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th
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ta.
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p
p
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m
a
c
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t
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d
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K
ey
w
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r
d
s
:
Ar
tific
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n
eu
r
al
n
etwo
r
k
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Dee
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d
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Pre
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Stu
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p
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T
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s
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c
c
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rticle
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CC B
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se
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C
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p
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A
uth
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r
:
Mu
n
iap
p
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am
ar
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Dep
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tm
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t o
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C
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ter
Scie
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R
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am
C
o
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e
o
f
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r
ts
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d
Scien
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b
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e,
I
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d
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m
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r
am
ar
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p
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cs@
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m
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co
m
1.
I
NT
RO
D
UCT
I
O
N
E
d
u
ca
tio
n
al
d
ata
m
in
in
g
(
E
D
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is
a
g
r
o
win
g
f
ield
d
ed
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ed
to
u
n
co
v
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r
in
g
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alu
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d
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m
ex
ten
s
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c
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d
atasets
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B
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f
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tiv
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tili
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th
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d
ata,
in
s
t
it
u
tio
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s
ca
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f
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tu
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d
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in
d
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d
s
[
1
]
.
R
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p
r
o
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ess
in
m
ac
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in
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lear
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in
g
an
d
d
ee
p
lear
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i
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g
h
as
led
to
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cr
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o
f
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h
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cu
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m
o
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r
p
r
ed
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s
tu
d
en
t
p
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f
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[
2
]
.
Alth
o
u
g
h
tr
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lear
n
in
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s
an
d
s
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p
p
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t
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to
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m
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in
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s
(
SVMs),
h
av
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b
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ap
p
lied
to
th
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task
,
d
ee
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lear
n
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g
tech
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em
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s
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3
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ter
m
m
e
m
o
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y
(
L
STM
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n
etwo
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k
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[
4
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.
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n
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en
v
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VL
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s
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h
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,
d
atasets
ca
n
b
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s
p
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s
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r
im
b
alan
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,
m
ak
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g
s
im
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ler
m
o
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s
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c
h
as
d
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n
tr
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s
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g
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r
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n
,
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r
e
v
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en
s
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b
le
m
eth
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d
s
lik
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r
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m
f
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ests
m
o
r
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p
r
ac
tical
in
r
ea
l
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wo
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ld
ap
p
licatio
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s
[
5
]
.
L
ea
r
n
in
g
an
al
y
tics
(
L
A)
ty
p
ically
f
o
cu
s
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n
r
ea
l
-
tim
e,
ac
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ter
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co
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d
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r
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d
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m
in
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tech
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T
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p
r
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e
r
y
o
f
E
DM
,
as
ea
ch
o
p
er
ates
o
n
d
if
f
er
en
t
tem
p
o
r
al
an
d
m
eth
o
d
o
lo
g
ical
p
lan
es
[
6
]
.
A
d
ee
p
co
g
n
itiv
e
d
iag
n
o
s
is
m
o
d
el
(
DC
DM
)
f
o
r
p
r
e
d
ictin
g
s
tu
d
en
ts
’
p
er
f
o
r
m
a
n
ce
f
o
cu
s
es
o
n
en
h
an
cin
g
h
o
w
ac
cu
r
a
tely
we
ca
n
ass
ess
s
tu
d
en
t
k
n
o
wled
g
e
b
ased
o
n
t
h
eir
r
esp
o
n
s
es
to
v
ar
io
u
s
ass
ess
m
en
ts
[
7
]
.
An
o
th
er
is
s
u
e
is
th
e
n
ee
d
f
o
r
lar
g
e
-
s
ca
le,
h
ig
h
-
q
u
ality
d
ata
to
t
r
ain
s
u
ch
m
o
d
els
ef
f
ec
tiv
ely
.
I
n
m
an
y
e
d
u
ca
tio
n
al
co
n
te
x
ts
,
d
ata
m
ay
b
e
s
ca
r
ce
,
n
o
is
y
,
o
r
im
b
alan
ce
d
,
esp
ec
ia
lly
in
ter
m
s
o
f
ass
es
s
m
en
ts
f
o
r
s
p
ec
if
ic
lear
n
in
g
d
o
m
ain
s
o
r
m
in
o
r
ity
s
tu
d
e
n
t
g
r
o
u
p
s
[
8
]
.
Ma
n
y
d
ee
p
lear
n
in
g
-
b
ased
k
n
o
wled
g
e
tr
ac
in
g
(
DL
KT
)
m
o
d
els
ar
e
tr
ain
ed
o
n
s
p
ec
if
ic
ty
p
es
o
f
d
ata
(
e.
g
.
,
o
n
lin
e
lear
n
in
g
p
lat
f
o
r
m
s
an
d
s
tan
d
a
r
d
ized
test
s
)
.
T
h
e
s
u
r
v
ey
s
ee
k
s
to
ad
d
r
ess
t
h
e
p
r
o
b
lem
o
f
h
o
w
well
DL
KT
m
o
d
els
g
en
er
aliz
e
ac
r
o
s
s
d
iv
er
s
e
lear
n
in
g
co
n
t
ex
ts
.
Ho
wev
er
,
th
e
p
r
o
b
lem
r
em
ain
s
th
at
th
er
e
is
lim
ited
r
esear
ch
ex
p
lo
r
in
g
th
e
d
ir
ec
t r
o
le
ar
tific
ial
in
tellig
en
c
e
(
AI
)
ca
n
p
la
y
in
en
h
an
cin
g
a
ca
d
em
ic
o
u
tco
m
es
b
y
f
o
cu
s
in
g
o
n
b
o
t
h
s
tu
d
y
s
tr
ateg
ies
an
d
lear
n
in
g
d
is
ab
ilit
ies
[
9
]
.
A
n
o
v
el
m
ac
h
in
e
lear
n
in
g
m
o
d
el,
r
a
n
d
o
m
g
r
o
u
p
in
g
-
b
ased
d
ee
p
m
u
lti
-
m
o
d
al
lear
n
in
g
(
RG
-
DM
ML
)
,
wh
ich
is
co
u
p
led
with
an
en
s
em
b
le
lear
n
in
g
alg
o
r
ith
m
.
T
h
is
m
o
d
el
in
te
g
r
ates
v
ar
io
u
s
d
ata
s
o
u
r
ce
s
,
s
u
ch
as
ac
ad
em
ic
r
ec
o
r
d
s
an
d
d
em
o
g
r
ap
h
ic
in
f
o
r
m
atio
n
,
an
d
ap
p
lies
d
e
ep
lear
n
in
g
tech
n
iq
u
es
to
en
h
an
ce
p
r
e
d
ictio
n
ac
cu
r
ac
y
[
1
0
]
.
