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Science
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24
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3
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Dec
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R
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J
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4
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R
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Oct
14
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Acc
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ted
Oct
27
,
2
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2
1
Th
e
g
ra
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a
tes
w
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o
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a
v
e
fi
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ish
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th
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stu
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rit
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fica
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ra
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T
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sin
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lativ
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e
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th
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ro
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e
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g
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e
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d
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t'
s
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l
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n
tag
e
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sla
ted
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o
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lette
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a
ll
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re
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th
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e
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ra
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e
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ts
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st
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p
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tro
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n
e
w
e
q
u
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ti
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t
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m
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se
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m
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e
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ro
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d
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si
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th
re
e
a
c
tu
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l
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e
n
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h
m
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rk
s
c
o
ll
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c
ted
fro
m
t
h
re
e
d
iffere
n
t
c
o
l
leg
e
s
in
Be
n
i
-
S
u
e
f
u
n
i
v
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rsity
.
Th
e
o
b
tain
e
d
re
su
lt
s
re
flec
ts
th
e
e
ffe
c
t
o
f
t
h
e
fu
z
z
y
lo
g
ic
i
n
h
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lp
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g
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v
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CG
P
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m
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m
e
a
su
re
in
e
d
u
c
a
ti
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l
s
y
ste
m
s
.
K
ey
w
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s
:
C
u
m
u
lativ
e
g
r
ad
e
p
o
in
t
a
v
er
a
g
e
Fu
zz
y
lo
g
ic
Gr
ad
e
p
o
in
t a
v
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ag
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Me
m
b
er
s
h
ip
f
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ctio
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Per
ce
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tag
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T
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s
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c
c
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ss
a
rticle
u
n
d
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r th
e
CC B
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SA
li
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se
.
C
o
r
r
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s
p
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A
uth
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r
:
Far
id
Ali M
o
u
s
a
I
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Dep
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B
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Un
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s
ity
B
en
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B
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Go
v
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ate
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E
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p
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ali@
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eg
1.
I
NT
RO
D
UCT
I
O
N
T
h
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p
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f
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m
a
n
ce
o
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s
tu
d
en
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in
an
ac
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p
r
o
g
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am
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m
ea
s
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r
ed
b
y
th
ei
r
f
in
al
g
r
a
d
es
in
th
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eq
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is
ite
co
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r
s
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T
h
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g
r
a
d
es
co
m
e
f
r
o
m
th
e
d
is
tr
ib
u
t
io
n
o
f
letter
s
o
r
n
u
m
b
er
s
g
i
v
en
b
y
th
e
co
u
r
s
e
in
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tr
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cto
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u
m
m
ar
ize
all
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e
ass
e
s
s
m
en
ts
o
f
th
e
s
tu
d
en
t'
s
e
v
alu
atio
n
r
esu
lts
.
T
h
e
s
ca
le
u
s
ed
to
ass
ig
n
g
r
ad
es
is
th
er
ef
o
r
e
p
ar
ticu
lar
ly
s
ig
n
if
i
ca
n
t,
am
o
n
g
o
th
er
f
ac
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s
.
Stu
d
en
t
g
r
ad
e
p
o
in
t
a
v
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ag
e
(
GPA)
is
a
s
tatis
tic
th
at
r
ef
lects
h
o
w
well
o
r
h
o
w
h
ig
h
y
o
u
h
av
e
ac
h
ie
v
ed
in
y
o
u
r
co
u
r
s
es
[
1
]
.
I
t
h
elp
s
to
ass
e
s
s
y
o
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d
u
r
in
g
y
o
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r
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ies
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1
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d
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em
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tr
ates
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o
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tal
class
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k
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ig
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w.
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n
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m
b
e
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s
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in
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wh
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a
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d
cr
iter
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s
et
b
y
th
e
f
ac
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lty
d
eg
r
ee
[
2
]
.
Yo
u
r
GPA
is
b
asically
th
e
o
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ly
in
d
icato
r
o
f
h
o
w
s
u
cc
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f
u
l y
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ar
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a
n
d
wh
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to
r
y
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r
g
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ad
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atio
n
,
in
y
o
u
r
u
n
iv
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s
i
ty
e
d
u
ca
tio
n
.
I
f
y
o
u
ar
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clea
r
l
y
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cio
u
s
o
f
y
o
u
r
p
ass
ag
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an
d
ex
ce
llen
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in
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o
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r
class
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r
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o
f
f
er
s
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in
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r
o
f
y
o
u
r
o
v
er
all
s
k
ills
an
d
r
atin
g
s
[
3
]
.
Fo
r
ex
am
p
le;
m
a
n
y
ed
u
ca
tio
n
al
s
y
s
tem
s
in
s
o
m
e
co
u
n
tr
ies,
g
r
ad
es
ar
e
ty
p
ically
g
iv
en
i
n
t
wo
way
s
.
First,
b
y
u
s
in
g
th
e
av
er
ag
e
g
r
ad
e
p
er
ce
n
tag
e
o
f
co
u
r
s
es.
Ad
d
in
g
th
e
m
a
r
k
s
o
f
all
co
u
r
s
e
s
an
d
th
en
d
iv
id
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
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J
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&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
em
b
er
2
0
2
1
:
1
8
2
3
-
1
8
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1
1824
th
is
v
alu
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b
y
th
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n
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m
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f
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r
s
es
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ltip
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to
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o
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m
eth
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[
4
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h
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tag
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n
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lated
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ask
s
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d
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s
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s
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tain
ed
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in
th
at
letter
.
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f
two
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tu
d
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ts
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av
e
th
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s
am
e
f
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al
s
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at
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al
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h
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eq
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u
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t
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ay
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ca
n
s
till
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ate
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ad
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e,
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r
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Dec
r
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s
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g
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e
r
an
g
e
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f
letter
s
co
r
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with
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lu
s
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ag
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lts
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f
s
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d
en
ts
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m
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e
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s
.
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e
ass
u
m
p
tio
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,
h
o
wev
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,
th
at
th
e
g
ap
in
r
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lts
b
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o
r
3
p
e
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ce
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ta
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p
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ts
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im
p
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tan
t.
T
h
e
f
ac
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lty
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tio
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s
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eliab
ly
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u
is
h
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er
f
o
r
m
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ce
s
r
an
g
es.
I
n
th
is
r
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ch
;
we
in
tr
o
d
u
ce
a
n
ew
eq
u
at
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ased
o
n
f
u
zz
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s
tem
th
at
h
elp
s
in
tr
an
s
f
o
r
m
in
g
b
etwe
en
th
e
awa
r
d
e
d
c
u
m
u
lativ
e
g
r
ad
e
p
o
i
n
t
av
e
r
ag
e
an
d
th
e
awa
r
d
ed
p
er
ce
n
tag
e
r
an
k
in
g
to
h
elp
e
d
u
ca
tio
n
al
s
y
s
tem
s
in
ac
ce
p
ti
n
g
tr
an
s
f
er
r
in
g
s
tu
d
en
ts
an
d
r
an
k
in
g
th
e
s
tu
d
en
ts
b
etwe
en
d
if
f
er
e
n
t
u
n
i
v
er
s
ities
th
at
u
s
e
d
if
f
er
e
n
t e
v
alu
atio
n
m
eth
o
d
s
.
E
d
u
ca
tio
n
d
eg
r
ee
s
ar
e
th
e
m
e
th
o
d
o
f
ap
p
l
y
in
g
u
n
if
o
r
m
m
et
r
ics
o
f
v
ar
io
u
s
s
tan
d
ar
d
s
o
f
e
f
f
icien
cy
.
Gr
ad
es
ca
n
b
e
d
is
tr
ib
u
ted
as
l
etter
s
(
e.
g
.
,
A
to
F),
as
a
s
er
ies
(
e.
g
.
,
1
to
6
,
as
a
p
er
ce
n
ta
g
e
o
f
th
e
to
tal
n
u
m
b
e
r
o
f
co
r
r
ec
t
an
s
wer
s
to
th
e
q
u
es
tio
n
s
o
r
as
a
n
u
m
b
er
f
r
o
m
a
p
o
s
s
ib
le
to
tal
(
e.
g
.
,
o
u
t
o
f
2
0
o
r
1
0
0
)
.
T
h
e
o
v
e
r
all
p
r
o
p
o
r
tio
n
o
f
all
class
es
in
ce
r
tain
co
u
n
tr
ies
o
r
a
g
r
a
d
e
p
o
in
t
av
er
ag
e
(
GPA)
is
av
er
ag
ed
b
y
all
g
r
a
d
es
o
f
all
cu
r
r
en
t
class
es.
T
h
e
GPA
is
d
eter
m
in
ed
b
y
th
e
g
r
ad
e
p
o
in
ts
g
ain
ed
b
y
a
s
tu
d
e
n
t
in
a
g
iv
en
p
er
io
d
o
f
tim
e.
Fo
r
b
ac
h
elo
r
an
d
g
r
a
d
u
ate
s
t
u
d
en
t
s
in
m
o
s
t
u
n
iv
er
s
ities
,
GPAs
also
ar
e
d
eter
m
in
ed
.
T
h
e
GPA
m
ay
b
e
u
s
ed
f
o
r
ass
es
s
in
g
an
d
co
m
p
ar
in
g
ca
n
d
id
ates
f
o
r
p
r
o
s
p
ec
tiv
e
em
p
lo
y
er
s
o
r
ed
u
ca
tio
n
al
in
s
titu
tio
n
s
[
5
]
.
C
alcu
latin
g
th
e
to
tal
ea
r
n
ed
p
o
i
n
ts
o
f
a
s
tu
d
e
n
t
d
iv
id
ed
b
y
th
e
p
o
s
s
ib
le
n
u
m
b
er
o
f
p
o
in
ts
,
is
k
n
o
wn
as
a
cu
m
u
lativ
e
g
r
a
d
e
p
o
in
t
av
er
ag
e
(
C
GPA)
.
T
h
e
av
er
ag
e
o
f
th
is
r
an
k
i
n
g
s
ch
e
m
e
f
o
r
all
o
f
th
e
s
tu
d
en
t'
s
e
d
u
ca
tio
n
ca
r
ee
r
is
d
eter
m
in
ed
[
6
]
.
Gr
ad
e
p
o
in
t
a
v
er
ag
es
co
u
ld
b
e
u
n
weig
h
ted
;
w
h
er
e
all
g
r
o
u
p
s
with
th
e
s
am
e
cr
ed
its
h
av
e
th
e
s
am
e
ef
f
e
ct
o
n
th
e
GPA,
o
r
co
u
l
d
b
e
weig
h
ted
;
wh
er
e
s
o
m
e
class
es
th
an
o
th
er
s
a
r
e
g
iv
e
n
m
o
r
e
in
f
lu
en
ce
.
A
d
eg
r
ee
s
ch
em
e
is
e
s
tab
lis
h
ed
b
y
m
ea
n
s
o
f
th
e
ac
ce
p
tan
ce
p
r
o
ce
s
s
f
o
r
u
n
d
er
g
r
a
d
u
ate
o
r
g
r
a
d
u
ate
co
u
r
s
es.
