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er
s
a
d
ata
-
d
r
iv
e
n
m
eth
o
d
o
l
o
g
y
to
id
e
n
tify
an
d
m
itig
ate
th
e
f
ac
to
r
s
in
f
lu
en
cin
g
ac
ad
em
ic
r
esu
lts
.
T
h
e
n
o
v
elty
lies
in
in
teg
r
atin
g
d
em
o
g
r
a
p
h
ic
c
h
ar
ac
ter
is
tics
an
d
ass
ig
n
m
en
t
-
s
p
ec
if
ic
p
er
f
o
r
m
an
ce
to
d
ev
elo
p
a
h
ig
h
ly
a
cc
u
r
ate
p
r
e
d
ictiv
e
m
o
d
el.
2.
R
E
V
I
E
W
O
F
L
I
T
E
R
A
T
U
R
E
T
h
e
s
tu
d
y
co
m
p
ar
es
th
e
ac
cu
r
ac
y
an
d
p
er
f
o
r
m
an
ce
o
f
f
iv
e
t
ec
h
n
iq
u
es
b
ef
o
r
e
p
r
o
p
o
s
in
g
a
m
u
lti
-
class
p
r
ed
ictio
n
m
o
d
el
f
o
r
im
b
ala
n
ce
d
m
u
lti
-
class
d
atasets
.
Sy
n
t
h
etic
m
in
o
r
ity
o
v
er
s
am
p
lin
g
a
n
d
f
ea
t
u
r
e
s
elec
tio
n
in
cr
ea
s
e
u
n
b
ala
n
ce
d
d
ataset
ac
cu
r
ac
y
c
o
m
p
ar
e
d
to
th
e
f
i
v
e
m
eth
o
d
s
[
1
]
.
Ma
ch
in
e
lea
r
n
in
g
m
o
d
els
f
r
o
m
W
o
lk
ite
Un
iv
er
s
ity
d
ata
p
r
ed
i
ct
s
tu
d
en
t
p
er
f
o
r
m
an
ce
a
n
d
i
d
en
tify
lo
w
p
er
f
o
r
m
er
s
[
2
]
.
An
o
th
er
s
tu
d
y
f
in
d
s
th
at
o
n
lin
e
lear
n
in
g
,
ev
alu
a
tio
n
g
r
a
d
es,
an
d
ac
ad
em
ic
em
o
tio
n
s
p
r
ed
ict
ac
ad
em
ic
p
er
f
o
r
m
an
ce
[
3
]
.
R
esam
p
lin
g
s
tr
ateg
ies
d
em
o
n
s
tr
ate
th
at
r
an
d
o
m
f
o
r
est
(
R
F)
an
d
SVM
-
SMOT
E
im
p
r
o
v
e
u
n
b
alan
ce
d
d
ataset
p
er
f
o
r
m
an
ce
,
with
RF
b
ein
g
th
e
b
est
[
4
]
.
An
o
th
e
r
I
n
d
ian
r
esear
ch
s
u
g
g
ests
n
eu
r
al
n
et
wo
r
k
s
m
ay
p
r
ed
ict
ed
u
ca
tio
n
al
s
u
cc
ess
d
esp
ite
d
if
f
icu
lties
[
5
]
.
An
o
th
er
ar
ticle
f
in
d
s
th
at
co
u
r
s
e
g
r
ad
es
p
r
e
d
ict
g
r
ad
u
atio
n
b
etter
th
an
GPAs
,
an
d
s
p
ar
s
e
lin
ea
r
an
d
lo
w
-
r
an
k
m
atr
ix
f
ac
t
o
r
i
za
tio
n
in
cr
ea
s
es
ac
cu
r
ac
y
[
6
]
,
[
7
]
.
Po
s
tg
r
ad
u
ate
r
esear
ch
s
h
o
wed
ANN
p
r
e
d
icts
C
GP
A
well
[
8
]
.
An
o
th
er
s
tu
d
y
u
s
es
R
F
an
d
m
u
lti
-
class
p
r
ed
ictio
n
m
o
d
els
to
ass
es
s
f
ir
s
t
-
s
em
ester
g
r
ad
es
[
9
]
,
[
1
0
]
.