E
d
u
ca
tio
n
al
in
s
titu
tio
n
s
s
tr
u
g
g
le
to
id
en
tif
y
at
-
r
is
k
s
tu
d
en
ts
ea
r
ly
e
n
o
u
g
h
to
in
ter
v
en
e
ef
f
ec
tiv
ely
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
E
DM
h
as
s
ee
n
r
a
p
id
g
r
o
wth
o
v
er
th
e
last
d
ec
ad
e,
d
r
iv
e
n
b
y
th
e
in
cr
ea
s
in
g
av
ailab
ilit
y
o
f
e
d
u
ca
tio
n
al
d
ata
f
r
o
m
o
n
lin
e
lear
n
in
g
p
l
atf
o
r
m
s
,
s
tu
d
en
t
in
f
o
r
m
atio
n
s
y
s
tem
s
,
an
d
o
th
e
r
d
i
g
ital
to
o
ls
.
E
ar
ly
r
esear
ch
f
o
cu
s
ed
o
n
r
u
le
-
b
ased
s
y
s
tem
s
an
d
s
tatis
tical
m
o
d
els,
wh
ich
,
wh
ile
ef
f
ec
tiv
e
in
ce
r
tain
s
ce
n
ar
io
s
,
s
tr
u
g
g
led
to
s
ca
le
with
in
cr
ea
s
in
g
d
ata
co
m
p
lex
ity
.
R
ath
i
et
a
l
.
[
1
1
]
h
as
p
r
esen
ts
th
e
h
y
b
r
id
ap
p
r
o
ac
h
co
m
b
in
in
g
th
e
s
elf
-
s
u
p
er
v
is
ed
r
o
b
u
s
t
o
p
tim
izatio
n
alg
o
r
ith
m
(
SS
-
R
OA)
an
d
d
ee
p
L
STM
n
etwo
r
k
s
.
T
h
is
m
o
d
el
lev
e
r
ag
es
th
e
s
tr
en
g
th
s
o
f
d
ee
p
lear
n
in
g
in
h
an
d
lin
g
tim
e
-
s
er
ies
d
ata
wh
ile
o
p
tim
izin
g
f
ea
tu
r
e
s
elec
tio
n
an
d
m
o
d
el
tr
ain
in
g
u
s
in
g
th
e
SS
-
R
OA
tech
n
iq
u
e.
Din
g
[
1
2
]
h
as
illu
s
tr
ate
o
n
d
ee
p
lear
n
in
g
m
o
d
els
ca
n
an
aly
ze
s
tu
d
en
t
m
o
v
em
en
ts
th
r
o
u
g
h
v
i
d
eo
d
a
ta,
p
r
o
v
id
in
g
r
ea
l
-
tim
e
co
r
r
ec
tio
n
s
o
r
f
ee
d
b
ac
k
o
n
tech
n
iq
u
e
an
d
p
o
s
tu
r
e.
I
n
m
u
s
ic,
AI
-
d
r
iv
e
n
m
o
d
els
ca
n
ass
ess
p
itch
,
tim
in
g
,
an
d
e
x
p
r
ess
io
n
d
u
r
i
n
g
p
e
r
f
o
r
m
an
ce
s
,
o
f
f
er
i
n
g
s
tu
d
e
n
ts
d
etailed
f
ee
d
b
ac
k
o
n
ar
ea
s
f
o
r
im
p
r
o
v
em
en
t.
Au
la
k
h
et
a
l
.
[
1
3
]
aim
s
to
e
x
am
in
e
t
h
e
in
ter
s
ec
tio
n
o
f
e
-
lear
n
in
g
an
d
E
DM
d
u
r
in
g
th
e
C
OVI
D
-
1
9
p
an
d
e
m
ic.
I
t
ex
p
lo
r
es
v
ar
i
o
u
s
E
DM
m
eth
o
d
s
ap
p
lied
in
e
-
lear
n
in
g
,
s
u
ch
as
clu
s
ter
in
g
,
class
if
icatio
n
,
an
d
r
eg
r
ess
io
n
an
aly
s
is
.
Sar
k
er
et
a
l
.
[
1
4
]
an
aly
zin
g
s
tu
d
en
ts
’
ac
a
d
em
ic
p
er
f
o
r
m
an
ce
th
r
o
u
g
h
E
DM
h
as
e
m
e
r
g
ed
as
a
v
alu
ab
le
a
p
p
r
o
ac
h
f
o
r
im
p
r
o
v
i
n
g
e
d
u
ca
tio
n
al
o
u
tc
o
m
es
an
d
in
s
titu
tio
n
al
d
ec
is
io
n
-
m
ak
i
n
g
.
Fen
g
an
d
Fan
[
1
5
]
h
as
in
v
esti
g
ate
h
o
w
E
DM
ca
n
im
p
r
o
v
e
t
h
e
lear
n
in
g
p
r
o
ce
s
s
b
y
ev
alu
atin
g
lear
n
in
g
b
e
h
av
io
r
s
,
p
r
e
d
ictin
g
s
tu
d
en
t
s
u
cc
ess
,
an
d
v
is
u
alizin
g
d
ata
in
a
way
t
h
at
s
u
p
p
o
r
ts
d
ec
is
io
n
-
m
ak
in
g
in
e
d
u
ca
tio
n
.
Den
g
et
a
l
.
[
1
6
]
h
as
in
tr
o
d
u
ce
s
a
n
o
v
el
d
ee
p
l
ea
r
n
in
g
-
b
ased
p
r
ed
ictiv
e
m
o
d
el,
ca
p
ab
le
o
f
a
n
aly
zin
g
v
ar
io
u
s
f
ac
to
r
s
s
u
ch
as
s
elf
-
esteem
lev
els,
ten
d
en
cies
to
war
d
s
in
d
iv
id
u
alis
m
,
an
d
t
h
eir
co
m
b
i
n
ed
im
p
ac
t
o
n
p
er
f
o
r
m
an
ce
m
etr
ics.
L
am
et
a
l
.
