I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
,
p
p
.
45
2
~
46
2
I
SS
N:
2
502
-
4
7
52
,
DOI
: 1
0
.
1
1
5
9
1
/ijee
cs
.v
3
7
.
i
1
.
pp
45
2
-
46
2
452
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs
.
ia
esco
r
e.
co
m
AI in Mo
ro
ccan
e
duca
tion: eva
lua
t
ing
studen
t
a
ccep
tance using
ma
chine learning
cla
ss
ificatio
n mo
d
els
K
ho
ua
l Mo
ha
m
ed,
E
lk
a
im
bi
lla
h Z
ine
b,
M
cha
rf
i Zi
neb,
E
l A
s
ri
B
o
uc
hra
I
M
S
Te
a
m,
A
D
M
I
R
La
b
o
r
a
t
o
r
y
R
a
b
a
t
I
T
C
e
n
t
e
r
,
EN
S
I
A
S
,
M
o
h
a
mm
e
d
V
U
n
i
v
e
r
si
t
y
i
n
R
a
b
a
t
,
R
a
b
a
t
,
M
o
r
o
c
c
o
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
4
,
2
0
2
4
R
ev
is
ed
Sep
10
,
2
0
2
4
Acc
ep
ted
Sep
29
,
2
0
2
4
P
e
rso
n
a
li
z
e
d
lea
rn
i
n
g
is
b
e
c
o
m
in
g
a
re
a
li
ty
i
n
e
d
u
c
a
ti
o
n
th
a
n
k
s
to
th
e
rise
o
f
AI.
Th
is
st
u
d
y
in
v
e
stig
a
tes
th
e
p
o
ss
ib
il
it
ies
o
f
AI
with
i
n
t
h
e
re
a
lm
o
f
e
d
u
c
a
ti
o
n
,
fo
c
u
si
n
g
o
n
th
e
i
n
d
i
v
id
u
a
li
z
a
ti
o
n
o
f
th
e
lea
rn
in
g
e
x
p
e
rien
c
e
.
Th
e
re
se
a
rc
h
is b
a
se
d
o
n
th
e
re
sp
o
n
se
s o
f
3
9
5
stu
d
e
n
ts f
r
o
m
v
a
rio
u
s fac
u
lt
ies
in
M
o
ro
c
c
o
.
T
h
e
q
u
e
stio
n
n
a
ire
a
ime
d
to
a
ss
e
ss
th
e
stu
d
e
n
ts
’
o
p
in
i
o
n
s
o
f
AI,
th
e
ir
lev
e
l
o
f
k
n
o
wle
d
g
e
,
th
e
ir
p
r
e
v
io
u
s
e
x
p
e
rien
c
e
s,
a
n
d
t
h
e
ir
p
e
r
c
e
p
ti
o
n
o
f
th
e
a
p
p
li
c
a
ti
o
n
o
f
AI
with
in
e
d
u
c
a
ti
o
n
a
l
se
tt
i
n
g
s.
Emp
l
o
y
i
n
g
c
la
ss
ifi
c
a
ti
o
n
tec
h
n
iq
u
e
s
su
c
h
a
s
d
e
c
isio
n
tree
s
(DT),
m
u
lt
il
a
y
e
r
p
e
rc
e
p
tro
n
(
M
LP
),
a
n
d
ra
n
d
o
m
fo
re
sts
(RF
),
o
u
r
a
im
wa
s
to
p
re
d
ict
th
e
re
c
e
p
ti
v
it
y
o
f
AI
in
e
d
u
c
a
ti
o
n
.
Th
e
fi
n
d
in
g
s
h
i
g
h
li
g
h
t
sig
n
if
ica
n
t
d
iffere
n
c
e
s
in
h
o
w
M
o
ro
c
c
a
n
stu
d
e
n
ts
p
e
rc
e
iv
e
A
I,
id
e
n
ti
fy
i
n
g
k
e
y
fa
c
to
rs
s
u
c
h
a
s
fa
m
il
iarit
y
wit
h
t
h
e
tec
h
n
o
l
o
g
y
,
e
t
h
ica
l
c
o
n
c
e
rn
s,
a
n
d
p
e
rc
e
p
ti
o
n
o
f
it
s
p
o
ten
ti
a
l
im
p
a
c
t
o
n
t
h
e
lea
rn
in
g
e
x
p
e
rien
c
e
.
Clas
sifica
ti
o
n
m
o
d
e
ls
sh
o
we
d
v
a
ried
p
e
rfo
rm
a
n
c
e
in
a
n
ti
c
ip
a
ti
n
g
t
h
e
se
a
tt
it
u
d
e
s.
T
h
is
stu
d
y
h
i
g
h
li
g
h
ts
t
h
e
c
rit
ica
l
imp
o
rtan
c
e
o
f
u
n
d
e
rsta
n
d
i
n
g
st
u
d
e
n
ts
’
p
e
rsp
e
c
t
iv
e
s
o
n
AI
in
e
d
u
c
a
ti
o
n
.
Th
e
se
fin
d
in
g
s
o
ffe
r
c
ru
c
ial
in
sig
h
ts
fo
r
e
d
u
c
a
t
io
n
p
o
li
c
y
m
a
k
e
rs
a
s
we
ll
a
s
d
e
sig
n
e
rs
o
f
e
d
u
c
a
ti
o
n
a
l
tec
h
n
o
lo
g
y
so
l
u
ti
o
n
s
in
M
o
ro
c
c
o
.
T
h
e
fi
n
d
i
n
g
s
c
a
n
b
e
u
se
d
a
s
a
g
u
i
d
e
to
a
d
a
p
t
t
h
e
in
c
o
rp
o
ra
ti
o
n
o
f
AI
i
n
to
t
h
e
e
d
u
c
a
ti
o
n
se
c
to
r
with
d
isc
e
rn
m
e
n
t,
tak
i
n
g
in
to
a
c
c
o
u
n
t
stu
d
e
n
ts
’
p
e
rc
e
p
ti
o
n
s a
n
d
p
re
fe
re
n
c
e
s.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
C
las
s
if
icatio
n
m
o
d
els
E
d
u
ca
tio
n
al
s
ec
to
r
s
Mo
r
o
cc
an
u
n
iv
er
s
ity
Per
s
o
n
alize
d
lear
n
in
g
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Kh
o
u
al
Mo
h
a
m
ed
I
MS
T
ea
m
,
ADM
I
R
L
ab
o
r
ato
r
y
R
ab
at
I
T
C
en
ter
,
E
NSI
AS,
Mo
h
am
m
ed
V
U
n
iv
er
s
ity
in
R
ab
at
R
ab
at,
Mo
r
o
cc
o
E
m
ail:
m
o
h
am
ed
_
k
h
o
u
al@
u
m
5
.
ac
.
m
a
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
ed
u
ca
tio
n
al
s
ec
to
r
is
p
r
o
g
r
ess
iv
ely
in
co
r
p
o
r
atin
g
ar
tifi
cial
in
tellig
en
ce
(
AI
)
,
a
tech
n
o
lo
g
y
with
th
e
p
o
te
n
tial
to
r
ev
o
lu
tio
n
ize
b
o
th
lea
r
n
in
g
an
d
teac
h
in
g
m
eth
o
d
s
[
1
]
.
T
h
is
a
d
v
an
ce
d
tec
h
n
o
lo
g
y
e
n
ab
les
th
e
cu
s
to
m
izatio
n
o
f
lear
n
in
g
ex
p
er
ien
ce
s
b
y
ad
ju
s
tin
g
p
ed
ag
o
g
ical
tech
n
iq
u
es
to
m
ee
t
th
e
s
p
ec
if
ic
n
ee
d
s
o
f
ea
ch
s
tu
d
en
t,
th
er
eb
y
p
r
o
v
id
in
g
m
o
r
e
p
er
s
o
n
alize
d
an
d
e
f
f
ec
tiv
e
lear
n
in
g
o
p
p
o
r
tu
n
ities
[
2
]
,
[
3
]
.
Desp
ite
its
co
n
s
id
er
ab
le
p
o
te
n
tial,
th
er
e
is
a
co
n
s
p
icu
o
u
s
lack
o
f
co
m
p
r
eh
en
s
iv
e
r
esear
ch
o
n
th
e
im
p
ac
t
o
f
AI
in
th
e
ed
u
ca
tio
n
al
lan
d
s
ca
p
e
o
f
M
o
r
o
cc
o
.
T
h
is
d
ata
d
ef
icien
c
y
u
n
d
er
s
co
r
es
th
e
im
p
o
r
tan
ce
o
f
u
n
d
er
s
tan
d
i
n
g
th
e
d
is
tin
ct
attitu
d
es
[
4
]
,
b
elief
s
,
a
n
d
p
r
e
f
er
en
ce
s
o
f
Mo
r
o
cc
an
s
tu
d
en
ts
to
war
d
s
AI
tech
n
o
lo
g
y
.
AI
in
ed
u
ca
tio
n
h
o
ld
s
g
r
ea
t p
r
o
m
is
e
f
o
r
p
er
s
o
n
alizin
g
lear
n
i
n
g
p
ath
way
s
,
wh
ich
in
clu
d
es th
e
f
lex
ib
le
ad
ju
s
tm
en
t
o
f
c
o
n
ten
t,
tea
ch
in
g
m
eth
o
d
s
,
an
d
ev
alu
atio
n
s
ac
co
r
d
in
g
to
ea
ch
s
tu
d
en
t
’
s
p
r
o
g
r
ess
an
d
p
r
ef
er
en
ce
s
[
5
]
.
Ho
wev
e
r
,
th
e
i
n
teg
r
atio
n
o
f
AI
in
to
ed
u
ca
tio
n
p
r
esen
ts
u
n
i
q
u
e
c
h
allen
g
es,
m
ak
in
g
it
cr
u
cial
to
u
n
d
er
s
tan
d
s
tu
d
en
t
s
’
p
er
s
p
ec
tiv
es
to
o
p
tim
ize
i
ts
d
ep
lo
y
m
e
n
t
[
6
]
,
[
7
]
.
B
y
co
m
p
ar
in
g
s
ev
er
al
class
if
icatio
n
m
eth
o
d
s
,
th
is
s
tu
d
y
aim
s
to
ad
d
r
ess
th
is
r
esear
ch
g
ap
b
y
p
r
e
d
ictin
g
th
e
lik
elih
o
o
d
o
f
A
I
ac
ce
p
tan
ce
am
o
n
g
Mo
r
o
cc
an
s
tu
d
en
ts
.
T
h
is
r
esear
ch
ev
alu
a
tes
th
e
o
p
in
io
n
s
,
k
n
o
wled
g
e,
an
d
ex
p
e
r
ien
ce
s
o
f
3
9
5
s
tu
d
e
n
ts
f
r
o
m
v
ar
io
u
s
f
ac
u
lties
in
Mo
r
o
cc
o
r
eg
a
r
d
in
g
AI
in
ed
u
ca
tio
n
[
8
].
T
h
e
s
tu
d
y
u
t
ilizes
clas
s
if
icatio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
A
I
in
Mo
r
o
cc
a
n
ed
u
c
a
tio
n
:
ev
a
lu
a
tin
g
s
tu
d
e
n
t a
cc
ep
t
a
n
ce
u
s
in
g
ma
ch
in
e
lea
r
n
in
g
…
(
K
h
o
u
a
l Mo
h
a
med
)
453
m
o
d
els,
in
clu
d
in
g
d
ec
is
io
n
tr
ee
s
(
DT
)
,
r
an
d
o
m
f
o
r
ests
(
R
F
)
,
an
d
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
s
(
ML
P),
to
f
o
r
ec
ast
h
o
w
s
tu
d
en
ts
will
ad
o
p
t
th
is
i
n
n
o
v
ativ
e
tech
n
o
lo
g
y
[
9
]
.
T
h
e
g
o
al
is
to
d
eter
m
in
e
th
e
m
o
s
t
ac
cu
r
ate
m
o
d
el
b
y
co
m
p
ar
in
g
th
eir
in
d
i
v
id
u
al
p
e
r
f
o
r
m
a
n
ce
s
an
d
s
elec
tin
g
th
e
o
n
e
th
at
p
r
o
v
id
es
th
e
m
o
s
t
p
r
ec
is
e
p
r
ed
ictio
n
s
f
o
r
AI
ac
ce
p
tan
ce
.