An
o
t
h
er
s
tu
d
y
f
o
u
n
d
t
h
at
s
u
p
p
o
r
t
v
ec
to
r
r
eg
r
ess
io
n
(
SVR
)
b
est
p
r
ed
icts
Nig
er
ian
s
tu
d
en
ts
’
C
GPA,
with
ag
e
an
d
o
th
er
ch
ar
ac
ter
is
ti
cs
less
im
p
o
r
tan
t
[
1
1
]
.
Acc
o
r
d
in
g
to
p
u
b
licatio
n
s
[
1
2
]
-
[
1
4
]
,
m
ac
h
i
n
e
lear
n
i
n
g
is
b
ein
g
em
p
l
o
y
ed
f
o
r
ea
r
ly
s
tu
d
en
t
p
er
f
o
r
m
an
ce
i
n
ter
v
en
tio
n
.
A
s
tu
d
y
d
em
o
n
s
tr
ates
Naiv
e
B
ay
es
ca
teg
o
r
izatio
n
ac
cu
r
ately
p
r
e
d
i
cts
s
tu
d
en
t
ac
h
iev
em
en
t
[
1
5
]
.
An
o
th
er
r
esear
c
h
f
o
u
n
d
th
at
ANN
p
r
ed
icts
co
m
p
u
ter
s
cien
ce
s
tu
d
en
t
r
esu
lts
b
est
[
1
6
]
.
R
esea
r
ch
s
h
o
ws
th
a
t
m
ac
h
in
e
lear
n
in
g
m
o
d
els
p
r
ed
ict
GPA
an
d
wo
r
k
lo
ad
with
ap
p
r
o
x
im
atel
y
7
5
%
ac
cu
r
ac
y
[
1
7
]
.
As
a
r
esu
lt
o
f
im
p
r
o
v
in
g
p
r
ed
ictio
n
ac
cu
r
ac
y
an
d
p
r
o
v
id
in
g
ass
is
tan
ce
to
s
tu
d
en
ts
with
p
o
o
r
p
er
f
o
r
m
a
n
ce
,
m
ac
h
in
e
lear
n
in
g
(
ML
)
tech
n
o
lo
g
ies
h
av
e
a
n
im
p
ac
t
o
n
ed
u
ca
tio
n
[
1
8
]
.
T
h
e
an
a
ly
s
is
r
ev
ea
ls
th
at
lo
g
is
t
ic
r
eg
r
ess
io
n
ac
cu
r
ately
p
r
ed
icts
h
ig
h
er
e
d
u
ca
tio
n
s
tu
d
en
t
ac
h
ie
v
em
en
t
[
1
9
]
.
T
h
e
en
r
ich
ed
p
lan
t
g
r
o
wth
o
p
tim
is
ed
ANN
m
eth
o
d
p
r
ed
icts
ac
ad
em
ic
ac
h
iev
em
e
n
t
b
etter
[
2
0
]
.
An
o
th
er
r
esear
ch
co
m
p
ar
es
ML
w
o
r
k
lo
a
d
DB
MS
s
to
co
m
m
o
n
f
r
am
ewo
r
k
s
[
2
1
]
.
A
p
a
p
er
s
h
o
ws
th
at
th
e
RF
c
lass
if
ier
p
r
e
d
icts
co
m
p
u
te
r
s
cien
ce
s
tu
d
en
t
p
er
f
o
r
m
an
ce
9
4
%
ac
cu
r
ately
[
2
2
]
.
An
o
th
er
r
es
ea
r
ch
f
o
u
n
d
cr
itical
C
OVI
D
-
1
9
s
tu
d
en
t
r
eten
tio
n
c
h
ar
ac
t
er
is
tics
u
s
in
g
d
ata
m
in
in
g
[
2
3
]
.
His
to
r
ical
d
ata
s
u
g
g
ests
it
im
p
r
o
v
es
s
ch
o
o
lin
g
[
2
4
]
.
C
o
lleg
e
alg
eb
r
a
s
u
cc
ess
r
esear
ch
em
p
lo
y
s
k
-
n
ea
r
est
n
eig
h
b
o
u
r
s
(
KNN)
an
d
d
ec
is
io
n
t
r
ee
s
w
ith
8
5
%
ac
cu
r
ac
y
[
2
5
]
.