[
1
7
]
in
tr
o
d
u
ce
s
a
r
o
b
u
s
t
f
r
am
ewo
r
k
th
at
lev
er
ag
es
m
ac
h
in
e
lear
n
in
g
tech
n
i
q
u
es
to
ac
cu
r
ately
p
r
ed
ict
s
tu
d
en
t
p
er
f
o
r
m
an
ce
,
en
ab
lin
g
p
r
o
ac
tiv
e
id
en
tific
atio
n
o
f
lear
n
er
s
at
ac
ad
em
i
c
r
is
k
.
B
y
u
tili
zin
g
alg
o
r
ith
m
s
s
u
ch
as
k
-
m
ea
n
s
,
h
ier
ar
ch
ical
clu
s
ter
in
g
,
a
n
d
d
en
s
ity
-
b
ased
s
p
atial
clu
s
ter
in
g
o
f
ap
p
licatio
n
s
with
n
o
is
e
(
DB
SC
AN)
,
th
e
s
tu
d
y
s
ee
k
s
to
u
n
co
v
er
p
atter
n
s
t
h
at
ca
n
in
f
o
r
m
ed
u
ca
t
o
r
s
ab
o
u
t
th
e
d
iv
er
s
e
n
ee
d
s
o
f
th
eir
s
tu
d
en
ts
.
Pen
g
et
a
l
.
[
1
8
]
t
h
e
a
ch
iev
em
e
n
t
o
f
t
h
is
r
esear
ch
lies
in
its
ab
ilit
y
to
f
ac
ilit
ate
tar
g
eted
in
ter
v
en
tio
n
s
,
p
er
s
o
n
alize
d
lea
r
n
in
g
p
ath
way
s
,
an
d
u
ltima
tely
en
h
a
n
ce
ed
u
ca
tio
n
al
o
u
tc
o
m
es.
R
ejeb
et
a
l
.
[
1
9
]
aim
s
to
ex
am
in
e
h
o
w
C
h
atGPT
is
b
ein
g
u
tili
ze
d
in
v
ar
io
u
s
ed
u
ca
tio
n
al
co
n
tex
ts
an
d
to
ass
ess
it
s
in
f
lu
en
ce
o
n
t
ea
ch
in
g
m
et
h
o
d
s
,
lear
n
in
g
e
x
p
er
ien
ce
s
,
an
d
o
v
er
all
ed
u
ca
tio
n
al
o
u
tco
m
es.
B
h
a
r
d
waj
et
a
l
.
[
2
0
]
d
e
m
o
n
s
t
r
ates
t
h
at
d
e
ep
le
ar
n
i
n
g
m
o
d
e
ls
,
s
u
c
h
as
co
n
v
o
l
u
ti
o
n
al
n
eu
r
a
l
n
e
tw
o
r
k
s
(
C
NNs
)
an
d
L
S
T
M
n
et
wo
r
k
s
,
ar
e
ef
f
ec
ti
v
e
to
o
ls
f
o
r
p
r
e
d
ic
ti
n
g
a
n
d
an
al
y
z
in
g
s
t
u
d
e
n
t
e
n
g
a
g
e
m
e
n
t
i
n
e
-
le
ar
n
in
g
en
v
i
r
o
n
m
e
n
ts
.
I
t
ai
m
s
t
o
i
d
e
n
t
if
y
p
att
er
n
s
o
f
e
n
g
a
g
e
m
e
n
t
,
p
r
ed
i
ct
s
t
u
d
e
n
t
b
e
h
av
io
r
s
,
a
n
d
p
r
o
v
i
d
e
p
er
s
o
n
a
liz
ed
in
t
er
v
en
ti
o
n
s
to
im
p
r
o
v
e
l
ea
r
n
i
n
g
o
u
t
c
o
m
es.
Al
K
a'
b
i
[
2
1
]
h
as
in
tr
o
d
u
ce
s
a
n
o
v
el
A
I
a
lg
o
r
it
h
m
a
n
d
d
ee
p
lea
r
n
i
n
g
tec
h
n
i
q
u
es
t
ail
o
r
e
d
f
o
r
e
n
h
a
n
ci
n
g
t
h
e
q
u
al
it
y
o
f
h
i
g
h
e
r
e
d
u
ca
ti
o
n
.
L
i
n
et
a
l
.
[
2
2
]
a
im
s
to
s
tr
ea
m
li
n
e
lea
r
n
i
n
g
p
r
o
ce
s
s
es,
i
m
p
r
o
v
e
ed
u
c
ati
o
n
al
o
u
t
co
m
es
,
a
n
d
o
p
ti
m
i
ze
i
n
s
ti
tu
ti
o
n
al
m
a
n
a
g
e
m
e
n
t
b
y
p
r
o
v
id
in
g
p
e
r
s
o
n
ali
ze
d
l
ea
r
n
in
g
e
x
p
er
ie
n
ce
s
,
p
r
e
d
i
cti
v
e
a
n
al
y
t
ics
,
a
n
d
a
u
t
o
m
ate
d
ad
m
i
n
is
tr
ati
v
e
tas
k
s
.
Far
h
o
o
d
et
a
l
.
[
2
3
]
co
n
tr
ib
u
tes
to
th
e
f
ield
o
f
E
D
M
b
y
in
tr
o
d
u
cin
g
g
e
n
er
ativ
e
a
d
v
er
s
ar
ial
n
etwo
r
k
s
(
GANs)
a
s
a
n
o
v
el
ap
p
r
o
ac
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
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tell
I
SS
N:
2252
-
8
9
3
8
E
d
u
ca
tio
n
a
l d
a
t
a
min
in
g
a
p
p
r
o
a
ch
fo
r
p
r
ed
ictin
g
s
tu
d
en
t p
e
r
fo
r
ma
n
ce
a
n
d
…
(
Mu
n
ia
p
p
a
n
R
a
ma
r
a
j
)
4115
f
o
r
im
p
r
o
v
i
n
g
s
tu
d
e
n
t
o
u
tc
o
m
e
p
r
ed
ictio
n
s
.
T
h
e
f
o
cu
s
is
o
n
ex
tr
ac
tin
g
m
ea
n
in
g
f
u
l
p
atter
n
s
an
d
in
s
ig
h
ts
f
r
o
m
tex
tu
al
o
r
co
m
m
u
n
icatio
n
d
a
ta
g
en
er
ated
d
u
r
in
g
lear
n
in
g
p
r
o
ce
s
s
es,
s
u
ch
as
o
n
lin
e
d
is
cu
s
s
io
n
s
,
wr
itten
ass
ig
n
m
en
ts
,
o
r
f
ee
d
b
ac
k
.
R
iaz
et
a
l
.
