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p
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ac
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m
a
1.
I
NT
RO
D
UCT
I
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h
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cr
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is
s
u
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o
f
in
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s
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d
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in
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ca
tio
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atch
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m
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ely
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atch
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ated
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ar
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B
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aly
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t
s
tu
d
en
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in
m
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in
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ased
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ata
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lg
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r
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s
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Fo
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tan
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Ou
ati
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l.
[
1
]
f
o
u
n
d
Naïv
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-
B
ay
e
s
m
o
s
t
s
u
itab
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Mr
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[
2
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f
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[
3
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f
av
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m
f
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f
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E
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Mr
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[
4
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tr
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t
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E
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id
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ter
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to
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tatio
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f
ailu
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Kem
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[
5
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co
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DT
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ac
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tu
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o
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k
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SVM
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,
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lo
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r
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r
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Ou
atik
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a
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[
6
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ac
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iev
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8
7
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7
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[
8
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c
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d
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a
co
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f
DT
s
,
Naïv
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ay
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tu
d
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u
id
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Z
h
o
u
et
a
l.
[
9
]
d
ev
elo
p
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a
ch
at
b
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t
-
b
ased
v
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ca
tio
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B
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Facu
lt
y
o
f
Scien
ce
s
s
tu
d
e
n
ts
.
Nab
il
et
a
l.
[
1
0
]
e
m
p
lo
y
e
d
d
ee
p
l
ea
r
n
in
g
t
o
f
o
r
ec
ast
s
tu
d
en
t
s
u
cc
ess
b
ased
o
n
c
o
u
r
s
e
d
ata.
Nab
izad
e
h
et
a
l.
[
1
1
]
e
x
p
lo
r
ed
ML
tec
h
n
iq
u
e
s
to
f
o
r
ec
ast
f
in
al
s
tu
d
en
ts
’
p
er
f
o
r
m
a
n
ce
s
in
g
a
m
if
ied
lear
n
i
n
g
e
n
v
ir
o
n
m
en
ts
.
Sán
ch
ez
-
Po
z
o
et
a
l.
[
1
2
]
co
m
p
ar
ed
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
m
o
d
els
to
p
r
ed
ict
h
ig
h
s
ch
o
o
l
s
tu
d
en
t
ac
a
d
em
ic
s
u
cc
ess
.
Ujwal
an
d
Ma
lik
[
1
3
]
d
eter
m
in
e
d
cr
itical
s
tu
d
en
t
attr
ib
u
tes
f
o
r
ac
ad
em
ic
p
e
r
f
o
r
m
an
ce
p
r
ed
ictio
n
to
in
f
o
r
m
s
tu
d
en
t
o
r
i
en
tatio
n
s
tr
ateg
ies.
R
eb
ai
et
a
l.
[
1
4
]
em
p
lo
y
ed
a
v
is
u
al
m
ac
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
to
f
o
r
ec
ast
s
ec
o
n
d
a
r
y
s
ch
o
o
l
p
e
r
f
o
r
m
an
ce
,
ap
p
licab
le
to
o
r
ien
tatio
n
s
y
s
te
m
s
.
I
n
an
o
th
er
wo
r
k
,
Haz
r
a
et
a
l.
[
1
5
]
em
p
lo
y
ed
h
y
b
r
id
alg
o
r
ith
m
s
,
in
clu
d
i
n
g
Naïv
e
-
B
ay
es,
SVM
to
d
etec
t b
r
ea
s
t c
an
ce
r
.
T
h
e
p
a
p
er
is
o
r
g
an
ize
d
in
to
t
h
r
ee
s
ec
tio
n
s
.
T
h
e
s
ec
o
n
d
p
a
r
t
d
etails
th
e
m
eth
o
d
o
lo
g
y
,
f
o
l
lo
wed
b
y
r
esu
lts
an
d
d
is
cu
s
s
io
n
in
th
e
th
ir
d
s
ec
tio
n
.
T
h
e
p
ap
er
co
n
clu
d
es with
a
f
in
al
s
ec
tio
n
.
2.
M
E
T
H
O
D
T
h
is
r
esear
ch
in
v
esti
g
ates
th
e
r
ep
er
cu
s
s
io
n
s
o
f
in
c
o
r
r
ec
t
s
tu
d
en
t
o
r
ien
tatio
n
o
n
s
ch
o
o
l
d
r
o
p
o
u
t
r
ates.
W
e
em
p
lo
y
a
h
y
b
r
id
m
et
h
o
d
o
lo
g
y
th
at
le
v
er
ag
es
th
e
s
tr
en
g
th
s
o
f
b
o
th
n
eu
r
al
n
etwo
r
k
s
an
d
r
an
d
o
m
f
o
r
est
alg
o
r
ith
m
s
in
o
u
r
an
aly
s
is
.
T
h
is
co
m
b
in
ed
s
tr
ateg
y
aim
s
to
o
f
f
er
a
th
o
r
o
u
g
h
an
d
r
o
b
u
s
t
co
m
p
r
eh
en
s
io
n
o
f
th
e
elem
en
ts
im
p
ac
tin
g
s
tu
d
en
t
o
r
ien
tatio
n
an
d
t
h
eir
co
n
n
ec
tio
n
to
d
r
o
p
o
u
t r
ates w
ith
in
th
is
s
p
ec
if
ic
s
ettin
g
.
2
.
1
.
B
ig
da
t
a
a
na
ly
t
ics
T
h
e
co
n
ce
p
t
o
f
b
ig
d
ata
en
co
m
p
ass
es
ex
ten
s
iv
e
d
ataset
s
[
1
6
]
,
in
clu
d
in
g
b
o
th
s
tr
u
c
tu
r
ed
a
n
d
u
n
s
tr
u
ctu
r
ed
d
ata,
th
at
ca
n
b
e
an
aly
ze
d
to
p
r
o
d
u
ce
m
ea
n
in
g
f
u
l
in
s
ig
h
ts
.
Ou
r
s
tu
d
y
h
ar
n
ess
es
th
e
p
o
wer
o
f
b
i
g
d
ata
to
c
o
n
d
u
ct
a
t
h
o
r
o
u
g
h
ex
am
in
atio
n
o
f
th
e
co
m
p
lex
ities
s
u
r
r
o
u
n
d
in
g
s
tu
d
en
t
o
r
ien
tatio
n
a
n
d
its
r
elatio
n
s
h
ip
with
d
r
o
p
o
u
t
r
ates.
B
y
in
teg
r
atin
g
b
ig
d
ata
a
n
aly
tics
in
to
o
u
r
h
y
b
r
i
d
ap
p
r
o
ac
h
,
we
aim
to
u
n
c
o
v
e
r
n
u
an
ce
d
p
atter
n
s
an
d
in
s
ig
h
ts
m
ay
f
ac
ilit
ate
th
e
cr
ea
tio
n
o
f
m
o
r
e
ef
f
ec
tiv
e
in
ter
v
en
tio
n
s
an
d
s
tr
ateg
ies
f
o
r
lo
wer
in
g
d
r
o
p
o
u
t r
ates
.