Var
io
u
s
s
tu
d
ies h
av
e
ex
p
lo
r
e
d
d
if
f
er
en
t a
s
p
ec
ts
o
f
AI
in
e
d
u
ca
tio
n
,
in
clu
d
in
g
ac
ad
em
ic
p
er
f
o
r
m
an
ce
,
s
tu
d
en
t
en
g
ag
em
en
t,
an
d
p
er
s
o
n
alize
d
lea
r
n
in
g
.
Pach
ec
o
-
M
en
d
o
za
et
a
l.
[
1
0
]
u
tili
ze
d
DT
to
p
r
e
d
ict
ac
ad
e
m
ic
p
er
f
o
r
m
an
ce
,
h
ig
h
lig
h
tin
g
th
e
im
p
o
r
ta
n
ce
o
f
ag
e
an
d
s
tu
d
y
h
a
b
its
with
an
ac
cu
r
ac
y
o
f
8
5
%.
Similar
ly
,
Kab
ak
ch
iev
a
[
1
1
]
ap
p
lied
DT
to
id
en
tify
at
-
r
is
k
s
tu
d
en
t
s
b
ased
o
n
p
r
ev
io
u
s
r
esu
lts
an
d
en
g
ag
e
m
en
t,
ac
h
iev
in
g
an
ac
cu
r
ac
y
o
f
8
7
%
.
Aitk
in
a
n
d
Fo
x
all
[
1
2
]
em
p
lo
y
ed
ML
P
to
m
o
d
el
teac
h
er
b
e
h
av
io
r
al
in
ten
tio
n
s
,
d
em
o
n
s
tr
atin
g
th
e
a
b
ilit
y
to
h
an
d
le
co
m
p
lex
r
elatio
n
s
h
ip
s
with
an
a
cc
u
r
ac
y
o
f
8
9
%.
L
iv
ier
is
et
a
l.
[
1
3
]
u
s
ed
ML
P
to
p
r
ed
ict
s
tu
d
e
n
t
p
e
r
f
o
r
m
an
ce
b
y
id
en
tif
y
in
g
m
o
tiv
ati
o
n
an
d
en
g
ag
em
en
t
f
ac
to
r
s
,
w
ith
an
ac
cu
r
ac
y
o
f
9
0
%.
Al
-
R
ah
m
i
et
a
l.
[
1
4
]
in
teg
r
ated
R
F
to
p
r
ed
ict
s
tu
d
en
t
attitu
d
es
to
war
d
s
AI
-
b
ased
MO
OC
s
,
s
h
o
win
g
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
in
u
s
er
b
eh
av
io
r
m
o
d
elin
g
with
an
a
c
cu
r
ac
y
o
f
9
2
%.
Dev
asia
et
a
l.
[
1
5
]
u
tili
ze
d
R
F
to
p
r
ed
ict
ac
ad
em
ic
p
er
f
o
r
m
an
ce
,
d
em
o
n
s
tr
atin
g
h
ig
h
ac
c
u
r
ac
y
in
id
en
tify
in
g
s
u
cc
ess
f
ac
to
r
s
with
an
ac
cu
r
ac
y
o
f
9
1
%.
Sev
er
al
s
tu
d
ies
h
av
e
ex
am
i
n
ed
th
e
in
teg
r
ati
o
n
an
d
ac
ce
p
tan
ce
o
f
e
d
u
ca
tio
n
al
tech
n
o
lo
g
ies
in
Mo
r
o
cc
o
.
E
jjam
i
[
1
6
]
an
al
y
ze
d
th
e
im
p
ac
t
o
f
AI
(
ML
an
d
L
L
M)
o
n
M
o
r
o
cc
a
n
ed
u
ca
tio
n
,
em
p
h
asizin
g
th
e
p
o
ten
tial
to
p
e
r
s
o
n
alize
lear
n
in
g
an
d
r
ed
u
ce
ed
u
ca
tio
n
al
d
is
p
ar
ities
.
Ham
d
an
i
[
1
7
]
in
v
esti
g
ated
s
tu
d
en
ts
’
sa
tis
f
ac
tio
n
with
d
is
tan
ce
lear
n
in
g
,
r
ev
ea
lin
g
d
is
s
atis
f
ac
tio
n
d
u
e
to
in
a
d
eq
u
ate
tech
n
o
l
o
g
ical
in
f
r
astru
ctu
r
e
an
d
h
ig
h
in
ter
n
et
c
o
s
ts
.
T
h
ese
s
tu
d
ies
u
n
d
er
s
co
r
e
th
e
ef
f
icac
y
o
f
d
if
f
e
r
en
t
m
ac
h
in
e
lear
n
in
g
m
o
d
els
in
f
o
r
ec
asti
n
g
ed
u
ca
tio
n
al
o
u
tco
m
es
an
d
u
s
e
r
ac
c
ep
tan
ce
o
f
tec
h
n
o
lo
g
ies.
Ho
wev
er
,
th
ey
p
r
ed
o
m
in
an
tly
f
o
cu
s
o
n
ac
ad
e
m
ic
p
er
f
o
r
m
an
ce
an
d
teac
h
e
r
ad
o
p
tio
n
,
leav
in
g
a
g
ap
in
u
n
d
e
r
s
tan
d
in
g
AI
ac
ce
p
tan
ce
am
o
n
g
s
tu
d
en
ts
in
th
e
Mo
r
o
cc
an
ed
u
ca
tio
n
al
co
n
tex
t.
Desp
ite
ad
v
an
ce
m
en
ts
in
AI
-
r
elate
d
ed
u
c
atio
n
al
r
esear
c
h,
s
ev
er
al
ch
allen
g
es
r
em
ain
:
lim
ited
f
o
cu
s
o
n
s
tu
d
en
t
ac
ce
p
tan
ce
,
g
eo
g
r
a
p
h
ical
an
d
cu
ltu
r
al
g
ap
s
,
an
d
a
lack
o
f
co
m
p
r
eh
e
n
s
iv
e
co
m
p
ar
is
o
n
s
b
etwe
en
d
if
f
er
en
t
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els
to
p
r
ed
ict
AI
ac
ce
p
tan
ce
am
o
n
g
s
tu
d
en
ts
[
1
8
].
T
h
is
s
tu
d
y
aim
s
to
b
r
id
g
e
th
ese
g
ap
s
b
y
c
o
n
d
u
ctin
g
a
co
m
p
ar
ativ
e
an
aly
s
is
o
f
v
ar
io
u
s
class
if
icatio
n
m
o
d
els
to
p
r
ed
ict
AI
ac
ce
p
ta
n
ce
am
o
n
g
M
o
r
o
cc
an
s
tu
d
en
t
s
.
B
y
ex
am
in
in
g
th
e
r
esp
o
n
s
es
o
f
3
9
5
s
tu
d
e
n
ts
f
r
o
m
d
if
f
e
r
en
t
f
ac
u
lties
ac
r
o
s
s
Mo
r
o
cc
o
,
th
e
r
esear
ch
ass
ess
es
th
eir
p
er
ce
p
tio
n
s
,
k
n
o
wled
g
e,
an
d
ex
p
e
r
ien
ce
s
with
AI
in
ed
u
ca
tio
n
.
E
m
p
lo
y
in
g
class
if
icatio
n
m
o
d
els
s
u
ch
as
DT
,
ML
P,
an
d
R
F
,
th
e
s
tu
d
y
en
d
ea
v
o
r
s
to
f
o
r
ec
ast
s
tu
d
en
t
ad
o
p
tio
n
o
f
th
is
in
n
o
v
ativ
e
tech
n
o
lo
g
y
.
T
h
e
g
o
al
is
to
id
en
tify
th
e
m
o
s
t
ac
cu
r
ate
m
o
d
el
b
y
ev
alu
atin
g
t
h
eir
r
esp
ec
tiv
e
p
er
f
o
r
m
a
n
ce
s
an
d
s
elec
tin
g
t
h
e
o
n
e
th
at
p
r
o
v
id
es
th
e
b
est
p
r
ed
ictio
n
s
f
o
r
AI
ac
ce
p
tan
ce
.
C
o
n
tr
ib
u
tio
n
s
o
f
t
h
e
s
tu
d
y
:
−
C
o
m
p
ar
ativ
e
an
al
y
s
is
o
f
class
if
icatio
n
m
o
d
els
:
e
v
alu
ate
an
d
co
m
p
a
r
e
th
e
p
e
r
f
o
r
m
an
ce
s
o
f
DT
,
ML
P,
an
d
R
F m
o
d
els in
ter
m
s
o
f
ac
cu
r
ac
y
f
o
r
p
r
ed
ictin
g
s
tu
d
en
ts
’
r
ec
e
p
tiv
ity
to
AI
in
e
d
u
ca
tio
n
.
−
Un
d
er
s
tan
d
in
g
s
tu
d
e
n
t
p
er
c
ep
tio
n
s
:
p
r
o
v
i
d
e
a
d
etailed
an
aly
s
is
o
f
Mo
r
o
cc
an
s
tu
d
en
ts
’
attitu
d
es,
p
er
ce
p
tio
n
s
,
a
n
d
co
n
ce
r
n
s
r
e
g
ar
d
in
g
AI
i
n
ed
u
ca
tio
n
.
−
R
ec
o
m
m
en
d
atio
n
s
f
o
r
p
o
licy
m
ak
er
s
:
o
f
f
er
i
n
s
ig
h
ts
to
g
u
id
e
ed
u
ca
tio
n
al
p
o
licies
an
d
th
e
in
teg
r
atio
n
o
f
AI
in
th
e
Mo
r
o
cc
an
ed
u
ca
tio
n
al
s
ec
to
r
,
co
n
s
id
er
i
n
g
s
tu
d
e
n
ts
’
p
r
ef
er
en
ce
s
an
d
p
er
ce
p
tio
n
s
.
−
I
m
p
licatio
n
s
f
o
r
ed
u
ca
tio
n
al
s
o
lu
tio
n
d
esig
n
er
s
:
p
r
o
p
o
s
e
g
u
id
elin
es
f
o
r
d
ev
elo
p
in
g
AI
-
b
a
s
ed
ed
u
ca
tio
n
al
to
o
ls
tailo
r
ed
to
th
e
n
ee
d
s
an
d
ex
p
ec
tatio
n
s
o
f
s
tu
d
e
n
ts
.
2.
M
E
T
H
O
D
2
.
1
.
Abo
ut
t
he
da
t
a
s
et
2
.
1
.
1
.
Sa
m
pli
ng
m
e
t
ho
do
lo
g
y
a
nd
da
t
a
co
llect
io
n pro
ce
s
s
I
n
o
u
r
r
esear
ch
,
we
o
p
ted
to
co
n
d
u
ct
a
n
o
n
lin
e
s
u
r
v
e
y
u
s
in
g
Go
o
g
le
Fo
r
m
s
as
th
e
p
r
im
ar
y
d
ata
co
llectio
n
to
o
l.
T
h
e
s
u
r
v
e
y
co
n
s
is
ted
o
f
2
0
d
etailed
q
u
esti
o
n
s
,
wh
ich
ar
e
li
s
ted
in
T
ab
le
1
.
All
wr
itten
in
Fre
n
ch
-
th
e
p
r
im
ar
y
lan
g
u
a
g
e
o
f
in
s
tr
u
ctio
n
in
Mo
r
o
cc
o
.
T
h
is
ap
p
r
o
ac
h
allo
wed
u
s
to
en
g
ag
e
a
d
iv
er
s
e
g
r
o
u
p
o
f
r
esp
o
n
d
en
ts
an
d
ef
f
ec
tiv
ely
g
ath
er
th
ei
r
i
n
s
ig
h
ts
.
B
y
th
o
u
g
h
tf
u
lly
d
esig
n
in
g
th
e
q
u
esti
o
n
s
,
we
aim
ed
to
ex
p
lo
r
e
v
ar
i
o
u
s
a
s
p
ec
ts
o
f
AI
in
teg
r
atio
n
in
ed
u
ca
tio
n
.