[
2
4
]
in
tr
o
d
u
ce
s
T
r
a
n
s
L
STM
,
a
n
o
v
el
h
y
b
r
i
d
ar
ch
ite
ctu
r
e
co
m
b
i
n
in
g
th
e
s
tr
en
g
th
s
o
f
L
STM
an
d
T
r
an
s
f
o
r
m
er
m
o
d
els to
p
e
r
f
o
r
m
f
in
e
-
g
r
ain
ed
s
u
g
g
esti
o
n
m
in
in
g
.
3.
M
E
T
H
O
D
T
h
e
m
eth
o
d
o
lo
g
y
f
o
r
p
r
ed
icti
n
g
s
tu
d
en
t
p
er
f
o
r
m
a
n
ce
th
r
o
u
g
h
E
DM
,
th
is
s
tu
d
y
em
p
l
o
y
s
a
m
u
lti
-
s
tep
m
eth
o
d
o
l
o
g
y
u
tili
zin
g
d
ee
p
lear
n
in
g
tech
n
i
q
u
es
[
2
5
]
.
T
h
e
ap
p
r
o
ac
h
b
eg
in
s
with
d
ata
co
llectio
n
,
wh
er
e
ac
ad
em
ic
r
ec
o
r
d
s
,
d
e
m
o
g
r
ap
h
ic
d
etails,
an
d
b
eh
av
io
r
al
p
atter
n
s
ar
e
ag
g
r
e
g
ated
.
T
h
e
d
ata
u
n
d
er
g
o
es
p
r
ep
r
o
ce
s
s
in
g
to
clea
n
an
d
n
o
r
m
alize
it,
f
o
llo
wed
b
y
f
ea
tu
r
e
s
elec
tio
n
to
id
en
tify
th
e
m
o
s
t
r
elev
an
t
attr
ib
u
tes
f
o
r
p
r
ed
ictio
n
[
2
6
]
.
Fig
u
r
e
1
illu
s
tr
ates
o
n
th
e
p
r
ed
ictin
g
s
tu
d
en
t
p
er
f
o
r
m
an
ce
u
s
in
g
d
ee
p
lear
n
in
g
i
n
E
D
M
in
v
o
lv
es
s
ev
er
al
k
ey
s
tep
s
.
I
n
itially
,
a
d
ataset
co
m
p
r
is
in
g
ac
ad
em
ic
r
ec
o
r
d
s
,
d
em
o
g
r
a
p
h
ic
d
etails,
an
d
b
eh
a
v
io
r
al
d
at
a
is
co
llected
an
d
p
r
ep
r
o
ce
s
s
ed
to
h
an
d
le
m
is
s
in
g
v
alu
es
a
n
d
n
o
r
m
alize
f
ea
t
u
r
es.
T
o
id
en
tif
y
th
e
m
o
s
t
r
elev
an
t
f
ea
tu
r
es
f
o
r
p
r
ed
ictio
n
,
f
ea
tu
r
e
s
elec
tio
n
is
ca
r
r
ied
o
u
t,
f
o
llo
wed
b
y
s
p
litt
in
g
th
e
d
ata
in
to
tr
ain
in
g
a
n
d
test
in
g
s
ets
to
en
s
u
r
e
r
eliab
le
m
o
d
e
l
ev
alu
atio
n
.
Ad
v
an
ce
d
d
ee
p
lear
n
in
g
m
o
d
els,
s
u
ch
as
y
o
u
o
n
l
y
lo
o
k
o
n
ce
(
YOL
O
)
,
f
ast
r
eg
io
n
-
b
ased
c
o
n
v
o
lu
ti
o
n
al
n
e
u
r
al
n
etwo
r
k
s
(
Fas
t
R
C
N
N)
,
ANNs,
an
d
L
STM
n
etwo
r
k
s
,
ar
e
em
p
lo
y
ed
to
ca
p
t
u
r
e
co
m
p
le
x
p
atter
n
s
with
in
th
e
d
ata
[
2
7
]
.
T
h
ese
m
o
d
els
ar
e
tr
ain
ed
o
n
th
e
tr
ain
in
g
s
et
an
d
ass
es
s
ed
o
n
th
e
test
in
g
s
et,
u
s
in
g
m
etr
ics
lik
e
ac
cu
r
ac
y
,
p
r
e
cisi
o
n
,
an
d
r
ec
all
to
g
au
g
e
th
eir
p
er
f
o
r
m
an
c
e.
A
co
m
p
ar
ativ
e
an
al
y
s
is
is
p
er
f
o
r
m
ed
ag
ain
s
t
tr
ad
itio
n
al
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els,
in
clu
d
in
g
d
ec
is
io
n
tr
ee
s
an
d
SVMs,
to
h
ig
h
lig
h
t
th
e
s
u
p
er
i
o
r
p
r
ed
ictiv
e
ac
c
u
r
ac
y
o
f
d
ee
p
lear
n
in
g
tech
n
iq
u
es.
T
h
is
m
eth
o
d
o
lo
g
y
aim
s
to
p
r
o
v
id
e
p
r
ec
is
e
in
s
ig
h
ts
in
t
o
s
tu
d
en
t
p
er
f
o
r
m
a
n
ce
,
e
n
a
b
lin
g
m
o
r
e
e
f
f
ec
tiv
e
an
d
t
ar
g
eted
e
d
u
ca
tio
n
al
in
ter
v
en
tio
n
s
.
Fig
u
r
e
1
.
Pro
p
o
s
ed
e
d
u
ca
tio
n
a
l d
ata
m
in
in
g
m
o
d
els
3
.
1
.
SVM
m
et
ho
d us
ed
f
o
r
E
DM
w
it
h
s
pa
t
ia
l py
ra
m
id po
o
lin
g
SVM
is
a
p
o
wer
f
u
l
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
u
s
ed
f
o
r
b
o
th
class
if
icatio
n
an
d
r
eg
r
ess
io
n
task
s
.
SVM
is
o
f
ten
ap
p
lied
to
p
r
e
d
ict
s
tu
d
en
t
p
er
f
o
r
m
an
ce
b
y
class
if
y
in
g
s
tu
d
en
ts
in
to
d
if
f
er
en
t
p
er
f
o
r
m
an
c
e
ca
teg
o
r
ies
o
r
p
r
ed
ictin
g
co
n
tin
u
o
u
s
s
co
r
es
[
2
8
]
.