Fo
r
m
o
s
t
ac
ad
em
ic,
tech
n
ical
a
n
d
jo
b
f
o
cu
s
ed
co
u
r
s
es,
let
ter
r
atin
g
s
ch
em
es
(
g
r
ad
es
ap
p
lic
ab
le
to
GPA)
ar
e
m
o
s
tly
u
s
ed
.
L
etter
g
r
ad
es
ca
n
also
b
e
u
s
ed
in
th
e
ca
s
e
o
f
n
o
n
-
cr
e
d
it
co
u
r
s
es,
wh
er
e
GPA
r
atin
g
s
ar
e
n
o
t
av
ailab
le.
T
h
e
m
ea
n
in
g
o
f
th
e
f
i
n
al
letter
g
r
ad
e
is
b
ased
o
n
r
eg
u
lar
p
er
ce
n
tag
e
e
q
u
iv
alen
ts
.
T
h
e
f
in
a
l
p
er
ce
n
tag
e
is
r
o
u
n
d
ed
t
o
th
e
n
ea
r
est
in
te
g
e
r
b
y
n
o
r
m
al
s
tatis
tical
p
r
o
ce
d
u
r
e
b
ef
o
r
e
tr
a
n
s
latin
g
it
to
a
letter
g
r
a
d
e
(
i.e
.
,
r
o
u
n
d
in
g
to
th
e
n
e
x
t h
ig
h
e
r
in
t
eg
er
if
th
e
f
ir
s
t d
ig
it a
f
ter
th
e
d
ec
im
al
p
lace
is
5
o
r
h
ig
h
e
r
)
[
7
]
.
T
h
e
s
tu
d
en
t’
s
cr
ed
its
ar
e
b
ase
d
o
n
th
e
ac
ad
e
m
ic
lo
ad
cr
ed
it
s
o
f
th
e
p
ass
ed
m
o
d
u
les.
T
h
e
ca
lcu
latio
n
o
f
cu
m
u
lativ
e
cr
ed
it
h
o
u
r
s
o
f
th
e
r
ep
ea
ted
m
o
d
u
les
will
b
e
co
u
n
ted
o
n
ce
.
I
n
o
r
d
er
to
m
ea
s
u
r
e
to
tal
GPA,
th
e
h
ig
h
est
ac
h
iev
ed
GP
is
u
s
ed
.
T
h
e
ac
cu
m
u
lated
GPA
esti
m
ate
f
o
r
ea
ch
s
tu
d
en
t
b
eg
in
s
f
r
o
m
th
e
f
ir
s
t
s
em
ester
an
d
is
u
p
d
ated
till
th
e
s
tu
d
en
t
g
r
ad
u
atio
n
in
ea
ch
s
em
ester
.
T
h
e
s
tu
d
en
t'
s
s
em
es
ter
G
PA
is
th
e
weig
h
ted
av
er
ag
e
o
f
th
e
g
r
ad
e
p
o
i
n
ts
o
b
tain
ed
in
th
e
m
o
d
u
les
tak
en
d
u
r
in
g
th
at
p
ar
ticu
lar
s
em
ester
as
ca
lcu
lated
in
(
1
)
a
n
d
(
2
)
[
8
]
.
=
∑
×
ℎ
ℎ
∑
ℎ
ℎ
(
1
)
=
∑
×
ℎ
ℎ
∑
ℎ
(
2
)
2.
P
RO
P
O
SE
D
SYS
T
E
M
T
h
e
g
o
al
o
f
th
is
p
ap
er
is
to
tr
an
s
late
th
e
awa
r
d
ed
cu
m
u
lat
iv
e
GPA
in
to
an
eq
u
iv
alen
t
p
er
ce
n
t
ag
e,
wh
ich
will
h
elp
s
tu
d
en
ts
tr
an
s
f
er
in
two
ed
u
c
atio
n
al
s
y
s
tem
s
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
i
s
h
elp
f
u
l
in
m
an
y
s
itu
atio
n
s
;
o
n
e
o
f
th
em
is
tr
an
s
f
er
r
in
g
s
tu
d
e
n
ts
f
r
o
m
o
n
e
u
n
i
v
er
s
ity
to
a
n
o
th
er
th
at
h
av
e
d
i
f
f
er
en
t
ass
ess
m
en
t;
also
ca
n
h
elp
in
ca
s
e
o
f
r
a
n
k
in
g
s
tu
d
en
t
s
f
r
o
m
d
if
f
e
r
en
t
u
n
iv
er
s
ities
.
Sin
ce
th
e
n
u
m
b
e
r
o
f
p
eo
p
le
wh
o
g
r
a
d
u
ated
f
r
o
m
a
u
n
i
v
er
s
ity
h
as
in
cr
ea
s
ed
.
T
h
e
r
e
is
a
h
ig
h
co
m
p
etitio
n
am
o
n
g
th
e
g
r
ad
u
ates
in
wh
ite
co
llar
j
o
b
m
ar
k
et.
On
e
o
f
th
e
in
d
icato
r
s
th
at
h
ig
h
lig
h
t
th
e
u
n
iv
er
s
ity
s
tu
d
en
ts
’
q
u
alif
icati
o
n
is
th
e
ac
ad
em
ic
p
er
f
o
r
m
a
n
ce
,
th
er
e
ar
e
d
if
f
er
en
t
ev
al
u
atio
n
s
in
u
n
iv
er
s
ities
,
th
e
p
r
o
p
o
s
ed
m
o
d
el
u
s
ed
to
f
i
n
d
a
way
to
co
m
p
ar
e
all
s
tu
d
e
n
ts
in
o
n
e
way
.
Alg
o
r
ith
m
1
,
d
escr
ib
es
th
e
b
asic
s
tep
s
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
eq
u
atio
n
s
b
ased
o
n
t
h
e
f
u
zz
y
lo
g
ic
m
eth
o
d
o
l
o
g
y
:
Alg
o
r
ith
m
1
:
p
r
o
p
o
s
ed
f
u
z
zy
m
o
d
el
to
c
o
n
v
er
t
C
GPA
to
eq
u
iv
alen
t
p
er
ce
n
tag
e
v
al
u
e
:
a)
I
n
p
u
t:
−
Ma
tr
ix
M
with
l
etter
g
r
ad
es a
n
d
co
r
r
esp
o
n
d
in
g
GPA
with
th
e
awa
r
d
ed
d
e
g
r
ee
f
o
r
a
g
iv
en
f
a
cu
lty
.
−
C
u
m
u
lativ
e
GPA
.
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:
2502
-
4
7
5
2
C
o
n
ve
r
tin
g
cu
mu
la
tive
g
r
a
d
e
p
o
in
t a
ve
r
a
g
e
to
a
n
eq
u
iv
a
len
t
p
ercen
ta
g
e…
(
I
b
r
a
h
im
E
ld
es
o
u
ky
F
a
tto
h
)
1825
b)
C
alcu
late
th
e
mi
n
im
u
m
p
o
in
ts
_
an
d
m
a
x
i
m
u
m
p
o
in
ts
_
f
o
r
th
e
g
iv
en
m
atr
i
x
M:
=
(
)
(
3
)
=
(
)
(
4
)
c)
C
a
l
c
u
l
a
t
e
t
h
e
m
i
n
i
m
u
m
p
e
r
c
e
n
t
a
g
e
_
a
n
d
m
a
x
i
m
u
m
p
e
r
c
e
n
t
a
g
e
_
f
o
r
t
h
e
g
i
v
e
n
m
a
t
r
i
x
M
:
=
(
)
(
5
)
=
(
)
(
6
)
d)
C
alcu
late
th
e
m
em
b
er
s
h
ip
v
al
u
e
f
o
r
t
h
e
_
:
=
_
−
_
[
_
]
−
_
(
7
)
e)
Ou
tp
u
t f
o
r
th
is
cu
m
u
lativ
e
GP
A
is
:
=
[
×
]
+
_
(
8
)
Su
ch
th
at
:
f)
=
[
_
]
−
_
(
9
)
T
h
e
in
p
u
ts
to
th
e
p
r
o
p
o
s
ed
m
eth
o
d
a
r
e
t
h
e
m
atr
i
x
M
w
i
th
g
r
ad
e
letter
s
a
n
d
c
o
r
r
esp
o
n
d
in
g
GPA
an
d
th
e
c
u
m
u
lativ
e
GPA
th
at
s
h
o
u
ld
b
e
co
n
v
er
ted
to
p
er
ce
n
tag
e
.
T
h
e
s
y
s
tem
will
s
tar
t
b
y
ca
lcu
latin
g
Min
im
u
m
p
o
in
t
u
s
in
g
(
3
)
th
at
will
ta
k
e
th
e
m
in
im
u
m
p
o
in
t
to
t
h
e
c
o
r
r
esp
o
n
d
in
g
c
u
m
u
lativ
e
GPA
f
r
o
m
t
h
e
m
atr
i
x
M.
W
ith
th
e
s
am
e
we
wil
l
u
s
ed
(
4
)
to
g
et
th
e
ma
x
im
u
m
p
o
in
t
to
th
e
in
p
u
t
cu
m
u
lativ
e
GPA.
T
h
e
m
in
im
u
m
an
d
m
ax
im
u
m
p
er
ce
n
ta
g
e
to
ea
ch
g
r
ad
e
letter
in
m
atr
ix
M
will
ca
lcu
lated
u
s
in
g
(
5
)
an
d
(
6
)
.
T
h
e
n
ex
t
s
tep
is
ca
lcu
latin
g
th
e
m
em
b
e
r
s
h
ip
v
alu
e
to
t
h
e
en
ter
ed
c
u
m
u
lativ
e
GPA
u
s
in
g
(
7
)
.
T
h
e
n
o
v
el
m
em
b
er
s
h
ip
f
u
n
ctio
n
in
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
g
u
ar
an
tee
o
f
p
ick
in
g
a
v
al
u
e
1
to
h
ig
h
est
cu
m
u
lativ
e
GP
A
an
d
a
v
alu
e
0
t
o
m
in
im
u
m
c
u
m
u
lativ
e
GPA.
Af
ter
ca
lcu
latin
g
t
h
e
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
µ
,
it
will
b
e
m
u
ltip
lied
b
y
th
e
p
er
ce
n
tag
e
d
if
f
er
en
ce
o
f
th
e
ap
p
r
o
p
r
iate
r
an
g
e
wh
er
e
th
e
cu
m
u
lativ
e
GPA
f
all
in
b
et
wee
n
,
f
o
llo
wed
b
y
ad
d
in
g
t
h
is
v
alu
e
to
th
e
m
in
im
u
m
p
e
r
ce
n
tag
e
as sh
o
wn
in
(
8
)
an
d
(
9
)
.
3.
P
RO
P
O
SE
D
SYS
T
E
M
T
h
e
n
o
v
el
eq
u
atio
n
s
u
s
ed
in
th
is
p
ap
er
is
b
ased
o
n
f
u
zz
y
lo
g
ic,
th
e
m
ath
em
atica
l
b
asis
o
f
th
e
f
u
zz
y
lo
g
ic
lies
in
th
e
f
u
zz
y
s
et
t
h
eo
r
y
o
f
a
class
ical
s
et
th
e
o
r
y
,
wh
ic
h
ca
n
b
e
v
iewe
d
a
s
a
g
en
er
aliza
tio
n
.
