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th
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ai
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m
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ac
q
u
ir
ed
f
r
o
m
lear
n
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g
[
1
]
.
T
h
u
s
,
o
n
e
o
f
t
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p
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[
2
]
.
T
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k
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[
3
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.
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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2
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8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
10
,
No
.
4
,
Dec
em
b
er
2
0
2
1
:
8
3
9
-
846
840
e
n
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4
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[
5
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[
6
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[
7
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.
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ased
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ased
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m
ittee
w
o
u
ld
n
o
t
s
h
ar
e
a
s
i
m
ilar
v
ie
w
w
h
e
n
j
u
d
g
in
g
a
p
r
o
b
lem
.
A
p
an
el
o
f
ex
p
er
ts
m
a
y
co
m
e
t
o
a
d
is
ag
r
ee
m
en
t
ca
u
s
ed
b
y
d
if
f
er
e
n
t
o
p
in
io
n
s
o
n
th
e
r
atin
g
o
f
th
e
alter
n
ativ
e
s
o
r
th
e
m
er
it
o
f
th
e
cr
iter
ia.
A
r
r
iv
i
n
g
at
th
e
b
est
r
eso
lu
tio
n
d
esp
ite
s
u
c
h
d
if
f
er
en
ce
s
b
ec
o
m
es
a
s
i
g
n
if
ican
t
i
s
s
u
e
in
g
r
o
u
p
-
b
ased
d
ec
is
io
n
m
ak
in
g
.
I
f
ex
p
er
ts
r
ea
lize
th
at
u
s
i
n
g
a
n
u
m
er
ic
s
ca
le
f
o
r
ex
p
r
ess
i
n
g
t
h
eir
o
p
in
io
n
s
is
co
n
v
e
n
ien
t,
it
w
o
u
ld
b
e
u
s
e
f
u
l to
co
n
s
id
er
av
er
a
g
in
g
t
h
e
s
co
r
es
as
a
s
i
m
p
le
w
a
y
f
o
r
ag
g
r
eg
ati
n
g
co
n
flicti
n
g
as
s
ess
m
en
ts
.
B
es
id
es,
i
f
t
h
e
o
p
in
io
n
s
o
f
t
h
e
m
e
m
b
er
s
o
f
a
g
r
o
u
p
d
o
n
o
t
ca
r
r
y
th
e
s
a
m
e
w
ei
g
h
t,
th
e
n
it
w
o
u
ld
b
e
ess
e
n
tial
to
i
m
p
le
m
e
n
t
a
w
ei
g
h
ted
av
er
ag
i
n
g
s
ca
le
to
s
p
ec
if
y
t
h
eir
r
elat
iv
e
i
m
p
o
r
ta
n
ce
.
Ho
w
e
v
er
,
to
s
o
l
v
e
a
n
I
T
o
r
en
g
in
ee
r
i
n
g
e
v
al
u
atio
n
p
r
o
b
lem
,
it
is
m
o
r
e
i
m
p
o
r
tan
t
to
ar
r
iv
e
at
th
e
r
ig
h
t
lev
el
o
f
co
n
s
e
n
s
u
s
a
m
o
n
g
t
h
e
ex
p
er
ts
;
th
r
o
u
g
h
en
co
u
r
a
g
i
n
g
th
e
m
to
r
ec
o
n
s
id
er
th
eir
ass
e
s
s
m
e
n
ts
r
at
h
er
th
a
n
a
g
g
r
e
g
ate
t
h
eir
s
co
r
es.
T
h
is
is
t
h
e
co
r
e
o
f
th
e
d
elp
h
i
m
et
h
o
d
.
T
h
e
d
elp
h
i m
e
th
o
d
,
h
av
i
n
g
a
r
ep
etitiv
e
p
r
o
ce
d
u
r
e,
ai
m
s
at
m
a
k
i
n
g
v
ar
io
u
s
s
u
b
j
ec
tiv
e
o
p
in
io
n
s
co
n
v
er
g
e
in
to
m
o
r
e
w
id
el
y
ac
ce
p
tab
le
v
ie
w
p
o
in
t
s
[
8
]
,
[
9
]
.
T
h
e
d
if
f
icu
l
t
y
f
ac
ed
b
y
a
n
y
r
esear
ch
s
u
p
er
v
i
s
o
r
in
s
elec
t
in
g
a
GR
A
co
u
ld
b
e
attr
ib
u
ted
to
s
ev
er
al
r
ea
s
o
n
s
[
10
]
-
[1
3
]:
i)
t
h
e
v
ar
iet
y
o
f
e
v
alu
a
tio
n
cr
iter
ia
a
n
d
ch
ar
ac
t
er
is
tics
(
t
h
er
e
is
n
o
s
tan
d
ar
d
f
o
r
th
e
G
R
A
ev
al
u
at
io
n
an
d
s
elec
tio
n
cr
iter
ia)
,
ii)
t
h
e
p
r
o
ce
s
s
o
f
ass
e
s
s
i
n
g
th
e
s
k
il
ls
o
f
r
esear
c
h
er
p
er
f
o
r
m
a
n
ce
d
u
e
to
t
h
e
d
if
f
er
en
t t
y
p
e
o
f
r
esear
c
h
ac
ti
v
ities
.
I
n
th
i
s
ca
s
e,
a
s
u
p
er
v
i
s
o
r
w
o
u
ld
f
ac
e
d
if
f
ic
u
ltie
s
i
n
cr
ea
tin
g
a
r
esear
ch
g
r
o
u
p
w
it
h
b
alan
ce
d
in
d
iv
id
u
al
a
n
d
g
r
o
u
p
w
o
r
k
s
k
ill
s
.
I
n
o
r
d
er
to
d
o
s
o
,
a
s
u
p
er
v
is
o
r
m
u
s
t
e
v
al
u
ate
s
u
ch
s
k
il
ls
b
y
e
m
p
lo
y
i
n
g
u
s
e
f
u
l
v
ar
iab
les.
T
h
er
e
is
a
lar
g
e
n
u
m
b
er
o
f
ev
al
u
atio
n
v
ar
i
ab
les
th
at
ca
n
b
e
u
s
ed
i
n
th
i
s
r
esp
ec
t.
Ho
w
ev
er
,
th
is
lar
g
e
n
u
m
b
er
m
ak
e
s
it
h
ar
d
er
f
o
r
s
u
p
er
v
is
o
r
s
to
p
in
p
o
in
t
t
h
e
m
o
s
t
r
ele
v
an
t
v
ar
iab
l
es
to
e
v
al
u
ate
th
e
r
esear
ch
s
k
ills
p
er
f
o
r
m
an
ce
b
ased
o
n
d
if
f
er
en
t
c
h
ar
ac
ter
is
t
ics.