2
.
1
.
1
.
H
a
do
o
p
Had
o
o
p
is
a
d
is
tr
ib
u
ted
co
m
p
u
tin
g
p
latf
o
r
m
f
o
r
s
to
r
in
g
a
n
d
p
r
o
ce
s
s
in
g
lar
g
e
am
o
u
n
ts
o
f
d
ata
in
b
atch
es
[
1
7
]
,
[
1
8
]
,
p
lay
s
a
cr
u
cial
r
o
le
in
o
u
r
r
esear
ch
.
T
h
is
f
r
am
ewo
r
k
’
s
c
o
r
e
p
r
in
cip
le
i
n
v
o
lv
es
d
is
tr
ib
u
ted
p
r
o
ce
s
s
in
g
ac
r
o
s
s
m
u
ltip
le
n
o
d
es,
s
ig
n
if
ican
tly
en
h
an
cin
g
c
o
m
p
u
tin
g
an
d
s
to
r
ag
e
ca
p
ab
il
ities
to
ef
f
ec
tiv
ely
m
an
ag
e
ex
ten
s
iv
e
d
ata
v
o
lu
m
es.
T
h
is
is
p
ar
ticu
lar
ly
b
en
ef
icial
f
o
r
o
u
r
an
aly
s
is
co
n
s
id
er
in
g
s
tu
d
en
t
d
ata
ac
r
o
s
s
s
ev
er
al
ac
ad
em
ic
y
ea
r
s
.
Had
o
o
p
o
f
f
e
r
s
n
u
m
er
o
u
s
ad
v
an
tag
es:
i)
R
ap
id
s
to
r
ag
e
an
d
p
r
o
ce
s
s
in
g
:
lar
g
e
d
atasets
ca
n
b
e
s
to
r
ed
an
d
p
r
o
ce
s
s
ed
ef
f
icien
tly
,
a
cr
u
cial
asp
ec
t
f
o
r
an
aly
zin
g
s
tu
d
e
n
t d
ata
en
c
o
m
p
ass
in
g
g
r
ad
es a
n
d
ch
o
s
en
o
r
i
en
tatio
n
s
o
v
er
m
u
ltip
le
y
ea
r
s
.
ii)
Scalab
ilit
y
:
t
h
e
s
y
s
tem
is
in
h
er
en
tly
s
ca
lab
le;
ad
d
itio
n
al
n
o
d
es
ca
n
b
e
s
ea
m
less
ly
in
teg
r
ate
d
as
d
ata
n
ee
d
s
g
r
o
w,
m
in
im
izin
g
ad
m
in
is
tr
ativ
e
ef
f
o
r
t.
T
h
is
f
lex
ib
ilit
y
is
im
p
o
r
tan
t a
s
we
an
ticip
ate
p
o
ten
t
ially
co
llectin
g
ev
en
m
o
r
e
d
ata
p
o
in
ts
in
th
e
f
u
tu
r
e
(
e.
g
.
,
e
x
tr
ac
u
r
r
ic
u
lar
ac
ti
v
ities
)
.
iii)
Dis
tr
ib
u
ted
co
m
p
u
tin
g
a
n
d
f
au
lt
to
ler
an
ce
:
d
is
tr
ib
u
ted
p
r
o
ce
s
s
in
g
f
ac
ilit
ates
s
wif
t
d
at
a
an
aly
s
is
,
with
in
cr
ea
s
ed
co
m
p
u
tatio
n
n
o
d
es
tr
an
s
latin
g
to
en
h
an
ce
d
p
r
o
c
ess
in
g
p
o
wer
.
Ad
d
itio
n
ally
,
Had
o
o
p
en
s
u
r
es
d
ata
an
d
ap
p
licatio
n
in
teg
r
ity
b
y
r
e
d
ir
ec
tin
g
task
s
to
alter
n
a
tiv
e
n
o
d
es
in
th
e
ev
e
n
t
o
f
h
a
r
d
war
e
f
ailu
r
es,
p
r
ev
en
tin
g
d
is
r
u
p
tio
n
d
u
r
in
g
d
is
tr
ib
u
ted
co
m
p
u
tatio
n
.
iv
)
C
o
s
t
-
ef
f
ec
tiv
en
ess
:
as
a
n
o
p
en
-
s
o
u
r
ce
f
r
am
ewo
r
k
,
Had
o
o
p
lev
er
a
g
es
s
tan
d
ar
d
m
ac
h
in
es
f
o
r
s
to
r
in
g
ex
ten
s
iv
e
d
ata
v
o
l
u
m
es,
o
f
f
er
i
n
g
a
co
s
t
-
ef
f
ec
tiv
e
s
o
lu
tio
n
.
2
.
1
.
2
.
H
a
do
o
p
dis
t
ribute
d f
ile
s
y
s
t
em
Had
o
o
p
d
is
tr
ib
u
ted
f
ile
s
y
s
tem
(
HDFS
)
s
er
v
es
a
s
th
e
u
n
d
er
ly
in
g
s
to
r
ag
e
s
o
lu
tio
n
with
in
o
u
r
b
ig
d
ata
ar
ch
itectu
r
e
[
1
9
]
.
Desig
n
ed
f
o
r
co
m
m
o
d
ity
h
ar
d
war
e,
it
p
r
o
v
id
es
r
o
b
u
s
t
f
au
lt
to
ler
an
ce
ca
p
ab
ilit
ies.
E
ac
h
p
iece
o
f
d
ata
(
s
tu
d
en
t
i
n
f
o
r
m
atio
n
f
o
r
ea
c
h
y
ea
r
)
is
r
ep
licated
ac
r
o
s
s
m
u
ltip
le
lo
ca
tio
n
s
with
in
th
e
Had
o
o
p
clu
s
ter
,
en
s
u
r
in
g
its
av
ailab
ilit
y
u
n
d
er
an
y
cir
cu
m
s
t
an
ce
s
an
d
m
itig
atin
g
th
e
r
is
k
o
f
d
ata
co
r
r
u
p
tio
n
.
Her
e
’
s
h
o
w
HDFS sp
ec
if
icall
y
b
en
ef
its
o
u
r
r
esear
ch
:
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
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
9
0
4
-
1
9
1
0
1906
−
Op
tim
ized
f
o
r
lar
g
e
d
atasets
:
HDFS
u
til
izes
lar
g
er
d
ata
b
lo
ck
s
izes
co
m
p
ar
ed
to
c
o
n
v
en
tio
n
al
f
ile
s
y
s
tem
s
,
allo
win
g
f
o
r
ef
f
icien
t
s
to
r
ag
e
an
d
r
etr
iev
al
o
f
o
u
r
s
tu
d
en
t
d
ata
en
co
m
p
ass
in
g
g
r
ad
es
an
d
ch
o
s
en
o
r
ien
tatio
n
s
ac
r
o
s
s
m
u
ltip
le
y
e
ar
s
[
2
0
]
.