Fu
zz
y
lo
g
ic
is
a
s
tr
o
n
g
p
r
o
b
lem
-
s
o
lv
in
g
tech
n
iq
u
e
f
o
r
a
v
ar
iety
o
f
em
b
ed
d
e
d
co
n
tr
o
l
an
d
in
f
o
r
m
atio
n
tech
n
o
lo
g
y
ap
p
licatio
n
s
.
Fu
zz
y
p
r
o
v
id
es
a
s
u
r
p
r
is
in
g
ly
clea
r
way
in
wh
ich
f
i
n
al
ju
d
g
m
en
ts
ar
e
d
er
iv
ed
f
r
o
m
v
ag
u
e,
u
n
clea
r
o
r
in
co
r
r
ec
t
in
f
o
r
m
atio
n
.
I
n
s
o
m
e
s
en
s
e,
f
u
zz
y
lo
g
ic
r
esem
b
les
th
e
ab
ilit
y
o
f
th
e
h
u
m
a
n
b
ein
g
to
f
u
n
ctio
n
with
ap
p
r
o
x
im
ate
d
ata
an
d
f
in
d
s
p
ec
if
ic
s
o
lu
tio
n
s
.
Ov
er
th
e
last
f
ew
y
ea
r
s
,
f
u
zz
y
lo
g
ic
h
as
b
ee
n
wid
ely
ac
ce
p
ted
as
a
m
eth
o
d
f
o
r
s
o
lv
in
g
p
r
o
b
lem
s
.
Mo
r
e
t
h
an
two
th
o
u
s
an
d
c
o
m
m
er
cia
l
p
r
o
d
u
cts,
r
an
g
in
g
f
r
o
m
wash
in
g
m
ac
h
in
es
t
o
h
ig
h
-
s
p
ee
d
t
r
ain
s
,
ar
e
u
s
ab
le
v
ia
f
u
zz
y
lo
g
ic.
Fu
zz
y
lo
g
ic
b
en
e
f
its
ca
n
b
e
r
ea
lized
in
its
p
er
f
o
r
m
a
n
ce
,
s
im
p
licity
,
lo
wer
co
s
t a
n
d
ef
f
icien
cy
in
e
v
er
y
ap
p
licatio
n
[
9
]
.
Fu
zz
y
is
a
co
m
p
u
tin
g
tech
n
iq
u
e
f
o
cu
s
ed
o
n
“d
eg
r
ee
s
o
f
tr
u
t
h
”
an
d
n
o
t
o
n
“tr
u
e
o
r
f
alse”
t
ec
h
n
iq
u
es
(
1
o
r
0
)
as
th
e
ty
p
ically
b
asi
s
o
f
th
e
d
ig
ital
co
m
p
u
ter
.
L
o
tf
i
Z
ad
eh
f
r
o
m
th
e
Un
i
v
er
s
ity
o
f
C
alif
o
r
n
ia
in
B
er
k
eley
,
was
th
e
f
ir
s
t
to
in
v
e
n
t
th
e
n
o
tio
n
o
f
f
u
zz
y
lo
g
ic
in
th
e
s
ix
ties
.
T
h
e
d
ilem
m
a
t
h
at
p
r
o
m
p
te
d
Z
a
d
eh
t
o
lear
n
ab
o
u
t th
e
f
u
zz
y
lo
g
ic
was to
o
v
er
c
o
m
e
th
e
p
r
o
b
lem
o
f
co
m
p
u
ter
k
n
o
wled
g
e
o
f
n
atu
r
a
l la
n
g
u
ag
e
[
1
0
]
.
T
r
an
s
latin
g
th
e
n
atu
r
al
la
n
g
u
ag
e
in
to
0
an
d
1
is
v
er
y
co
m
p
licated
.
T
wo
e
x
tr
em
e
ca
s
es
o
f
th
e
f
ac
t
(
tr
u
th
)
,
wh
ich
ar
e
ze
r
o
an
d
o
n
e
ar
e
p
ar
t
o
f
f
u
zz
y
lo
g
ic
.
I
t
also
co
v
er
s
th
e
v
ar
i
o
u
s
ca
s
e
s
o
f
f
a
ct
(
tr
u
th
)
in
b
etwe
en
,
e.
g
.
,
t
h
e
o
u
tco
m
es
o
f
a
co
m
p
ar
is
o
n
o
f
two
item
s
ca
n
'
t
b
e
'tall
'
o
r
'sh
o
r
t'
o
n
ly
,
b
u
t
a
v
alu
e
o
f
‘
0
.
3
8
talln
ess
'
.
An
ea
s
ier
way
to
s
im
p
lify
th
is
co
n
ce
p
t
is
allo
win
g
m
o
r
e
v
alu
es
f
r
o
m
1
to
0
.
I
n
p
ar
ticu
la
r
,
s
o
m
e
alter
n
ativ
es h
av
e
th
e
p
o
ten
tial
to
b
e
allo
wed
b
etwe
en
th
e
lim
its
0
to
1
,
i.e
.
,
t
h
e
in
ter
v
al
o
f
t
h
e
u
n
it I
=
[
0
,
1
]
.
I
t
is
m
u
ch
h
ar
d
e
r
to
t
r
an
s
late
th
e
n
u
m
b
er
s
allo
ca
ted
to
ea
ch
elem
en
t.
T
h
is
im
p
lies
th
at
th
e
n
u
m
b
er
1
will
b
e
ass
ig
n
ed
to
an
elem
en
t
,
if
th
e
elem
en
t
is
in
s
et
'
A'
.
I
f
th
e
elem
en
t
is
n
o
t
in
'
A'
s
et,
t
h
at
im
p
lies
th
at
an
elem
en
t w
ill b
e
g
iv
en
t
h
e
n
u
m
b
er
0
.
All o
t
h
er
v
al
u
es m
ea
n
t
h
at
th
e
s
et
'
A
'
is
s
tead
ily
in
co
r
p
o
r
ated
[
1
0
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
em
b
er
2
0
2
1
:
1
8
2
3
-
1
8
3
1
1826
3
.
1
.
Cha
ra
c
t
er
is
t
ics o
f
f
uzzy
lo
g
ic
I
n
1
9
9
2
,
Z
a
d
eh
laid
d
o
wn
a
s
et
o
f
f
u
n
d
am
e
n
tal
r
u
les o
f
f
u
zz
y
lo
g
ic
[
1
1
]
:
−
C
o
r
r
ec
t r
ea
s
o
n
in
g
s
h
all
b
e
tr
ea
ted
as a
n
esti
m
ated
r
ea
s
o
n
in
g
s
ce
n
ar
io
.
−
E
v
er
y
th
in
g
is
co
n
s
id
er
ed
a
m
a
tter
o
f
d
e
g
r
ee
.
−
Kn
o
wled
g
e
is
u
n
d
er
s
to
o
d
as a
n
elastic,
f
u
zz
y
co
n
s
tr
ain
t o
n
a
g
r
o
u
p
o
f
v
ar
iab
les.
−
I
n
f
er
en
ce
s
h
all
b
e
tr
ea
ted
as a
n
elastic c
o
n
s
tr
ain
t p
r
o
p
ag
atio
n
m
ec
h
an
is
m
.
−
An
y
lo
g
ical
s
y
s
tem
s
h
all
b
e
"f
u
zz
y
".
T
h
er
e
ar
e
two
m
ain
f
ea
t
u
r
e
s
o
f
f
u
zz
y
s
y
s
tem
s
,
p
r
o
v
i
d
e
im
p
r
o
v
e
d
p
er
f
o
r
m
an
ce
f
o
r
p
ar
ticu
lar
ap
p
licatio
n
s
:
−
Fu
zz
y
s
y
s
tem
s
ar
e
s
u
itab
le,
i
n
p
ar
ticu
lar
f
o
r
th
e
s
y
s
tem
w
ith
a
m
ath
em
atica
l
m
o
d
el
th
a
t
is
d
if
f
icu
lt
to
ex
tr
ac
t a
n
d
id
ea
l
f
o
r
u
n
clea
r
o
r
ap
p
r
o
x
im
ate
p
u
r
p
o
s
es.
−
Fu
zz
y
lo
g
ic
h
e
l
p
s
d
ec
is
io
n
s
with
ap
p
r
o
x
im
ate
v
alu
es in
in
co
m
p
lete
o
r
am
b
ig
u
o
u
s
in
f
o
r
m
at
io
n
.
3
.
2
.
M
em
bersh
ip cr
ea
t
io
n
T
h
e
f
u
zz
y
r
elatio
n
co
n
ce
p
t
o
f
a
class
ical
r
elatio
n
s
h
ip
is
g
en
er
alize
d
b
y
i
n
tr
o
d
u
cin
g
a
p
ar
tial
m
em
b
er
s
h
ip
b
etwe
en
x
an
d
y
elem
en
ts
.
E
x
am
p
les
o
f
f
u
zz
y
r
elatio
n
s
h
ip
s
ar
e
“
s
im
ilar
,
ap
p
r
o
x
im
ately
e
q
u
al
an
d
m
u
ch
lar
g
er
”
.
T
h
e
ca
r
tesi
an
p
r
o
d
u
ct
o
f
X
an
d
Y
d
ef
i
n
e
s
th
e
f
u
zz
y
r
elatio
n
b
etwe
en
s
et
'
X
'
an
d
s
et
'
Y
'
i
s
estab
lis
h
ed
.
T
ak
in
g
a
s
u
b
s
et
o
f
a
ce
r
tain
ca
r
tesi
an
p
r
o
d
u
ct
'
XY'
.
A
f
u
zz
y
s
et
'
A'
m
em
b
er
s
h
ip
f
u
n
ctio
n
o
n
th
e
d
is
co
u
r
s
e
u
n
iv
er
s
e
is
well
-
d
ef
in
ed
as:
µ
A
:
→
[
0
,
1
]
,
wh
er
e
t
h
e
v
alu
e
o
f
ea
ch
elem
en
t
is
m
ap
p
ed
f
r
o
m
0
to
1
.
T
h
is
v
al
u
e
is
ca
lled
a
m
e
m
b
er
s
h
ip
v
alu
e
o
r
m
em
b
er
s
h
i
p
d
e
g
r
ee
,
q
u
an
tifie
s
t
h
e
m
em
b
er
s
h
ip
g
r
a
d
e
o
f
t
h
e
elem
en
ts
in
X
to
th
e
f
u
zz
y
s
et
'
A
'
.
Me
m
b
er
s
h
ip
f
u
n
ctio
n
s
allo
ws
g
r
a
p
h
ical
r
ep
r
esen
tati
o
n
o
f
a
f
u
zz
y
s
et,
wh
er
e
th
e
x
-
a
x
is
r
ep
r
esen
ts
th
e
d
is
co
u
r
s
e
u
n
iv
e
r
s
e,
wh
ile
t
h
e
y
-
a
x
is
r
ep
r
esen
ts
t
h
e
m
em
b
e
r
s
h
ip
d
e
g
r
ee
s
in
s
id
e
th
e
in
ter
v
al
[
0
,
1
]
[
1
1
]
.
T
o
co
n
s
tr
u
ct
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
,
s
im
p
le
f
u
n
ctio
n
s
ar
e
u
s
ed
.
Usi
n
g
co
m
p
lex
f
u
n
ctio
n
s
in
f
u
zz
y
co
n
ce
p
ts
d
o
n
’
t
o
f
f
er
m
o
r
e
ac
cu
r
ac
y
.