Giv
en
a
d
ataset
o
f
s
tu
d
e
n
t
f
ea
tu
r
e
s
(
s
u
ch
as
g
r
ad
es,
atten
d
an
ce
,
an
d
d
e
m
o
g
r
a
p
h
i
c
d
ata)
,
th
e
g
o
al
is
to
cl
ass
if
y
s
tu
d
en
ts
in
to
ca
teg
o
r
ies
lik
e
p
as
s
/f
ail,
h
ig
h
/m
ed
iu
m
/lo
w
p
e
r
f
o
r
m
an
c
e,
o
r
p
r
e
d
ict
th
eir
f
in
al
s
co
r
es.
T
h
e
p
r
im
a
r
y
o
b
jectiv
e
o
f
SVM
is
to
id
e
n
tify
a
h
y
p
er
p
lan
e
th
at
o
p
tim
ally
s
e
p
ar
ates
d
ata
p
o
in
ts
(
s
tu
d
en
ts
)
in
to
d
is
tin
ct
class
es.
I
n
s
tu
d
en
t
p
er
f
o
r
m
an
ce
p
r
ed
ictio
n
,
t
h
e
h
y
p
er
p
la
n
e
s
ep
ar
ates stu
d
en
ts
b
ased
o
n
th
eir
p
er
f
o
r
m
an
ce
lev
els.
+
=
0
(
1
)
(
)
=
+
(
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
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4
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5
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Octo
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er
2
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5
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1
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3
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1
2
2
4116
W
h
er
e
is
th
e
we
ig
h
t
v
ec
to
r
(
wh
ich
d
eter
m
in
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)
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x
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f
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tu
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e
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r
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in
p
u
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s
u
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en
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es),
b
is
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ias
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o
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ig
in
)
,
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0
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ef
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e
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e
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y
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lan
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an
d
th
is
is
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e
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ec
is
io
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b
o
u
n
d
ar
y
.
I
f
f
(
x
)
≥
0
th
e
s
tu
d
en
t
is
class
if
ied
in
t
o
o
n
e
ca
teg
o
r
y
(
e.
g
.
,
p
ass
)
,
I
f
f
(
x
)
<0
th
e
s
tu
d
en
t is cla
s
s
if
ied
in
to
th
e
o
th
er
ca
te
g
o
r
y
(
e.
g
.
,
f
ail)
.
3
.
2
.
YO
L
O
m
et
ho
d us
ed
f
o
r
E
DM
wit
h
s
pa
t
ia
l py
ra
m
id po
o
lin
g
E
DM
,
to
th
e
d
ir
ec
t
a
p
p
licatio
n
o
f
YOL
O
f
o
r
s
tu
d
en
t
p
er
f
o
r
m
an
ce
p
r
ed
ictio
n
is
u
n
co
n
v
e
n
tio
n
al,
as
YOL
O
is
f
u
n
d
am
en
tally
d
esig
n
ed
f
o
r
im
a
g
e
-
b
ased
task
s
.
Ho
wev
er
,
with
s
o
m
e
cr
ea
tiv
e
m
o
d
if
icatio
n
,
YOL
O
-
lik
e
ar
ch
itectu
r
es
c
o
u
ld
th
eo
r
e
tically
b
e
ad
a
p
ted
f
o
r
E
DM
ta
s
k
s
,
esp
ec
ially
if
im
ag
e
-
lik
e
d
ata
r
ep
r
esen
tatio
n
s
(
e.
g
.
,
h
ea
tm
ap
s
,
tim
e
s
er
ies,
o
r
v
is
u
al
p
atter
n
s
o
f
s
tu
d
en
t
ac
tiv
ity
)
ar
e
u
s
ed
[
2
9
]
.
n
tr
a
d
itio
n
al
YOL
O,
th
e
o
b
jectiv
e
is
to
p
r
ed
ict
b
o
u
n
d
in
g
b
o
x
es
a
r
o
u
n
d
o
b
jects
in
an
im
ag
e
an
d
class
if
y
th
em
.
T
T
h
e
alg
o
r
ith
m
s
eg
m
en
ts
th
e
im
ag
e
in
to
an
S×S
g
r
id
,
wh
er
e
ea
ch
g
r
id
ce
ll
p
r
ed
icts
m
u
ltip
le
b
o
u
n
d
in
g
b
o
x
es
alo
n
g
with
co
r
r
esp
o
n
d
in
g
c
o
n
f
id
en
ce
s
co
r
es a
n
d
class
p
r
o
b
ab
ilit
ies.
=
∑
(
(
)
−
(
)
)
2
=
1
+
∑
(
−
)
2
=
1
+
∑
∑
(
,
−
,
)
2
=
1
=
1
(
3
)
W
h
e
r
e
,
,
a
r
e
h
y
p
e
r
p
a
r
a
m
e
t
e
r
s
t
h
a
t
c
o
n
t
r
o
l
t
h
e
r
e
la
t
i
v
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m
p
o
r
t
a
n
c
e
o
f
e
a
c
h
l
o
s
s
t
e
r
m
,
(
(
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a
r
e
t
h
e
t
r
u
e
a
n
d
p
r
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d
i
ct
e
d
p
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a
b
i
l
i
ti
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s
o
f
s
t
u
d
e
n
t
i
i
i
b
el
o
n
g
i
n
g
t
o
a
c
e
r
t
ai
n
p
e
r
f
o
r
m
a
n
c
e
c
l
ass
,
(
,
−
,
)
a
r
e
t
h
e
t
r
u
e
a
n
d
p
r
e
d
i
c
t
e
d
f
e
a
t
u
r
e
v
a
l
u
e
s
f
o
r
s
t
u
d
e
n
t
i
a
n
d
f
e
a
t
u
r
e
j
.
3.
3
.
F
a
s
t
RCNN
m
et
ho
d us
ed
f
o
r
E
DM
wit
h
s
pa
t
ia
l py
ra
m
id po
o
lin
g
Fas
t
R
C
N
N
is
a
co
m
p
u
ter
v
is
io
n
alg
o
r
ith
m
ty
p
ically
u
s
ed
f
o
r
o
b
ject
d
etec
tio
n
in
im
a
g
es.
W
h
ile
it
's
n
o
t
d
ir
ec
tly
ap
p
licab
le
to
E
D
M,
it
ca
n
cr
ea
tiv
ely
ad
ap
t
th
e
p
r
in
cip
les
o
f
Fas
t
R
C
NN
f
o
r
s
tu
d
en
t
p
er
f
o
r
m
an
ce
p
r
ed
ictio
n
.