An
o
th
e
r
s
tu
d
y
r
ev
ea
ls
th
at
R
F
an
d
en
s
em
b
le
m
o
d
els
b
est
p
r
ed
ic
t
s
tu
d
en
t
ac
h
iev
e
m
en
t
[
2
6
]
.
An
o
th
er
p
ap
er
s
h
o
wed
R
B
M
ac
cu
r
ately
p
r
ed
icts
elec
tr
ical
en
g
in
ee
r
in
g
g
r
ad
es
[
2
7
]
.
Fin
ally
,
a
n
ar
ticle
d
i
f
f
er
en
tiates
GPA
-
in
f
lu
en
cin
g
co
m
p
o
n
en
ts
in
to
p
s
y
ch
o
lo
g
ical,
s
o
cial,
an
d
s
tu
d
y
elem
en
ts
[
2
8
]
,
wh
ile
an
o
th
er
s
tu
d
y
p
r
o
p
o
s
es
a
m
o
d
el
th
a
t
p
r
ed
icts
f
in
al
test
g
r
ad
es with
7
0
-
7
5
% a
cc
u
r
ac
y
f
r
o
m
m
i
d
ter
m
r
esu
lts
[
2
9
].
3.
RE
S
E
ARCH
M
E
T
H
O
DO
L
O
G
Y
A
s
u
r
v
ey
(
G
-
f
o
r
m
s
)
is
u
s
ed
to
g
ath
er
d
ata
o
n
a
co
lle
g
e
s
tu
d
en
t
’
s
ac
tiv
ities
in
o
r
d
er
to
f
o
r
ec
ast
th
eir
SGPA.
T
h
e
d
ataset
in
clu
d
es
m
an
y
p
a
r
am
eter
s
p
er
tain
i
n
g
t
o
s
tu
d
en
t
ac
tiv
ities
,
in
clu
d
in
g
s
tu
d
y
tim
e,
wak
e
-
u
p
tim
e,
p
r
ev
io
u
s
y
ea
r
’
s
s
em
ester
s
co
r
e,
s
leep
tim
e,
o
v
er
-
th
e
-
to
p
(
OT
T
)
u
s
e,
T
V
v
ie
win
g
,
s
o
cial
m
ed
ia
en
g
ag
em
e
n
t,
o
n
lin
e
g
am
i
n
g
,
p
ar
ticip
atio
n
in
s
o
cial
ev
en
ts
,
tim
e
s
p
en
t
in
c
o
lleg
e,
an
d
in
v
o
lv
em
en
t
in
s
p
o
r
ts
.
T
h
ese
elem
en
ts
h
av
e
a
r
o
le
in
d
ec
id
in
g
th
e
s
tu
d
e
n
ts
’
ac
tio
n
s
an
d
en
h
an
cin
g
th
eir
f
u
tu
r
e
SGPA.
T
h
e
d
ataset
h
as
a
co
m
b
in
atio
n
o
f
ca
teg
o
r
y
an
d
n
u
m
er
ical
d
ata.
Ou
t
o
f
th
ese
elev
en
co
lu
m
n
s
,
th
er
e
is
o
n
e
th
at
is
in
d
ep
en
d
en
t
an
d
o
n
e
th
a
t
is
d
ep
en
d
en
t.
I
n
th
is
co
n
te
x
t,
o
u
r
aim
is
to
d
eter
m
in
e
th
e
tar
g
et
v
ar
iab
le,
wh
ich
is
th
e
SEM
s
co
r
e
f
r
o
m
th
e
p
r
ev
io
u
s
y
ea
r
.
T
h
is
s
co
r
e
is
in
f
lu
e
n
ce
d
b
y
th
e
i
n
d
ep
e
n
d
en
t
f
ac
t
o
r
s
in
clu
d
ed
in
th
e
d
ataset.
T
h
e
d
ataset
h
as
5
0
0
r
o
ws
an
d
1
2
co
lu
m
n
s
.
T
h
e
o
b
j
ec
tiv
e
o
f
th
e
p
r
o
ject
is
to
f
o
r
e
ca
s
t
s
tu
d
en
ts
’
f
u
tu
r
e
SGPA
b
y
u
s
in
g
p
r
ev
io
u
s
s
em
ester
d
ata,
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
,
an
d
s
tu
d
y
h
o
u
r
s
.