C
h
o
o
s
in
g
Go
o
g
le
Fo
r
m
s
en
s
u
r
ed
ea
s
e
o
f
p
ar
ticip
atio
n
f
o
r
r
esp
o
n
d
en
ts
wh
ile
s
tr
ea
m
lin
in
g
d
ata
co
llectio
n
f
o
r
r
esear
c
h
er
s
.
Ov
er
all,
o
u
r
s
tr
ateg
y
em
p
h
asized
ef
f
icien
cy
,
ac
ce
s
s
ib
ilit
y
,
an
d
cu
ltu
r
al
s
en
s
itiv
ity
,
en
ab
lin
g
u
s
to
g
ath
e
r
v
alu
a
b
le
in
f
o
r
m
atio
n
o
n
Mo
r
o
cc
an
s
tu
d
e
n
ts
’
attitu
d
es to
war
d
AI
in
e
d
u
ca
tio
n
.
Fro
m
Feb
r
u
ar
y
to
Ma
y
2
0
2
3
,
3
9
5
s
tu
d
en
ts
v
o
lu
n
tar
ily
p
ar
ti
cip
ated
in
th
i
s
s
u
r
v
ey
wh
ile
m
ain
tain
in
g
th
e
an
o
n
y
m
ity
o
f
th
ei
r
r
esp
o
n
s
es.
No
p
er
s
o
n
al
in
f
o
r
m
atio
n
was
r
eq
u
ested
o
r
co
llected
as
p
ar
t
o
f
th
is
d
ata
co
llectio
n
p
r
o
ce
s
s
.
T
h
e
q
u
est
io
n
s
in
clu
d
e
d
in
th
e
s
u
r
v
ey
wer
e
m
eticu
lo
u
s
ly
d
esig
n
ed
t
o
en
co
m
p
ass
b
o
th
q
u
an
titativ
e
an
d
q
u
al
itativ
e
a
s
p
ec
ts
.
Qu
an
titativ
e
in
q
u
ir
ies
f
o
cu
s
ed
o
n
m
ea
s
u
r
es
s
u
ch
as
th
e
f
r
eq
u
en
cy
a
n
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
45
2
-
46
2
454
d
u
r
atio
n
o
f
u
s
in
g
AI
to
o
ls
,
o
v
er
all
s
atis
f
ac
tio
n
r
atin
g
s
,
an
d
th
e
p
er
ce
iv
ed
im
p
ac
t
o
n
a
ca
d
em
ic
o
u
tco
m
es.
Simu
ltan
eo
u
s
ly
,
q
u
alitativ
e
q
u
esti
o
n
s
ex
p
lo
r
ed
s
tu
d
en
ts
’
in
d
iv
id
u
al
p
e
r
s
p
ec
tiv
es,
th
eir
au
to
n
o
m
y
in
lear
n
i
n
g
,
th
e
r
eliab
ilit
y
o
f
in
f
o
r
m
atio
n
p
r
o
v
id
ed
b
y
AI
to
o
ls
,
an
d
co
n
c
er
n
s
ass
o
ciate
d
with
th
eir
u
s
ag
e
[
8
]
.
T
ab
le1
.
Stu
d
en
t
-
r
elate
d
s
u
r
v
e
y
q
u
esti
o
n
s
S
u
r
v
e
y
q
u
e
s
t
i
o
n
s
H
a
v
e
t
h
e
u
s
e
o
f
A
I
t
e
c
h
n
o
l
o
g
i
e
s
h
e
l
p
e
d
y
o
u
i
n
c
o
n
so
l
i
d
a
t
i
n
g
y
o
u
r
a
c
q
u
i
r
e
d
d
a
t
a
m
e
mo
r
y
?
H
a
v
e
y
o
u
n
o
t
i
c
e
d
a
n
i
m
p
r
o
v
e
m
e
n
t
i
n
t
h
e
q
u
a
l
i
t
y
o
f
y
o
u
r
e
d
u
c
a
t
i
o
n
a
n
d
l
e
a
r
n
i
n
g
s
k
i
l
l
s
w
i
t
h
t
h
e
u
se
o
f
A
I
t
e
c
h
n
o
l
o
g
i
e
s
?
D
o
y
o
u
t
h
i
n
k
t
h
a
t
t
h
e
u
s
e
o
f
A
I
t
e
c
h
n
o
l
o
g
i
e
s
h
a
s
i
n
f
l
u
e
n
c
e
d
y
o
u
r
a
b
i
l
i
t
y
t
o
i
n
d
e
p
e
n
d
e
n
t
l
y
s
o
l
v
e
p
r
o
b
l
e
ms?
A
r
e
A
I
t
e
c
h
n
o
l
o
g
i
e
s
su
f
f
i
c
i
e
n
t
i
n
e
f
f
e
c
t
i
v
e
l
y
a
n
sw
e
r
i
n
g
y
o
u
r
q
u
e
r
i
e
s
?
D
o
A
I
t
e
c
h
n
o
l
o
g
i
e
s
c
o
n
t
r
i
b
u
t
e
t
o
b
e
t
t
e
r
u
n
d
e
r
st
a
n
d
i
n
g
o
f
c
o
m
p
l
e
x
c
o
n
c
e
p
t
s?
D
o
y
o
u
f
r
e
q
u
e
n
t
l
y
r
e
l
y
o
n
A
I
t
e
c
h
n
o
l
o
g
i
e
s
t
o
c
o
m
p
l
e
t
e
y
o
u
r
u
n
i
v
e
r
si
t
y
a
ss
i
g
n
m
e
n
t
s?
I
s t
h
e
p
o
t
e
n
t
i
a
l
d
e
p
e
n
d
e
n
c
e
o
n
A
I
t
e
c
h
n
o
l
o
g
i
e
s f
o
r
y
o
u
r
st
u
d
i
e
s a
so
u
r
c
e
o
f
c
o
n
c
e
r
n
f
o
r
y
o
u
?
H
a
v
e
y
o
u
n
o
t
i
c
e
d
a
n
i
m
p
r
o
v
e
m
e
n
t
i
n
y
o
u
r
a
c
a
d
e
mi
c
p
e
r
f
o
r
m
a
n
c
e
si
n
c
e
y
o
u
s
t
a
r
t
e
d
u
s
i
n
g
A
I
t
e
c
h
n
o
l
o
g
i
e
s?
H
a
s t
h
e
a
d
o
p
t
i
o
n
o
f
A
I
t
e
c
h
n
o
l
o
g
i
e
s
a
f
f
e
c
t
e
d
y
o
u
r
i
n
d
e
p
e
n
d
e
n
c
e
i
n
a
c
q
u
i
r
i
n
g
k
n
o
w
l
e
d
g
e
?
A
r
e
t
h
e
s
o
l
u
t
i
o
n
s
p
r
o
v
i
d
e
d
b
y
A
I
t
e
c
h
n
o
l
o
g
i
e
s
c
o
n
s
i
st
e
n
t
w
i
t
h
t
h
o
se
f
r
o
m
o
t
h
e
r
v
e
r
i
f
i
e
d
so
u
r
c
e
s?
I
n
y
o
u
r
o
p
i
n
i
o
n
,
a
r
e
A
I
t
e
c
h
n
o
l
o
g
i
e
s
a
n
a
s
set
t
o
y
o
u
r
l
e
a
r
n
i
n
g
p
r
o
c
e
ss?
A
r
e
y
o
u
c
o
mf
o
r
t
a
b
l
e
u
si
n
g
A
I
t
e
c
h
n
o
l
o
g
i
e
s
i
n
y
o
u
r
s
t
u
d
i
e
s?
H
a
s
y
o
u
r
u
n
d
e
r
st
a
n
d
i
n
g
o
f
t
h
e
s
u
b
j
e
c
t
s y
o
u
st
u
d
i
e
d
b
e
e
n
e
n
h
a
n
c
e
d
b
y
i
n
t
e
g
r
a
t
i
n
g
A
I
t
e
c
h
n
o
l
o
g
i
e
s?
H
a
s y
o
u
r
a
p
p
r
o
a
c
h
t
o
i
n
f
o
r
ma
t
i
o
n
se
a
r
c
h
c
h
a
n
g
e
d
w
i
t
h
t
h
e
i
n
t
r
o
d
u
c
t
i
o
n
o
f
A
I
t
e
c
h
n
o
l
o
g
i
e
s
i
n
t
o
y
o
u
r
r
o
u
t
i
n
e
?
A
r
e
y
o
u
sa
t
i
sf
i
e
d
w
i
t
h
t
h
e
a
c
c
u
r
a
c
y
o
f
t
h
e
i
n
f
o
r
ma
t
i
o
n
p
r
o
v
i
d
e
d
b
y
A
I
t
e
c
h
n
o
l
o
g
i
e
s?
H
a
v
e
y
o
u
se
e
n
a
n
i
n
c
r
e
a
s
e
i
n
y
o
u
r
e
f
f
i
c
i
e
n
c
y
i
n
e
d
u
c
a
t
i
o
n
a
l
a
c
t
i
v
i
t
i
e
s
d
u
e
t
o
t
h
e
u
se
o
f
A
I
t
e
c
h
n
o
l
o
g
i
e
s
?
H
a
v
e
y
o
u
e
v
e
r
e
n
c
o
u
n
t
e
r
e
d
i
n
a
c
c
u
r
a
t
e
i
n
f
o
r
ma
t
i
o
n
p
r
o
v
i
d
e
d
b
y
A
I
t
e
c
h
n
o
l
o
g
i
e
s?
H
a
v
e
A
I
t
e
c
h
n
o
l
o
g
i
e
s e
v
e
r
p
r
o
v
i
d
e
d
y
o
u
w
i
t
h
o
u
t
d
a
t
e
d
i
n
f
o
r
mat
i
o
n
?
D
i
d
y
o
u
so
l
e
l
y
r
e
l
y
o
n
A
I
t
e
c
h
n
o
l
o
g
i
e
s fo
r
i
n
f
o
r
mat
i
o
n
r
e
p
r
o
d
u
c
t
i
o
n
?
O
n
a
v
e
r
a
g
e
,
h
o
w
m
u
c
h
t
i
me
d
o
y
o
u
s
p
e
n
d
u
s
i
n
g
A
I
t
e
c
h
n
o
l
o
g
i
e
s?
2
.
1
.
2
.
P
er
s
o
na
l inf
o
rma
t
io
n
a
nd
dem
o
g
ra
ph
ic
da
t
a
a
bo
u
t
s
t
ud
ent
s
A
th
o
r
o
u
g
h
ex
am
in
atio
n
o
f
t
h
e
p
ar
ticip
an
ts
’
p
er
s
o
n
al
an
d
d
em
o
g
r
ap
h
ic
tr
aits
is
s
h
o
wn
in
Fig
u
r
e
1
.
No
tab
ly
,
m
o
s
t
o
f
th
e
p
ar
ticip
a
n
ts
h
av
e
attain
ed
a
h
i
g
h
d
eg
r
e
e
o
f
e
d
u
ca
tio
n
an
d
p
o
s
s
ess
a
s
u
b
s
tan
tial
n
u
m
b
e
r
o
f
u
n
iv
e
r
s
ity
d
eg
r
ee
s
.
T
o
b
e
m
o
r
e
p
r
ec
is
e,
8
0
%
o
f
p
ar
ticip
an
t
s
s
aid
th
ey
h
ad
ea
r
n
e
d
a
b
a
ch
elo
r
’
s
d
eg
r
ee
,
1
5
%
s
aid
th
ey
h
ad
a
m
aster
’
s
d
e
g
r
ee
,
an
d
5
%
s
aid
th
ey
h
ad
a
d
o
cto
r
ate,
d
em
o
n
s
tr
atin
g
th
e
r
an
g
e
o
f
ac
ad
e
m
ic
lev
els r
ep
r
esen
ted
.
T
h
is
in
clu
s
iv
e
g
r
o
u
p
in
clu
d
es st
u
d
en
ts
o
f
v
ar
io
u
s
ag
es a
n
d
ac
ad
e
m
ic
b
a
ck
g
r
o
u
n
d
s
.
Am
o
n
g
th
em
,
9
4
%
wer
e
ag
e
d
b
etwe
e
n
1
8
an
d
3
8
,
wh
ile
th
e
r
est
we
r
e
o
v
er
3
9
y
ea
r
s
o
ld
.