T
o
s
o
lv
e
th
is
p
r
o
b
le
m
,
w
e
e
x
p
lo
r
ed
ea
r
lier
s
tu
d
ie
s
p
er
tain
i
n
g
to
t
h
e
ev
a
lu
atio
n
an
d
s
elec
tio
n
o
f
GR
A
to
id
en
ti
f
y
th
e
co
m
m
o
n
l
y
u
s
ed
ev
alu
a
tio
n
cr
iter
ia
.
T
h
e
cr
iter
ia
ar
e
th
e
n
d
i
s
tr
ib
u
ted
to
ex
p
er
ts
to
ev
a
lu
ate
f
o
llo
w
ed
b
y
t
h
e
ap
p
licatio
n
o
f
th
e
f
u
zz
y
d
elp
h
i
p
r
o
g
r
am
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
er
e
ar
e
tw
o
m
aj
o
r
p
h
ases
in
th
is
r
esear
ch
.
F
ir
s
t
is
t
h
e
id
en
ti
f
icatio
n
o
f
cr
iter
ia
th
r
o
u
g
h
liter
atu
r
es
r
ev
ie
w
s
.
T
h
e
s
ec
o
n
d
p
h
ase
is
th
e
e
v
al
u
at
io
n
o
f
t
h
e
cr
iter
ia
t
h
r
o
u
g
h
ex
p
er
ts
’
o
p
i
n
io
n
w
h
ic
h
ar
e
i
m
p
le
m
e
n
ted
u
s
i
n
g
th
e
f
u
zz
y
d
elp
h
i
m
et
h
o
d
(
FDM)
.
2
.
1
.
Crit
er
ia
i
dentif
ica
t
io
n
L
iter
at
u
r
es
r
elate
d
to
r
esear
c
h
s
k
ills
ar
e
i
m
p
o
r
tan
t
p
r
i
m
ar
y
s
o
u
r
ce
s
to
id
e
n
ti
f
y
cr
iter
ia
t
h
a
t
co
u
ld
b
e
ad
o
p
ted
to
ass
ess
th
e
GR
A
s
k
ill
s
.
T
h
er
e
a
r
e
n
u
m
er
o
u
s
s
k
i
lls
w
h
ic
h
co
m
m
o
n
l
y
ap
p
ea
r
ed
an
d
p
r
o
p
o
s
ed
in
liter
atu
r
es
as
i
m
p
o
r
ta
n
t
r
esear
ch
s
k
il
ls
.
T
h
ese
s
k
ill
s
co
u
ld
b
e
g
r
o
u
p
ed
in
to
co
r
r
esp
o
n
d
in
g
ca
te
g
o
r
y
.
E
v
er
y
g
r
o
u
p
w
o
u
ld
in
cl
u
d
e
a
co
llecti
o
n
o
f
cr
iter
ia
to
f
ac
ilit
ate
t
h
e
p
r
o
ce
s
s
o
f
m
at
h
e
m
atica
l c
alc
u
l
atio
n
s
[
1
4
]
,
[
1
5
]
.
2
.
2
.
I
m
ple
m
e
nta
t
io
n o
f
t
he
f
uzzy
delph
i
m
et
ho
d
Su
c
h
lar
g
e
n
u
m
b
er
o
f
cr
iter
ia
ex
tr
ac
ted
f
r
o
m
t
h
e
liter
at
u
r
e
r
ev
ie
w
w
o
u
ld
n
ee
d
to
b
e
e
v
al
u
ated
u
s
i
n
g
th
e
d
elp
h
i
f
u
zz
y
to
ac
h
ie
v
e
t
h
e
co
n
s
en
s
u
s
o
f
ex
p
er
ts
o
n
th
e
m
o
s
t
u
s
e
f
u
l
cr
iter
ia.
I
n
o
t
h
er
w
o
r
d
s
,
th
e
cr
iter
ia
w
o
u
ld
b
e
ex
a
m
in
ed
a
n
d
test
ed
in
t
h
e
i
n
ter
v
ie
w
s
w
i
th
e
x
p
er
ts
.
Fig
u
r
e
1
p
r
esen
ts
th
e
f
lo
w
o
f
s
tep
s
i
n
v
o
l
v
ed
in
th
e
i
m
p
le
m
en
tatio
n
o
f
t
h
e
FD
M
to
d
eter
m
i
n
e
t
h
e
s
u
itab
le
e
v
alu
a
tio
n
cr
iter
ia
o
f
G
R
A
.
T
h
e
s
tep
s
ar
e
f
u
r
th
er
ex
p
lain
ed
i
n
s
ec
tio
n
s
2
.
2
.
1
to
2
.
2
.
4
.
2
.
2
.
1
.
Select
io
n o
f
t
he
ex
pert
s
I
n
th
is
s
tu
d
y
,
ex
p
er
ts
ar
e
d
e
f
i
n
ed
as
r
esear
c
h
er
s
w
h
o
h
a
v
e
w
id
e
r
esear
c
h
ex
p
er
ie
n
ce
an
d
s
u
p
er
v
i
s
es
m
an
y
p
o
s
tg
r
ad
u
ate
s
t
u
d
en
ts
a
n
d
G
R
A
.
I
n
ter
m
s
o
f
th
e
n
u
m
b
er
s
o
f
e
x
p
er
ts
f
o
r
t
h
e
s
tu
d
y
,
a
co
n
s
e
n
s
u
s
h
as
to
b
e
r
ea
ch
ed
in
t
h
i
s
r
eg
ar
d
s
.
P
r
ev
i
o
u
s
r
esear
ch
er
s
s
u
g
g
e
s
ted
th
a
t
th
e
n
u
m
b
er
o
f
e
x
p
er
ts
r
a
n
g
e
s
b
et
w
ee
n
3
to
2
5
as
an
o
p
ti
m
al
n
u
m
b
er
in
Delp
h
i
m
et
h
o
d
[
1
6
]
-
[
2
0
]
.
Nev
er
th
ele
s
s
,
a
p
r
ec
o
n
d
itio
n
to
th
e
ex
p
er
ts
is
th
a
t
t
h
e
y
m
u
s
t
h
av
e
t
h
e
ap
titu
d
e
to
p
r
o
ce
s
s
in
f
o
r
m
a
tio
n
an
d
g
iv
e
d
ec
is
i
o
n
s
.
I
n
th
i
s
s
t
u
d
y
,
w
e
ch
o
s
e
2
3
ex
p
er
ts
am
o
n
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
A
g
en
era
l fra
mewo
r
k
fo
r
s
ele
ctin
g
a
p
p
r
o
p
r
ia
te
crit
eria
o
f st
u
d
en
t a
s
r
esea
r
ch
…
(
S
u
la
ima
n
A
b
d
A
n
ter
)
841
p
r
o
f
ess
o
r
s
,
ass
o
ciate
p
r
o
f
ess
o
r
s
,
an
d
lect
u
r
er
s
o
f
th
e
I
T
an
d
en
g
i
n
ee
r
i
n
g
d
ep
ar
t
m
en
t
s
i
n
t
w
o
p
ar
ticip
atin
g
u
n
i
v
er
s
i
ties
;
t
h
e
U
n
i
v
er
s
it
y
o
f
An
b
ar
an
d
th
e
U
n
i
v
er
s
it
y
o
f
T
ec
h
n
o
lo
g
y
i
n
B
ag
h
d
ad
.