T
h
e
id
ea
is
to
le
v
er
ag
e
its
u
n
d
e
r
ly
in
g
f
r
am
e
wo
r
k
f
o
r
an
aly
zin
g
s
eg
m
en
t
ed
d
ata
r
eg
io
n
s
an
d
m
ak
in
g
p
r
ed
ictio
n
s
,
a
n
d
m
a
p
t
h
ese
co
n
ce
p
ts
o
n
t
o
th
e
f
ea
tu
r
e
s
an
d
p
er
f
o
r
m
an
ce
p
r
e
d
ictio
n
t
ask
s
in
E
DM
.
=
−
∑
∑
=
1
=
1
l
og
(
)
(
4
)
W
h
er
e
is
th
e
tr
u
e
p
er
f
o
r
m
a
n
c
e
class
f
o
r
s
tu
d
en
t
i’
s
r
eg
io
n
j,
l
og
(
)
is
th
e
p
r
ed
icted
p
r
o
b
ab
ilit
y
o
f
th
e
tr
u
e
class
f
o
r
r
eg
io
n
j.
Fo
r
ea
ch
s
tu
d
en
t,
we
m
ak
e
p
r
ed
ictio
n
s
f
o
r
ea
ch
r
eg
io
n
o
f
f
ea
tu
r
es
an
d
th
en
ag
g
r
eg
ate
th
ese
to
m
a
k
e
a
f
in
al
d
ec
i
s
io
n
ab
o
u
t
th
e
s
tu
d
en
t'
s
o
v
er
all
p
er
f
o
r
m
an
ce
.
T
h
e
alg
o
r
ith
m
ca
n
class
if
y
p
er
f
o
r
m
an
ce
o
r
p
r
ed
ict
s
co
r
e
s
f
o
r
ea
ch
f
ea
tu
r
e
s
et
an
d
th
en
ag
g
r
eg
ate
th
ese
p
r
e
d
ictio
n
s
to
m
ak
e
a
f
in
al
d
ec
is
io
n
o
n
s
tu
d
e
n
t p
er
f
o
r
m
a
n
ce
.
3.
4
.
ANN
wit
h L
ST
M
m
et
ho
d us
ed
f
o
r
E
DM
wit
h
s
pa
t
i
a
l py
ra
m
id po
o
lin
g
ANNs
co
m
b
in
ed
with
L
STM
u
n
its
ar
e
wid
ely
u
s
ed
f
o
r
tim
e
s
er
ies
p
r
ed
ictio
n
an
d
s
eq
u
en
tial
d
ata
m
o
d
elin
g
.
I
n
E
DM
,
th
is
co
m
b
in
atio
n
ca
n
b
e
h
ig
h
l
y
ef
f
ec
ti
v
e
f
o
r
s
tu
d
e
n
t
p
er
f
o
r
m
an
ce
p
r
ed
ictio
n
,
esp
ec
ially
wh
en
th
e
r
e
is
a
tem
p
o
r
al
a
s
p
ec
t
to
th
e
d
ata
(
e.
g
.
,
p
r
e
d
ictin
g
p
er
f
o
r
m
a
n
ce
o
v
er
m
u
ltip
le
s
em
ester
s
o
r
ass
es
s
m
en
ts
)
.
A
n
eu
r
al
n
etwo
r
k
with
f
u
lly
co
n
n
ec
ted
la
y
er
s
,
ty
p
ically
u
s
ed
f
o
r
lear
n
in
g
f
r
o
m
n
o
n
-
s
eq
u
en
tial,
s
tatic
d
ata.
I
n
E
DM
,
an
ANN
ca
n
b
e
u
s
ed
to
m
o
d
el
r
ela
tio
n
s
h
ip
s
b
etwe
en
s
tu
d
en
t
f
e
atu
r
es
(
e.
g
.
,
g
r
ad
es,
atten
d
an
ce
,
an
d
ass
ig
n
m
en
t
s
co
r
es)
an
d
t
h
eir
f
in
al
p
er
f
o
r
m
an
ce
.
L
STM
s
ar
e
p
ar
ticu
lar
ly
u
s
ef
u
l
in
m
o
d
elin
g
tim
e
-
d
ep
en
d
e
n
t r
elatio
n
s
h
ip
s
,
s
u
ch
as a
s
tu
d
en
t’
s
p
er
f
o
r
m
an
ce
o
v
er
m
u
ltip
le
p
er
io
d
s
o
r
tas
k
s
.
=
−
∑
=
1
l
og
(
)
(
5
)
=
1
⁄
∑
(
−
̂
)
2
=
1
(
6
)
W
h
er
e
is
th
e
tr
u
e
p
er
f
o
r
m
an
c
e
class
f
o
r
s
tu
d
e
n
t
i,
l
og
(
)
is
th
e
p
r
ed
icted
p
r
o
b
ab
ilit
y
f
o
r
th
e
t
r
u
e
class
.
T
h
en
,
n
e
x
t
eq
u
atio
n
is
th
e
tr
u
e
p
er
f
o
r
m
an
ce
class
f
o
r
s
tu
d
e
n
t
I
,
̂
is
th
e
p
r
ed
icted
p
er
f
o
r
m
an
ce
s
co
r
e.
Fo
r
ea
ch
s
tu
d
en
t,
th
e
L
STM
p
r
o
c
ess
es
th
e
s
eq
u
en
tial
f
ea
tu
r
es,
an
d
th
e
ANN
lay
er
s
m
ak
e
th
e
f
in
al
p
er
f
o
r
m
an
ce
p
r
ed
ictio
n
b
ased
o
n
th
e
lear
n
e
d
r
ep
r
esen
tatio
n
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
ANN
an
d
L
STM
m
o
d
el
s
d
em
o
n
s
tr
ated
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
in
p
r
ed
ictin
g
s
tu
d
en
t
o
u
tco
m
es
co
m
p
ar
ed
to
tr
a
d
itio
n
al
m
ac
h
i
n
e
lear
n
in
g
m
eth
o
d
s
.
T
h
e
L
STM
,
in
p
ar
ticu
lar
,
ex
ce
lled
at
ca
p
tu
r
in
g
tem
p
o
r
al
p
atter
n
s
in
th
e
d
ata,
lead
in
g
to
h
ig
h
er
ac
cu
r
ac
y
in
p
r
ed
ictin
g
lo
n
g
-
ter
m
s
tu
d
e
n
t
p
er
f
o
r
m
an
c
e.