Ad
d
itio
n
ally
,
an
an
aly
s
is
o
f
g
en
d
er
d
is
tr
ib
u
tio
n
r
ev
ea
ls
th
a
t 5
6
% o
f
p
a
r
ticip
an
ts
wer
e
f
e
m
ale,
wh
ile
4
4
% we
r
e
m
ale
[
8
]
.
Fig
u
r
e
1
.
Stu
d
e
n
ts
’
p
er
s
o
n
al
i
n
f
o
r
m
atio
n
/
d
em
o
g
r
ap
h
ic
d
ata
2
.
2
.
M
et
ho
do
lo
g
y
T
o
ass
ess
an
d
c
o
m
p
ar
e
th
e
r
es
u
lts
o
f
class
if
icatio
n
m
o
d
els
i
n
p
r
ed
ictin
g
th
e
ac
ce
p
tan
ce
o
f
AI
am
o
n
g
Mo
r
o
cc
an
s
tu
d
en
ts
,
a
r
ig
o
r
o
u
s
m
eth
o
d
o
lo
g
y
was e
s
tab
lis
h
ed
.
T
h
is
m
eth
o
d
o
lo
g
y
u
n
f
o
ld
ed
t
h
r
o
u
g
h
s
ev
er
al
k
ey
s
tag
es,
as d
ep
icted
in
Fig
u
r
e
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
A
I
in
Mo
r
o
cc
a
n
ed
u
c
a
tio
n
:
ev
a
lu
a
tin
g
s
tu
d
e
n
t a
cc
ep
t
a
n
ce
u
s
in
g
ma
ch
in
e
lea
r
n
in
g
…
(
K
h
o
u
a
l Mo
h
a
med
)
455
Fig
u
r
e
2
.
Pro
p
o
s
ed
m
et
h
o
d
o
lo
g
y
2
.
2
.
1
.
Da
t
a
co
llect
io
n
T
h
is
p
r
o
ce
s
s
in
v
o
lv
es g
ath
er
in
g
r
elev
an
t in
f
o
r
m
atio
n
f
r
o
m
a
d
iv
er
s
e
g
r
o
u
p
o
f
s
tu
d
e
n
ts
ac
r
o
s
s
v
ar
io
u
s
f
ac
u
lties
an
d
u
n
iv
er
s
ities
in
Mo
r
o
cc
o
.
W
e
will
u
tili
ze
m
u
ltip
le
ch
an
n
els
s
u
ch
as
u
n
iv
er
s
ity
m
ailin
g
lis
ts
,
s
o
cial
m
ed
ia
p
latf
o
r
m
s
,
an
d
s
tu
d
en
t
clu
b
s
to
en
s
u
r
e
b
r
o
ad
p
ar
ticip
atio
n
.
T
h
e
p
r
im
a
r
y
to
o
l
f
o
r
d
ata
co
llectio
n
will
b
e
a
m
u
ltip
le
-
ch
o
ice
q
u
e
s
tio
n
n
air
e,
d
esig
n
ed
to
elicit
d
etailed
an
d
s
tr
u
ctu
r
ed
r
esp
o
n
s
es.
T
h
is
d
ata
will
p
r
o
v
id
e
c
r
u
cial
in
s
ig
h
ts
in
to
Mo
r
o
cc
an
s
tu
d
e
n
ts
’
ac
ce
p
tan
c
e
o
f
AI
in
ed
u
ca
tio
n
,
f
o
r
m
in
g
th
e
f
o
u
n
d
atio
n
f
o
r
f
u
r
th
er
a
n
aly
s
is
an
d
in
ter
p
r
etatio
n
in
o
u
r
s
tu
d
y
.
2
.
2
.
2
.
Da
t
a
prepro
ce
s
s
ing
Data
p
r
ep
r
o
ce
s
s
in
g
in
v
o
lv
es
clea
n
in
g
th
e
d
ata,
p
r
o
ce
s
s
in
g
m
is
s
in
g
v
alu
es,
c
o
d
in
g
ca
teg
o
r
ical
attr
ib
u
tes
an
d
n
o
r
m
alizin
g
t
h
e
d
ata
wh
e
r
e
n
ec
ess
ar
y
.
At
t
h
is
s
tag
e,
th
e
d
ata
is
ch
ec
k
ed
f
o
r
d
u
p
licates
an
d
m
is
s
in
g
v
alu
es.
As
s
h
o
wn
in
Fig
u
r
e
3
,
th
e
r
e
ar
e
n
o
m
is
s
in
g
v
a
l
u
es
in
t
h
is
d
ataset,
s
o
th
er
e
is
n
o
n
ee
d
to
r
em
o
v
e
g
ap
s
o
r
d
u
p
licates [
1
9
].
Fig
u
r
e
3
.
Data
s
tatis
tics
2
.
2
.
3
.
Da
t
a
s
pli
t
t
ing
T
h
is
p
r
o
ce
d
u
r
e
e
n
tails
d
iv
id
i
n
g
th
e
d
ataset
in
to
th
r
ee
s
ep
a
r
ate
s
u
b
s
ets
f
o
r
th
e
p
u
r
p
o
s
e
o
f
tr
ain
in
g
,
v
alid
atin
g
,
an
d
test
in
g
th
e
m
a
ch
in
e
lear
n
in
g
m
o
d
el,
as
s
h
o
wn
in
F
ig
u
r
e
4
.
T
h
e
g
o
al
is
to
g
u
ar
a
n
tee
th
at
th
e
m
o
d
el
ex
h
ib
its
g
o
o
d
g
en
e
r
aliza
tio
n
to
n
ew
d
ata,
h
en
ce
p
r
ev
e
n
tin
g
o
v
er
f
itti
n
g
:
a.
T
r
ain
in
g
s
et:
th
e
m
o
d
el
is
tr
ai
n
ed
o
n
t
h
is
s
u
b
s
et
in
o
r
d
er
to
h
elp
it
id
e
n
tify
p
atter
n
s
a
n
d
m
o
d
if
y
p
ar
am
eter
s
in
o
r
d
e
r
to
r
e
d
u
ce
p
r
ed
ictio
n
m
is
tak
es.
b.
Valid
atio
n
s
et:
th
is
s
u
b
s
et
i
s
u
tili
ze
d
in
th
e
tr
ain
in
g
p
h
as
e
to
o
p
tim
ize
h
y
p
er
p
ar
a
m
ete
r
s
.
I
t
o
f
f
er
s
an
im
m
ed
iate
an
d
im
p
ar
tial
ass
ess
m
en
t
o
f
t
h
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
,
aid
in
g
in
th
e
ch
o
ice
o
f
th
e
m
o
s
t
ef
f
ec
tiv
e
m
o
d
el.
c.
T
est
s
et:
o
n
ce
th
e
m
o
d
el
h
as
u
n
d
er
g
o
n
e
tr
ain
i
n
g
an
d
v
alid
ati
o
n
,
th
is
s
u
b
s
et
is
u
s
ed
to
ev
al
u
ate
th
e
m
o
d
e
l
’
s
ca
p
ac
ity
to
ap
p
l
y
its
k
n
o
wle
d
g
e
to
f
r
esh
,
u
n
f
am
iliar
d
ata
.
I
t
p
r
o
v
id
es
an
im
p
a
r
tial
ass
ess
m
en
t
o
f
th
e
m
o
d
el
’
s
r
ea
l
-
wo
r
l
d
p
er
f
o
r
m
a
n
ce
[
2
0
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
45
2
-
46
2
456
B
y
ca
r
ef
u
lly
s
p
litt
in
g
th
e
d
ata
,
th
e
p
r
o
ce
s
s
e
n
s
u
r
es
th
at
t
h
e
m
ac
h
in
e
lear
n
in
g
m
o
d
el
ca
n
e
f
f
ec
tiv
ely
b
alan
ce
u
n
d
er
f
itti
n
g
a
n
d
o
v
er
f
itti
n
g
,
lead
in
g
to
a
r
o
b
u
s
t a
n
d
r
eliab
le
p
r
ed
ictiv
e
p
er
f
o
r
m
an
c
e.
Fig
u
r
e
4
.
Data
s
p
litt
in
g
2
.
2
.
4
.
M
o
del t
ra
ini
ng
I
n
th
is
s
tu
d
y
,
we
em
p
lo
y
e
d
th
r
ee
m
ac
h
in
e
lear
n
in
g
m
o
d
els
to
p
r
e
d
ict
AI
ac
ce
p
tan
ce
am
o
n
g
Mo
r
o
cc
an
s
tu
d
e
n
ts
:
ML
P,
DT
,
an
d
R
F.
E
ac
h
m
o
d
el
was
s
elec
ted
f
o
r
its
d
is
tin
ct
s
tr
en
g
th
s
an
d
s
u
itab
ilit
y
f
o
r
v
ar
io
u
s
d
ata
ty
p
es
an
d
a
n
aly
ti
ca
l
task
s
[
21
]
.
B
elo
w
is
a
d
et
ailed
ex
p
lan
atio
n
o
f
t
h
e
tr
ain
i
n
g
p
r
o
ce
s
s
f
o
r
ea
c
h
m
o
d
el
u
tili
ze
d
in
t
h
is
s
tu
d
y
.
A.
M
ultila
y
er
p
erce
ptr
o
ns
ML
P
ar
e
s
o
p
h
is
ticated
f
ee
d
f
o
r
war
d
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
co
m
p
o
s
ed
o
f
m
u
ltip
le
in
ter
co
n
n
ec
te
d
lay
er
s
o
f
n
o
d
es,
o
f
ten
k
n
o
wn
as n
eu
r
o
n
s
.
T
h
e
n
etwo
r
k
is
s
tr
u
ctu
r
ed
in
a
way
t
h
at
ea
ch
lay
er
is
f
u
lly
co
n
n
ec
ted
to
th
e
n
ex
t,
allo
win
g
it
to
e
f
f
ec
tiv
ely
ca
p
t
u
r
e
a
n
d
u
n
d
e
r
s
tan
d
in
tr
icate
,
n
o
n
-
lin
ea
r
p
atte
r
n
s
in
t
h
e
d
ata
b
y
u
tili
zin
g
ac
tiv
atio
n
f
u
n
ctio
n
s
,
as d
ep
icted
in
Fig
u
r
e
5
.
T
h
e
tr
ain
in
g
p
r
o
ce
d
u
r
e
o
f
a
M
L
P c
o
n
s
is
ts
o
f
th
e
f
o
llo
win
g
s
t
ep
s
:
1.
I
n
itializatio
n
:
t
h
e
n
etwo
r
k
’
s
weig
h
ts
an
d
b
iases
ar
e
r
an
d
o
m
l
y
s
et
at
th
e
b
eg
in
n
in
g
o
f
th
e
le
ar
n
in
g
p
r
o
ce
s
s
.
2.
Fo
r
war
d
p
r
o
p
a
g
atio
n
:
t
h
e
in
p
u
t
d
ata
is
s
eq
u
en
tially
p
r
o
ce
s
s
ed
v
ia
th
e
n
etwo
r
k
,
o
n
e
lay
e
r
at
a
tim
e.
E
v
er
y
in
d
iv
id
u
al
n
eu
r
o
n
u
s
e
an
ac
tiv
atio
n
f
u
n
ctio
n
to
p
r
o
ce
s
s
its
in
p
u
t a
n
d
p
r
o
d
u
ce
a
n
o
u
t
p
u
t.
3.
L
o
s
s
ca
lcu
latio
n
:
th
e
m
o
d
el
’
s
p
r
ed
ictio
n
e
r
r
o
r
is
ass
ess
ed
b
y
co
m
p
ar
in
g
t
h
e
ex
p
ec
te
d
o
u
tp
u
t
with
th
e
ac
tu
al
o
u
tp
u
t
u
s
in
g
a
lo
s
s
f
u
n
c
tio
n
,
s
u
ch
as c
r
o
s
s
-
en
tr
o
p
y
f
o
r
class
if
icatio
n
task
s
.
4.