Fig
u
r
e
1
.
Flo
w
c
h
ar
t o
f
FDM
i
m
p
le
m
e
n
tat
io
n
f
o
r
GR
A
cr
iter
ia
ev
alu
a
tio
n
2
.
2
.
2
.
Dev
elo
p
m
ent
o
f
qu
esti
o
nn
a
ire
W
e
d
ev
elo
p
ed
a
q
u
esti
o
n
n
air
e
to
co
llect
th
e
d
ata
(
o
p
in
io
n
s
o
f
th
e
ex
p
er
ts
)
th
at
ar
e
g
r
o
u
n
d
ed
o
n
th
e
cr
iter
ia
in
f
er
r
ed
f
r
o
m
t
h
e
a
n
al
y
s
i
s
a
n
d
co
m
b
in
a
tio
n
o
f
ex
is
ti
n
g
g
u
id
eli
n
es.
R
ah
i
m
et
a
l
.
[
21
]
s
u
g
g
es
ted
th
at
i
n
th
e
d
i
g
ital
a
g
e,
r
esear
c
h
er
s
m
a
y
p
lace
t
h
e
q
u
e
s
tio
n
n
air
e
o
n
li
n
e
to
a
v
o
id
d
ela
y
a
n
d
b
u
r
d
en
.
Hen
ce
,
th
e
q
u
esti
o
n
a
ir
es
w
er
e
b
u
il
t
an
d
d
is
tr
ib
u
ted
v
ia
th
e
u
s
e
o
f
g
o
o
g
le
f
o
r
m
o
n
l
in
e
s
u
r
v
e
y
.
T
h
e
q
u
esti
o
n
n
air
e
s
co
m
p
r
is
e
s
o
f
t
w
o
p
ar
ts
;
p
ar
t
o
n
e
is
r
elate
d
to
th
e
p
er
s
o
n
a
l
in
f
o
r
m
atio
n
o
f
t
h
e
ex
p
er
t
a
n
d
p
ar
t
t
w
o
co
n
tai
n
s
th
e
lis
t
o
f
id
en
ti
f
ied
cr
iter
ias
to
b
e
s
co
r
ed
.
T
h
e
ass
es
s
m
en
t
w
a
s
d
o
n
e
b
y
u
s
i
n
g
th
e
f
u
zz
y
lik
er
t
w
it
h
f
i
v
e
s
ca
le
r
esp
o
n
s
e:
s
tr
o
n
g
l
y
ag
r
ee
,
ag
r
e
e,
n
eu
tr
al,
d
is
a
g
r
ee
,
an
d
s
tr
o
n
g
l
y
d
is
a
g
r
ee
.
2.
2
.
3
.
Da
t
a
co
llect
io
n
I
n
th
i
s
q
u
esti
o
n
n
air
e
s
,
an
ele
ctr
o
n
ic
f
o
r
m
o
f
a
n
s
w
er
s
w
a
s
p
r
esen
ted
an
d
s
u
b
m
itted
to
th
e
s
elec
ted
g
r
o
u
p
o
f
p
r
o
f
es
s
o
r
s
,
ass
i
s
tan
t
p
r
o
f
ess
o
r
s
an
d
p
r
o
f
es
s
o
r
s
w
h
o
h
av
e
lo
n
g
e
x
p
er
ien
ce
i
n
s
u
p
er
v
is
i
n
g
s
t
u
d
en
t
s
in
th
e
u
n
i
v
er
s
it
ies.
T
h
e
r
esp
o
n
s
e
s
f
o
r
th
e
q
u
esti
o
n
n
air
e
s
w
er
e
co
llected
th
r
o
u
g
h
g
o
o
g
le
f
o
r
m
s
a
n
d
d
o
w
n
lo
ad
ed
as
m
icr
o
s
o
f
t
ex
ce
l
f
i
le
f
o
r
e
ase
o
f
an
a
l
y
s
is
,
w
h
ic
h
in
v
o
l
v
ed
f
i
n
d
in
g
t
h
e
av
er
a
g
e
o
f
t
h
r
es
h
o
ld
v
alu
e,
t
h
e
av
er
ag
e
p
er
ce
n
ta
g
e
o
f
ex
p
er
t'
co
n
s
e
n
s
u
s
a
n
d
a
v
er
ag
e
f
u
zz
y
s
co
r
e.
I
t
is
s
u
g
g
ested
t
h
at
m
ax
i
m
u
m
a
n
d
m
i
n
i
m
u
m
m
et
h
o
d
u
s
es
a
c
u
m
u
lati
v
e
f
r
eq
u
en
c
y
d
i
s
tr
ib
u
ti
o
n
an
d
f
u
zz
y
s
co
r
in
g
i
n
o
r
d
er
to
d
ea
l
w
ith
t
h
e
o
p
in
io
n
o
f
ex
p
er
ts
w
i
th
r
esp
ec
t to
th
e
f
u
zz
y
n
u
m
b
er
s
r
es
u
lti
n
g
f
r
o
m
t
h
e
F
DM
[
2
2
]
,
[
2
3
].
2
.
2
.
4
.
F
uzzy
d
elphi
da
t
a
a
na
l
y
s
is
T
h
e
FDM
is
u
s
ed
to
d
eter
m
i
n
e
th
e
b
est
t
y
p
e
o
f
cr
iter
ia
a
n
d
to
s
et
t
h
e
t
y
p
e
o
f
f
ac
to
r
s
ap
p
er
tain
i
n
g
to
th
is
s
t
u
d
y
.
T
h
e
f
o
llo
w
in
g
ar
e
t
h
e
s
tep
s
p
er
f
o
r
m
ed
i
n
f
u
zz
y
d
elp
h
i d
ata
an
al
y
s
i
s
:
C
o
n
v
er
t
th
e
L
in
g
u
is
t
ic
v
ar
iab
l
es
to
tr
ian
g
u
lar
f
u
zz
y
n
u
m
b
er
s
.
T
h
e
lin
g
u
is
tic
v
ar
iab
les
ar
e
f
o
r
w
ei
g
h
ti
n
g
th
e
ag
r
ee
m
en
t
o
f
th
e
e
x
p
er
ts
.