A
p
r
o
g
n
o
s
tic
m
o
d
el
is
d
ev
elo
p
e
d
,
p
r
o
v
id
in
g
tailo
r
ed
s
u
g
g
esti
o
n
s
an
d
an
in
tu
itiv
e
in
ter
f
ac
e.
T
h
e
m
o
d
el
u
n
d
er
g
o
es
co
n
tin
u
al
u
p
d
ates,
p
r
o
m
o
tin
g
th
e
estab
lis
h
m
en
t
o
f
attain
ab
le
o
b
jectiv
es
an
d
p
r
o
v
id
in
g
to
o
ls
to
e
n
h
an
ce
s
tu
d
y
h
a
b
its
an
d
tim
e
m
an
ag
em
en
t a
b
ilit
ies.
Ma
ch
in
e
lear
n
in
g
alg
o
r
ith
m
m
o
d
els in
clu
d
e:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
:
66
-
73
68
a)
L
in
ea
r
r
eg
r
ess
io
n
:
l
in
ea
r
r
eg
r
e
s
s
io
n
is
a
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
u
s
ed
to
p
r
e
d
ict
th
e
tar
g
et
v
ar
iab
le
,
an
d
it
b
u
ild
s
th
e
co
r
r
elatio
n
b
etwe
en
a
tar
g
et
v
ar
iab
le
an
d
o
n
e
o
r
m
o
r
e
f
ea
tu
r
e
v
a
r
iab
les,
co
n
s
id
er
ed
t
o
b
e
lin
ea
r
.
I
t tr
ies to
i
d
en
tify
t
h
e
b
est
-
f
itti
n
g
lin
e
th
r
o
u
g
h
th
e
d
ata
p
o
in
ts
:
=
0
+
1
∗
(
1
)
w
h
er
e
y
i
s
t
he
d
e
pe
n
d
en
t
v
ar
ia
bl
e
an
d
t
h
e
i
n
de
p
e
nd
e
nt
v
a
r
ia
bl
e
x.
A
s
s
u
m
i
n
g
a
l
in
e
ar
r
el
a
ti
on
s
hi
p,
li
n
e
ar
r
e
gr
e
s
s
i
o
n
i
s
a
m
a
ch
in
e
-
l
ear
ni
n
g
a
pp
r
o
a
c
h
t
h
at
pr
e
di
ct
s
th
e
t
ar
ge
t
v
ar
i
a
bl
e
b
y
co
n
s
tr
u
ct
i
n
g
a
li
n
k
b
et
w
e
e
n
a
d
e
p
e
nd
e
nt
va
r
i
ab
l
e
a
n
d
o
n
e
or
m
or
e
i
nd
e
p
en
d
e
nt
v
ar
i
a
b
l
e
s
.
I
t
s
e
ek
s
to
i
d
e
nt
i
f
y
th
e
li
n
e
t
h
at
f
it
s
th
e
d
at
a
p
o
i
nt
s
t
h
e
b
e
s
t.
b)
KNN
:
i
t
ca
l
cu
la
t
e
s
di
s
ta
n
c
e
s
be
t
w
ee
n
a
n
e
w
d
at
a
po
in
t
a
nd
ot
h
er
tr
ai
ni
n
g
p
oi
nt
s
u
s
i
n
g
m
e
tr
i
c
s
li
k
e
E
u
cl
id
e
a
n
d
i
s
t
an
c
e,
M
a
n
h
at
t
a
n
di
s
t
an
c
e,
o
r
Mi
n
k
o
w
s
ki
d
is
ta
n
c
e.
T
h
e
n
u
m
be
r
of
n
ea
r
e
s
t
n
ei
gh
b
or
s
is
c
h
o
s
e
n,
a
nd
t
h
e
m
aj
or
i
ty
cl
a
s
s
i
s
a
s
s
i
g
n
e
d
to
th
e
n
e
w
da
t
a
p
oi
nt
.