−
Data
r
eliab
ilit
y
an
d
a
v
ailab
ilit
y
:
th
e
b
lo
c
k
r
e
p
licatio
n
f
ea
tu
r
e
en
s
u
r
es
d
ata
r
elia
b
ilit
y
.
E
v
e
n
if
a
n
o
d
e
f
ails
,
s
tu
d
en
t
d
ata
ca
n
s
till
b
e
ac
ce
s
s
ed
f
r
o
m
o
th
er
n
o
d
es
h
o
ld
in
g
r
ep
licas
o
f
th
e
s
am
e
b
lo
ck
.
T
h
is
is
cr
u
cial
f
o
r
m
ain
tain
in
g
d
ata
i
n
teg
r
ity
th
r
o
u
g
h
o
u
t o
u
r
an
aly
s
is
.
Ov
er
all,
HDFS
p
r
o
v
id
es
a
r
esil
ien
t
an
d
ef
f
icien
t
s
to
r
ag
e
s
o
lu
tio
n
f
o
r
o
u
r
lar
g
e
s
tu
d
en
t
d
ataset,
f
ac
ilit
atin
g
r
eliab
le
d
ata
ac
ce
s
s
an
d
r
etr
iev
al
f
o
r
an
al
y
s
is
u
s
in
g
m
ac
h
in
e
l
ea
r
n
in
g
alg
o
r
ith
m
s
.
2
.
2
.
Cla
s
s
if
ica
t
io
n
a
lg
o
rit
hm
s
T
h
is
r
esear
ch
in
v
esti
g
ates
th
e
ef
f
ec
tiv
en
ess
o
f
en
s
em
b
le
le
ar
n
in
g
tech
n
iq
u
es
f
o
r
p
r
ed
ictin
g
o
p
tim
al
s
tu
d
en
t o
r
ien
tatio
n
in
Mo
r
o
cc
o
.
W
e
p
r
o
p
o
s
e
a
s
p
ec
if
ic
en
s
em
b
le
lear
n
in
g
m
o
d
el
an
d
c
o
m
p
ar
e
its
p
er
f
o
r
m
a
n
ce
with
a
s
elec
tio
n
o
f
estab
lis
h
e
d
class
ical
cla
s
s
if
icat
io
n
alg
o
r
ith
m
s
.
T
h
is
co
m
p
ar
ativ
e
an
a
ly
s
is
allo
w
s
u
s
to
id
en
tify
th
e
m
o
s
t su
itab
le
ap
p
r
o
ac
h
f
o
r
s
tu
d
en
t
o
r
ien
tatio
n
p
r
ed
ictio
n
in
th
is
s
p
ec
if
ic
co
n
tex
t.
2
.
2
.
1
.
Cla
s
s
ica
l
a
lg
o
rit
hm
s
Naïv
e
-
B
ay
es
:
i
s
co
n
s
id
er
ed
to
b
e
th
e
m
o
s
t
ef
f
ec
tiv
e
class
if
icatio
n
alg
o
r
ith
m
[
2
1
]
.
I
ts
in
d
icatio
n
,
b
y
an
ticip
atin
g
th
e
lik
elih
o
o
d
th
at
a
r
ec
o
r
d
in
th
e
d
atab
ase
is
ass
o
ciate
d
with
a
s
p
ec
if
ic
class
,
an
d
th
i
s
class
if
icatio
n
m
o
d
el
is
b
ased
o
n
th
e
s
tatis
tical
n
o
tio
n
o
f
B
ay
e
s
’
th
eo
r
em
.
(
|
)
=
(
|
)
∗
(
)
(
)
(
1
)
P
(
Y
|
N
)
is
th
e
p
r
o
b
a
b
ilit
y
o
f
Y
b
ein
g
tr
u
e
g
iv
en
th
at
N
is
tr
u
e.
(
|
)
is
th
e
o
p
p
o
s
ite
o
f
th
e
p
r
ev
io
u
s
o
n
e.
I
t is th
e
p
r
o
b
ab
ilit
y
o
f
N
b
ein
g
tr
u
e
g
iv
e
n
th
at
Y
is
tr
u
e
.
P
(
Y
)
is
th
e
p
r
o
b
a
b
ilit
y
o
f
Y
b
ein
g
t
r
u
e.
P
(
N
)
is
th
e
p
r
o
b
a
b
ilit
y
o
f
N
b
ein
g
t
r
u
e.
T
h
e
K
NN:
is
a
p
r
ed
ictiv
e
m
et
h
o
d
th
at
in
v
o
lv
es
co
m
p
ar
in
g
t
h
e
f
ea
tu
r
es
o
f
a
n
ew
r
ec
o
r
d
wi
th
th
o
s
e
o
f
ex
is
tin
g
r
ec
o
r
d
s
in
th
e
d
ataset
[
2
2
]
.
B
y
id
en
tify
in
g
th
e
K
NN
b
ased
o
n
s
im
ilar
ity
(
ty
p
ically
u
s
in
g
d
is
tan
ce
m
etr
ics),
it
p
r
ed
icts
th
e
u
n
k
n
o
wn
v
alu
e
o
f
th
e
n
ew
r
ec
o
r
d
.
Fo
r
class
if
icatio
n
task
s
,
it
a
s
s
ig
n
s
th
e
p
r
ev
alen
t
class
w
ith
in
th
e
k
n
eig
h
b
o
r
s
to
th
e
n
ew
r
ec
o
r
d
,
wh
ile
f
o
r
r
eg
r
ess
io
n
task
s
,
it
co
m
p
u
tes
th
e
av
er
ag
e
o
r
weig
h
ted
m
ea
n
o
f
th
e
ta
r
g
et
v
alu
es f
r
o
m
th
e
k
n
eig
h
b
o
r
s
as th
e
p
r
ed
icted
v
alu
e.
DT
:
is
a
m
o
d
elin
g
tech
n
iq
u
e
d
ep
icted
in
a
tr
ee
-
lik
e
s
tr
u
ctu
r
e,
as
its
n
am
e
im
p
lies
[
2
3
]
,
[
2
4
]
.
W
ith
in
th
is
f
r
am
ewo
r
k
,
ea
c
h
n
o
d
e
r
e
p
r
esen
ts
a
d
ec
is
io
n
p
o
in
t
b
as
ed
o
n
s
p
ec
if
ic
f
ea
tu
r
es,
an
d
t
h
e
b
r
an
c
h
es
s
ig
n
if
y
p
o
s
s
ib
le
o
u
tco
m
es
o
r
class
if
icatio
n
s
.