T
ab
le
1
as
s
h
o
w
n
in
t
h
e
p
r
o
ce
s
s
of
li
n
g
u
i
s
tic
v
ar
iab
le
s
f
o
r
w
ei
g
h
ti
n
g
t
h
e
ag
r
ee
m
e
n
t o
f
t
h
e
ex
p
er
ts
[
1
9
]
,
[
2
4
].
T
ab
le
1.
L
in
g
u
i
s
tic
v
ar
iab
les o
f
th
e
a
g
r
ee
m
e
n
t
L
i
n
g
u
i
st
i
c
v
a
r
i
a
b
l
e
s
F
u
z
z
y
l
i
k
e
r
t
S
t
r
o
n
g
l
y
D
i
sag
r
e
e
(
1
)
0
0
0
.
2
D
i
sag
r
e
e
(
2
)
0
0
.
2
0
.
4
N
e
u
t
r
a
l
(
3
)
0
.
2
0
.
4
0
.
6
A
g
r
e
e
(
4
)
0
.
4
0
.
6
0
.
8
S
t
r
o
n
g
l
y
A
g
r
e
e
(
5
)
0
.
6
0
.
8
1
C
alcu
late
t
h
e
a
v
er
ag
e
v
alu
e
b
ased
o
n
t
h
e
to
tal
o
f
n
u
m
b
er
o
f
ea
c
h
i
te
m
an
d
th
e
n
d
i
v
id
ed
b
y
t
h
e
n
u
m
b
er
o
f
ex
p
er
ts
[
2
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
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I
n
t J
A
r
ti
f
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tell
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Vo
l.
10
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No
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4
,
Dec
em
b
er
2
0
2
1
:
8
3
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C
alcu
late
th
e
d
is
ta
n
ce
b
et
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en
t
w
o
f
u
zz
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y
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d
ev
iatio
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b
et
w
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en
th
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v
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ag
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f
u
zz
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v
al
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d
ata
a
n
d
t
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ex
p
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'
e
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alu
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tio
n
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i
n
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1
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Data
v
er
tex
m
e
th
o
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u
s
e
d
to
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lcu
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th
e
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is
ta
n
ce
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h
ich
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s
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h
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e
(
)
o
f
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t
w
o
(
2
)
f
u
zz
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m
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er
s
=
(
1
,
2
,
3
)
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d
=
(
1
,
2
,
3
)
,
w
h
ic
h
ar
e
t
h
en
av
er
a
g
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f
o
r
all
th
e
r
esu
lts
.
T
h
e
las
t
av
er
a
g
e
r
ep
r
esen
ts
t
h
e
to
tal
th
r
e
s
h
o
l
d
v
alu
e
(
)
f
o
r
th
at
p
ar
ticu
lar
cr
it
er
ia.
(
̃
,
̃
)
=
√
1
3
[
(
1
−
1
)
2
+
(
2
−
2
)
2
+
(
3
−
3
)
2
)
]
(
1)
C
alcu
late
th
e
p
er
ce
n
tag
e
f
o
r
ea
ch
ite
m
,
w
h
en
t
h
e
th
r
es
h
o
ld
v
alu
e
(
r
esp
o
n
s
e)
f
o
r
ea
ch
cr
it
er
ia
w
it
h
d
<
=
0
.
2
.
T
h
e
d
ata
an
al
y
s
i
s
is
b
ased
o
n
th
e
tr
ian
g
u
lar
f
u
zz
y
n
u
m
b
er
w
h
er
e
it
ai
m
s
to
g
et
t
h
r
esh
o
l
d
v
alu
e
(
)
.
T
h
er
ef
o
r
e,
th
e
f
ir
s
t
r
eq
u
ir
e
m
en
t
to
b
e
f
o
llo
w
ed
is
t
h
r
es
h
o
ld
v
al
u
e
(
)
m
u
s
t
b
e
les
s
o
r
eq
u
al
to
0
.
2
.
P
er
ce
n
tag
es
f
o
r
ce
r
tai
n
ite
m
if
r
ea
ch
a
n
ag
r
ee
m
en
t
o
f
e
x
p
er
ts
e
x
ce
ed
in
g
7
5
.
0
%,
t
h
e
n
t
h
is
ite
m
is
ac
ce
p
ted
.
I
n
s
tead
,
if
it is
les
s
t
h
an
7
5
.
0
%,
it
m
ea
n
s
t
h
at
t
h
is
i
te
m
n
ee
d
to
b
e
r
ej
ec
ted
.
T
h
e
av
er
ag
e
f
u
zz
y
s
co
r
e
w
a
s
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eter
m
in
ed
b
ased
o
n
th
e
v
al
u
e
o
f
α
-
c
u
t,
w
h
ic
h
is
0
.
5
.
I
f
th
e
av
er
ag
e
f
u
zz
y
s
co
r
e
(
)
is
m
o
r
e
th
a
n
o
r
eq
u
al
to
0
.
5
,
th
e
ele
m
e
n
ts
ar
e
r
eg
ar
d
ed
as
h
as
ac
h
ie
v
ed
th
e
co
n
s
e
n
s
u
s
o
f
th
e
ex
p
er
ts
.
T
h
e
f
o
r
m
u
la
u
s
ed
f
o
r
d
ef
u
zz
i
f
icatio
n
in
(
2
)
:
=
1
3
(
1
+
2
+
3
)
(
2
)
3.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
I
O
N
I
n
th
i
s
s
t
u
d
y
,
s
u
c
h
f
in
d
i
n
g
s
t
h
at
r
ep
r
esen
t
t
h
e
FDM
a
n
d
t
h
e
f
i
n
al
s
et
o
f
cr
iter
ia
g
ath
er
ed
f
r
o
m
th
e
liter
atu
r
e
r
ev
ie
w
.
T
h
is
is
in
o
r
d
er
to
f
o
r
m
u
late
t
h
e
q
u
esti
o
n
n
air
e
an
d
th
en
s
elec
t
th
e
s
u
itab
l
e
cr
iter
ia.
T
o
d
o
s
o
,
th
er
e
ar
e
n
u
m
er
o
u
s
m
e
th
o
d
s
f
o
r
th
e
id
en
tif
icat
io
n
o
f
th
e
r
elatio
n
s
h
ip
a
m
o
n
g
v
ar
io
u
s
th
e
cr
i
ter
ia.
T
h
is
id
en
ti
f
icatio
n
i
s
b
ased
o
n
th
e
c
h
ar
ac
ter
is
tic
s
o
f
s
u
ch
cr
iter
ia.
3
.
1
.
I
dentif
ied
c
rit
er
ia
T
h
e
r
esu
lts
o
f
t
h
e
s
t
u
d
en
t
c
la
s
s
i
f
icatio
n
p
r
o
ce
s
s
ar
e
p
r
esen
t
ed
in
T
ab
le
2
.