T
h
e
al
g
or
it
h
m
al
s
o
u
s
e
s
th
e
a
ve
r
a
g
e
va
lu
e
of
t
h
e
s
e
n
ei
g
h
bo
r
s
f
o
r
r
e
gr
e
s
s
io
n
pr
ed
i
ct
i
o
n
s
:
=
(
1
−
11
)
2
+
(
2
−
12
)
2
+
⋯
+
(
−
1
)
2
(
2
)
w
h
er
e
:
1
,
2
,
3
,
4
…
.
.
,
xn
ar
e
t
h
e
f
e
at
ur
e
s
of
t
h
e
n
e
w
d
at
a
po
in
t
yo
u
w
a
nt
t
o
cl
a
s
s
i
f
y
or
pr
ed
i
ct
11
,
12
,
13
,
14
….
1
ar
e
t
h
e
f
e
at
ur
e
s
of
a
d
at
a
po
in
t
i
n
t
h
e
tr
ai
ni
n
g
s
et
t
h
a
t
w
e
ar
e
c
o
m
pa
r
i
ng
.
D
i
s
t
h
e
E
u
cl
id
e
a
n
d
i
s
t
a
nc
e
b
et
w
e
e
n
t
h
e
s
e
t
w
o
d
at
a
p
oi
nt
s
,
ca
lc
u
la
te
d
u
s
i
ng
th
ei
r
r
e
s
p
e
ct
i
v
e
f
e
a
tu
r
e
s
.
c)
S
V
R
:
i
t
i
s
a
l
e
ar
ni
ng
m
et
ho
d
u
s
e
d
b
y
m
a
c
hi
n
e
s
f
or
r
e
gr
e
s
s
i
o
n
pr
ob
le
m
s
.
S
V
R
’
s
o
bj
e
ct
iv
e
i
s
t
o
f
i
n
d
th
e
b
e
s
t
h
y
p
er
p
l
an
e
a
n
d
cl
a
s
s
if
y
t
he
da
ta
po
in
t
s
.
W
e
ha
v
e
c
on
s
i
de
r
e
d
o
ur
d
at
a
s
et
’
s
ke
r
n
el
,
w
hi
c
h
i
s
a
li
n
e
ar
f
un
ct
i
on
,
a
nd
p
er
f
or
m
e
d
t
he
m
o
d
el
f
i
tt
i
n
g
w
it
h
t
h
e
f
i
v
e
t
e
s
t
r
at
io
s
:
=
+
(
3
)
w
h
er
e
y
i
s
t
he
pr
ed
ic
t
ed
va
l
u
e,
x
i
s
th
e
i
np
ut
f
e
at
ur
e
v
e
c
to
r
,
w
i
s
th
e
w
ei
g
ht
v
e
ct
or
t
h
at
d
et
er
m
in
e
s
t
h
e
di
r
e
ct
i
o
n
of
t
he
h
y
pe
r
p
la
n
e,
a
nd
b
i
s
t
h
e
bi
a
s
t
er
m
.
d)
B
a
g
gi
ng
:
i
t
cr
ea
tes d
iv
er
s
e
s
u
b
s
ets o
f
th
e
tr
ain
in
g
d
ata
th
r
o
u
g
h
b
o
o
ts
tr
ap
p
in
g
an
d
tr
ai
n
s
a
b
ase
m
o
d
el
o
n
ea
ch
s
u
b
s
et.
B
ag
g
in
g
is
ef
f
e
ctiv
e
f
o
r
r
ed
u
ci
n
g
o
v
er
f
itti
n
g
an
d
i
n
cr
ea
s
in
g
th
e
s
tab
ilit
y
o
f
th
e
m
o
d
el,
esp
ec
ially
in
h
ig
h
-
v
ar
ian
ce
al
g
o
r
ith
m
s
lik
e
d
ec
is
io
n
tr
ee
s
.
e)
D
e
ci
s
i
o
n
tr
e
e
:
t
h
e
d
ec
is
io
n
tr
ee
is
a
ty
p
e
o
f
b
ag
g
in
g
wh
er
e
it
is
u
s
ed
to
h
an
d
le
n
o
n
-
d
ata
s
et
ef
f
ec
tiv
ely
an
d
f
alls
u
n
d
er
n
o
n
-
p
ar
am
etr
i
c
s
u
p
er
v
is
ed
lear
n
i
n
g
.
f)
R
a
n
do
m
f
or
e
s
t
:
t
h
i
s
as
s
em
b
les
s
ev
er
al
d
ec
is
io
n
tr
ee
s
an
d
m
er
g
es
th
eir
f
o
r
ec
asts
b
y
ch
o
o
s
in
g
ar
b
itra
r
y
s
u
b
s
ets
o
f
in
f
o
r
m
atio
n
a
n
d
c
h
ar
ac
ter
is
tics
f
o
r
ev
er
y
tr
ee
,
i
t
p
r
o
d
u
ce
s
d
iv
er
s
ity
.