T
h
e
ter
m
in
al
n
o
d
es,
o
r
leav
es,
c
o
r
r
esp
o
n
d
to
t
h
e
f
in
a
l
p
r
ed
icted
class
es
o
r
v
alu
es.
DT
alg
o
r
ith
m
s
ar
e
u
tili
ze
d
to
r
ec
u
r
s
iv
ely
s
p
li
t
th
e
d
ataset
in
to
s
u
b
s
ets
b
ased
o
n
th
e
m
o
s
t
in
f
o
r
m
ativ
e
f
ea
tu
r
es,
en
a
b
lin
g
ef
f
ec
tiv
e
class
if
icatio
n
an
d
p
r
e
d
ictio
n
task
s
.
2
.
2
.
2
.
E
ns
em
ble
l
ea
rning
Ou
r
ap
p
r
o
ac
h
in
v
o
l
v
es
th
e
im
p
lem
en
tatio
n
o
f
en
s
em
b
le
lear
n
in
g
,
co
m
b
in
in
g
two
p
o
wer
f
u
l
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es.
Fo
r
ea
c
h
b
ase
class
if
ier
,
s
u
ch
as r
an
d
o
m
f
o
r
est
class
if
ier
an
d
ML
P
c
lass
if
ier
,
we
g
en
er
ate
d
is
tin
ct
tr
ain
in
g
s
ets
(
D1
an
d
D2
)
f
r
o
m
th
e
p
r
o
v
id
ed
d
ataset
D.
T
h
e
r
a
n
d
o
m
f
o
r
est
class
if
ier
is
tr
ain
ed
u
s
in
g
D1
.
I
n
c
o
n
tr
ast,
th
e
ML
P
c
las
s
if
ier
is
tr
ain
ed
u
s
in
g
D2
.
Af
ter
tr
ain
in
g
b
o
th
b
ase
class
if
ier
s
,
th
eir
p
r
e
d
ictio
n
s
ar
e
co
m
b
in
ed
th
r
o
u
g
h
a
v
o
ti
n
g
m
ec
h
an
is
m
(
r
ef
e
r
to
Fig
u
r
e
1
)
.
W
h
en
a
n
ew
d
ata
tu
p
le
is
p
r
esen
ted
f
o
r
class
if
icatio
n
,
ea
ch
b
ase
class
i
f
ier
o
f
f
e
r
s
its
class
p
r
ed
ictio
n
(
o
r
ien
tatio
n
c
h
o
ice)
.
Su
b
s
eq
u
e
n
tly
,
th
e
en
s
em
b
le
class
if
ier
ag
g
r
eg
ates
th
ese
p
r
e
d
ictio
n
s
v
ia
m
ajo
r
ity
v
o
tin
g
,
u
ltima
tely
y
ield
in
g
th
e
f
in
al
c
lass
p
r
ed
ictio
n
f
o
r
th
e
n
ew
d
ata
tu
p
le.
−
R
an
d
o
m
f
o
r
est:
is
an
en
s
em
b
le
lear
n
in
g
tec
h
n
iq
u
e
th
at
co
m
b
in
es
m
u
ltip
le
DT
s
to
m
ak
e
p
r
ed
ictio
n
s
[
2
5
]
.
I
t
is
a
p
o
wer
f
u
l
m
eth
o
d
f
o
r
h
an
d
lin
g
b
o
th
n
u
m
er
ical
an
d
ca
teg
o
r
ical
f
ea
t
u
r
es,
en
a
b
lin
g
it
to
ca
p
t
u
r
e
co
m
p
lex
r
elatio
n
s
h
ip
s
with
in
th
e
d
ata.
−
Neu
r
al
n
etwo
r
k
:
in
o
u
r
ap
p
r
o
ac
h
,
we
em
p
lo
y
th
e
ML
PC
la
s
s
if
ier
,
a
n
eu
r
al
n
etwo
r
k
m
o
d
el
th
at
u
tili
ze
s
d
ee
p
lear
n
in
g
tech
n
iq
u
es
to
c
o
m
p
r
eh
e
n
d
in
tr
icate
p
atter
n
s
with
in
th
e
d
ata.
Neu
r
al
n
etw
o
r
k
s
co
n
s
is
t
o
f
in
ter
co
n
n
ec
ted
n
o
d
es
(
n
e
u
r
o
n
s
)
ar
r
an
g
ed
in
lay
e
r
s
an
d
th
eir
p
ar
am
eter
s
ar
e
o
p
ti
m
ized
th
r
o
u
g
h
b
ac
k
p
r
o
p
ag
atio
n
[
2
6
]
.
T
h
is
allo
ws
th
e
m
o
d
el
to
ad
e
p
tly
ca
p
tu
r
e
in
tr
icate
r
elatio
n
s
h
ip
s
an
d
p
atter
n
s
with
in
th
e
d
ataset.
2
.
3
.
We
k
a
I
n
o
u
r
wo
r
k
,
we
u
tili
ze
W
ek
a
[
2
7
]
f
o
r
v
a
r
io
u
s
task
s
,
in
clu
d
i
n
g
d
ata
p
r
ep
r
o
c
ess
in
g
,
f
ea
tu
r
e
s
elec
tio
n
,
an
d
im
p
lem
en
tin
g
th
e
b
ase
cl
ass
if
ier
s
(
r
an
d
o
m
f
o
r
est
class
i
f
ier
an
d
ML
P
c
lass
if
ier
)
b
ef
o
r
e
co
m
b
in
in
g
th
eir
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
B
ig
d
a
ta
a
n
d
en
s
emb
le
lea
r
n
i
n
g
fo
r
effec
tive
s
tu
d
en
t o
r
ien
ta
tio
n
in
Mo
r
o
cc
o
(
Mo
r
a
d
B
a
d
r
a
n
i
)
1907
p
r
ed
ictio
n
s
.
W
ek
a,
also
k
n
o
w
n
as
th
e
W
aik
ato
en
v
ir
o
n
m
en
t
f
o
r
k
n
o
wled
g
e
an
aly
s
is
,
is
a
co
m
p
r
eh
en
s
iv
e
s
et
o
f
J
av
a
-
b
ased
to
o
ls
f
o
r
d
ata
m
a
n
i
p
u
latio
n
an
d
a
n
aly
s
is
.
I
t o
f
f
er
s
a
wid
e
ar
r
ay
o
f
ar
tific
ial
in
tell
ig
en
ce
alg
o
r
ith
m
s
,
s
u
ch
as
DT
s
an
d
n
eu
r
al
n
etw
o
r
k
s
,
f
o
r
task
s
lik
e
s
u
p
er
v
is
e
d
o
r
u
n
s
u
p
er
v
is
ed
class
if
icati
o
n
.