C
o
llected
f
r
o
m
liter
atu
r
e
r
ev
ie
w
,
th
e
cr
iter
ia
ar
e
g
r
o
u
p
ed
in
to
f
o
u
r
ca
teg
o
r
ies;
n
a
m
e
l
y
Hu
m
a
n
B
eh
a
v
io
r
,
Me
th
o
d
o
lo
g
y
s
k
ill
s
,
Me
n
ta
l
an
d
P
er
s
o
n
al
s
k
i
lls
.
T
h
ese
ca
teg
o
r
ies
w
er
e
d
er
iv
ed
f
r
o
m
r
elev
an
t
liter
at
u
r
es
an
d
s
u
g
g
es
ted
b
y
e
x
p
er
ts
i
n
a
clo
s
e
f
o
r
m
at.
3
.
2
.
Da
t
a
a
na
ly
s
is
us
ing
f
uzz
y
delph
i
T
ab
le
3
s
h
o
w
s
t
h
e
r
esu
lts
o
f
t
h
e
ac
ce
p
t
ed
cr
iter
ia
ex
tr
ac
ted
f
r
o
m
th
e
FDM
.
C
o
n
s
i
s
te
n
c
y
a
m
o
n
g
t
h
e
r
esu
lt
s
o
f
ea
c
h
g
r
o
u
p
o
f
cr
it
er
ia
d
o
es
ex
i
s
t
a
n
d
d
e
m
o
n
s
tr
ated
th
r
o
u
g
h
t
h
e
r
es
u
lt
s
.
T
h
e
s
e
ac
ce
p
ted
cr
iter
ia
w
h
er
e
its
p
er
ce
n
ta
g
e
i
s
g
r
ea
ter
th
an
o
r
eq
u
al
to
7
5
%,
w
er
e
d
i
v
id
ed
in
to
co
h
er
en
t
ca
teg
o
r
ies
.
T
h
is
p
r
o
ce
s
s
is
to
f
ac
ilit
at
ed
t
h
e
u
n
d
er
s
ta
n
d
i
n
g
an
d
m
ath
e
m
at
ical
o
p
er
atio
n
s
in
th
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A
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ter
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843
T
ab
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2
.
C
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r
izatio
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f
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ti
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cr
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ass
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s
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GR
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k
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N
o
.
C
r
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t
e
r
i
a
C
a
t
e
g
o
r
i
e
s
1.
A
d
a
p
t
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c
h
a
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g
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t
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c
h
n
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H
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a
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2.
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sh
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B
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r
5.
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6.
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H
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7.
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8.
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s d
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g
n
M
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t
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k
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9.
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d
M
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k
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1
0
.
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p
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f
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t
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k
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1
1
.
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p
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1
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.
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.
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1
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.
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1
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.
A
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.
P
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s i
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k
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1
9
.
P
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sse
ss c
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o
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M
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y
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k
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2
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.
R
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q
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.
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2
1
.
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2
2
.
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.
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2
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.
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.
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2
6
.
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.
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2
9
.
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k
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3
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.
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3
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.
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.
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845
RE
F
E
R
E
NC
E
S
[1
]
M
.
Yo
rk
e
,
“
F
o
rm
a
ti
v
e
a
ss
e
ss
m
e
n
t
in
h
ig
h
e
r
e
d
u
c
a
ti
o
n
:
M
o
v
e
s
to
w
a
rd
s
th
e
o
ry
a
n
d
th
e
e
n
h
a
n
c
e
m
e
n
t
o
f
p
e
d
a
g
o
g
ic
p
ra
c
ti
c
e
,
”
Hig
h
er
Ed
u
c
u
c
a
t
io
n
,
v
o
l.
4
5
,
n
o
.
4
,
p
p
.
4
7
7
-
5
0
1
,
2
0
0
3
,
d
o
i:
1
0
.
1
0
2
3
/A
:1
0
2
3
9
6
7
0
2
6
4
1
3
.
[2
]
A
.
K.
Ka
b
le,
C.
A
rth
u
r,
T
.
Lev
e
tt
‐Jo
n
e
s,
a
n
d
K.
Re
id
‐S
e
a
rl,
“
S
t
u
d
e
n
t
e
v
a
lu
a
ti
o
n
o
f
sim
u
latio
n
in
u
n
d
e
rg
ra
d
u
a
te
n
u
rsin
g
p
ro
g
ra
m
s
in
A
u
stra
li
a
u
sin
g
q
u
a
li
ty
in
d
ica
to
rs,”
Nu
rs
i
n
g
a
n
d
He
a
lt
h
S
c
i
e
n
c
e
s
,
v
o
l.
1
5
,
n
o
.
2
,
p
p
.
2
3
5
-
2
4
3
,
2
0
1
3
,
d
o
i:
1
0
.
1
1
1
1
/
n
h
s.
1
2
0
2
5
.
[3
]
B.
A
ll
iso
n
,
A
.
Hilt
o
n
,
T
.
O’S
u
ll
iv
a
n
,
A
.
Ow
e
n
,
a
n
d
A
.
Ro
t
h
w
e
ll
,
Re
se
a
rc
h
sk
il
ls
f
o
r
stu
d
e
n
ts.
Ro
u
tl
e
d
g
e
,
2
0
1
6
.
[4
]
H.
Et
z
k
o
w
it
z
,
“
Re
se
a
rc
h
g
ro
u
p
s
a
s
‘q
u
a
si
-
f
ir
m
s’:
th
e
in
v
e
n
ti
o
n
o
f
th
e
e
n
trep
re
n
e
u
rial
u
n
iv
e
rsity
,
”
Res
e
a
rc
h
Po
li
c
y
,
v
o
l.
3
2
,
n
o
.
1
,
p
p
.
1
0
9
–
1
2
1
,
2
0
0
3
,
d
o
i
:
1
0
.
1
0
1
6
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0
0
4
8
-
7
3
3
3
(0
2
)0
0
0
0
9
-
4
.
[5
]
M
.
L
a
m
b
o
v
sk
a
,
"
Co
n
tro
l
o
n
tea
m
s:
A
m
o
d
e
l
a
n
d
e
m
p
iri
c
a
l
e
v
id
e
n
c
e
f
ro
m
Bu
lg
a
ria.
"
S
e
rb
ia
n
J
o
u
r
n
a
l
o
f
M
a
n
a
g
e
me
n
t
,
v
o
l.
1
3
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o
.
2
,
p
p
.
3
1
1
-
3
2
2
,
2
0
1
8
,
d
o
i:
1
0
.
5
9
3
7
/sjm
1
3
-
1
4
6
3
3
.
[6
]
H.
Et
z
k
o
w
it
z
,
M
.
Ra
n
g
a
,
a
n
d
J.
Dz
isa
h
,
“
W
h
it
h
e
r
t
h
e
u
n
iv
e
rsity
?