I
t
th
en
co
m
b
in
es
an
d
p
r
o
v
id
es a
f
in
al
p
r
e
d
ictio
n
th
at
is
m
o
r
e
ac
cu
r
ate
a
n
d
less
p
r
o
n
e
to
o
v
er
f
itti
n
g
.
g)
B
o
o
s
ti
n
g
:
i
t
’
s
a
m
et
ho
d
u
s
e
d
i
n
m
ac
hi
n
e
l
e
ar
ni
n
g
to
r
e
du
c
e
er
r
or
s
i
n
pr
ed
i
ct
i
v
e
d
a
ta
a
na
ly
s
i
s
.
I
t
cr
e
at
e
s
a
n
en
s
e
m
bl
e
m
o
d
el
b
y
c
o
m
b
in
g
s
e
v
er
al
w
e
a
k
d
e
c
is
io
n
tr
ee
s
s
eq
u
e
nt
i
al
ly
a
n
d
a
s
s
i
g
ni
n
g
t
h
e
th
e
o
u
tp
ut
of
i
n
di
vi
d
ua
l
tr
e
e
s
.
h)
XG
-
B
oo
s
t:
XG
-
B
o
o
s
t
i
s
de
s
ig
n
ed
t
o
be
v
er
y
ef
f
i
ci
en
t
a
nd
ca
n
h
a
nd
l
e
d
if
f
er
e
nt
t
y
p
e
s
of
d
at
a.
I
t
in
cl
u
de
s
t
e
ch
ni
q
u
e
s
li
k
e
r
e
g
ul
ar
i
z
at
io
n,
w
hi
c
h
he
lp
s
t
o
p
r
e
v
en
t
o
v
er
f
i
tt
i
n
g,
an
d
p
ar
al
l
el
pr
o
ce
s
s
i
ng
,
w
hi
c
h
m
ak
e
s
i
t
f
a
s
t
er
t
o
tr
ai
n
an
d
m
a
k
e
pr
e
di
ct
io
n
s
i)
Ada
-
B
o
o
s
t
:
Ada
-
B
o
o
s
t
a
d
a
pt
s
an
d
t
r
i
e
s
t
o
s
e
lf
-
co
r
r
e
c
t
,
it
’
s
n
ot
a
s
s
en
s
it
i
v
e
a
s
ot
h
er
b
o
o
s
ti
n
g
al
g
or
it
h
m
s
.
C
o
m
bi
n
e
s
m
ul
ti
pl
e
w
ea
k
l
e
ar
ne
r
s
in
to
a
r
o
b
u
s
t
m
od
el
.
I
t
a
s
s
i
g
n
s
gr
e
at
er
w
ei
g
ht
t
o
d
at
a
po
i
nt
s
w
i
th
l
ar
g
er
er
r
or
s
,
f
o
c
u
s
i
n
g
o
n
ar
ea
s
w
h
er
e
t
h
e
m
o
d
el
p
er
f
or
m
s
p
o
or
l
y.
j)
G
r
a
di
e
nt
-
B
o
o
s
t
:
G
r
ad
ie
nt
-
B
oo
s
t
i
s
a
s
e
q
ue
nt
i
al
tr
ai
ni
n
g
te
ch
ni
q
u
e.
I
t
f
o
c
u
s
e
s
o
n
m
in
i
m
i
zi
ng
t
h
e
lo
s
s
f
un
ct
io
n,
gr
a
d
u
al
l
y
r
ef
i
ni
n
g
pr
ed
i
ct
i
o
n
s
w
it
h
e
a
ch
it
e
r
a
ti
on
.