W
h
en
co
m
p
ar
ed
to
d
ec
is
io
n
tr
ee
s
an
d
SVMs,
d
ee
p
lear
n
in
g
m
o
d
els
s
h
o
wed
a
m
ar
k
e
d
im
p
r
o
v
em
en
t
in
p
r
ed
ictio
n
ac
cu
r
ac
y
.
T
h
e
ANN
a
n
d
L
STM
m
o
d
els
r
ed
u
ce
d
th
e
er
r
o
r
r
ate
b
y
a
p
p
r
o
x
im
ately
1
0
-
1
5
%,
h
ig
h
lig
h
tin
g
th
eir
ef
f
ec
tiv
en
ess
in
id
en
tify
in
g
n
o
n
-
lin
ea
r
r
elatio
n
s
h
i
p
s
an
d
co
m
p
lex
p
atter
n
s
in
s
tu
d
e
n
t d
ata.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
E
d
u
ca
tio
n
a
l d
a
t
a
min
in
g
a
p
p
r
o
a
ch
fo
r
p
r
ed
ictin
g
s
tu
d
en
t p
e
r
fo
r
ma
n
ce
a
n
d
…
(
Mu
n
ia
p
p
a
n
R
a
ma
r
a
j
)
4117
4
.
1
.
Cla
s
s
if
ica
t
io
n a
cc
ura
c
y
T
h
e
class
if
icatio
n
ac
cu
r
ac
y
f
o
r
s
tu
d
en
t
p
er
f
o
r
m
a
n
ce
p
r
ed
ict
io
n
in
th
e
r
ea
l
tim
e
d
ataset,
t
o
f
in
d
th
e
ac
cu
r
ac
y
is
a
co
m
m
o
n
ly
u
s
ed
m
etr
ic
to
ev
alu
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
a
class
if
icatio
n
m
o
d
el.
Acc
u
r
ac
y
m
ea
s
u
r
es th
e
p
r
o
p
o
r
tio
n
o
f
co
r
r
ec
t p
r
ed
ictio
n
s
m
a
d
e
b
y
th
e
m
o
d
el
r
elativ
e
to
th
e
to
tal
n
u
m
b
er
o
f
p
r
e
d
ictio
n
s
.
=
1
⁄
∑
1
=
1
(
̂
=
)
(
7
)
W
h
er
e
n
is
th
e
to
tal
n
u
m
b
er
o
f
s
tu
d
en
ts
,
̂
is
p
r
ed
icted
class
eith
er
0
o
r
1
,
is
th
e
tr
u
e
class
o
f
i.
I
n
th
e
c
o
n
tex
t
o
f
s
tu
d
e
n
t
p
e
r
f
o
r
m
an
ce
p
r
ed
ictio
n
,
ac
cu
r
ac
y
m
ea
s
u
r
es
h
o
w
well
th
e
m
o
d
el
class
if
ies
s
tu
d
en
ts
in
to
th
e
co
r
r
ec
t
p
er
f
o
r
m
an
ce
ca
teg
o
r
ies (
e.
g
.
,
p
ass
/f
ail,
h
ig
h
/m
ed
iu
m
/lo
w
p
e
r
f
o
r
m
an
ce
)
.
L
et’
s
s
ay
th
e
m
o
d
el
is
p
r
ed
ictin
g
wh
eth
e
r
a
s
tu
d
en
t
will
p
ass
o
r
f
ail
b
ased
o
n
th
ei
r
f
ea
t
u
r
es
(
s
u
c
h
as g
r
ad
es,
atten
d
an
ce
,
an
d
ass
ig
n
m
en
ts
)
.
I
f
t
h
e
m
o
d
el
cla
s
s
if
ies
a
s
tu
d
en
t
as
p
ass
in
g
,
an
d
th
e
s
tu
d
e
n
t
ac
tu
ally
p
ass
es,
it
is
a
tr
u
e
p
o
s
itiv
e
(
T
P)
.
I
f
it p
r
ed
icts
f
ailu
r
e,
a
n
d
th
e
s
tu
d
en
t f
ails
,
it is
a
tr
u
e
n
e
g
ativ
e
(
T
N)
.
4
.
2
.
P
re
cisi
o
n,
re
ca
ll
,
a
nd
F
-
m
ea
s
ures
Pre
cisi
o
n
ca
lcu
lates
th
e
p
r
o
p
o
r
tio
n
o
f
TP
p
r
ed
ictio
n
s
am
o
n
g
all
p
o
s
itiv
e
p
r
ed
ictio
n
s
(
in
clu
d
in
g
b
o
th
TP
an
d
f
alse
p
o
s
itiv
es
(
FP
)
)
.
I
t
ad
d
r
ess
es
th
e
q
u
esti
o
n
:
"Ou
t
o
f
all
th
e
s
tu
d
en
ts
p
r
e
d
icted
to
s
u
cc
ee
d
,
h
o
w
m
an
y
ac
tu
ally
d
id
?"
R
ec
all,
also
r
ef
er
r
ed
to
as
s
en
s
itiv
ity
o
r
th
e
tr
u
e
p
o
s
itiv
e
r
ate
(
T
PR
)
,
m
ea
s
u
r
es
th
e
r
atio
o
f
TP
p
r
ed
ictio
n
s
to
all
ac
tu
al
p
o
s
itiv
es
(
TP
an
d
f
alse
n
eg
ativ
es
(
FN)
)
.
I
t
an
s
wer
s
:
"Ou
t
o
f
all
th
e
s
tu
d
en
ts
wh
o
ac
tu
ally
s
u
cc
ee
d
ed
,
h
o
w
m
an
y
wer
e
co
r
r
ec
tly
p
r
ed
ict
ed
b
y
th
e
m
o
d
el?"
T
h
e
F1
-
s
co
r
e,
wh
ich
is
th
e
h
ar
m
o
n
ic
m
ea
n
o
f
p
r
ec
is
io
n
a
n
d
r
ec
all,
o
f
f
er
s
a
s
in
g
le
m
etr
i
c
th
at
b
ala
n
ce
s
th
e
two
.
T
h
is
s
co
r
e
is
p
a
r
ticu
lar
ly
v
alu
ab
le
wh
en
th
er
e
is
a
n
ee
d
to
b
alan
ce
p
r
ec
is
io
n
an
d
r
ec
al
l,
s
u
ch
as
wh
en
b
o
th
FP
an
d
FN
h
av
e
s
ig
n
if
ican
t
co
n
s
eq
u
en
ce
s
.