T
h
e
No
v
u
m
T
ri
v
iu
m
a
n
d
th
e
tran
siti
o
n
f
ro
m
in
d
u
strial
t
o
k
n
o
w
led
g
e
so
c
iet
y
,
”
Soc
ia
l
S
c
i
e
n
c
e
I
n
f
o
rm
a
t
io
n
,
v
o
l.
5
1
,
n
o
.
2
,
p
p
.
1
4
3
-
1
6
4
,
2
0
1
2
,
d
o
i:
1
0
.
1
1
7
7
/0
5
3
9
0
1
8
4
1
2
4
3
7
0
9
9
.
[7
]
C.
M
.
Ka
rd
a
sh
,
“
Ev
a
lu
a
ti
o
n
o
f
u
n
d
e
rg
ra
d
u
a
te
re
se
a
rc
h
e
x
p
e
rien
c
e
:
P
e
rc
e
p
ti
o
n
s
o
f
u
n
d
e
rg
ra
d
u
a
te
in
tern
s
a
n
d
t
h
e
ir
f
a
c
u
lt
y
m
e
n
to
rs,”
J
o
u
r
n
a
l
o
f
E
d
u
c
a
t
io
n
a
l
Psy
c
h
o
l
o
g
y
,
v
o
l
.
9
2
,
n
o
.
1
,
p
p
.
1
9
1
-
2
0
1
,
2
0
0
0
,
d
o
i:
1
0
.
1
0
3
7
/0
0
2
2
-
0
6
6
3
.
9
2
.
1
.
1
9
1
.
[8
]
M
.
A
.
M
ir
e
t
a
l.
,
“
A
p
p
l
ica
ti
o
n
o
f
T
OP
S
IS
a
n
d
V
IKO
R
im
p
ro
v
e
d
v
e
rsio
n
s
in
a
m
u
lt
i
c
rit
e
ria
d
e
c
isio
n
a
n
a
ly
sis
to
d
e
v
e
lo
p
a
n
o
p
ti
m
ize
d
m
u
n
icip
a
l
so
li
d
w
a
ste
m
a
n
a
g
e
m
e
n
t
m
o
d
e
l,
”
J
o
u
rn
a
l
o
f
En
v
iro
n
me
n
ta
l
M
a
n
a
g
e
me
n
t
,
v
o
l.
1
6
6
,
p
p
.
1
0
9
-
1
1
5
,
2
0
1
5
,
d
o
i:
1
0
.
1
0
1
6
/
j.
jen
v
m
a
n
.
2
0
1
5
.
0
9
.
0
2
8
.
[9
]
K.
F
.
T
a
m
rin
,
B.
Ra
h
m
a
tu
ll
a
h
,
a
n
d
S
.
M
.
S
a
m
u
ri,
“
A
n
e
x
p
e
ri
m
e
n
tal
in
v
e
stig
a
ti
o
n
o
f
th
re
e
-
d
im
e
n
sio
n
a
l
p
a
rti
c
le
a
g
g
r
e
g
a
ti
o
n
u
sin
g
d
ig
it
a
l
h
o
lo
g
r
a
p
h
ic
m
icro
sc
o
p
y
,
”
Op
t
ics
a
n
d
L
a
se
rs
En
g
i
n
e
e
rin
g
,
v
o
l.
6
8
,
p
p
.
9
3
-
1
0
3
,
2
0
1
5
,
d
o
i:
1
0
.
1
0
1
6
/j
.
o
p
tl
a
se
n
g
.
2
0
1
4
.
1
2
.
0
1
1
.
[1
0
]
D.
F
.
F
e
ld
o
n
e
t
a
l.
,
"
G
ra
d
u
a
te
stu
d
e
n
ts’
tea
c
h
in
g
e
x
p
e
rien
c
e
s
im
p
ro
v
e
th
e
ir
m
e
th
o
d
o
lo
g
ica
l
re
se
a
rc
h
s
k
il
ls,"
S
c
ien
c
e
,
v
o
l.
3
3
3
,
n
o
.
6
0
4
5
,
p
p
.
1
0
3
7
-
1
0
3
9
,
2
0
1
1
,
d
o
i:
1
0
.
1
1
2
6
/sc
i
e
n
c
e
.
1
2
0
4
1
0
9
.
[1
1
]
D.
L
o
p
a
tt
o
,
“
S
u
rv
e
y
o
f
u
n
d
e
rg
ra
d
u
a
te res
e
a
rc
h
e
x
p
e
rien
c
e
s (S
URE
):
F
irst
f
in
d
i
n
g
s,”
Ce
ll
Bi
o
l
ogy
E
d
u
c
a
ti
o
n
,
v
o
l.
3
,
n
o
.
4
,
p
p
.
2
7
0
-
2
7
7
,
2
0
0
4
,
d
o
i:
1
0
.
1
1
8
7
/cb
e
.
0
4
-
07
-
0
0
4
5
.
[1
2
]
D
.
L
o
p
a
tt
o
,
“
Un
d
e
rg
ra
d
u
a
te
re
se
a
rc
h
e
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3
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W
.
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u
e
r
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d
J.
S
.
Be
n
n
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tt
,
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lu
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e
J
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6
.
2
0
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7
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.
[1
4
]
Z.
T
a
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m
u
d
i,
F
.
A
.
M
u
h
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d
i
n
,
M
.
Ro
ss
d
y
,
a
n
d
N.
W
.
D.
T
a
m
sin
,
“
F
u
z
z
y
d
e
lp
h
i
m
e
th
o
d
f
o
r
e
v
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lu
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ti
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g
e
ff
e
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ti
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e
tea
c
h
in
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b
a
se
d
o
n
stu
d
e
n
ts’
p
e
rsp
e
c
ti
v
e
,
”
e
-
Aca
d
e
mia
J
o
u
rn
a
l
UiT
M
T
,
v
o
l.
5
,
n
o
.
1
,
p
p
.
1
-
1
0
,
2
0
1
6
.
[1
5
]
B.
Ra
h
m
a
tu
ll
a
h
,
I.
S
a
rris,
J.
A
.
No
b
le,
a
n
d
A
.
T
.
P
a
p
a
g
e
o
rg
h
io
u
.
"
OP
2
6
.
0
8
:
A
u
to
m
a
ted
sta
n
d
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rd
p
lan
e
se
lec
ti
o
n
f
ro
m
f
e
t
a
l
a
b
d
o
m
in
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l
u
lt
ra
so
u
n
d
v
o
lu
m
e
s
u
sin
g
a
m
a
c
h
in
e
lea
r
n
in
g
a
lg
o
rit
h
m
,
"
Ultra
so
u
n
d
in
Ob
ste
trics
a
n
d
Gy
n
e
c
o
lo
g
y
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l
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4
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.
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1
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4
.
[1
6
]
C.