T
h
e
s
im
p
lest
m
em
b
e
r
s
h
ip
f
u
n
cti
o
n
s
ar
e
g
en
er
ated
b
y
s
tr
aig
h
t
lin
es
.
T
h
e
ea
s
iest
o
f
th
ese
is
th
e
tr
ia
n
g
u
lar
f
u
n
ctio
n
m
em
b
e
r
s
h
ip
(
3
)
,
a
n
d
its
n
am
e
is
tr
im
f
.
I
t'
s
ju
s
t
a
th
r
ee
-
p
o
i
n
t
s
et
m
ak
in
g
a
tr
ian
g
le.
T
h
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
o
f
th
e
t
r
ap
ez
o
i
d
al
(
4
)
,
tr
a
p
m
f
,
h
as
a
f
lat
to
p
an
d
is
r
ea
lly
ju
s
t
a
tr
u
n
ca
ted
tr
ian
g
le
cu
r
v
e.
T
h
e
b
en
ef
it
o
f
th
ese
s
tr
aig
h
t
lin
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
is
s
im
p
licity
.
Her
e
ar
e
s
o
m
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
u
s
ed
in
th
e
liter
atu
r
e
f
o
r
s
o
lv
in
g
m
an
y
ap
p
licatio
n
s
[
1
2
]
.
−
T
r
ian
g
u
lar
f
u
n
ctio
n
is
d
ef
in
e
d
as a
lo
wer
lim
it ‘
a’
,
a
v
al
u
e
o
f
‘
m
’
an
d
an
u
p
p
er
lim
it ‘
b
’
,
w
h
er
e
a
<
m
<
b
:
(
)
=
{
0
,
≤
−
−
,
<
≤
−
−
<
<
0
,
>
}
(
10
)
−
T
r
ap
ez
o
id
al
f
u
n
ctio
n
is
d
ef
in
e
d
as
a
lo
wer
lim
it
‘
a’
,
a
l
o
wer
s
u
p
p
o
r
t
lim
it
‘
b
’
,
an
u
p
p
er
l
im
it
‘
d
’
an
d
an
u
p
p
er
s
u
p
p
o
r
t lim
it ‘
c’
,
wh
e
r
e
a
<
b
<
c
<
d
.
(
)
{
0
,
(
<
)
(
>
)
−
−
,
<
≤
1
<
<
−
−
,
≤
≤
}
(
11
)
−
Gau
s
s
ian
f
u
n
ctio
n
is
k
n
o
wn
as
'
m
'
,
ce
n
tr
al
v
alu
e
an
d
'
k
'
,
a
s
tan
d
ar
d
d
e
v
iatio
n
>
0
.
T
h
e
s
m
aller
th
e
k
,
th
e
n
ar
r
o
wer
t
h
e
b
ell.
(
)
=
−
(
−
)
2
2
2
(
1
2
)
3.
3
.
F
uzzy
lo
g
ic
in educa
t
io
na
l sy
s
t
em
I
n
th
is
s
ec
tio
n
,
s
o
m
e
o
f
t
h
e
f
u
zz
y
m
o
s
t
r
ec
en
t
r
esear
c
h
ar
ea
s
ar
e
in
t
r
o
d
u
ce
d
f
o
r
ad
d
r
ess
in
g
p
ar
ticu
lar
p
r
o
b
l
em
s
in
d
iv
er
s
e
f
ield
s
o
f
s
tu
d
y
.
O
n
e
o
f
th
e
v
e
r
y
ea
r
ly
ap
p
r
o
ac
h
es
in
e
-
lear
n
in
g
m
o
d
elin
g
was
th
e
f
u
zz
y
lo
g
ic.
I
n
[
1
3
]
,
th
e
r
ef
lectio
n
o
f
an
ass
ess
m
en
t
m
eth
o
d
,
lear
n
i
n
g
en
v
ir
o
n
m
en
t
was
p
r
o
p
o
s
e
d
.
Fu
zz
y
l
o
g
ic
h
el
p
s
to
lay
all
th
e
k
n
o
wled
g
e
in
a
c
o
m
p
u
tatio
n
al
wa
y
an
d
m
ak
e
it
h
ig
h
ly
im
p
r
ec
is
e
[
1
4
]
.
I
t
im
p
l
em
en
ted
a
m
eth
o
d
ca
lled
tap
s
f
o
r
g
r
a
d
in
g
s
tu
d
e
n
ts
,
wh
ich
u
s
es
f
u
zz
y
lo
g
ic
to
ass
ig
n
g
r
ad
es
o
f
m
em
b
er
s
h
ip
to
lan
g
u
ag
e
la
b
els.
Sy
s
tem
s
lik
e
Sh
er
lo
ck
I
I
[
1
5
]
an
d
m
e
d
iu
m
-
d
en
s
ity
f
ib
e
r
b
o
a
r
d
(
MD
F
)
tu
t
o
r
[
1
6
]
h
a
v
e
u
s
ed
f
u
zz
y
d
is
tr
ib
u
tio
n
s
an
d
a
v
a
r
iety
o
f
r
u
les
to
r
e
p
r
esen
t,
id
en
tify
,
a
n
d
u
p
d
ate
s
tu
d
en
t
b
e
h
av
io
r
al
i
n
s
tab
ilit
y
.
T
h
e
b
r
illi
an
t
s
ch
o
la
r
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:
2502
-
4
7
5
2
C
o
n
ve
r
tin
g
cu
mu
la
tive
g
r
a
d
e
p
o
in
t a
ve
r
a
g
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to
a
n
eq
u
iv
a
len
t
p
ercen
ta
g
e…
(
I
b
r
a
h
im
E
ld
es
o
u
ky
F
a
tto
h
)
1827
s
e
r
i
es
1
(
B
S
S
1
)
t
u
t
o
r
i
n
g
f
r
a
m
e
w
o
r
k
i
n
[
1
7
]
a
n
d
s
y
p
r
o
s
in
[
1
8
]
h
a
v
e
b
e
e
n
d
e
v
e
l
o
p
e
d
t
o
b
e
i
n
t
r
o
d
u
c
e
d
w
i
t
h
a
f
u
z
z
y
l
o
g
i
c
e
n
g
i
n
e
.
I
t
p
r
o
v
i
d
es
a
n
o
p
t
i
m
i
ze
d
s
t
u
d
e
n
t
l
e
a
r
n
i
n
g
m
a
n
a
g
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m
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n
t
b
a
s
e
d
o
n
t
h
e
s
e
s
t
r
a
t
e
g
i
e
s
.
D
e
g
r
e
e
[
1
9
]
i
s
a
m
e
t
h
o
d
t
h
a
t
e
n
a
b
l
e
s
k
n
o
w
l
e
d
g
e
t
o
b
e
r
e
t
r
i
e
v
e
d
a
t
a
n
u
m
b
e
r
o
f
s
t
a
g
e
s
.
T
h
e
i
r
a
u
t
h
o
r
s
a
s
s
e
s
s
a
n
d
m
o
d
e
l
t
h
e
v
a
r
i
a
b
l
es
w
it
h
l
i
n
g
u
i
s
t
i
c
v
ar
i
a
b
l
e
s
i
n
q
u
a
l
i
t
at
i
v
e
t
e
r
m
s
.
T
h
e
l
e
a
r
n
i
n
g
p
a
r
t
n
e
r
s
h
i
p
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et
h
o
d
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s
d
e
f
i
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d
f
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t
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is
p
o
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n
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o
f
v
i
e
w
.
T
h
e
f
u
z
z
y
i
n
f
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n
c
e
m
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t
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o
d
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x
p
l
a
i
n
s
t
h
e
g
r
o
u
p
b
e
h
a
v
i
o
r
i
n
c
o
m
p
l
i
a
n
c
e
w
i
t
h
g
r
o
u
p
r
u
l
e
s
a
n
d
v
a
r
i
a
b
l
e
s
.
T
h
e
p
r
e
v
i
o
u
s
w
o
r
k
o
v
e
r
l
o
o
k
e
d
t
h
e
p
a
r
t
i
c
u
l
a
r
f
a
c
e
ts
o
f
t
h
e
d
o
m
a
i
n
.
I
n
t
h
i
s
r
e
s
p
e
c
t
,
th
e
D
o
m
o
S
i
m
-
T
PC
s
y
s
te
m
[
2
0
]
a
u
t
o
m
a
t
i
c
a
ll
y
u
s
es
m
e
t
h
o
d
s
t
o
a
l
l
o
w
e
d
u
c
at
i
o
n
p
r
o
f
e
s
s
i
o
n
al
s
t
o
e
v
al
u
a
t
e
t
h
e
o
u
t
co
m
e
s
o
f
c
l
a
s
s
r
o
o
m
a
c
t
i
v
i
ti
es
.
F
u
z
z
y
i
n
f
e
r
e
n
c
e
c
a
n
b
e
u
s
e
d
i
n
c
o
l
l
ec
t
i
v
e
le
a
r
n
i
n
g
to
d
r
a
w
d
e
c
i
s
i
o
n
s
a
b
o
u
t
s
t
u
d
e
n
ts
a
n
d
t
h
e
i
r
a
ct
i
o
n
s
.
I
n
[
2
1
]
,
f
u
zz
y
r
u
les o
f
ass
o
ciatio
n
wer
e
u
s
ed
to
e
x
p
lain
th
e
c
o
n
n
ec
tio
n
s
am
o
n
g
th
e
d
if
f
er
e
n
t p
atter
n
’
s
b
eh
av
io
r
o
f
a
lear
n
er
.
T
h
e
p
ar
am
eter
s
u
n
d
er
ex
a
m
in
atio
n
in
clu
d
e
th
e
n
u
m
b
er
o
f
q
u
es
tio
n
s
an
s
wer
ed
,
th
e
o
n
lin
e
tim
e
s
p
en
t
an
d
th
e
n
u
m
b
er
o
f
ar
ticles
r
ea
d
an
d
wr
it
ten
.
T
h
is
tec
h
n
iq
u
e
is
d
o
n
e
b
y
th
e
f
u
zz
y
ap
p
r
o
a
c
h
to
c
h
an
g
e
t
y
p
ical
s
ite
lo
g
s
.
T
o
r
ef
lect
th
e
ev
al
u
atio
n
s
o
f
th
e
teac
h
er
,
f
u
zz
y
in
f
e
r
en
ce
s
ar
e
s
u
g
g
ested
in
[
2
2
]
.
I
t
co
n
tr
ib
u
tes
b
y
p
r
o
ce
s
s
in
g
an
d
ag
g
r
eg
atin
g
m
em
b
e
r
s
h
ip
r
u
les
to
in
f
er
lear
n
er
s
'
awa
r
en
ess
an
d
co
g
n
itiv
e
s
k
ills
.
Ad
d
itio
n
al
g
r
ad
in
g
task
s
ar
e
in
[
2
0
]
.
I
t r
esear
ch
ed
t
h
e
p
er
s
p
ec
tiv
es o
f
s
p
ec
ialis
ts
to
c
r
ea
te
a
n
ew
way
o
f
m
ea
s
u
r
in
g
th
e
ac
ad
em
ic
s
u
cc
ess
o
f
s
tu
d
en
ts
.