B
ac
k
war
d
p
r
o
p
a
g
atio
n
:
th
e
b
ac
k
p
r
o
p
ag
atio
n
tech
n
iq
u
e
is
u
s
ed
to
co
m
p
u
te
th
e
g
r
a
d
ie
n
ts
o
f
th
e
lo
s
s
f
u
n
ctio
n
with
r
esp
ec
t to
th
e
w
eig
h
ts
an
d
b
iases
,
en
ab
lin
g
er
r
o
r
co
r
r
ec
tio
n
[
2
2
]
.
5.
Op
tim
izatio
n
:
u
s
in
g
o
p
tim
izat
io
n
m
eth
o
d
s
lik
e
A
d
am
o
r
s
t
o
ch
asti
c
g
r
ad
ien
t
d
escen
t
(
SGD)
,
th
e
weig
h
ts
an
d
b
iases
ar
e
m
o
d
if
ied
to
m
in
im
ize
th
e
lo
ss.
6.
I
ter
atio
n
:
t
h
e
p
ar
am
eter
s
ar
e
c
o
n
tin
u
o
u
s
ly
im
p
r
o
v
ed
to
in
cr
ea
s
e
p
r
ed
ictio
n
ac
cu
r
ac
y
an
d
d
ec
r
ea
s
e
er
r
o
r
s
.
T
h
is
cy
cle
o
f
f
o
r
war
d
p
r
o
p
ag
a
tio
n
,
lo
s
s
ca
lcu
latio
n
,
b
ac
k
war
d
p
r
o
p
ag
atio
n
,
a
n
d
o
p
tim
izatio
n
is
co
n
tin
u
e
d
f
o
r
a
p
r
ed
eter
m
i
n
ed
n
u
m
b
er
o
f
ep
o
ch
s
o
r
u
n
til th
e
m
o
d
el
c
o
n
v
er
g
es.
Fig
u
r
e
5
.
ML
P st
r
u
ctu
r
e
with
2
4
in
p
u
ts
,
2
h
id
d
en
lay
e
r
an
d
1
o
u
tp
u
t
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
A
I
in
Mo
r
o
cc
a
n
ed
u
c
a
tio
n
:
ev
a
lu
a
tin
g
s
tu
d
e
n
t a
cc
ep
t
a
n
ce
u
s
in
g
ma
ch
in
e
lea
r
n
in
g
…
(
K
h
o
u
a
l Mo
h
a
med
)
457
B.
Dec
is
io
n
t
rees
DT
ar
e
n
o
n
-
p
ar
am
etr
ic
alg
o
r
ith
m
s
u
s
ed
in
s
u
p
er
v
is
ed
lear
n
i
n
g
f
o
r
b
o
th
class
if
icatio
n
an
d
r
eg
r
ess
io
n
task
s
.
T
h
ey
o
p
er
ate
b
y
iter
at
iv
ely
d
iv
i
d
in
g
th
e
d
ataset
in
t
o
s
m
aller
g
r
o
u
p
s
b
ased
o
n
f
ea
tu
r
e
v
al
u
es
[
23
]
,
r
esu
ltin
g
in
a
h
ier
a
r
ch
ical
s
tr
u
ctu
r
e
wh
er
e
ea
c
h
n
o
d
e
co
r
r
esp
o
n
d
s
to
a
d
ec
is
io
n
p
o
i
n
t,
as d
e
p
icted
in
Fig
u
r
e
6
.
T
h
e
tr
ain
in
g
p
r
o
ce
d
u
r
e
f
o
r
DT
co
m
p
r
is
es sev
er
al
ess
en
tial st
e
p
s
:
1.
R
o
o
t
n
o
d
e
c
r
ea
tio
n
:
u
s
in
g
p
ar
am
eter
s
lik
e
in
f
o
r
m
ati
o
n
g
ai
n
o
r
Gin
i
im
p
u
r
ity
,
t
h
e
d
ataset
is
ex
am
in
ed
to
id
en
tify
th
e
f
ea
tu
r
e
th
at
o
f
f
er
s
th
e
b
est s
p
lit.
2.
Sp
litt
in
g
:
t
h
e
d
ataset
is
s
ep
ar
ated
in
to
s
u
b
s
ets d
ep
en
d
in
g
o
n
t
h
e
s
elec
ted
f
ea
tu
r
e
v
alu
es.
3.
R
ec
u
r
s
iv
e
s
p
litt
in
g
:
t
h
is
m
eth
o
d
o
f
s
p
litt
in
g
d
iv
id
es
ea
ch
s
u
b
s
et
r
ep
ea
ted
ly
,
p
r
o
d
u
cin
g
n
ew
n
o
d
es
an
d
b
r
an
ch
es e
ac
h
tim
e,
u
n
til th
e
s
to
p
p
in
g
r
e
q
u
ir
em
e
n
ts
ar
e
s
ati
s
f
ied
(
s
u
ch
as
o
b
tain
in
g
a
m
in
im
u
m
n
u
m
b
er
o
f
s
am
p
les p
er
leaf
o
r
a
m
ax
im
u
m
tr
ee
d
ep
th
)
.
4.
L
ea
f
n
o
d
e
cr
ea
tio
n
:
t
h
e
last
l
ea
f
n
o
d
es
ar
e
f
o
r
m
ed
,
wh
ich
co
n
tain
th
e
m
o
d
el
’
s
p
r
ed
ictio
n
s
,
s
u
ch
as
th
e
class
lab
els f
o
r
class
if
icatio
n
t
ask
s
.
Fig
u
r
e
6
.
S
tr
u
ctu
r
e
o
f
a
DT
C.
Ra
nd
o
m
f
o
re
s
t
s
Ov
er
f
itti
n
g
is
a
v
o
id
e
d
a
n
d
p
r
ed
ictio
n
ac
c
u
r
ac
y
is
in
cr
ea
s
e
d
v
ia
RF
,
a
k
in
d
o
f
en
s
em
b
le
lear
n
in
g
tech
n
iq
u
e.
T
h
is
is
ac
co
m
p
lis
h
ed
b
y
g
en
er
atin
g
m
u
ltip
le
DT
an
d
co
m
b
in
in
g
th
eir
o
u
t
co
m
es.
E
ac
h
tr
ee
is
g
r
o
wn
u
s
in
g
a
b
o
o
ts
tr
ap
s
am
p
le
o
f
t
h
e
d
ata
a
n
d
u
tili
ze
s
a
r
an
d
o
m
ly
s
elec
ted
s
u
b
s
et
o
f
attr
ib
u
tes
to
m
ak
e
d
iv
is
io
n
s
,
as d
ep
icted
in
Fig
u
r
e
7
.
T
h
e
tr
ain
in
g
p
r
o
ce
d
u
r
e
f
o
r
RF
co
m
p
r
is
es th
e
s
u
b
s
eq
u
e
n
t step
s
:
1.
B
o
o
ts
tr
ap
s
am
p
lin
g
:
m
u
ltip
le
b
o
o
ts
tr
ap
s
am
p
les
ar
e
g
en
er
at
ed
f
r
o
m
th
e
o
r
ig
in
al
d
ataset
to
p
r
o
d
u
ce
v
ar
ied
tr
ain
in
g
s
ets f
o
r
ea
ch
tr
ee
.
2.
T
r
ee
tr
ain
in
g
:
e
v
e
r
y
DT
is
tr
a
in
ed
u
s
in
g
a
d
is
tin
ct
b
o
o
ts
tr
ap
s
am
p
le.
At
ev
er
y
n
o
d
e,
a
r
a
n
d
o
m
s
u
b
s
et
o
f
ch
ar
ac
ter
is
tics
is
tak
en
in
to
ac
co
u
n
t
f
o
r
d
i
v
is
io
n
,
wh
ich
b
r
in
g
s
in
v
a
r
iety
an
d
d
im
in
i
s
h
es
co
r
r
elatio
n
am
o
n
g
t
h
e
tr
ee
s
.
3.
Ag
g
r
eg
atio
n
:
t
h
e
u
ltima
te
r
esu
lt
o
f
th
e
m
o
d
el
is
ac
h
iev
ed
b
y
co
m
b
in
i
n
g
th
e
p
r
ed
ictio
n
s
m
ad
e
b
y
ea
ch
in
d
iv
id
u
al
tr
ee
.
Fo
r
class
if
icatio
n
task
s
,
th
e
u
s
u
al
ap
p
r
o
ac
h
is
to
ch
o
o
s
e
th
e
o
u
tco
m
e
b
ase
d
o
n
a
m
ajo
r
ity
v
o
te.
I
n
co
n
tr
ast,
f
o
r
r
e
g
r
ess
io
n
task
s
,
th
e
p
r
ed
ictio
n
s
ar
e
o
f
t
en
av
er
ag
e
d
to
g
et
th
e
f
in
al
r
esu
lt.
B
y
f
o
llo
win
g
th
is
co
m
p
r
eh
en
s
iv
e
tr
ain
in
g
p
r
o
ce
s
s
,
ea
ch
m
o
d
el
is
ef
f
ec
tiv
ely
tr
ain
ed
to
u
n
d
er
s
tan
d
an
d
p
r
e
d
ict
AI
ac
ce
p
tan
ce
a
m
o
n
g
M
o
r
o
cc
an
s
tu
d
en
ts
,
en
s
u
r
in
g
r
o
b
u
s
t
an
d
r
eliab
le
o
u
tco
m
es.
T
h
is
m
u
lti
-
m
o
d
el
ap
p
r
o
ac
h
allo
ws
f
o
r
a
th
o
r
o
u
g
h
ev
alu
atio
n
o
f
d
if
f
er
en
t
p
r
ed
ictiv
e
tech
n
iq
u
es,
en
h
an
cin
g
th
e
o
v
er
all
r
eliab
ilit
y
an
d
ac
cu
r
ac
y
o
f
th
e
s
tu
d
y
’
s
f
in
d
in
g
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
45
2
-
46
2
458
Fig
u
r
e
7
.
Stru
ctu
r
e
o
f
a
RF
m
o
d
el
2
.
2
.
5
.
I
nte
rpre
t
a
t
io
n o
f
r
esu
lt
s
T
h
e
f
in
al
s
tep
i
n
v
o
lv
es
an
al
y
zin
g
t
h
e
o
u
tco
m
es
f
r
o
m
th
e
s
elec
ted
m
o
d
el
to
d
e
r
iv
e
m
ea
n
in
g
f
u
l
in
s
ig
h
ts
an
d
co
n
clu
s
io
n
s
.
T
h
i
s
in
clu
d
es
u
n
d
er
s
tan
d
i
n
g
t
h
e
im
p
licatio
n
s
o
f
th
e
m
o
d
el
’
s
p
r
ed
ictio
n
s
an
d
h
o
w
th
ey
ca
n
b
e
ap
p
lied
in
r
ea
l
-
wo
r
ld
ed
u
ca
tio
n
al
s
ettin
g
s
.
I
n
ter
p
r
etatio
n
s
tep
s
:
1.
An
aly
ze
p
r
ed
ictio
n
s
:
e
x
am
in
e
th
e
p
r
ed
ictio
n
s
m
a
d
e
b
y
th
e
s
e
lecte
d
m
o
d
el
to
i
d
en
tify
p
atter
n
s
an
d
tr
en
d
s
.
2.
I
d
en
tify
k
e
y
f
ac
to
r
s
:
d
eter
m
i
n
e
wh
ich
f
ea
tu
r
es
ar
e
m
o
s
t
in
f
lu
en
tial
in
p
r
ed
ictin
g
AI
ac
ce
p
tan
ce
am
o
n
g
s
tu
d
en
ts
.
3.
Dr
aw
c
o
n
clu
s
io
n
s
:
s
u
m
m
ar
ize
th
e
k
ey
f
in
d
in
g
s
an
d
th
eir
im
p
licatio
n
s
f
o
r
in
teg
r
atin
g
AI
in
ed
u
ca
t
io
n
.
4.
Pro
v
id
e
r
ec
o
m
m
e
n
d
atio
n
s
:
o
f
f
er
p
r
ac
tical
r
ec
o
m
m
e
n
d
atio
n
s
f
o
r
p
o
licy
m
a
k
er
s
,
ed
u
ca
to
r
s
,
a
n
d
tech
n
o
lo
g
y
d
ev
elo
p
er
s
to
en
h
a
n
ce
AI
ac
ce
p
tan
ce
in
th
e
e
d
u
ca
tio
n
al
s
ec
to
r
.