=
+
(
8
)
=
+
(
9
)
−
=
2
×
×
+
(
10
)
W
h
er
e
T
P
r
ef
er
to
t
h
e
n
u
m
b
er
o
f
ca
s
es
wh
er
e
p
o
s
itiv
e
o
u
tco
m
es
ar
e
c
o
r
r
ec
tly
p
r
ed
i
cted
(
e.
g
.
,
s
tu
d
en
ts
co
r
r
ec
tly
id
en
tifie
d
as
p
ass
in
g
)
.
FP
r
ep
r
esen
t
in
s
tan
ce
s
w
h
er
e
th
e
m
o
d
el
in
co
r
r
ec
tly
p
r
ed
icts
a
p
o
s
itiv
e
o
u
tco
m
e
(
e
.
g
.
,
s
tu
d
e
n
ts
p
r
ed
i
cted
to
p
ass
b
u
t
ac
tu
ally
f
ail
)
.
FN
ar
e
th
e
ca
s
es
wh
er
e
th
e
m
o
d
el
wr
o
n
g
ly
p
r
ed
icts
a
n
e
g
ativ
e
o
u
tco
m
e
(
e.
g
.
,
s
tu
d
e
n
ts
p
r
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p
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t p
r
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.
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ic
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o
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c
h
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R
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)
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e
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ce
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teg
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ical
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ates
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el
to
class
if
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s
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en
ts
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PR
.
=
+
,
=
+
(
11
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h
is
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esen
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n
e
p
o
in
t
o
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e
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d
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alu
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p
lo
t th
e
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n
tire
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4
.
4
.
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im
e
c
a
lcula
t
io
n
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r
s
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d
en
t
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f
o
r
m
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ce
p
r
ed
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lcu
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e
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m
e
co
m
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lex
ity
o
f
th
e
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o
r
ith
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d
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e
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n
p
r
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s
ig
h
ts
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to
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i
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cy
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o
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el.
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im
e
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m
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lex
ity
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e
n
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ally
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m
p
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f
u
n
ctio
n
o
f
th
e
len
g
th
o
f
th
e
in
p
u
t.
(
.
2
)
(
12
)
W
h
er
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n
is
th
e
n
u
m
b
er
o
f
in
s
tan
ce
s
an
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n
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es.
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h
e
co
m
p
u
tatio
n
i
n
v
o
lv
es
ca
lcu
latin
g
th
e
co
ef
f
icien
ts
u
s
in
g
th
e
least sq
u
ar
es m
eth
o
d
.
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m
a
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:
jo
th
is
h
c
h
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m
b
a
th
1
2
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g
m
a
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c
o
m
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e
lv
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id
h
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wo
rk
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s
a
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As
so
c
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P
ro
fe
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h
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De
p
a
rtme
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Co
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ter
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KPR
Co
ll
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g
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o
f
Arts
S
c
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a
Re
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a
rc
h
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Co
imb
a
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S
h
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h
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m
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th
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d
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x
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d
iffere
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t
v
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rti
c
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h
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is
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re
v
iew
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tern
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l
jo
u
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a
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d
li
fe
-
ti
m
e
m
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m
b
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r
in
IAENG
.
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r
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se
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in
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lso
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m
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a
n
1
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p
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tern
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m
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h
e
c
a
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c
o
n
tac
ted
a
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m
a
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:
v
id
h
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sa
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4
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m
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.
c
o
m
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.
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Th
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g
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rk
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sista
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ro
fe
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p
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rtme
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t
o
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t
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rp
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g
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m
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a
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f
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h
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r
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u
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a
ti
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o
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.
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re
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tl
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sc
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ted
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p
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ti
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tec
h
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d
re
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p
ro
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m
s
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lu
ste
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c
ry
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t
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ra
p
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se
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l
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d
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o
m
p
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ti
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g
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rti
ficia
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telli
g
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n
t
sy
ste
m
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in
fo
rm
a
ti
o
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se
c
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ty
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l
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rg
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tab
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se
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d
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ta
m
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i
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g
a
s
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ll
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s
th
e
str
o
n
g
tea
c
h
in
g
e
x
p
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rien
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e
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d
o
c
to
ra
l
d
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rtatio
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a
lso
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o
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n
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d
s
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c
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ste
m
s
with
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lo
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p
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a
n
d
h
e
h
a
s
p
u
b
li
sh
e
d
m
o
re
th
a
n
1
3
p
u
b
li
c
a
ti
o
n
s i
n
re
p
u
ted
jo
u
rn
a
ls,
wh
ic
h
he
fin
d
s
wo
u
ld
b
e
a
g
re
a
t
a
d
d
it
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u
c
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r
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h
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n
d
re
se
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rc
h
d
e
p
a
rtme
n
t
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
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m
a
il
:
d
r
th
a
n
g
a
ra
su
.
n
@k
a
h
e
d
u
.
e
d
u
.
i
n
.
Bh
a
a
r
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t
h
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Il
a
n
g
o
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wo
r
k
in
g
a
s
a
n
As
sista
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t
P
r
o
fe
ss
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r
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t
h
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p
a
rtme
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t
o
f
Co
m
p
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ter
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c
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c
e
a
t
Ra
th
i
n
a
m
Co
ll
e
g
e
o
f
Arts
a
n
d
S
c
ien
c
e
,
Co
imb
a
to
re
.
He
h
o
l
d
s
a
NET.
Qu
a
li
fica
ti
o
n
in
Co
m
p
u
ter
S
c
ien
c
e
a
t
NTA
in
t
h
e
y
e
a
r
o
f
2
0
2
3
.
with
sp
e
c
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ti
o
n
i
n
d
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ta
m
in
in
g
with
ima
g
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p
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ss
a
n
d
a
lso
fu
z
z
y
lo
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ic
in
th
e
ima
g
e
a
n
a
l
y
sis.
His
re
se
a
rc
h
a
re
a
s
a
re
d
a
ta
m
in
in
g
,
in
t
h
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v
a
rio
u
s
field
s.
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h
a
s
p
u
b
l
ish
e
d
m
o
re
re
se
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rc
h
a
rti
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le
in
th
e
re
p
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te
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v
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s
n
a
ti
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n
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l
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n
d
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tern
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ti
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a
l
jo
u
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n
d
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lso
fil
e
d
t
h
e
p
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te
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t
s
in
th
e
sa
m
e
field
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
b
h
a
a
ra
th
i.
c
s@
ra
th
in
a
m
.
in
.
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