L
in
,
“
A
p
p
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c
a
ti
o
n
o
f
f
u
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z
y
De
lp
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m
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th
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(F
DM)
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f
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n
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l
y
ti
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iera
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h
y
p
ro
c
e
ss
(F
A
HP
)
to
c
rit
e
ria
w
e
i
g
h
ts
f
o
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f
a
sh
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n
d
e
sig
n
sc
h
e
m
e
e
v
a
lu
a
ti
o
n
,
”
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ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Clo
t
h
in
g
S
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ien
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e
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n
d
T
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c
h
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o
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y
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2
5
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o
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5
6
2
2
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1
3
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.
[1
7
]
B.
Ra
h
m
a
tu
ll
a
h
,
a
n
d
J.
A
.
No
b
le.
"
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n
a
to
m
ic
a
l
o
b
jec
t
d
e
tec
ti
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n
in
f
e
tal
u
lt
ra
so
u
n
d
:
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o
m
p
u
ter
-
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x
p
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rt
a
g
re
e
m
e
n
ts,"
in
In
ter
n
a
ti
o
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a
l
Co
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fer
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n
c
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Bi
o
me
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ica
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In
fo
rm
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ti
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[1
8
]
O.
M
a
n
o
li
a
d
is,
I.
T
so
las
,
a
n
d
A.
Na
k
o
u
,
“
S
u
sta
i
n
a
b
le
c
o
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u
c
ti
o
n
a
n
d
d
r
iv
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rs
o
f
c
h
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n
g
e
in
G
re
e
c
e
:
a
D
e
lp
h
i
stu
d
y
,
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n
str
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c
ti
o
n
M
a
n
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g
e
me
n
t
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n
d
Eco
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l.
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0
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o
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4
4
6
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0
5
0
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4
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0
4
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[1
9
]
S
.
N.
A
.
M
o
h
a
m
a
d
,
M
.
A
.
E
m
b
i,
a
n
d
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No
rd
i
n
,
“
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ter
m
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-
p
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lem
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in
lea
rn
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g
p
ro
c
e
ss
u
sin
g
f
u
z
z
y
d
e
lp
h
i
a
n
a
ly
sis.,
”
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t
e
rn
a
ti
o
n
a
l
E
d
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c
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ti
o
n
a
l
S
tu
d
ies
,
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l.
8
,
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o
.
9
,
p
p
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1
7
1
-
1
7
6
,
2
0
1
5
,
d
o
i:
1
0
.
5
5
3
9
/i
e
s.v
8
n
9
p
1
7
1
.
[2
0
]
B.
Ra
h
m
a
tu
ll
a
h
a
n
d
R.
Be
sa
r,
“
A
n
a
l
y
si
s
o
f
se
m
i
-
a
u
to
m
a
ted
m
e
t
h
o
d
f
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r
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e
m
u
r
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g
th
m
e
a
su
re
m
e
n
t
f
ro
m
f
o
e
tal
u
lt
ra
so
u
n
d
,
”
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o
u
rn
a
l
o
f
M
e
d
ica
l
En
g
in
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e
rin
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a
n
d
T
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c
h
n
o
l
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y
,
v
o
l
.
3
3
,
n
o
.
6
,
2
0
0
9
,
d
o
i:
1
0
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1
0
8
0
/0
3
0
9
1
9
0
0
8
0
2
4
5
1
2
3
2
.
[2
1
]
A
.
A
.
A
.
R
a
h
i
m
,
M
.
A
.
Em
b
i,
S
.
Hu
ss
in
,
N.
M
.
No
o
r
,
N.
Y.
Kh
a
m
is
a
n
d
R
.
Di
n
,
"
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u
z
z
y
De
lp
h
i
m
e
t
h
o
d
re
f
in
e
m
e
n
t
o
f
m
o
b
il
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lan
g
u
a
g
e
le
a
rn
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g
f
r
a
m
e
w
o
rk
e
le
m
e
n
ts
f
o
r
tec
h
n
ica
l
a
n
d
e
n
g
in
e
e
rin
g
c
o
n
tex
ts,"
2
0
1
8
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1
0
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h
In
ter
n
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t
io
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a
l
C
o
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n
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E
n
g
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rin
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E
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u
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ti
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n
(
ICEE
D),
2
0
1
8
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p
p
.
1
7
3
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1
7
5
,
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o
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1
0
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1
1
0
9
/ICE
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2
0
1
8
.
8
6
2
6
9
0
5
.
[2
2
]
H.
-
M
.
Hs
u
a
n
d
C.
-
T
.
Ch
e
n
,
“
A
g
g
re
g
a
ti
o
n
o
f
f
u
z
z
y
o
p
in
i
o
n
s u
n
d
e
r
g
ro
u
p
d
e
c
isio
n
m
a
k
in
g
,
”
Fu
zz
y
S
e
ts a
n
d
S
y
ste
ms
,
v
o
l.
7
9
,
n
o
.
3
,
p
p
.
2
7
9
-
2
8
5
,
1
9
9
6
,
d
o
i:
1
0
.
1
0
1
6
/0
1
6
5
-
0
1
1
4
(9
5
)0
0
1
8
5
-
9
.
[2
3
]
H.
Ha
ss
a
n
,
B.
Ra
h
m
a
tu
l
lah
,
a
n
d
N.
M
o
h
a
m
a
d
No
rd
in
,
“
T
o
w
a
rd
s
sc
h
o
o
l
m
a
n
a
g
e
m
e
n
t
s
y
ste
m
(S
M
S
)
su
c
c
e
ss
in
tea
c
h
e
r’s p
e
rc
e
p
ti
o
n
,
”
M
a
l
a
y
sia
n
On
li
n
e
J
o
u
rn
a
l
o
f
E
d
u
c
a
t
io
n
a
l
T
e
c
h
n
o
l
ogy
,
v
o
l.
2
,
n
o
.
4
,
p
p
.
5
0
-
6
0
,
2
0
1
4
.
[2
4
]
O.
T
a
y
lan
,
A
.
O.
Ba
fa
il
,
R.
M
.
S
.
A
b
d
u
laa
l,
a
n
d
M
.
R.
Ka
b
li
,
“
Co
n
stru
c
ti
o
n
p
r
o
jec
ts
se
lec
ti
o
n
a
n
d
risk
a
ss
e
ss
m
e
n
t
b
y
f
u
z
z
y
A
HP
a
n
d
f
u
z
z
y
T
O
P
S
IS
m
e
th
o
d
o
l
o
g
ies
,
”
Ap
p
li
e
d
S
o
ft
C
o
mp
u
ti
n
g
,
v
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l.
1
7
,
p
p
.
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-
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0
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o
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a
so
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.
2
0
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4
.
0
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0
3
.
[2
5
]
L
.
Ya
n
,
C.
M
.
Eck
e
rt,
a
n
d
C.