A
v
is
u
al
r
ep
r
esen
tatio
n
o
f
th
e
d
etailed
m
o
d
el
tr
ain
i
n
g
an
d
ev
alu
atio
n
p
r
o
ce
s
s
d
escr
ib
ed
ab
o
v
e,
as
d
ep
icted
in
Fig
u
r
e
8
.
W
e
’
ll b
r
ea
k
d
o
wn
t
h
e
m
eth
o
d
o
lo
g
y
in
t
o
a
clea
r
an
d
s
tr
u
ctu
r
e
d
d
iag
r
a
m
.
Fig
u
r
e
8
.
Vis
u
al
r
ep
r
esen
tatio
n
o
f
th
e
d
etailed
m
o
d
el
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
A
I
in
Mo
r
o
cc
a
n
ed
u
c
a
tio
n
:
ev
a
lu
a
tin
g
s
tu
d
e
n
t a
cc
ep
t
a
n
ce
u
s
in
g
ma
ch
in
e
lea
r
n
in
g
…
(
K
h
o
u
a
l Mo
h
a
med
)
459
3.
R
E
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
q
u
esti
o
n
“
Ar
e
y
o
u
c
o
m
f
o
r
tab
le
u
s
in
g
AI
to
o
ls
f
o
r
y
o
u
r
s
tu
d
ies?
”
s
er
v
es a
s
th
e
tar
g
et
v
ar
iab
le
f
o
r
o
u
r
class
if
icatio
n
an
aly
s
is
.
T
h
is
cr
u
cial
q
u
esti
o
n
u
n
d
er
p
in
s
o
u
r
p
r
e
d
ictiv
e
m
o
d
els,
in
clu
d
in
g
R
F,
DT
,
an
d
MLP
[2
4
]
.
B
y
f
o
c
u
s
in
g
o
n
th
is
q
u
esti
o
n
,
we
aim
to
p
r
e
d
ict
an
d
u
n
d
er
s
tan
d
th
e
ac
ce
p
t
an
ce
o
f
AI
am
o
n
g
Mo
r
o
cc
an
s
tu
d
en
ts
in
th
eir
e
d
u
ca
tio
n
al
jo
u
r
n
ey
.
T
h
is
s
p
ec
if
ic
q
u
esti
o
n
r
ef
lects
s
tu
d
en
ts
’
co
m
f
o
r
t
lev
els
with
AI
to
o
ls
,
allo
win
g
u
s
to
em
p
lo
y
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
to
d
is
ce
r
n
p
atter
n
s
,
ca
teg
o
r
ize
s
tu
d
en
ts
b
ased
o
n
th
eir
r
esp
o
n
s
es,
an
d
co
n
d
u
ct
a
co
m
p
ar
ativ
e
an
aly
s
is
o
f
m
o
d
el
p
er
f
o
r
m
a
n
ce
in
p
r
ed
ictin
g
s
tu
d
en
t
ac
ce
p
tan
ce
o
f
AI
in
e
d
u
ca
tio
n
.
−
S
tu
d
en
t
co
m
f
o
r
t
lev
els
with
A
I
t
o
o
ls
:
t
h
e
f
in
d
in
g
s
,
illu
s
tr
ated
in
Fig
u
r
e
9
,
in
d
icate
th
at
6
4
%
o
f
Mo
r
o
cc
an
s
tu
d
en
ts
f
ee
l
co
m
f
o
r
tab
le
u
s
in
g
AI
tech
n
o
lo
g
ies
f
o
r
th
eir
s
t
u
d
ies,
wh
er
ea
s
2
3
%
o
f
p
a
r
ticip
an
ts
ex
p
r
ess
ed
eith
er
less
co
m
f
o
r
t
o
r
r
elu
cta
n
ce
in
u
s
in
g
th
ese
t
o
o
ls
.
T
h
is
h
ig
h
lig
h
ts
th
e
n
ee
d
f
o
r
f
u
r
th
er
in
v
esti
g
atio
n
in
to
th
e
f
ac
t
o
r
s
in
f
lu
e
n
cin
g
s
tu
d
en
ts
’
attitu
d
es
to
wa
r
d
s
AI
i
n
ed
u
ca
tio
n
an
d
em
p
h
asizes
th
e
im
p
o
r
tan
ce
o
f
ad
d
r
ess
in
g
p
o
te
n
tial b
ar
r
ier
s
t
o
ac
ce
p
tan
ce
.
Fig
u
r
e
9
.
T
h
e
in
f
lu
e
n
ce
o
f
AI
o
n
ed
u
ca
tio
n
3
.
1
.
M
o
del per
f
o
rma
nce
m
e
t
rics
3
.
1
.
1
.
Ra
nd
o
m
f
o
re
s
t
−
Acc
u
r
ac
y
: 0
.
9
1
1
−
Per
f
o
r
m
an
ce
:
t
h
e
RF
m
o
d
el
d
i
s
p
lay
ed
th
e
h
ig
h
est
p
r
ec
is
io
n
,
in
d
icatin
g
its
r
o
b
u
s
tn
ess
in
ca
p
tu
r
in
g
in
tr
icate
d
ata
p
atter
n
s
e
f
f
ec
tiv
ely
.
Fo
r
ex
am
p
le,
a
RF
m
o
d
el
ca
n
p
r
e
d
ict
wh
eth
e
r
Mo
r
o
cc
a
n
s
tu
d
en
ts
ar
e
co
m
f
o
r
ta
b
le
u
s
in
g
AI
to
o
ls
b
ased
o
n
th
eir
r
esp
o
n
s
es
to
v
ar
i
o
u
s
s
u
r
v
ey
q
u
es
tio
n
s
,
ev
e
n
if
th
eir
r
esp
o
n
s
es
ar
e
co
m
p
le
x
o
r
in
clu
d
e
s
u
b
tle
p
atter
n
s
.
T
h
is
s
u
p
er
i
o
r
p
er
f
o
r
m
an
ce
m
ay
b
e
attr
i
b
u
ted
t
o
th
e
m
o
d
el
’
s
a
b
ilit
y
to
r
ed
u
ce
o
v
er
f
itti
n
g
b
y
u
s
in
g
m
u
ltip
le
DT
.
E
ac
h
tr
ee
is
tr
ain
ed
o
n
a
r
a
n
d
o
m
s
u
b
s
et
o
f
th
e
d
ata,
an
d
p
r
ed
ictio
n
s
ar
e
m
ad
e
b
y
av
e
r
ag
in
g
t
h
e
p
r
ed
ictio
n
s
o
f
all
t
r
ee
s
,
h
elp
in
g
to
m
itig
ate
th
e
r
i
s
k
o
f
o
v
er
f
itti
n
g
[
2
5
]
,
as sh
o
w
n
in
Fig
u
r
e
1
0.
Fig
u
r
e
10
.
RF
m
o
d
el
p
er
f
o
r
m
a
n
ce
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
45
2
-
46
2
460
3
.
1
.
2
.
Dec
is
io
n
t
ree
-
Acc
u
r
ac
y
: 0
.
8
8
6
-
Per
f
o
r
m
an
ce
:
a
lth
o
u
g
h
t
h
e
DT
ex
h
ib
ited
s
lig
h
tly
l
o
wer
p
r
e
cisi
o
n
,
its
s
im
p
licity
an
d
in
ter
p
r
etab
ilit
y
m
a
k
e
it
a
r
elev
an
t
m
o
d
el.
Fo
r
in
s
tan
ce
,
a
DT
m
o
d
el
ca
n
p
r
e
d
ict
wh
eth
er
a
M
o
r
o
cc
a
n
s
tu
d
en
t
is
co
m
f
o
r
tab
le
u
s
in
g
AI
to
o
ls
b
ased
o
n
f
ac
to
r
s
s
u
ch
as
ag
e,
g
en
d
er
,
an
d
ac
ad
em
ic
b
ac
k
g
r
o
u
n
d
.
T
h
e
r
esu
ltin
g
tr
ee
s
tr
u
ctu
r
e
is
ea
s
y
to
u
n
d
er
s
tan
d
an
d
i
n
ter
p
r
et,
m
ak
in
g
it
s
u
itab
le
f
o
r
s
itu
atio
n
s
wh
er
e
tr
an
s
p
ar
en
cy
a
n
d
ex
p
lain
ab
ilit
y
ar
e
im
p
o
r
tan
t,
a
s
s
h
o
wn
in
Fig
u
r
e
1
1
.
3
.
1
.
3
.
M
ultila
y
er
perc
ept
ro
n
-
Acc
u
r
ac
y
: 0
.
8
8
5
-
Per
f
o
r
m
an
ce
:
t
h
e
ML
P
’
s
p
er
f
o
r
m
an
ce
is
v
er
y
s
im
ilar
to
th
a
t
o
f
th
e
DT
,
b
u
t
its
co
m
p
le
x
ity
r
eq
u
ir
es
f
in
e
-
tu
n
in
g
o
f
h
y
p
er
p
ar
am
eter
s
to
b
etter
id
en
tify
p
atter
n
s
in
th
e
d
ata.
Fo
r
ex
am
p
le,
in
M
L
P
m
o
d
els
with
m
u
ltip
le
h
id
d
e
n
lay
e
r
s
,
p
ar
am
eter
s
s
u
ch
as
lear
n
in
g
r
ate,
b
a
tch
s
ize,
an
d
n
u
m
b
er
o
f
n
e
u
r
o
n
s
n
ee
d
ca
r
ef
u
l
tu
n
in
g
to
ac
h
iev
e
o
p
tim
al
p
e
r
f
o
r
m
a
n
ce
.
W
ith
o
u
t
p
r
o
p
er
tu
n
in
g
,
th
e
m
o
d
el
m
ay
s
tr
u
g
g
l
e
to
e
f
f
ec
tiv
ely
ca
p
tu
r
e
th
e
n
u
an
ce
s
o
f
th
e
d
at
a,
as sh
o
wn
in
Fig
u
r
e
1
2
.
Fig
u
r
e
1
1
.
DT
m
o
d
el
p
er
f
o
r
m
an
ce
Fig
u
r
e
1
2
.
ML
P
m
o
d
el
p
er
f
o
r
m
an
ce
3
.
2
.
Co
m
pa
riso
n o
f
m
o
del p
er
f
o
rma
nce
T
h
e
d
i
f
f
er
en
ce
s
in
p
er
f
o
r
m
an
ce
m
etr
ics
u
n
d
er
s
co
r
e
th
e
im
p
o
r
tan
ce
o
f
s
elec
tin
g
t
h
e
m
o
s
t
s
u
itab
le
m
o
d
el
b
ased
o
n
t
h
e
d
ata
’
s
c
h
ar
ac
ter
is
tics
an
d
s
p
ec
if
ic
p
r
ed
ictio
n
g
o
als.
W
h
en
p
r
e
d
ictin
g
AI
ac
ce
p
tan
ce
am
o
n
g
Mo
r
o
cc
an
s
tu
d
en
ts
,
f
a
cto
r
s
s
u
ch
as
th
e
co
m
p
lex
ity
o
f
th
e
d
ata,
th
e
n
ee
d
f
o
r
in
ter
p
r
etab
ilit
y
,
an
d
th
e
tr
ad
e
-
o
f
f
b
etwe
en
p
r
ec
is
io
n
a
n
d
co
m
p
lex
ity
m
u
s
t
b
e
co
n
s
id
er
ed
.
T
h
e
RF
m
o
d
el
’
s
s
u
p
er
i
o
r
p
er
f
o
r
m
an
ce
,
d
u
e
to
its
ab
ilit
y
to
ca
p
tu
r
e
in
tr
ica
te
d
ata
p
atter
n
s
an
d
r
e
d
u
ce
o
v
er
f
itti
n
g
,
m
a
k
es
it
th
e
b
est
ch
o
ice
in
t
h
is
co
n
tex
t.
Ho
wev
er
,
th
e
s
im
p
licity
an
d
in
ter
p
r
etab
ilit
y
o
f
DT
,
alo
n
g
with
th
e
p
o
te
n
tial
o
f
ML
Ps
w
h
en
p
r
o
p
e
r
ly
tu
n
ed
,
h
ig
h
li
g
h
t th
e
v
alu
e
o
f
th
ese
m
o
d
els in
d
if
f
e
r
en
t scen
ar
io
s
.