I
n
o
r
d
e
r
to
ac
co
m
p
lis
h
th
is
task
,
th
e
f
u
zz
y
r
u
les
we
r
e
ap
p
lied
,
wh
ich
in
clu
d
ed
a
f
u
zz
y
in
f
er
e
n
c
e
s
y
s
tem
an
d
a
n
ass
o
ciate
d
r
u
le
alg
o
r
ith
m
f
o
r
th
e
in
d
u
ctio
n
.
I
n
th
is
co
n
tex
t
,
co
n
ce
p
t
m
a
p
s
r
ep
r
esen
t
an
o
t
h
er
b
ig
ad
v
an
tag
e.
I
n
[
2
3
]
,
a
n
ew
tech
n
i
q
u
e
is
p
r
o
p
o
s
ed
to
co
n
s
tr
u
ct
th
em
au
to
m
atica
lly
.
I
t
was
th
en
e
x
ten
d
ed
t
o
m
u
ltip
le
a
d
ap
tiv
e
e
d
u
ca
tio
n
s
y
s
tem
s
.
T
h
is
wo
r
k
u
s
es
r
ea
s
o
n
in
g
tech
n
iq
u
es
an
d
f
u
zz
y
r
u
les.
I
t
p
r
o
d
u
ce
s
co
n
ce
p
t
m
a
p
s
an
d
ass
es
s
es
th
e
im
p
o
r
tan
ce
o
f
co
n
n
ec
tio
n
s
b
etwe
e
n
co
n
ce
p
ts
.
Su
e
et
a
l
.
[
2
4
]
b
u
ilt
th
e
co
n
ce
p
t
m
ap
au
to
m
atic
ally
u
s
in
g
th
e
h
is
to
r
ical
test
e
r
s
o
f
ed
u
ca
to
r
s
,
b
y
m
ea
n
s
o
f
a
two
-
p
h
ase
c
o
n
ce
p
t
m
ap
co
n
s
tr
u
ctio
n
(TP
-
C
MC).
Fu
zz
y
lo
g
ic
ta
k
es
p
r
io
r
ity
in
t
h
e
f
ir
s
t
s
tep
,
wh
er
e
th
e
n
u
m
er
ical
v
alu
es
o
f
t
h
e
te
s
t
r
ec
o
r
d
s
ar
e
co
n
v
er
ted
in
to
s
y
m
b
o
lic
d
ata.
I
n
th
is
s
tep
,
th
e
ap
p
r
o
ac
h
to
d
ata
m
in
in
g
is
u
s
ed
t
o
d
ef
in
e
th
e
r
u
les
o
f
th
e
f
u
zz
y
ass
o
ciatio
n
.
T
h
e
wr
iter
s
u
s
ed
s
ev
er
al
r
u
les
in
r
ea
lis
tic
lear
n
in
g
s
ce
n
ar
io
s
ac
co
r
d
in
g
t
o
th
eir
f
in
d
in
g
s
,
in
th
e
s
ec
o
n
d
p
h
ase.
A
f
u
ll
lear
n
in
g
ar
c
h
itectu
r
e
i
s
p
r
o
p
o
s
ed
in
[
2
5
]
b
ey
o
n
d
in
d
iv
i
d
u
al
tech
n
iq
u
es.
I
t
p
r
o
m
o
tes
in
d
iv
id
u
al
ed
u
ca
tio
n
f
o
cu
s
ed
o
n
a
m
eth
o
d
o
f
ed
u
ca
tio
n
b
u
ilt
f
o
r
in
tellig
en
t
ag
en
ts
.
T
h
e
r
u
les
s
et
in
th
is
ca
s
e
is
b
ased
o
n
a
f
u
zz
y
i
n
f
er
en
c
e
en
g
i
n
e.
I
t
al
lo
ws
m
ath
em
atica
l
lan
g
u
ag
e
r
u
les to
b
e
d
escr
ib
ed
an
d
co
d
e
to
b
e
cr
ea
ted
a
u
to
m
atica
lly
.
I
n
[
2
6
]
th
e
tech
n
iq
u
e
f
o
r
f
u
zz
y
in
d
u
ctiv
e
r
ea
s
o
n
in
g
f
o
r
ec
ast
s
t
h
e
ev
en
tu
al
o
u
tco
m
es
o
f
s
tu
d
en
ts
o
n
a
s
im
u
lated
ca
m
p
u
s
.
R
ea
l
ass
ess
m
en
t
ev
alu
atio
n
s
wer
e
p
er
f
o
r
m
ed
,
l
o
w
b
u
g
s
wer
e
r
en
d
e
r
e
d
an
d
th
e
teac
h
er
'
s
task
s
wer
e
d
ec
r
ea
s
ed
in
d
if
f
ic
u
lty
.
T
h
e
s
tu
d
y
in
[
2
7
]
s
u
g
g
es
ts
an
o
th
er
m
eth
o
d
o
f
f
u
zz
y
in
f
er
en
ce
m
ec
h
an
is
m
f
o
r
r
ec
o
m
m
e
n
d
in
g
th
e
o
p
tio
n
o
f
co
n
ten
t
f
o
r
an
y
s
tu
d
en
t
b
y
ed
u
ca
t
o
r
s
.
T
h
e
y
e
n
co
u
r
a
g
e
ed
u
ca
to
r
s
to
b
e
d
ir
ec
ted
with
th
eir
s
tu
d
en
ts
alo
n
g
with
th
e
d
esire
s
o
f
lear
n
e
r
s
,
an
aly
tical
h
ier
ar
ch
y
p
r
o
ce
s
s
es
an
d
m
em
o
r
ies
lo
o
p
u
p
d
ates.
W
h
ile
i
n
[
2
8
]
,
a
m
o
d
er
n
r
ec
o
m
m
en
d
atio
n
s
y
s
tem
o
f
f
u
zz
y
m
atch
in
g
r
u
les
is
p
r
o
v
id
ed
in
th
e
ad
ap
tiv
e
an
d
in
tellig
en
t
web
-
b
ased
ed
u
ca
tio
n
s
y
s
tem
to
f
in
d
th
e
r
eq
u
ir
e
d
lear
n
in
g
c
o
n
ten
t
th
at
b
est
s
u
its
th
e
n
ee
d
s
o
f
ea
c
h
s
tu
d
en
t.
T
h
is
r
e
co
m
m
en
d
e
d
s
y
s
tem
co
u
ld
ass
is
t
s
tu
d
en
ts
an
d
aid
teac
h
in
g
o
n
lin
e.
Als
o
,
th
e
f
u
zz
y
lo
g
ic
was
u
s
ed
as
m
eth
o
d
f
o
r
ev
alu
atin
g
s
tu
d
en
ts
p
er
f
o
r
m
an
ce
an
d
as
a
g
r
ad
in
g
m
o
d
el
f
o
r
s
tu
d
e
n
ts
as
in
[
2
9
]
,
[
3
0
]
.
Fr
o
m
th
e
liter
atu
r
e,
w
e
o
b
s
er
v
ed
th
at
th
e
f
u
zz
y
lo
g
ic
is
u
s
ed
in
m
an
y
ed
u
ca
tio
n
al
s
y
s
tem
s
f
o
r
d
if
f
e
r
en
t
p
u
r
p
o
s
es,
wh
ile
th
is
r
ese
ar
ch
is
co
n
s
id
er
ed
th
e
f
ir
s
t
o
n
e,
wh
ich
u
s
es
th
e
f
u
zz
y
lo
g
ic
to
co
n
v
er
t
f
r
o
m
o
n
e
g
r
a
d
in
g
s
y
s
tem
to
a
n
o
th
er
o
n
e.
T
h
is
will
h
elp
ed
u
ca
t
o
r
s
an
d
ed
u
ca
tio
n
al
o
r
g
an
izatio
n
s
in
ac
ce
p
tin
g
tr
a
n
s
f
er
r
ed
s
tu
d
en
ts
f
o
r
m
ed
u
ca
ti
o
n
al
o
r
g
an
iza
tio
n
to
an
o
t
h
er
o
n
e,
also
h
elp
in
r
an
k
in
g
th
e
s
tu
d
en
ts
ac
co
r
d
in
g
to
th
eir
g
r
ad
es
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
o
ev
alu
ate
th
e
p
r
o
p
o
s
ed
m
o
d
el,
we
h
av
e
c
o
llected
th
r
e
e
d
if
f
er
en
t
d
atasets
f
r
o
m
th
r
e
e
d
if
f
er
en
t
f
ac
u
lties
in
th
e
U
n
iv
er
s
ity
o
f
B
en
i
-
Su
ef
.
T
h
e
th
r
ee
d
atasets
d
if
f
er
in
th
e
n
u
m
b
er
o
f
av
ail
ab
le
GPA
p
o
in
ts
in
ea
ch
o
n
e
a
n
d
th
e
awa
r
d
ed
d
eg
r
ee
an
d
th
e
r
an
g
e
o
f
p
er
ce
n
ta
g
e
v
alu
es a
cc
o
r
d
i
n
g
to
t
h
e
GPA
p
o
in
ts
.
4
.
1
.
F
a
cult
y
o
f
co
m
pu
t
er
s
a
nd
a
rt
if
icia
l int
ellig
ence
T
h
e
f
ir
s
t
d
ataset
is
co
llected
f
r
o
m
fa
cu
lty
o
f
co
m
p
u
ter
s
an
d
ar
tific
ial
in
tellig
en
ce
.
T
h
e
d
if
f
er
e
n
t
g
r
ad
in
g
p
o
in
ts
r
an
g
e
f
r
o
m
0
t
o
4
.
T
h
e
awa
r
d
e
d
d
eg
r
ee
f
r
o
m
ex
ce
llen
t
to
f
ail.
T
ab
le
1
,
s
h
o
ws
GPA
in
p
o
in
ts
,
th
e
awa
r
d
ed
d
eg
r
ee
a
n
d
th
e
co
r
r
esp
o
n
d
i
n
g
p
e
r
ce
n
tag
e
r
an
g
e
f
o
r
ea
ch
GPA
p
o
in
ts
r
an
g
e
.
T
ab
le
1
.
D
ataset
1
-
f
ac
u
lty
o
f
c
o
m
p
u
ter
s
an
d
ar
tific
ial
in
tellig
en
ce
M
a
x
_
P
e
r
M
i
n
_
P
e
r
M
a
x
-
P
o
i
n
t
s
M
i
n
_
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i
n
t
s
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e
r
c
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t
a
g
e
A
w
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d
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r
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t
s
1
0
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4
3
.
7
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P
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=
0
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
5
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d
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J
E
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n
g
&
C
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m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
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b
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2
0
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1
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A
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a
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l
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.
F
a
cult
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o
f
s
cience
T
h
e
s
ec
o
n
d
d
ataset
is
co
llected
f
r
o
m
f
ac
u
lty
o
f
s
cien
ce
.
T
h
e
r
an
g
e
o
f
GPA
p
o
in
ts
ar
e
less
th
an
th
e
r
an
g
e
in
d
ataset
1
.
T
ab
le
3
,
s
h
o
ws
th
r
ee
c
o
lu
m
n
s
th
at
r
ep
r
esen
ts
th
e
GPA
in
p
o
in
ts
,
th
e
awa
r
d
ed
d
e
g
r
ee
a
n
d
th
e
p
er
ce
n
ta
g
e
.