W
ek
a
p
r
o
v
id
es
J
av
a
class
es
f
o
r
d
ata
lo
a
d
in
g
an
d
h
an
d
lin
g
,
v
ar
io
u
s
cla
s
s
if
icatio
n
alg
o
r
ith
m
s
,
attr
i
b
u
te
s
elec
tio
n
to
o
ls
,
s
tatis
t
ical
an
aly
s
is
f
ea
tu
r
es,
an
d
v
is
u
aliza
tio
n
ca
p
ab
ilit
ies.W
h
ile
W
ek
a
m
ay
f
ac
e
ch
al
len
g
es
with
lar
g
e
v
o
lu
m
es
o
f
d
ata
[
2
8
]
,
p
ar
tic
u
lar
ly
in
th
e
c
o
n
tex
t
o
f
b
ig
d
ata,
it
a
d
d
r
ess
es
th
is
is
s
u
e
b
y
im
p
lem
en
tin
g
tech
n
iq
u
es
an
d
ar
ch
itectu
r
al
s
o
lu
tio
n
s
f
o
r
ef
f
ec
tiv
e
d
ata
m
an
ag
em
en
t.
A
d
d
itio
n
ally
,
W
ek
a
in
teg
r
ates
with
o
th
er
to
o
ls
an
d
f
r
a
m
ewo
r
k
s
d
esig
n
ed
f
o
r
h
a
n
d
lin
g
lar
g
e
d
a
tasets
,
s
u
ch
as
m
ass
iv
e
o
n
lin
e
an
aly
s
is
(
MO
A
)
,
an
o
p
e
n
-
s
o
u
r
ce
f
r
a
m
ewo
r
k
co
n
tain
in
g
a
d
i
v
er
s
e
s
et
o
f
lear
n
in
g
alg
o
r
ith
m
s
an
d
ass
ess
m
en
t
to
o
ls
.
B
y
lev
er
ag
in
g
W
ek
a
an
d
co
m
p
le
m
en
tar
y
f
r
am
ewo
r
k
s
lik
e
MO
A,
r
esear
ch
er
s
,
a
n
d
p
r
ac
titi
o
n
er
s
ca
n
n
a
v
ig
ate
th
e
co
m
p
lex
ities
o
f
b
i
g
d
ata
an
al
y
tics
an
d
d
er
iv
e
m
ea
n
in
g
f
u
l in
s
ig
h
ts
f
r
o
m
v
ast d
atasets
.
Fig
u
r
e
1
.
W
o
r
k
f
lo
w
o
f
co
m
b
i
n
in
g
class
if
ier
s
f
o
r
im
p
r
o
v
e
d
p
r
ed
ictio
n
3.
RE
SU
L
T
S AN
D
D
I
SS
CUSS
I
O
N
3
.
1
.
Da
t
a
a
nd
p
ro
ce
s
s
ing
W
e
co
llected
s
tu
d
en
t
d
ata
f
r
o
m
th
e
Pro
v
in
cial
E
d
u
ca
tio
n
Dir
ec
to
r
ate
in
Nad
o
r
,
Mo
r
o
cc
o
,
u
s
in
g
th
e
MA
SS
A
R
s
y
s
tem
.
T
h
e
d
ataset
en
co
m
p
ass
es
s
tu
d
en
t
d
ata
f
o
r
s
ev
er
al
ac
ad
em
ic
y
ea
r
s
,
in
clu
d
in
g
th
e
p
er
io
d
f
r
o
m
2
0
1
8
to
2
0
2
2
.
I
t
in
clu
d
e
s
f
ea
tu
r
es
s
u
ch
as
s
tu
d
en
t
I
D,
g
r
ad
es
in
v
ar
i
o
u
s
s
u
b
jects
(
Ma
th
em
atics,
E
n
g
lis
h
,
Ph
y
s
ics,
Hi
s
to
r
y
an
d
Geo
g
r
a
p
h
y
,
L
if
e
an
d
E
ar
t
h
Scien
ce
s
,
Fre
n
ch
,
Ph
y
s
ical
E
d
u
ca
tio
n
,
I
s
lam
ic
E
d
u
ca
tio
n
,
Ar
ab
ic)
,
a
n
d
th
eir
c
h
o
s
en
ac
ad
em
ic
o
r
ien
tatio
n
s
(
Scien
ce
Ma
th
em
atics
(
SM)
,
Ph
y
s
ics
(
SF
)
,
o
r
Scien
ce
o
f
L
if
e
an
d
E
ar
th
(
SVT)
)
.
W
h
ile
a
Py
th
o
n
en
v
ir
o
n
m
e
n
t
o
n
a
s
in
g
le
m
ac
h
in
e
m
ig
h
t
b
e
s
u
itab
le
f
o
r
b
asic
an
aly
s
is
o
f
s
tu
d
en
t
g
r
ad
es
o
v
er
a
lim
ited
tim
ef
r
a
m
e,
we
o
p
ted
f
o
r
a
Ha
d
o
o
p
c
lu
s
ter
with
3
d
atan
o
d
es
an
d
1
n
am
en
o
d
e
f
o
r
th
e
f
o
llo
win
g
r
ea
s
o
n
s
:
−
Scalab
ilit
y
:
Had
o
o
p
p
r
o
v
id
es
a
s
ca
lab
le
ar
ch
itectu
r
e
th
at
ca
n
ef
f
o
r
tles
s
ly
ac
co
m
m
o
d
ate
f
u
tu
r
e
g
r
o
wth
in
th
e
d
ataset.
Su
p
p
o
s
e
th
e
Pro
v
in
cial
E
d
u
ca
tio
n
Dir
ec
to
r
ate
ex
p
an
d
s
o
r
ad
d
itio
n
al
d
ata
p
o
i
n
ts
ar
e
co
llected
in
th
e
f
u
tu
r
e
(
e.
g
.
,
ex
tr
ac
u
r
r
icu
lar
ac
tiv
ities
,
d
ata
f
o
r
ev
e
n
m
o
r
e
y
ea
r
s
)
.
I
n
th
at
ca
s
e,
o
u
r
cu
r
r
en
t sy
s
tem
ca
n
h
an
d
le
th
e
i
n
cr
ea
s
ed
v
o
lu
m
e
with
o
u
t sig
n
if
ican
t m
o
d
if
icatio
n
s
.
−
Po
ten
tial
f
o
r
c
o
m
p
lex
an
aly
s
es:
in
clu
d
in
g
d
ata
f
o
r
f
o
u
r
a
ca
d
em
ic
y
ea
r
s
(
2
0
1
8
-
2
0
2
2
)
a
llo
ws
f
o
r
m
o
r
e
in
tr
icate
an
aly
s
es
o
f
s
tu
d
en
t
p
r
o
g
r
ess
an
d
h
o
w
th
eir
ch
o
s
en
o
r
ien
tatio
n
(
SM,
SF
,
o
r
SVT)
r
elate
s
to
th
eir
ac
ad
em
ic
p
er
f
o
r
m
a
n
ce
.