3
.
3
.
I
m
pli
ca
t
i
o
ns
f
o
r
AI a
cc
ept
a
nce
in M
o
ro
cc
a
n e
du
ca
t
io
n
T
h
e
r
esu
lts
o
f
th
is
s
tu
d
y
h
av
e
s
ig
n
if
ican
t
im
p
licatio
n
s
f
o
r
th
e
in
teg
r
atio
n
o
f
AI
in
Mo
r
o
cc
an
ed
u
ca
tio
n
.
Un
d
er
s
tan
d
in
g
s
tu
d
en
ts
’
co
m
f
o
r
t le
v
els with
AI
to
o
ls
ca
n
in
f
o
r
m
s
tr
ateg
ies to
en
h
an
ce
AI
ad
o
p
tio
n
an
d
ad
d
r
ess
b
ar
r
ier
s
to
ac
ce
p
t
an
ce
.
Po
licy
m
ak
er
s
,
ed
u
ca
to
r
s
,
an
d
tech
n
o
lo
g
y
d
ev
elo
p
er
s
ca
n
lev
er
ag
e
th
ese
in
s
ig
h
ts
to
d
esig
n
i
n
ter
v
en
tio
n
s
th
at
im
p
r
o
v
e
s
tu
d
e
n
ts
’
ex
p
er
ien
ce
s
with
AI
,
u
ltima
tely
f
o
s
ter
in
g
a
m
o
r
e
s
u
p
p
o
r
tiv
e
e
n
v
ir
o
n
m
en
t
f
o
r
AI
in
ed
u
ca
tio
n
.
3
.
4
.
L
im
it
a
t
io
ns
a
nd
f
uture
wo
rk
T
h
is
s
tu
d
y
h
as
s
ev
er
al
lim
itatio
n
s
.
First,
th
e
d
ata
co
llected
m
ay
n
o
t
f
u
lly
ca
p
t
u
r
e
th
e
d
iv
er
s
ity
o
f
s
tu
d
en
t
ex
p
e
r
ien
ce
s
an
d
attitu
d
es
to
war
d
s
AI
a
cr
o
s
s
d
if
f
er
en
t
r
e
g
io
n
s
a
n
d
in
s
titu
t
io
n
s
in
Mo
r
o
cc
o
.
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
A
I
in
Mo
r
o
cc
a
n
ed
u
c
a
tio
n
:
ev
a
lu
a
tin
g
s
tu
d
e
n
t a
cc
ep
t
a
n
ce
u
s
in
g
ma
ch
in
e
lea
r
n
in
g
…
(
K
h
o
u
a
l Mo
h
a
med
)
461
Ad
d
itio
n
ally
,
th
e
m
o
d
els
’
p
er
f
o
r
m
an
ce
m
a
y
b
e
in
f
lu
en
ce
d
b
y
th
e
q
u
ality
an
d
q
u
an
tity
o
f
th
e
d
ata
u
s
ed
f
o
r
tr
ain
in
g
.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
aim
t
o
c
o
llect
m
o
r
e
c
o
m
p
r
e
h
en
s
iv
e
d
ata
an
d
e
x
p
lo
r
e
o
th
e
r
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els
to
f
u
r
th
er
en
h
a
n
ce
th
e
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
o
f
p
r
ed
ictio
n
s
.
Ad
d
itio
n
ally
,
in
v
est
ig
atin
g
th
e
s
p
ec
if
ic
f
ac
to
r
s
th
at
in
f
lu
en
ce
s
tu
d
en
ts
’
co
m
f
o
r
t
lev
els
with
AI
to
o
ls
ca
n
p
r
o
v
id
e
d
ee
p
er
in
s
ig
h
ts
in
to
h
o
w
t
o
ef
f
ec
tiv
ely
in
teg
r
ate
AI
in
to
th
e
ed
u
ca
tio
n
al
lan
d
s
ca
p
e.
4.
CO
NCLU
SI
O
N
T
h
e
o
v
e
r
all
g
o
al
o
f
o
u
r
s
tu
d
y
was
to
u
s
e
a
v
ar
iety
o
f
class
if
icatio
n
m
o
d
els,
s
u
ch
as
RF
,
DT
,
an
d
MLP
,
to
p
r
ed
ict
Mo
r
o
cc
a
n
s
tu
d
en
ts
’
ad
o
p
tio
n
o
f
AI
.
B
ased
o
n
o
u
r
f
in
d
i
n
g
s
,
th
e
R
F m
o
d
el
p
er
f
o
r
m
ed
th
e
b
est,
ac
h
iev
in
g
an
a
cc
u
r
ac
y
o
f
0
.
9
1
1
.
W
ith
r
esp
ec
tiv
e
ac
cu
r
ac
ie
s
o
f
0
.
8
8
6
a
n
d
0
.
8
8
5
,
th
e
DT
an
d
ML
P
m
o
d
els
lik
ewise
d
em
o
n
s
tr
ated
r
em
ar
k
ab
le
p
er
f
o
r
m
an
ce
.
I
t is si
g
n
if
ican
t to
r
em
em
b
er
th
at
a
n
u
m
b
e
r
o
f
ch
ar
ac
ter
is
tics
,
in
clu
d
in
g
c
o
m
p
lex
ity
,
in
ter
p
r
e
tab
ilit
y
,
an
d
a
cc
u
r
ac
y
,
a
f
f
ec
t t
h
e
d
ec
is
io
n
o
f
wh
ich
m
o
d
el
is
b
est.
Fo
r
o
u
r
f
u
t
u
r
e
r
esear
c
h
,
we
p
lan
to
i
n
co
r
p
o
r
ate
NL
P
m
o
d
els
to
an
aly
ze
te
x
tu
al
d
ata
p
r
o
v
id
e
d
b
y
s
tu
d
en
ts
.
T
h
is
ap
p
r
o
ac
h
will a
llo
w
u
s
to
d
ee
p
e
n
o
u
r
u
n
d
er
s
tan
d
in
g
a
n
d
g
ai
n
m
o
r
e
co
m
p
r
eh
en
s
iv
e
in
s
ig
h
ts
in
to
Mo
r
o
cc
an
s
tu
d
e
n
ts
’
p
er
ce
p
ti
o
n
s
o
f
AI
.
B
y
ex
p
a
n
d
in
g
o
u
r
m
eth
o
d
o
lo
g
ical
to
o
lk
it,
we
aim
to
ad
d
r
ess
u
n
an
s
wer
ed
q
u
esti
o
n
s
an
d
c
o
n
tr
ib
u
te
to
th
e
d
ev
elo
p
m
en
t
o
f
m
o
r
e
ef
f
ec
tiv
e
AI
to
o
l
s
tailo
r
ed
to
th
e
ed
u
ca
tio
n
al
n
ee
d
s
o
f
Mo
r
o
cc
a
n
s
tu
d
en
ts
.
I
n
co
n
clu
s
io
n
,
o
u
r
s
t
u
d
y
p
r
o
v
i
d
es v
alu
ab
le
in
s
ig
h
ts
in
to
th
e
f
ac
to
r
s
in
f
lu
en
cin
g
AI
ac
ce
p
tan
ce
am
o
n
g
s
tu
d
en
ts
an
d
h
ig
h
lig
h
ts
th
e
im
p
o
r
tan
ce
o
f
m
o
d
el
s
elec
tio
n
in
p
r
ed
ictiv
e
an
al
y
tics
.
T
h
e
f
in
d
in
g
s
h
av
e
s
ig
n
if
ican
t
im
p
licatio
n
s
f
o
r
p
o
licy
m
ak
er
s
,
ed
u
ca
to
r
s
,
an
d
t
ec
h
n
o
lo
g
y
d
e
v
elo
p
er
s
aim
in
g
to
in
teg
r
ate
AI
in
t
o
ed
u
ca
tio
n
al
s
y
s
t
em
s
.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
co
n
tin
u
e
to
e
x
p
lo
r
e
th
ese
d
y
n
am
ics
to
en
h
a
n
ce
th
e
e
d
u
ca
tio
n
al
ex
p
er
ien
ce
a
n
d
f
o
s
ter
a
cu
ltu
r
e
o
f
in
n
o
v
atio
n
an
d
ac
ce
p
tan
ce
o
f
AI
tech
n
o
lo
g
ies in
e
d
u
ca
tio
n
.
RE
F
E
R
E
NC
E
S
[
1
]
C
.
K
.
Y
.
C
h
a
n
,
“
A
c
o
m
p
r
e
h
e
n
si
v
e
A
I
p
o
l
i
c
y
e
d
u
c
a
t
i
o
n
f
r
a
m
e
w
o
r
k
f
o
r
u
n
i
v
e
r
si
t
y
t
e
a
c
h
i
n
g
a
n
d
l
e
a
r
n
i
n
g
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
Ed
u
c
a
t
i
o
n
a
l
T
e
c
h
n
o
l
o
g
y
i
n
H
i
g
h
e
r
E
d
u
c
a
t
i
o
n
,
v
o
l
.
2
0
,
n
o
.
1
,
p
.
3
8
,
J
u
l
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
8
6
/
s4
1
2
3
9
-
0
2
3
-
0
0
4
0
8
-
3.
[
2
]
F
.
K
a
m
a
l
o
v
,
D
.
S
.
C
a
l
o
n
g
e
,
a
n
d
I
.
G
u
r
r
i
b
,
“
N
e
w
e
r
a
o
f
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
i
n
e
d
u
c
a
t
i
o
n
:
t
o
w
a
r
d
s
a
s
u
s
t
a
i
n
a
b
l
e
mu
l
t
i
f
a
c
e
t
e
d
r
e
v
o
l
u
t
i
o
n
,
”
S
u
s
t
a
i
n
a
b
i
l
i
t
y
,
v
o
l
.
1
5
,
n
o
.
1
6
,
p
.
1
2
4
5
1
,
A
u
g
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s
u
1
5
1
6
1
2
4
5
1
.
[3
]
I
.
D
r
a
c
h
e
t
a
l
.
,
“
T
h
e
u
se
o
f
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
i
n
h
i
g
h
e
r
e
d
u
c
a
t
i
o
n
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
S
c
i
e
n
t
i
f
i
c
J
o
u
r
n
a
l
o
f
U
n
i
v
e
rsi
t
i
e
s
a
n
d
L
e
a
d
e
rs
h
i
p
,
v
o
l
.
1
5
,
p
p
.
6
6
–
8
2
,
J
u
n
.
2
0
2
3
,
d
o
i
:
1
0
.
3
1
8
7
4
/
2
5
2
0
-
6
7
0
2
-
2
0
2
3
-
15
-
66
-
8
2
.
[
4
]
E.
M
.
A
l
q
o
d
s
i
,
I
.
M
.
J
a
d
a
l
h
a
q
,
M
.
E
-
H
.
E
l
-
M
a
k
n
o
u
z
i
,
a
n
d
I
.
E.
A
.
A
b
d
u
l
h
a
y
,
“
N
a
v
i
g
a
t
i
n
g
t
h
e
l
e
g
a
l
l
a
n
d
sc
a
p
e
,
”
i
n
C
o
m
p
u
t
e
rs
a
n
d
Ed
u
c
a
t
i
o
n
:
Ar
t
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
,
v
o
l
.
2
,
2
0
2
4
,
p
p
.
2
1
2
–
2
3
0
.
d
o
i
:
1
0
.
4
0
1
8
/
9
7
9
-
8
-
3
6
9
3
-
0
8
8
0
-
6
.
c
h
0
1
5
.
[
5
]
F
.
O
u
y
a
n
g
a
n
d
P
.
J
i
a
o
,
“
A
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
i
n
e
d
u
c
a
t
i
o
n
:
t
he
t
h
r
e
e
p
a
r
a
d
i
g
ms
,
”
C
o
m
p
u
t
e
rs
a
n
d
E
d
u
c
a
t
i
o
n
:
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
,
v
o
l
.
2
,
p
.
1
0
0
0
2
0
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
a
e
a
i
.
2
0
2
1
.
1
0
0
0
2
0
.
[
6
]
B
.
T
.
A
l
sh
a
h
r
a
n
i
,
S
.
F
.