Als
o
,
T
a
b
le
3
,
s
h
o
ws
th
e
ca
lc
u
lated
th
e
m
in
im
u
m
p
o
in
ts
(
Min
_
Po
in
ts
)
,
m
ax
im
u
m
p
o
in
ts
(
Ma
x
_
Po
in
ts
)
,
m
in
im
u
m
p
er
c
en
tag
e
(
Min
_
Per
)
an
d
m
a
x
im
u
m
p
er
ce
n
tag
e
(
Ma
x
_
Per
)
to
cu
m
u
lativ
e
GPA
f
o
r
d
ata
s
et
2
.
T
h
ese
v
al
u
es
th
at
w
ill
b
e
u
s
ed
in
eq
u
atio
n
7
to
c
o
m
p
u
te
th
e
m
em
b
er
s
h
i
p
f
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n
cti
o
n
,
th
e
n
eq
u
atio
n
s
8
an
d
9
to
ca
lcu
late
th
e
p
r
o
p
o
s
e
d
p
er
ce
n
tag
e
v
alu
e.
T
ab
le
4
a
n
d
Fig
u
r
e
2
,
s
h
o
w
th
e
n
o
v
el
e
q
u
atio
n
s
ar
e
test
ed
b
y
ca
lcu
latin
g
th
e
p
er
ce
n
tag
e
o
f
a
g
iv
e
n
cu
m
u
lativ
e
GPA
o
f
s
o
m
e
s
tu
d
e
n
ts
in
th
e
g
iv
e
n
d
ataset
s
h
o
wn
i
n
T
ab
le
4
.
Fig
u
r
e
1
.
R
elatio
n
b
etwe
en
cu
m
u
lativ
e
GPA
an
d
p
er
ce
n
tag
e
f
o
r
d
ataset
1
Fig
u
r
e
2
.
R
elatio
n
b
etwe
en
cu
m
u
lativ
e
GPA
an
d
p
er
ce
n
tag
e
f
o
r
d
ataset
2
T
ab
le
3
.
Data
s
et
2
-
f
ac
u
lty
o
f
s
cien
ce
P
o
i
n
t
s
A
w
a
r
d
e
d
d
e
g
r
e
e
P
e
r
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t
a
g
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M
i
n
_
P
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i
n
t
s
M
a
x
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P
o
i
n
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s
M
i
n
_
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e
r
M
a
x
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p
e
r
G
P
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>
=
3
.
5
Ex
c
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5
85
1
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.
5
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P
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6
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a
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l
6
0
>
P
e
r
c
e
n
t
a
g
e
0
<1
0
<
6
0
T
ab
le
4
.
Per
ce
n
ta
g
e
ca
lcu
latio
n
s
f
o
r
a
g
iv
en
c
u
m
u
lativ
e
GP
A
f
o
r
d
ataset
2
C
u
m
u
l
a
t
i
v
e
G
P
A
M
e
m
b
e
r
P
e
r
c
e
n
t
a
g
e
A
w
a
r
d
e
d
D
e
g
r
e
e
C
u
m
u
l
a
t
i
v
e
G
P
A
M
e
m
b
e
r
P
e
r
c
e
n
t
a
g
e
A
w
a
r
d
e
d
D
e
g
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5
1
.
0
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c
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t
2
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3
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8
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o
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d
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:
2502
-
4
7
5
2
C
o
n
ve
r
tin
g
cu
mu
la
tive
g
r
a
d
e
p
o
in
t a
ve
r
a
g
e
to
a
n
eq
u
iv
a
len
t
p
ercen
ta
g
e…
(
I
b
r
a
h
im
E
ld
es
o
u
ky
F
a
tto
h
)
1829
4
.3
.
F
a
cult
y
o
f
s
cience
T
h
e
s
ec
o
n
d
d
ataset
is
co
llecte
d
f
r
o
m
f
ac
u
lty
o
f
s
cien
ce
.
T
h
e
r
an
g
e
o
f
GPA
p
o
in
ts
ar
e
less
th
an
th
e
r
an
g
e
in
d
ataset
1
.
T
ab
le
3
,
s
h
o
ws
th
r
ee
c
o
lu
m
n
s
th
at
r
ep
r
esen
ts
th
e
GPA
in
p
o
in
ts
,
th
e
awa
r
d
ed
d
e
g
r
ee
a
n
d
th
e
p
er
ce
n
tag
e
.
T
ab
le
5
also
s
h
o
ws
t
h
e
ca
lcu
lated
m
in
im
u
m
p
o
in
ts
(
Min
_
Po
in
ts
)
,
m
ax
im
u
m
p
o
in
ts
(
M
ax
_
Po
in
ts
)
,
m
in
im
u
m
p
er
ce
n
tag
e
(
Min
_
Per
)
an
d
m
a
x
im
u
m
p
e
r
ce
n
tag
e
(
Ma
x
_
Per
)
to
cu
m
u
lativ
e
GPA
f
o
r
d
ataset
3
.
As
s
h
o
wn
in
T
ab
le
6
an
d
Fig
u
r
e
3
,
th
e
n
o
v
el
eq
u
atio
n
s
ar
e
test
ed
b
y
ca
lcu
latin
g
th
e
p
er
ce
n
tag
e
o
f
a
g
iv
en
cu
m
u
lativ
e
GPA
o
f
s
o
m
e
s
tu
d
en
ts
in
th
e
g
iv
en
d
ataset
s
h
o
wn
in
T
ab
le
5
.
Fro
m
th
e
p
r
e
v
io
u
s
ev
al
u
atio
n
s
an
d
r
esu
lts
,
we
ca
n
co
n
clu
d
e
th
at;
f
u
z
zy
s
et
th
e
o
r
y
h
as
th
e
p
o
ten
tial
to
p
r
o
d
u
ce
m
o
d
els
th
at
ar
e
m
o
r
e
c
o
m
p
r
e
h
en
s
ib
le,
less
co
m
p
lex
,
an
d
m
o
r
e
r
o
b
u
s
t;
f
u
zz
y
in
f
o
r
m
atio
n
g
r
an
u
latio
n
ap
p
e
a
r
s
to
b
e
an
id
ea
l to
o
l f
o
r
tr
ad
in
g
o
f
f
ac
cu
r
a
cy
ag
ain
s
t c
o
m
p
le
x
ity
an
d
u
n
d
er
s
tan
d
ab
ilit
y
.
−
Fu
zz
y
s
et
th
eo
r
y
ca
n
g
en
er
ate
m
o
r
e
co
m
p
r
eh
en
s
ib
le,
le
s
s
co
m
p
licated
an
d
m
o
r
e
s
t
ab
le
m
o
d
els;
f
u
zz
y
g
r
an
u
latio
n
o
f
in
f
o
r
m
a
tio
n
is
ap
p
ar
en
tly
a
n
ex
ce
ll
en
t
m
eth
o
d
t
o
tr
ad
o
f
f
ac
c
u
r
ac
y
v
e
r
s
u
s
co
m
p
lex
ity
a
n
d
ab
ilit
y
u
n
d
e
r
s
tan
d
in
g
.
−
Data
m
in
in
g
ap
p
ea
r
s
to
b
e
e
x
tr
em
ely
h
el
p
f
u
l
in
f
u
zz
y
ap
p
r
o
ac
h
es
to
r
ep
r
esen
t
am
b
ig
u
o
u
s
p
atter
n
s
,
wh
ich
is
cr
u
cial
in
m
an
y
ap
p
licatio
n
ar
ea
s
.
−
I
n
ac
c
o
r
d
an
ce
with
th
e
p
r
in
ci
p
le
o
f
p
r
o
b
a
b
ilit
y
,
f
ix
atio
n
i
n
d
ex
(
FST
)
will
m
ak
e
a
m
ajo
r
co
n
tr
ib
u
tio
n
to
m
o
d
elin
g
a
n
d
a
n
aly
zin
g
d
iv
er
s
e
s
o
u
r
ce
s
o
f
k
n
o
wled
g
e
th
at
a
r
e
u
n
k
n
o
wn
a
n
d
in
c
o
m
p
lete.
−
Fo
r
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
an
d
p
o
s
t p
r
o
ce
s
s
in
g
,
f
u
zz
y
ap
p
r
o
ac
h
es seem
to
b
e
esp
ec
ially
u
s
ef
u
l.
T
ab
le
5
.
Data
s
et
3
-
f
ac
u
lty
o
f
c
lin
ical
p
h
ar
m
ac
y
M
a
x
_
P
e
r
M
i
n
_
P
e
r
M
a
x
_
P
o
i
n
t
s
M
i
n
_
P
o
i
n
t
s
P
e
r
c
e
n
t
a
g
e
A
w
a
r
d
e
d
D
e
g
r
e
e
P
o
i
n
t
s
1
0
0
90
5
4
P
e
r
c
e
n
t
a
g
e
>
=
9
0
Ex
c
e
l
l
e
n
t
G
P
A
>
=
4
<
9
0
85
<4
3
.
7
9
0
>
P
e
r
c
e
n
t
a
g
e
>
=
8
5
Ex
c
e
l
l
e
n
t
4
>
G
P
A
>
=
3
.
7
<
8
5
8
2
.
5
<
3
.
7
3
.
3
8
5
>
P
e
r
c
e
n
t
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g
e
>
=
8
2
.
5
V
e
r
y
G
o
o
d
3
.
7
>
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P
A
>
=
3
.
3
<
8
2
.
5
7
7
.
5
<
3
.
3
3
8
2
.
5
>
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e
r
c
e
n
t
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g
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>
=
7
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.
5
V
e
r
y
G
o
o
d
3
.
3
>
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P
A
>
=
3
<
7
7
.
5
75
<3
2
.
7
7
7
.
5
>
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e
r
c
e
n
t
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g
e
>
=
7
5
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e
r
y
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o
o
d
3
>
G
P
A
>
=
2
.
7
<
7
5
7
2
.
5
<
2
.
7
2
.
3
7
5
>
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e
r
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e
n
t
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g
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>
=
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.
5
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o
o
d
2
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7
>
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P
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<
7
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6
7
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5
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5
>
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=
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o
o
d
2
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3
>
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P
A
>
=
2
<
6
7
.
5
65
<2
1
.
7
6
7
.
5
>
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e
r
c
e
n
t
a
g
e
>
=
6
5
G
o
o
d
2
>
G
P
A
>
=
1
.
7
<
6
5
6
2
.
5
<
1
.
7
1
.
3
6
5
>
P
e
r
c
e
n
t
a
g
e
>
=
6
2
.
5
P
a
ss
1
.
7
>
G
P
A
>
=
1
.
3
<
6
2
.
5
60
<
1
.
3
1
6
2
.
5
>
P
e
r
c
e
n
t
a
g
e
>
=
6
0
P
a
ss
1
.
3
>
G
P
A
>
=
1
<
6
0
0
<1
0
6
0
>
P
e
r
c
e
n
t
a
g
e
F
a
i
l
1
>
G
P
A
T
ab
le
6
.