Earl
,
"
A
re
v
ie
w
o
f
f
u
z
z
y
A
HP
m
e
th
o
d
s
f
o
r
d
e
c
isio
n
-
m
a
k
in
g
w
it
h
su
b
jec
ti
v
e
ju
d
g
e
m
e
n
ts,"
Exp
e
rt S
y
ste
ms
wit
h
Ap
p
li
c
a
ti
o
n
s
,
v
o
l.
1
6
1
,
p
p
.
1
1
3
7
3
8
,
2
0
2
0
,
d
o
i:
1
0
.
1
0
1
6
/
j.
e
sw
a
.
2
0
2
0
.
1
1
3
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3
8
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I
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Vo
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10
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.
4
,
Dec
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b
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2
0
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p
u
ti
n
g
a
n
d
Cre
a
ti
v
e
In
d
u
stry
,
S
u
lt
a
n
I
d
ris
Ed
u
c
a
ti
o
n
U
n
iv
e
rsity
,
M
a
la
y
sia
.
Ha
v
in
g
re
c
e
iv
e
d
a
B
.
En
g
.
(El
e
c
tri
c
a
l)
f
ro
m
V
a
n
d
e
rb
il
t
Un
iv
e
rsity
,
US
A
,
a
M
.
En
g
.
Sc
.
f
ro
m
M
u
lt
im
e
d
ia
Un
i
v
e
rsit
y
,
M
a
la
y
si
a
a
n
d
D
P
h
il
in
E
n
g
.
S
c
ien
c
e
f
ro
m
Un
iv
e
r
sit
y
of
Ox
f
o
rd
,
UK
,
sh
e
is
k
e
e
n
in
a
p
p
ly
in
g
th
e
tec
h
n
ica
l
a
n
d
re
se
a
rc
h
sk
il
ls
g
a
in
e
d
in
im
p
ro
v
in
g
th
e
q
u
a
li
ty
o
f
re
se
a
rc
h
a
n
d
e
d
u
c
a
ti
o
n
i
n
M
a
lay
sia
.
S
h
e
h
a
s
a
u
th
o
re
d
a
w
id
e
ra
n
g
e
o
f
p
u
b
li
c
a
ti
o
n
s
a
n
d
h
a
d
b
e
e
n
i
n
v
it
e
d
to
re
v
iew
a
rti
c
le
s
f
o
r
h
ig
h
im
p
a
c
t
jo
u
rn
a
ls
a
n
d
c
o
n
f
e
re
n
c
e
s.
Cu
rre
n
t
re
se
a
rc
h
in
tere
st
in
c
lu
d
e
s
I
m
a
g
e
a
n
d
S
ig
n
a
l
P
ro
c
e
ss
in
g
,
P
a
tt
e
rn
Re
c
o
g
n
it
io
n
,
M
a
c
h
i
n
e
L
e
a
rn
in
g
,
L
e
a
rn
in
g
A
n
a
l
y
ti
c
s,
Ch
il
d
De
v
e
lo
p
m
e
n
t,
ICT
a
n
d
E
d
u
c
a
t
io
n
.
S
h
ih
a
b
H
a
m
a
d
K
h
a
lee
f
a
h
re
c
e
iv
e
d
th
e
B.
S
c
.
d
e
g
re
e
in
C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
A
rti
f
icia
l
In
telli
g
e
n
c
e
f
ro
m
Un
iv
e
rsit
y
o
f
An
b
a
r,
Ira
q
,
M
.
S
c
.
d
e
g
re
e
in
A
rti
f
i
c
ial
In
telli
g
e
n
c
e
a
n
d
P
a
tt
e
r
n
Re
c
o
g
n
it
io
n
f
ro
m
N
a
ti
o
n
a
l
Un
iv
e
rs
it
y
o
f
M
a
la
y
sia
(U
KM)
.
Cu
rre
n
tl
y
h
e
i
s
p
u
rsu
i
n
g
th
e
P
h
.
D.
in
in
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
O
n
n
M
a
lay
sia
(U
T
HM)
.
He
is
a
l
e
c
tu
re
r
w
it
h
th
e
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
,
A
l
m
a
a
rif
Un
iv
e
rsit
y
Co
ll
e
g
e
,
Ira
q
.
His
r
e
se
a
r
c
h
in
tere
sts
in
c
lu
d
e
a
rti
f
icia
l
in
telli
g
e
n
c
e
,
im
a
g
e
p
ro
c
e
ss
in
g
,
p
a
tt
e
rn
re
c
o
g
n
it
i
o
n
a
n
d
i
n
f
o
rm
a
ti
o
n
tec
h
n
o
l
o
g
y
.
K
h
a
iru
l
Fi
k
r
i
T
a
m
r
i
n
is
a
se
n
io
r
lec
tu
re
r
a
t
Un
iv
e
rsiti
M
a
la
y
sia
S
a
ra
wa
k
(
UN
IM
A
S
).
He
is
a
c
h
a
rtere
d
e
n
g
in
e
e
r
(I
.
M
e
c
h
.
E
.
,
UK
)
a
n
d
P
r
o
f
e
ss
io
n
a
l
En
g
in
e
e
r
(Bo
a
rd
o
f
En
g
in
e
e
rs,
M
a
la
y
sia
)
w
it
h
re
se
a
rc
h
f
o
c
u
s
in
las
e
r
m
a
te
rials
p
ro
c
e
ss
in
g
a
n
d
a
rti
f
ici
a
l
in
telli
g
e
n
c
e
f
o
r
th
e
a
p
p
li
c
a
ti
o
n
s
in
m
icro
f
lu
id
ics
,
a
u
t
o
m
o
ti
v
e
,
a
e
ro
sp
a
c
e
a
n
d
m
a
n
u
f
a
c
tu
rin
g
se
c
to
rs.
In
a
d
d
it
io
n
,
h
e
h
a
d
d
e
sig
n
e
d
a
n
d
d
e
v
e
lo
p
e
d
a
n
u
m
b
e
r
o
f
lo
w
-
c
o
st
las
e
r
o
p
ti
c
a
l
m
e
a
su
re
m
e
n
t
in
stru
m
e
n
ts,
p
a
rti
c
le
im
a
g
e
v
e
lo
c
i
m
e
ter
a
n
d
d
ig
it
a
l
h
o
lo
g
ra
p
h
ic
m
icro
sc
o
p
e
f
o
r
b
io
m
e
d
ica
l
a
p
p
li
c
a
ti
o
n
.
He
c
o
n
ti
n
u
o
u
sly
p
u
b
li
sh
e
s
re
se
a
rc
h
f
in
d
in
g
s
o
f
h
is
n
a
ti
o
n
a
l
a
n
d
in
tern
a
ti
o
n
a
l
c
o
ll
a
b
o
ra
ti
v
e
re
se
a
rc
h
w
o
rk
in
h
ig
h
im
p
a
c
t
jo
u
r
n
a
ls
.
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