W
e
c
an
ex
p
l
o
r
e
tr
e
n
d
s
,
id
e
n
tify
y
ea
r
-
on
-
y
ea
r
v
ar
iatio
n
s
in
p
er
f
o
r
m
an
ce
with
in
ch
o
s
en
o
r
ien
tatio
n
s
,
an
d
p
o
te
n
tially
u
n
co
v
er
f
ac
to
r
s
in
f
lu
e
n
cin
g
s
tu
d
en
t
s
u
cc
ess
in
th
eir
ch
o
s
en
p
ath
s
.
Ad
d
itio
n
ally
,
we
f
o
r
esee
th
e
p
o
s
s
ib
ilit
y
o
f
ex
p
lo
r
in
g
m
o
r
e
co
m
p
lex
r
elatio
n
s
h
ip
s
with
in
th
e
d
ata
in
th
e
f
u
tu
r
e,
s
u
c
h
as id
en
tify
in
g
f
ac
t
o
r
s
in
f
lu
en
cin
g
s
tu
d
en
t su
cc
es
s
with
in
th
eir
ch
o
s
en
p
at
h
s
.
Had
o
o
p
’
s
p
ar
allel
p
r
o
ce
s
s
in
g
ca
p
ab
ilit
ies
ca
n
b
e
ad
v
an
tag
e
o
u
s
f
o
r
co
m
p
u
tatio
n
ally
in
ten
s
iv
e
task
s
lik
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
o
r
c
o
m
p
lex
s
tatis
ti
ca
l m
o
d
elin
g
t
h
at
m
ig
h
t
b
e
em
p
lo
y
ed
i
n
th
ese
f
u
tu
r
e
an
aly
s
e
s
.
3
.
2
.
Resul
t
s
Ou
r
ap
p
r
o
ac
h
in
v
o
l
v
es
th
e
im
p
lem
en
tatio
n
o
f
en
s
em
b
le
lear
n
in
g
,
co
m
b
in
in
g
two
p
o
wer
f
u
l
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es:
Neu
r
al
n
etwo
r
k
an
d
r
an
d
o
m
f
o
r
est
.
T
h
e
class
if
icatio
n
ac
cu
r
ac
y
[
2
9
]
,
p
r
ec
is
io
n
[
3
0
]
,
r
ec
all
[
3
1
]
,
an
d
F
-
m
ea
s
u
r
e
[
3
2
]
wer
e
em
p
lo
y
ed
to
co
n
tr
ast
th
e
ef
f
ec
tiv
en
ess
o
f
o
u
r
m
o
d
el
with
th
at
o
f
f
i
v
e
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
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
9
0
4
-
1
9
1
0
1908
class
if
icatio
n
alg
o
r
ith
m
s
,
co
m
p
r
is
in
g
Naïv
e
-
B
ay
es
,
KNN
,
DT
,
n
e
u
r
al
n
etwo
r
k
,
an
d
r
an
d
o
m
f
o
r
est.
O
u
r
o
b
tain
ed
r
esu
lts
ar
e
s
u
m
m
a
r
iz
ed
in
T
ab
le
1
.
T
ab
le
1
.
Alg
o
r
ith
m
s
p
er
f
o
r
m
a
n
ce
m
etr
ics
V
a
r
i
a
b
l
e
A
c
c
u
r
a
c
y
(
%)
P
r
e
c
i
s
i
o
n
(
%)
R
e
c
a
l
l
(
%)
F
-
mea
su
r
e
(
%)
N
a
ï
v
e
-
B
a
y
e
s
7
0
.
6
7
6
8
.
9
6
5
1
.
6
7
5
8
.
3
0
DT
8
6
.
8
3
8
2
.
4
4
8
4
.
3
1
8
3
.
3
0
K
N
N
8
7
.
1
1
8
5
.
3
4
8
1
.
4
2
8
3
.
2
5
R
a
n
d
o
m
f
o
r
e
s
t
9
2
.
7
3
9
1
.
3
1
8
9
.
7
1
9
0
.
4
0
N
e
u
r
a
l
n
e
t
w
o
r
k
9
5
.
9
1
9
5
.
9
0
9
5
.
9
1
9
5
.
8
9
O
u
r
m
o
d
e
l
9
8
.
6
0
9
8
.
5
9
9
8
.
6
0
9
8
.
5
8
T
h
e
f
in
d
in
g
s
o
f
th
is
r
esear
c
h
d
em
o
n
s
tr
ate
th
e
e
f
f
ec
tiv
en
ess
o
f
en
s
em
b
le
lear
n
i
n
g
i
n
p
r
ed
ictin
g
s
tu
d
en
t o
r
ien
tatio
n
in
th
e
M
o
r
o
cc
an
ed
u
ca
tio
n
al
co
n
tex
t.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el,
wh
ich
c
o
m
b
in
es
r
an
d
o
m
f
o
r
est
an
d
n
eu
r
al
n
etwo
r
k
alg
o
r
ith
m
s
,
ac
h
iev
ed
o
u
ts
tan
d
in
g
p
er
f
o
r
m
an
ce
m
etr
ics:
p
r
ec
is
io
n
(
9
8
.
5
9
%),
ac
c
u
r
ac
y
(
9
8
.
6
0
%),
r
ec
all
(
9
8
.
6
0
%),
a
n
d
F1
-
s
co
r
e
(
9
8
.
5
8
%).
T
h
is
ex
ce
p
tio
n
al
p
er
f
o
r
m
a
n
ce
s
u
r
p
ass
es
th
e
ca
p
ab
ilit
ies
o
f
in
d
iv
id
u
al
alg
o
r
it
h
m
s
,
h
ig
h
lig
h
tin
g
th
e
en
s
em
b
le
a
p
p
r
o
a
ch
’
s
s
tr
en
g
th
in
m
a
k
in
g
ac
cu
r
ate
p
r
ed
ictio
n
s
f
o
r
s
tu
d
en
t o
r
ien
tatio
n
T
h
is
s
u
cc
ess
ca
n
b
e
attr
ib
u
ted
to
th
e
en
s
em
b
le
m
o
d
el
’
s
ab
ilit
y
to
lev
er
ag
e
th
e
co
m
p
lem
en
tar
y
s
tr
en
g
th
s
o
f
its
co
n
s
titu
en
t
alg
o
r
ith
m
s
.
R
an
d
o
m
f
o
r
est
ex
ce
ls
at
h
an
d
lin
g
h
ig
h
-
d
im
en
s
io
n
al
d
ata
a
n
d
id
en
tify
in
g
co
m
p
le
x
p
atter
n
s
,
wh
ile
th
e
n
eu
r
al
n
etwo
r
k
o
f
f
e
r
s
en
h
an
ce
d
lear
n
in
g
ca
p
a
b
ilit
ies.