Per
ce
n
ta
g
e
ca
lcu
latio
n
s
f
o
r
a
g
iv
en
c
u
m
u
lativ
e
GP
A
f
o
r
d
ataset
3
C
u
m
u
l
a
t
i
v
e
G
P
A
M
e
m
b
e
r
P
e
r
c
e
n
t
a
g
e
A
w
a
r
d
e
d
D
e
g
r
e
e
4
.
2
0
.
2
0
9
2
.
0
0
Ex
c
e
l
l
e
n
t
4
0
.
0
0
9
0
.
0
Ex
c
e
l
l
e
n
t
3
.
5
0
.
5
0
8
3
.
7
5
V
e
r
y
G
o
o
d
2
.
8
0
.
3
3
7
5
.
8
3
V
e
r
y
G
o
o
d
2
.
1
0
.
3
3
6
9
.
1
7
G
o
o
d
1
.
8
0
.
3
3
6
5
.
8
3
G
o
o
d
1
.
5
0
.
5
0
6
3
.
7
5
P
a
ss
1
.
1
0
.
3
3
6
0
.
8
3
P
a
ss
Fig
u
r
e
3
.
R
elatio
n
b
etwe
en
cu
m
u
lativ
e
GPA
an
d
p
er
ce
n
tag
e
f
o
r
d
ataset
3
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
em
b
er
2
0
2
1
:
1
8
2
3
-
1
8
3
1
1830
5.
C
O
NCLU
SI
O
N
I
n
th
is
s
tu
d
y
,
we
p
r
esen
ted
a
n
o
v
el
m
o
d
el
th
at
tr
an
s
f
o
r
m
s
b
et
wee
n
th
e
awa
r
d
ed
cu
m
u
lativ
e
GPA
an
d
th
e
awa
r
d
ed
p
er
ce
n
tag
e
r
an
k
i
n
g
b
ased
o
n
f
u
zz
y
s
y
s
tem
.
T
h
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
will
h
elp
s
tu
d
en
ts
th
at
n
ee
d
to
tr
an
s
f
er
b
etwe
en
u
n
iv
er
s
ities
th
at
u
s
ed
d
if
f
er
en
t
awa
r
d
ed
c
r
iter
ia
an
d
h
elp
in
co
m
p
ar
i
n
g
b
etwe
en
g
r
ad
u
ates
s
tu
d
en
ts
if
th
e
y
awa
r
d
ed
in
d
if
f
er
en
t
c
r
iter
ia.
W
e
h
av
e
d
ec
id
ed
to
in
v
esti
g
ate
t
h
ese
m
ec
h
an
is
m
s
u
n
d
e
r
th
e
f
u
zz
y
p
ar
ad
i
g
m
as
it
is
co
n
s
id
er
ed
o
n
e
o
f
t
h
e
h
y
p
o
t
h
eses
th
at
b
est
d
escr
ib
e
th
o
s
e
el
em
en
ts
o
f
h
u
m
a
n
co
m
p
r
eh
e
n
s
io
n
an
d
r
ea
s
o
n
in
g
.
R
esu
lts
in
th
r
ee
d
if
f
er
en
t
d
at
a
s
ets
s
h
o
w
th
at
th
e
p
r
o
p
o
s
ed
m
o
d
el
ca
n
co
n
v
er
t
th
e
awa
r
d
ed
c
u
m
u
lativ
e
GPA
to
an
awa
r
d
e
d
p
er
ce
n
tag
e
.
RE
F
E
R
E
NC
E
S
[1
]
X.
D.
Ke
a
ti
n
g
e
t
a
l
.
,
“
Trac
k
i
n
g
c
h
a
n
g
e
s
o
f
C
h
in
e
se
p
re
-
se
rv
ice
t
e
a
c
h
e
rs’
a
e
ro
b
ic
fit
n
e
ss
,
b
o
d
y
m
a
ss
in
d
e
x
,
a
n
d
g
ra
d
e
p
o
i
n
t
a
v
e
ra
g
e
o
v
e
r
4
-
y
e
a
rs
o
f
c
o
ll
e
g
e
,
”
In
ter
n
a
ti
o
n
a
l
jo
u
rn
a
l
o
f
e
n
v
iro
n
me
n
ta
l
re
se
a
rc
h
a
n
d
p
u
b
li
c
h
e
a
lt
h
,
v
o
l.
1
6
,
p
.
9
6
6
,
2
0
1
9
,
d
o
i:
1
0
.
3
3
9
0
/i
jerp
h
1
6
0
6
0
9
6
6
.
[2
]
R.
D.
Rich
a
rd
so
n
,
a
n
d
R.
L
.
Wi
ll
iam
s,
“
Li
n
k
a
g
e
s
Be
twe
e
n
G
ra
d
e
P
o
in
t
Av
e
ra
g
e
a
n
d
S
t
u
d
e
n
t
Ra
ti
n
g
s,
”
J
o
u
rn
a
l
o
f
Ed
u
c
a
ti
o
n
a
l
Res
e
a
rc
h
a
n
d
Pra
c
ti
c
e
,
v
o
l.
1
1
,
p
.
2
,
2
0
2
1
,
d
o
i:
1
0
.
5
5
9
0
/JERAP
.
2
0
2
0
.
1
1
.
1
.
0
2
.
[3
]
M
.
N.
Au
n
g
,
V.
Ja
ro
o
n
v
a
n
ich
k
u
l
,
J.
De
e
ro
jan
a
wo
n
g
,
J.
S
o
m
b
o
o
n
wo
n
g
,
I.
Ah
m
a
d
,
a
n
d
P
.
Wan
n
a
k
ra
iro
t,
“
A
Ne
w
M
e
th
o
d
fo
r
S
e
tt
in
g
S
ta
n
d
a
rd
in
M
e
d
ica
l
Ed
u
c
a
ti
o
n
,
Ap
p
ly
i
n
g
P
re
v
io
u
s
Ye
a
r
Cu
m
u
lativ
e
G
P
A,
”
Eu
ro
p
e
a
n
J
o
u
rn
a
l
o
f
M
e
d
ica
l
a
n
d
He
a
l
th
S
c
ien
c
e
s
,
v
o
l.
1
,
2
0
1
9
,
d
o
i:
1
0
.
2
4
0
1
8
/ejm
e
d
.
2
0
1
9
.
1
.
5
.
1
1
4
.
[4
]
S
.
E
l
S
h
e
ik
h
,
Y.
Taw
fik
Ha
li
m
,
H.
Ib
ra
h
im
Ha
m
d
y
,
a
n
d
M
.
S
a
m
y
El
-
d
e
e
b
,
“
T
h
e
Im
p
a
c
t
o
f
En
h
a
n
c
in
g
th
e
Ac
a
d
e
m
ic
P
e
rfo
rm
a
n
c
e
o
n
S
t
u
d
e
n
t
S
a
ti
sfa
c
ti
o
n
o
f
P
ri
v
a
te
Bu
si
n
e
ss
F
a
c
u
lt
ies
:
Ne
w
B
u
sin
e
ss
M
o
d
e
l
fo
r
Eg
y
p
t
ian
P
riv
a
te
Un
iv
e
rsit
ies
,
”
J
o
u
rn
a
l
o
f
a
lex
a
n
d
ria
Un
ive
rs
it
y
fo
r
A
d
mi
n
istra
ti
v
e
S
c
ien
c
e
s
,
v
o
l.
5
7
,
p
p
.
1
-
3
4
,
2
0
2
0
,
d
o
i
:
1
0
.
2
1
6
0
8
/ac
j.
2
0
2
0
.
1
2
1
7
5
9
.
[5
]
O.
An
a
ly
ti
c
a
,
“
E
g
y
p
t
e
d
u
c
a
ti
o
n
w
il
l
d
e
c
li
n
e
u
n
d
e
r
d
e
m
o
g
ra
p
h
ic g
r
o
wth
,
”
Eme
ra
ld
Exp
e
rt B
rie
fi
n
g
s
,
2
0
1
9
.
[6
]
A.
M
u
tali
b
a
,
S
.
Ra
z
a
li
b
,
a
n
d
M
.
Aq
sz
a
c
,
“
As
se
ss
m
e
n
t
o
f
S
tu
d
e
n
t
Ac
h
iev
e
m
e
n
t
u
sin
g
t
h
e
Cu
m
u
lati
v
e
G
ra
d
e
P
o
in
t
Av
e
ra
g
e
(CG
P
A)
a
n
d
th
e
In
teg
ra
ted
Cu
m
u
lati
v
e
G
ra
d
e
P
o
in
t
Av
e
ra
g
e
(ICG
P
A),
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
In
n
o
v
a
ti
o
n
,
Cre
a
ti
v
it
y
a
n
d
Ch
a
n
g
e
,
v
o
l
.
9
,
n
o
.
6
,
2
0
1
9
.
[7
]
J.
Ha
n
se
n
,
P
.
S
a
d
ler,
a
n
d
G
.
S
o
n
n
e
rt,
“
Esti
m
a
ti
n
g
h
i
g
h
sc
h
o
o
l
G
P
A
we
ig
h
t
in
g
p
a
ra
m
e
ters
with
a
g
ra
d
e
d
re
sp
o
n
se
m
o
d
e
l,
”
Ed
u
c
a
ti
o
n
a
l
M
e
a
su
re
me
n
t:
Iss
u
e
s a
n
d
Pra
c
ti
c
e
,
v
o
l.
3
8
,
p
p
.
1
6
-
2
4
,
2
0
1
9
,
d
o
i:
1
0
.
1
1
1
1
/em
ip
.
1
2
2
0
3
.
[8
]
P
.
A.
Wes
tri
c
k
,
“
Re
li
a
b
il
i
t
y
e
stim
a
tes
fo
r
u
n
d
e
r
g
ra
d
u
a
te
g
ra
d
e
p
o
i
n
t
a
v
e
ra
g
e
,
”
Ed
u
c
a
t
io
n
a
l
Asse
ss
me
n
t
,
v
o
l.
2
2
,
p
p
.
231
-
2
5
2
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
8
0
/1
0
6
2
7
1
9
7
.
2
0
1
7
.
1
3
8
1
5
5
4
.
[9
]
P
.
K.
S
riv
a
sta
v
a
a
n
d
D.
C.
Bis
h
t,
“
Re
c
e
n
t
tren
d
s
a
n
d
a
p
p
li
c
a
ti
o
n
s
o
f
f
u
z
z
y
lo
g
ic,
”
I
n
Ad
v
a
n
c
e
d
Fu
zz
y
L
o
g
ic
Ap
p
ro
a
c
h
e
s i
n
E
n
g
i
n
e
e
rin
g
S
c
ien
c
e
,
2
0
1
9
,
p
p
.
3
2
7
-
3
4
0
,
d
o
i:
1
0
.
4
0
1
8
/
9
7
8
-
1
-
5
2
2
5
-
5
7
0
9
-
8
.
c
h
0
1
5
.
[1
0
]
U.
G
h
a
n
i,
I.
S
.
Ba
jwa
,
a
n
d
A.
As
h
fa
q
,
“
A
fu
z
z
y
lo
g
ic
b
a
se
d
in
telli
g
e
n
t
sy
ste
m
fo
r
m
e
a
su
rin
g
c
u
sto
m
e
r
lo
y
a
lt
y
a
n
d
d
e
c
isio
n
m
a
k
i
n
g
,
”
S
y
mm
e
tr
y
,
v
o
l
.
1
0
,
p
.