P
i
l
e
g
g
i
,
a
n
d
F
.
K
a
r
i
mi
,
“
A
s
o
c
i
a
l
p
e
r
s
p
e
c
t
i
v
e
o
n
a
i
i
n
t
h
e
h
i
g
h
e
r
e
d
u
c
a
t
i
o
n
s
y
st
e
m:
a
sem
i
s
y
st
e
ma
t
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
,
”
El
e
c
t
r
o
n
i
c
s
,
v
o
l
.
1
3
,
n
o
.
8
,
p
.
1
5
7
2
,
A
p
r
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s1
3
0
8
1
5
7
2
.
[
7
]
K
.
O
.
Ti
s
h
k
i
n
a
,
O
.
V
.
El
i
se
e
v
a
,
A
.
S
.
B
a
g
a
u
t
d
i
n
o
v
a
,
K
.
S
.
S
h
i
l
o
v
a
,
a
n
d
A
.
A
.
Ef
r
e
mo
v
a
,
“
I
n
d
i
v
i
d
u
a
l
e
d
u
c
a
t
i
o
n
a
l
p
r
o
g
r
a
m
a
s a
t
o
o
l
t
o
p
e
r
s
o
n
a
l
i
z
e
e
d
u
c
a
t
i
o
n
,
”
U
n
i
v
e
rs
i
t
y
Ma
n
a
g
e
m
e
n
t
:
Pr
a
c
t
i
c
e
a
n
d
A
n
a
l
y
s
i
s
,
v
o
l
.
2
7
,
n
o
.
1
,
p
p
.
3
4
–
4
1
,
A
p
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
5
8
2
6
/
u
m
p
a
.
2
0
2
3
.
0
1
.
0
0
4
.
[
8
]
K
.
M
o
h
a
me
d
,
Z.
E
l
k
a
i
m
b
i
l
l
a
h
,
a
n
d
B
.
El
A
sr
i
,
“
S
i
g
n
i
f
i
c
a
n
c
e
a
n
d
i
m
p
a
c
t
o
f
A
I
o
n
p
e
d
a
g
o
g
i
c
a
l
l
e
a
r
n
i
n
g
:
a
c
a
s
e
st
u
d
y
o
f
M
o
r
o
c
c
a
n
st
u
d
e
n
t
s
a
t
t
h
e
f
a
c
u
l
t
y
o
f
l
e
g
a
l
a
n
d
e
c
o
n
o
m
i
c
s
,
”
2
0
2
4
,
p
p
.
1
2
4
–
1
2
9
.
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
031
-
4
8
5
7
3
-
2
_
1
8
.
[
9
]
Z.
A
z
a
m,
M
.
M
.
I
sl
a
m
a
n
d
M
.
N
.
H
u
d
a
,
“
C
o
mp
a
r
a
t
i
v
e
A
n
a
l
y
si
s
o
f
I
n
t
r
u
si
o
n
D
e
t
e
c
t
i
o
n
S
y
st
e
ms
a
n
d
M
a
c
h
i
n
e
Le
a
r
n
i
n
g
-
B
a
se
d
M
o
d
e
l
A
n
a
l
y
s
i
s
Th
r
o
u
g
h
D
e
c
i
s
i
o
n
Tr
e
e
,
”
in
I
E
EE
Ac
c
e
ss
,
v
o
l
.
1
1
,
p
p
.
8
0
3
4
8
-
8
0
3
9
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ES
S
.
2
0
2
3
.
3
2
9
6
4
4
4
.
[
1
0
]
S
.
P
a
c
h
e
c
o
-
M
e
n
d
o
z
a
,
C
.
G
u
e
v
a
r
a
,
A
.
M
a
y
o
r
g
a
-
A
l
b
á
n
,
a
n
d
J
.
F
e
r
n
á
n
d
e
z
-
E
sco
b
a
r
,
“
A
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
i
n
h
i
g
h
e
r
e
d
u
c
a
t
i
o
n
:
A
P
r
e
d
i
c
t
i
v
e
M
o
d
e
l
f
o
r
A
c
a
d
e
mi
c
P
e
r
f
o
r
m
a
n
c
e
,
”
Ed
u
c
a
t
i
o
n
S
c
i
e
n
c
e
s
,
v
o
l
.
1
3
,
n
o
.
1
0
,
p
.
9
9
0
,
S
e
p
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
e
d
u
c
sci
1
3
1
0
0
9
9
0
.
[
1
1
]
D
.
K
a
b
a
k
c
h
i
e
v
a
,
“
P
r
e
d
i
c
t
i
n
g
st
u
d
e
n
t
p
e
r
f
o
r
m
a
n
c
e
b
y
u
s
i
n
g
d
a
t
a
m
i
n
i
n
g
m
e
t
h
o
d
s
f
o
r
c
l
a
ssi
f
i
c
a
t
i
o
n
,
”
C
y
b
e
r
n
e
t
i
c
s
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
i
e
s
,
v
o
l
.
1
3
,
n
o
.
1
,
p
p
.
6
1
–
7
2
,
M
a
r
.
2
0
1
3
,
d
o
i
:
1
0
.
2
4
7
8
/
c
a
i
t
-
2
0
1
3
-
0
0
0
6
.
[
1
2
]
M
.
A
i
t
k
i
n
a
n
d
R
.
F
o
x
a
l
l
,
“
S
t
a
t
i
st
i
c
a
l
mo
d
e
l
l
i
n
g
o
f
a
r
t
i
f
i
c
i
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s
u
s
i
n
g
t
h
e
m
u
l
t
i
-
l
a
y
e
r
p
e
r
c
e
p
t
r
o
n
,
”
S
t
a
t
i
s
t
i
c
s
a
n
d
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
3
,
n
o
.
3
,
p
p
.
2
2
7
–
2
3
9
,
2
0
0
3
,
d
o
i
:
1
0
.
1
0
2
3
/
A
:
1
0
2
4
2
1
8
7
1
6
7
3
6
.
[
1
3
]
I
.
E.
Li
v
i
e
r
i
s
,
K
.
D
r
a
k
o
p
o
u
l
o
u
,
V
.
T.
T
a
m
p
a
k
a
s,
T.
A
.
M
i
k
r
o
p
o
u
l
o
s,
a
n
d
P
.
P
i
n
t
e
l
a
s
,
“
P
r
e
d
i
c
t
i
n
g
se
c
o
n
d
a
r
y
s
c
h
o
o
l
st
u
d
e
n
t
s
’
p
e
r
f
o
r
m
a
n
c
e
u
t
i
l
i
z
i
n
g
a
sem
i
-
su
p
e
r
v
i
se
d
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
,
”
J
o
u
rn
a
l
o
f
Ed
u
c
a
t
i
o
n
a
l
C
o
m
p
u
t
i
n
g
R
e
se
a
rc
h
,
v
o
l
.
5
7
,
n
o
.
2
,
p
p
.
4
4
8
–
4
7
0
,
A
p
r
.
2
0
1
9
,
d
o
i
:
1
0
.
1
1
7
7
/
0
7
3
5
6
3
3
1
1
7
7
5
2
6
1
4
.
[
1
4
]
A
.
M
.
A
l
-
R
a
h
mi
,
A
.
S
h
a
ms
u
d
d
i
n
,
E.
W
a
h
a
b
,
W
.
M
.
A
l
-
R
a
h
mi
,
I
.
Y
.
A
l
y
o
u
ss
e
f
,
a
n
d
J
.
C
r
a
w
f
o
r
d
,
“
S
o
c
i
a
l
me
d
i
a
u
s
e
i
n
h
i
g
h
e
r
e
d
u
c
a
t
i
o
n
:
B
u
i
l
d
i
n
g
a
st
r
u
c
t
u
r
a
l
e
q
u
a
t
i
o
n
mo
d
e
l
f
o
r
s
t
u
d
e
n
t
sa
t
i
sf
a
c
t
i
o
n
a
n
d
p
e
r
f
o
r
ma
n
c
e
,
”
Fr
o
n
t
i
e
rs
i
n
P
u
b
l
i
c
H
e
a
l
t
h
,
v
o
l
.
1
0
,
S
e
p
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
8
9
/
f
p
u
b
h
.
2
0
2
2
.
1
0
0
3
0
0
7
.
[
1
5
]
T.
D
e
v
a
si
a
,
T
.
P
.
V
i
n
u
sh
r
e
e
,
a
n
d
V
.
H
e
g
d
e
,
“
P
r
e
d
i
c
t
i
o
n
o
f
s
t
u
d
e
n
t
s
p
e
r
f
o
r
m
a
n
c
e
u
si
n
g
e
d
u
c
a
t
i
o
n
a
l
d
a
t
a
mi
n
i
n
g
,
”
i
n
2
0
1
6
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
D
a
t
a
Mi
n
i
n
g
a
n
d
A
d
v
a
n
c
e
d
C
o
m
p
u
t
i
n
g
(
S
A
PI
EN
C
E)
,
I
EEE,
M
a
r
.
2
0
1
6
,
p
p
.
9
1
–
9
5
.
d
o
i
:
1
0
.
1
1
0
9
/
S
A
P
I
EN
C
E.
2
0
1
6
.
7
6
8
4
1
6
7
.
[
1
6
]
R
.
Ej
j
a
m
i
,
“
R
e
v
o
l
u
t
i
o
n
i
z
i
n
g
M
o
r
o
c
c
a
n
e
d
u
c
a
t
i
o
n
w
i
t
h
A
I
:
a
p
a
t
h
t
o
c
u
st
o
mi
z
e
d
l
e
a
r
n
i
n
g
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
r
n
a
l
F
o
r
Mu
l
t
i
d
i
s
c
i
p
l
i
n
a
ry
Re
s
e
a
r
c
h
,
v
o
l
.
6
,
n
o
.
3
,
M
a
y
2
0
2
4
,
d
o
i
:
1
0
.
3
6
9
4
8
/
i
j
f
mr
.
2
0
2
4
.
v
0
6
i
0
3
.
1
9
4
6
2
.
[
1
7
]
Y
.
H
a
md
a
n
i
,
“
Th
e
e
v
a
l
u
a
t
i
o
n
o
f
d
i
s
t
a
n
c
e
e
d
u
c
a
t
i
o
n
b
y
st
u
d
e
n
t
s
i
n
M
o
r
o
c
c
a
n
u
n
i
v
e
r
s
i
t
i
e
s
i
n
t
h
e
t
i
m
e
o
f
C
O
V
I
D
-
1
9
:
e
x
p
e
r
i
e
n
c
e
s
a
n
d
p
e
r
s
p
e
c
t
i
v
e
s,
”
R
e
v
u
e
M
a
r
o
c
a
i
n
e
d
e
l
’
Ev
a
l
u
a
t
i
o
n
e
t
d
e
l
a
rec
h
e
rc
h
e
e
n
E
d
u
c
a
t
i
o
n
,
v
o
l
.
5
,
p
p
.
4
5
6
–
4
7
8
,
2
0
2
1
,
d
o
i
:
h
t
t
p
s:
/
/
d
o
i
.
o
r
g
/
1
0
.
4
8
4
2
3
/
i
m
i
st
.
p
r
s
m/
r
mere
-
v
0
i
5
.
2
4
1
7
2
.
[
1
8
]
A
.
V
.
K
o
n
o
b
e
e
v
,
Y
.
A
.
Y
u
k
h
i
mu
k
,
V
.
D
.
V
o
y
t
se
k
h
o
v
sk
a
y
a
,
a
n
d
M
.
S
h
c
h
e
k
i
c
h
,
“
P
e
r
s
o
n
a
l
i
z
a
t
i
o
n
a
s
a
n
a
p
p
r
o
a
c
h
t
o
e
d
u
c
a
t
i
o
n
,
”
Pro
f
e
ss
i
o
n
a
l
D
i
s
c
o
u
rse
&
C
o
m
m
u
n
i
c
a
t
i
o
n
,
v
o
l
.
2
,
n
o
.
3
,
p
p
.
1
1
8
–
1
3
8
,
S
e
p
.
2
0
2
0
,
d
o
i
:
1
0
.
2
4
8
3
3
/
2
6
8
7
-
0
1
2
6
-
2
0
2
0
-
2
-
3
-
1
1
8
-
1
3
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.