B
y
co
m
b
in
in
g
th
ese
s
tr
en
g
th
s
,
th
e
en
s
em
b
le
m
o
d
el
g
e
n
er
ates
m
o
r
e
r
o
b
u
s
t
an
d
g
e
n
er
aliza
b
le
p
r
e
d
ictio
n
s
co
m
p
ar
ed
to
s
in
g
le
alg
o
r
ith
m
s
.
Fu
r
th
er
m
o
r
e
,
th
e
u
tili
za
tio
n
o
f
a
b
ig
d
ata
f
r
am
ewo
r
k
lik
e
Ha
d
o
o
p
an
d
HDFS
p
lay
ed
a
cr
u
c
ial
r
o
le
in
m
an
ag
in
g
an
d
p
r
o
ce
s
s
in
g
th
e
lar
g
e
-
s
ca
le
ed
u
ca
tio
n
al
d
ataset
em
p
lo
y
ed
in
th
is
s
tu
d
y
.
T
h
e
v
o
lu
m
e
an
d
co
m
p
lex
ity
o
f
th
is
d
ata
n
ec
e
s
s
itated
a
r
o
b
u
s
t
in
f
r
astru
ctu
r
e
f
o
r
ef
f
icien
t
h
an
d
lin
g
.
B
y
lev
er
ag
in
g
b
i
g
d
ata
tech
n
o
lo
g
ies,
we
wer
e
ab
le
to
ex
tr
ac
t
v
alu
ab
le
in
s
ig
h
ts
f
r
o
m
th
e
d
ataset,
allo
win
g
u
s
to
d
e
v
elo
p
an
d
tr
ain
th
e
en
s
em
b
le
lear
n
in
g
m
o
d
el
ef
f
ec
tiv
ely
.
T
h
is
s
u
cc
ess
f
u
l
in
te
g
r
atio
n
o
f
b
ig
d
ata
m
an
a
g
em
en
t
with
en
s
em
b
le
lear
n
in
g
tech
n
iq
u
es p
av
es th
e
way
f
o
r
f
u
tu
r
e
r
esear
ch
ex
p
lo
r
in
g
th
e
ap
p
licatio
n
o
f
ad
v
an
ce
d
m
ac
h
in
e
lear
n
in
g
o
n
lar
g
e
e
d
u
ca
tio
n
al
d
atasets
in
Mo
r
o
cc
o
an
d
b
ey
o
n
d
.
3
.
3
.
Co
m
pa
ra
is
o
n study
T
ab
le
2
s
u
m
m
a
r
izes
th
e
f
in
d
in
g
s
f
r
o
m
p
r
e
v
io
u
s
s
tu
d
ies
th
at
in
v
esti
g
ated
s
tu
d
en
t
o
r
ien
tatio
n
p
r
ed
ictio
n
u
s
in
g
v
ar
i
o
u
s
m
ac
h
in
e
-
lear
n
in
g
tech
n
iq
u
es.
As
ca
n
b
e
o
b
s
er
v
ed
,
o
u
r
en
s
em
b
le
l
ea
r
n
in
g
ap
p
r
o
ac
h
,
wh
ich
in
co
r
p
o
r
ates
b
o
th
r
an
d
o
m
f
o
r
est
an
d
n
eu
r
al
n
etwo
r
k
m
o
d
els,
ac
h
iev
es
th
e
h
ig
h
est
ac
cu
r
ac
y
o
f
9
8
.
6
0
%
in
2
0
2
4
.
T
h
is
s
u
g
g
ests
th
at
o
u
r
ap
p
r
o
ac
h
is
cu
r
r
en
tly
th
e
m
o
s
t
ef
f
ec
tiv
e
f
o
r
s
tu
d
en
ts
’
o
r
ien
tatio
n
.
T
h
e
ac
cu
r
ac
y
o
f
m
o
s
t
ap
p
r
o
a
ch
es
h
as
im
p
r
o
v
ed
o
v
er
tim
e
.
Fo
r
ex
am
p
le,
SVM
im
p
r
o
v
ed
f
r
o
m
9
2
.
1
0
%
in
2
0
2
1
t
o
9
2
.
7
3
%
in
2
0
2
2
.
T
h
is
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
u
n
d
er
s
co
r
es
th
e
ef
f
ec
tiv
e
n
ess
o
f
o
u
r
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ith
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ased
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ter
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.
Dev
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ch
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Evaluation Warning : The document was created with Spire.PDF for Python.
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[
1
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F
.
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O
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.
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.
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M
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a
c
a
d
e
m
ic
b
a
c
k
g
ro
u
n
d
in
th
is
d
o
m
a
in
.
His
a
c
a
d
e
m
ic
in
tere
sts
e
n
c
o
m
p
a
ss
d
a
ta
sc
ien
c
e
,
m
a
c
h
in
e
lea
rn
in
g
,
d
e
e
p
lea
rn
in
g
,
a
n
d
b
l
o
c
k
c
h
a
i
n
tec
h
n
o
lo
g
ies
.
He
is
d
e
e
p
ly
c
o
m
m
it
ted
t
o
h
a
rn
e
ss
in
g
t
h
e
p
o
ten
ti
a
l
o
f
t
h
e
se
tec
h
n
o
l
o
g
ies
to
f
o
ste
r
in
n
o
v
a
ti
o
n
a
n
d
b
rin
g
a
b
o
u
t
p
o
siti
v
e
c
h
a
n
g
e
s in
th
e
re
a
lm o
f
e
d
u
c
a
ti
o
n
.
He
c
a
n
b
e
c
o
n
tac
ted
at
e
m
a
il
:
m
.
b
a
d
ra
n
i@
u
m
p
.
a
c
.
m
a
.
Adi
l
M
a
r
o
u
a
n
is
a
P
h
.
D.
stu
d
e
n
t
c
u
rre
n
t
ly
a
ffil
iate
d
with
th
e
A
p
p
l
ied
M
a
th
e
m
a
ti
c
s
Lab
o
ra
to
r
y
o
f
t
h
e
O
rien
tal
(LaM
AO
)
a
t
ENCG
Ou
jd
a
.
His
re
se
a
rc
h
fo
c
u
s
c
e
n
ters
a
ro
u
n
d
Bl
o
c
k
c
h
a
i
n
Tec
h
n
o
l
o
g
y
f
o
r
E
d
u
c
a
ti
o
n
.
M
a
ro
u
a
n
’
s
a
c
a
d
e
m
ic
jo
u
r
n
e
y
is
m
a
rk
e
d
b
y
a
M
a
ste
r
’
s
S
p
e
c
ializa
ti
o
n
i
n
C
o
m
p
u
ter
En
g
in
e
e
rin
g
(M
.
2
.
I)
fro
m
th
e
F
a
c
u
l
ty
o
f
S
c
ien
c
e
s
i
n
Ou
jd
a
(F
.