7
6
1
,
2
0
1
8
,
d
o
i
:
1
0
.
3
3
9
0
/sy
m
1
0
1
2
0
7
6
1
.
[1
1
]
R.
Bě
lo
h
láv
e
k
,
J.
W.
Da
u
b
e
n
,
a
n
d
G
.
J.
Klir
,
“
F
u
z
z
y
lo
g
ic
a
n
d
m
a
th
e
m
a
ti
c
s:
a
h
isto
rica
l
p
e
rsp
e
c
ti
v
e
”
Ox
fo
rd
Un
ive
rs
it
y
Pre
ss
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
9
3
/o
so
/9
7
8
0
1
9
0
2
0
0
0
1
5
.
0
0
1
.
0
0
0
1
.
[1
2
]
P
.
D.
As
a
n
k
a
a
n
d
A.
S
.
P
e
re
ra
,
“
De
fin
in
g
fu
z
z
y
m
e
m
b
e
rsh
ip
fu
n
c
ti
o
n
u
sin
g
b
o
x
p
l
o
t,
”
In
ter
n
a
ti
o
n
a
l
jo
u
rn
a
l
o
f
re
se
a
rc
h
in
c
o
mp
u
ter
a
p
p
li
c
a
ti
o
n
s a
n
d
ro
b
o
ti
c
s
,
v
o
l.
5
,
p
p
.
1
-
1
0
,
2
0
1
7
.
[1
3
]
L.
W.
Ha
wk
e
s,
S
.
J.
De
rry
,
a
n
d
E.
A.
R
u
n
d
e
n
ste
in
e
r,
“
I
n
d
i
v
id
u
a
l
ize
d
tu
t
o
rin
g
u
sin
g
a
n
in
tel
li
g
e
n
t
fu
z
z
y
tem
p
o
ra
l
re
latio
n
a
l
d
a
tab
a
se
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
M
a
n
-
M
a
c
h
in
e
S
tu
d
ies
,
v
o
l
.
3
3
,
p
p
.
4
0
9
-
4
2
9
,
1
9
9
0
,
d
o
i:
1
0
.
1
0
1
6
/
S
0
0
2
0
-
7
3
7
3
(0
5
)8
0
0
4
0
-
9.
[1
4
]
L.
W.
Ha
wk
e
s
a
n
d
S
.
J.
De
rry
,
“
Ad
v
a
n
c
e
s
in
l
o
c
a
l
stu
d
e
n
t
m
o
d
e
li
n
g
u
sin
g
in
f
o
rm
a
l
fu
z
z
y
re
a
so
n
i
n
g
,
”
In
ter
n
a
ti
o
n
a
l
jo
u
rn
a
l
o
f
h
u
m
a
n
-
c
o
m
p
u
ter
st
u
d
i
e
s
,
v
o
l.
4
5
,
p
p
.
6
9
7
-
7
2
2
,
1
9
9
6
,
d
o
i:
1
0
.
1
0
0
6
/i
j
h
c
.
1
9
9
6
.
0
0
7
5
.
[1
5
]
L.
B.
Nilso
n
,
“
S
p
e
c
ifi
c
a
ti
o
n
s
g
ra
d
in
g
:
Re
sto
ri
n
g
rig
o
r,
m
o
ti
v
a
ti
n
g
st
u
d
e
n
ts,
a
n
d
sa
v
in
g
fa
c
u
lt
y
ti
m
e
,
”
S
tylu
s
Pu
b
li
s
h
in
g
,
L
L
C
,
2
0
1
5
.
[1
6
]
H.
-
J.
Ro
n
g
,
P
.
P
.
An
g
e
lo
v
,
X.
G
u
,
a
n
d
J.
-
M
.
Ba
i,
“
S
tab
i
li
ty
o
f
e
v
o
l
v
in
g
f
u
z
z
y
sy
ste
m
s
b
a
se
d
o
n
d
a
ta
c
lo
u
d
s,
”
IE
EE
T
ra
n
sa
c
ti
o
n
s
o
n
Fu
zz
y
S
y
ste
ms
,
v
o
l.
2
6
,
p
p
.
2
7
7
4
-
2
7
8
4
,
2
0
1
8
,
d
o
i:
1
0
.
1
1
0
9
/
TF
UZZ.
2
0
1
8
.
2
7
9
3
2
5
8
.
[1
7
]
K.
Ware
n
d
o
rf
a
n
d
S
.
J.
Tsa
o
,
“
Ap
p
li
c
a
ti
o
n
o
f
fu
z
z
y
l
o
g
ic
tec
h
n
iq
u
e
s
in
th
e
BS
S
1
t
u
t
o
rin
g
sy
ste
m
,
”
J
o
u
rn
a
l
o
f
Arti
fi
c
ia
l
I
n
telli
g
e
n
c
e
in
Ed
u
c
a
ti
o
n
,
v
o
l.
8
,
p
p
.
1
1
3
-
1
4
6
,
1
9
9
7
.
[1
8
]
C.
He
rz
o
g
,
“
F
u
z
z
y
tec
h
n
i
q
u
e
s
f
o
r
u
n
d
e
rsta
n
d
i
n
g
st
u
d
e
n
t
so
l
u
ti
o
n
s
in
in
telli
g
e
n
t
t
u
to
ri
n
g
sy
ste
m
s,
P
a
p
e
rs
fo
r
t
h
e
S
e
v
e
n
th
M
e
e
ti
n
g
o
f
G
I
S
e
c
ti
o
n
1
.
1
.
5
/7
.
0
.
1
,
I
n
telli
g
e
n
t
T
u
t
o
rin
g
S
y
ste
m
s,
”
Res
e
a
rc
h
In
st
it
u
te
f
o
r
A
p
p
li
c
a
ti
o
n
-
Or
ien
ted
Kn
o
wled
g
e
Pro
c
e
ss
in
g
(
FA
W
)
,
G
e
rm
a
n
y
,
1
9
9
4
.
[1
9
]
J.
Ho
lt
a
n
d
A.
W.
Lea
c
h
,
“
Li
n
g
u
isti
c
v
a
riab
les
a
s
f
u
z
z
y
se
ts
t
o
m
o
d
e
l
u
n
c
e
rtain
t
y
i
n
th
e
c
o
m
b
in
e
d
e
ffica
c
y
o
f
m
u
lt
ip
le
p
h
y
to
sa
n
it
a
r
y
m
e
a
su
re
s
in
p
e
st
risk
a
n
a
l
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2
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IEE
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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p
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N:
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[2
3
]
M
.
Al
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a
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d
P
.
Ne
wb
u
r
y
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p
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4
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P.
-
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,
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-
F
.
Wen
g
,
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-
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.
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.
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,
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5
]
A.
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les
,
A.
P
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ñ
a
,
R.
P
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re
d
o
,
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S
o
ss
a
,
a
n
d
A.
G
u
ti
é
rre
z
,
“
Ad
a
p
ti
v
e
a
n
d
in
telli
g
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t
we
b
b
a
se
d
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d
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c
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ti
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sy
ste
m
:
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in
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g
ra
l
a
rc
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it
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c
tu
re
a
n
d
fra
m
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wo
rk
,
”
Exp
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rt
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wit
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[2
6
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I.
Ly
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G
ian
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.
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rd
is,
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s
,
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7
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.
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.
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lam
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n
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n
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.
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a
k
i,
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ter
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.
[2
8
]
C.
Tro
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ss
a
s,
K.
C
h
ry
sa
fiad
i,
a
n
d
M
.
Virv
o
u
,
“
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in
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rn
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g
,
”
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e
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ste
ms
wit
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l.
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sw
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.
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0
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3
.
[2
9
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e
li
a
,
A.
G
.
Ab
d
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ll
a
h
,
a
n
d
Y.
M
u
l
y
a
d
i
,
“
M
e
ta
-
a
n
a
ly
sis
o
f
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d
e
n
t
p
e
rfo
rm
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ss
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ss
m
e
n
t
u
sin
g
f
u
z
z
y
l
o
g
ic,”
In
d
o
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e
sia
n
J
o
u
rn
a
l
o
f
S
c
ien
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T
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.
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0
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G
.
Va
sa
n
ti
,
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G
ra
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M
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sin
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F
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Lo
g
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I
n
ter
n
a
ti
o
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l
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rn
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I
n
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T
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o
lo
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y
a
n
d
Exp
l
o
rin
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En
g
i
n
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rin
g
(
IJ
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EE
)
,
v
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l.
8
,
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0
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d
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1
0
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4
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.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Fa
r
id
Ali
Mo
u
sa
is
an
a
ss
o
c
ia
te
p
ro
f.
in
in
f
o
rm
a
ti
o
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tec
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rtme
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rti
ficia
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i
n
telli
g
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n
c
e
,
Be
n
i
-
S
u
e
f
Un
i
v
e
rsity
.
He
r
e
c
e
iv
e
d
h
is
P
h
D
d
e
g
re
e
i
n
in
fo
rm
a
ti
o
n
tec
h
n
o
lo
g
y
fro
m
Ca
iro
Un
iv
e
rsity
.
Re
se
a
rc
h
i
n
tere
st
p
o
i
n
ts
in
c
l
u
d
e
s
o
ft
c
o
m
p
u
ti
n
g
,
d
a
ta m
in
i
g
,
m
a
c
h
i
n
e
l
e
a
rn
in
g
,
a
n
d
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a
tu
ra
l
lan
g
u
a
g
e
p
ro
c
e
ss
in
g
.
Ema
il
:
fa
re
d
.
a
li
@fc
is.b
su
.
e
d
u
.
e
g
a
n
d
fa
ly
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sa
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e
d
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.
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im
Eld
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k
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a
tto
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is
an
a
ss
o
c
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a
te
p
ro
f.
in
c
o
m
p
u
ter
sc
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rtme
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c
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lt
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m
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ters
a
n
d
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n
telli
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n
i
-
S
u
e
f
Un
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v
e
rsity
.
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r
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c
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iv
e
d
h
is
P
h
D
d
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re
e
i
n
c
o
m
p
u
ter
sc
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c
e
fr
o
m
He
lwa
n
Un
iv
e
rsity
.
Re
se
a
rc
h
i
n
tere
st
p
o
i
n
ts
i
n
c
lu
d
e
so
f
t
c
o
m
p
u
ti
n
g
,
d
a
ta m
in
ig
,
m
a
c
h
in
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le
a
rn
i
n
g
,
a
n
d
n
a
tu
ra
l
la
n
g
u
a
g
e
p
r
o
c
e
ss
in
g
.
Ema
il
:
Ib
ra
h
im_
d
e
so
k
y
@fc
is.b
s
u
.
e
d
u
.
e
g
a
n
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ra
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e
.
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g
.
S
o
h
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S
a
fwa
t
La
b
ib
is
an
a
ss
o
c
iate
p
ro
f
.
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n
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a
d
o
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so
f
twa
re
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g
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n
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ti
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d
tec
h
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lo
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t
h
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E
g
y
p
ti
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n
C
h
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e
se
Un
iv
e
rsity
.
S
h
e
re
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d
h
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P
h
D
d
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g
re
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in
c
o
m
p
u
ter
sc
o
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c
e
fro
m
Ca
iro
Un
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y
.
Re
se
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tere
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in
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so
ft
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