S
.
O)
a
t
M
o
h
a
m
m
e
d
P
re
m
ier
Un
iv
e
rsity
(U.M
.
P
).
P
rio
r
to
t
h
is,
h
e
e
a
rn
e
d
a
fu
n
d
a
m
e
n
tal
d
e
g
re
e
in
M
a
t
h
e
m
a
ti
c
a
l
S
c
ien
c
e
s
a
n
d
C
o
m
p
u
ter
S
c
ien
c
e
(S
.
M
.
I).
His
d
iv
e
rse
e
d
u
c
a
ti
o
n
a
l
b
a
c
k
g
r
o
u
n
d
re
flec
ts
h
is
c
o
m
m
it
m
e
n
t
to
a
d
v
a
n
c
in
g
h
is
k
n
o
wle
d
g
e
a
n
d
s
k
il
ls
in
t
h
e
re
a
lm
o
f
in
fo
rm
a
ti
o
n
tec
h
n
o
l
o
g
y
a
n
d
e
n
g
i
n
e
e
rin
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
d
il
.
m
a
ro
u
a
n
@
u
m
p
.
a
c
.
m
a
.
Pro
f.
Dr
.
Na
b
il
K
a
n
n
o
u
f
is
a
d
isti
n
g
u
ish
e
d
p
ro
fe
ss
o
r
o
f
C
o
m
p
u
ter
S
c
ien
c
e
,
h
o
l
d
in
g
a
P
h
.
D.
in
Co
m
p
u
ter
S
c
ien
c
e
s.
He
e
a
rn
e
d
h
is
M
a
ste
r
’
s
d
e
g
re
e
in
C
o
m
p
u
ter
En
g
i
n
e
e
rin
g
fro
m
th
e
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s
a
t
th
e
U
n
iv
e
rsit
y
o
f
M
o
h
a
m
e
d
P
re
m
ier
in
O
u
jd
a
,
M
o
ro
c
c
o
,
i
n
2
0
1
0
.
S
i
n
c
e
2
0
2
0
,
h
e
h
a
s
b
e
e
n
se
rv
i
n
g
a
s
a
p
ro
fe
s
so
r
o
f
c
o
m
p
u
ter
sc
ien
c
e
a
t
Ab
d
e
lma
lek
Essa
id
i
Un
i
v
e
rsity
i
n
M
o
ro
c
c
o
.
Wi
t
h
a
p
r
o
li
fic
a
c
a
d
e
m
ic
c
a
re
e
r,
he
h
a
s
a
u
t
h
o
re
d
a
n
d
p
u
b
li
sh
e
d
o
v
e
r
1
0
re
se
a
rc
h
p
a
p
e
rs.
F
o
r
fu
r
th
e
r
c
o
m
m
u
n
ica
ti
o
n
,
he
c
a
n
b
e
c
o
n
tac
ted
at
e
m
a
il
:
n
a
b
il
.
k
a
n
n
o
u
f@
g
m
a
il
.
c
o
m
.
Pro
f.
Dr
.
Abd
e
l
a
z
iz
Cheto
u
a
n
i
b
o
rn
o
n
M
a
y
1
0
,
1
9
7
3
,
i
n
Ou
j
d
a
,
M
o
r
o
c
c
o
,
is
a
h
ig
h
ly
a
c
c
o
m
p
li
sh
e
d
a
c
a
d
e
m
ic
a
n
d
re
se
a
rc
h
e
r.
He
b
e
g
a
n
h
is
a
c
a
d
e
m
ic
jo
u
rn
e
y
b
y
o
b
tai
n
in
g
a
Ba
c
h
e
lo
r
’
s d
e
g
re
e
in
Ap
p
li
e
d
M
a
th
e
m
a
ti
c
s fro
m
th
e
De
p
a
rtme
n
t
o
f
M
a
th
e
m
a
ti
c
s a
t
th
e
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s,
M
o
h
a
m
e
d
I
Un
i
v
e
rsity
,
O
u
jd
a
,
in
1
9
9
5
.
C
o
n
ti
n
u
in
g
h
is
a
c
a
d
e
m
ic
p
u
rs
u
it
s,
h
e
e
a
rn
e
d
a
M
a
ste
r
’
s
d
e
g
re
e
in
Ap
p
l
ied
M
a
t
h
e
m
a
ti
c
s
fro
m
th
e
sa
m
e
i
n
stit
u
t
io
n
i
n
1
9
9
7
.
In
2
0
0
3
,
Ab
d
e
laz
iz
su
c
c
e
ss
fu
ll
y
c
o
m
p
lete
d
h
is
D
o
c
to
ra
te
in
Nu
m
e
rica
l
M
e
t
h
o
d
s
o
f
N
o
n
li
n
e
a
r
P
a
ra
b
o
li
c
P
ro
b
lem
s
with
N
o
n
l
o
c
a
l
Bo
u
n
d
a
ry
Co
n
d
it
io
n
s
a
n
d
Ap
p
li
c
a
ti
o
n
s
a
t
th
e
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s,
M
o
h
a
m
e
d
I
Un
iv
e
rsit
y
,
Ou
j
d
a
.
Cu
rre
n
tl
y
se
rv
in
g
a
s
a
P
ro
fe
ss
o
r
in
th
e
De
p
a
rtme
n
t
o
f
CTAM
a
t
ENCG
O,
M
o
h
a
m
e
d
I
Un
iv
e
rsity
,
Ou
j
d
a
,
sin
c
e
2
0
0
7
,
h
e
r
re
s
e
a
rc
h
in
tere
sts
e
n
c
o
m
p
a
ss
n
u
m
e
rica
l
a
n
a
ly
sis,
a
rti
ficia
l
i
n
tel
li
g
e
n
c
e
,
a
n
d
b
i
o
m
a
th
e
m
a
ti
c
s
.
His
imp
re
ss
iv
e
a
c
a
d
e
m
ic
trac
k
re
c
o
rd
in
c
l
u
d
e
s
t
h
e
a
u
th
o
rsh
i
p
o
f
1
9
p
a
p
e
rs
a
n
d
2
th
e
se
s.
Ad
d
it
io
n
a
ll
y
,
h
e
r
a
c
ti
v
e
ly
c
o
n
tr
ib
u
tes
to
sc
ien
ti
fic
o
rg
a
n
iza
ti
o
n
s,
h
o
l
d
i
n
g
m
e
m
b
e
rsh
ip
s
in
CIM
P
A
(2
0
1
9
,
2
0
2
2
),
CIM
AM
(
2
0
2
2
)
,
a
n
d
ROSA
(2
0
2
2
).
F
o
r
f
u
rth
e
r
in
q
u
ir
ies
,
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
.
c
h
e
to
u
a
n
i@
u
m
p
.
a
c
.
m
a
.
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