B
y
c
o
m
bi
ni
n
g
th
e
s
tr
e
ng
th
s
of
m
ul
t
ip
l
e
w
e
a
k
l
e
ar
ne
r
s
,
s
u
c
h
a
s
d
e
ci
s
io
n
tr
e
e
s
,
G
r
a
di
en
t
B
o
o
s
ti
n
g
pr
o
d
u
c
e
s
a
p
o
w
er
f
ul
r
e
gr
e
s
s
i
on
m
o
de
l
c
a
p
a
bl
e
of
c
a
pt
ur
i
n
g
c
o
m
pl
ex
r
e
la
ti
on
s
h
ip
s
in
t
h
e
d
at
a.
k)
R
id
g
e
r
e
gr
e
s
s
i
o
n
:
r
i
d
ge
r
e
gr
e
s
s
i
o
n
i
s
on
e
of
th
e
r
e
g
ul
ar
iz
at
io
n
t
ec
h
ni
q
u
e
s
u
s
ed
t
o
o
v
er
co
m
e
t
he
pr
ob
le
m
of
o
v
er
f
it
ti
ng
.
I
n
r
i
d
g
e
r
e
gr
e
s
s
io
n,
w
e
a
d
d
a
p
e
n
a
lt
y
te
r
m
e
q
u
al
t
o
t
he
c
o
ef
f
i
ci
e
nt
s
’
s
qu
ar
e.
T
h
e
m
at
h
e
m
at
i
c
al
e
qu
at
io
n
s
of
r
i
d
g
e
r
e
gr
e
s
s
i
o
n
ar
e
a
s
:
∑
(
−
̂
)
2
=
1
+
∑
2
=
1
(
4
)
w
h
er
e
:
-
n
i
s
t
h
e
n
u
m
b
er
of
d
at
a
p
oi
nt
s
,
p
i
s
t
he
n
u
m
b
er
of
f
e
at
ur
e
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
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f
&
C
o
m
m
u
n
T
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h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Ma
p
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ca
d
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u
tco
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s
tu
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t ro
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t
h
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pr
ed
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t
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ta
r
g
et
v
al
u
e
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i
s
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ul
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r
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et
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on
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ol
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t
h
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tr
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of
r
eg
ul
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iz
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t
h
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co
ef
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t
he
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r
eg
r
e
s
s
i
on
m
o
d
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l)
L
a
s
s
o
r
e
gr
e
s
s
i
o
n
:
l
a
s
s
o
ef
f
e
ct
i
v
el
y
s
hr
i
n
k
s
l
e
s
s
i
m
po
r
t
an
t
f
e
at
ur
e
co
ef
f
i
ci
en
t
s
to
z
er
o,
al
lo
w
i
ng
f
o
r
f
e
at
ur
e
s
e
le
c
ti
on
an
d
m
i
ti
g
at
i
n
g
ov
er
f
it
t
in
g.
L
a
s
s
o
r
e
gr
e
s
s
i
o
n
m
i
ni
m
iz
e
s
t
he
s
u
m
o
f
s
q
u
ar
ed
r
e
s
i
d
u
al
s
pl
u
s
th
e
s
u
m
of
the
a
b
s
o
lu
te
va
lu
e
s
of
th
e
c
o
ef
f
i
ci
e
nt
s
m
u
lt
i
pl
i
ed
b
y
a
r
e
gu
l
ar
i
z
at
i
o
n
p
ar
a
m
e
t
er
.
∑
(
−
̂
)
2
=
1
+
∑
|
|
=
1
(
5
)
W
h
e
r
e
|
|
r
ep
r
es
en
t
s
th
e
ab
so
l
u
te
v
a
lu
e o
f
th
e co
ef
f
ic
i
en
t
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
NS
T
h
e
da
ta
m
us
t
be
ev
a
lu
a
te
d
af
te
r
th
e
c
le
an
s
in
g
pr
oc
ed
ur
e
ha
s
be
en
co
m
pl
e
te
d
,
an
d
it
is
ne
cess
a
r
y
t
o
in
ve
s
ti
ga
te
th
e
in
t
r
a
-
an
d
i
nt
e
r
r
el
a
ti
on
s
h
ip
s
be
tw
een
t
he
da
ta
s
e
t
’
s
ch
a
r
ac
te
r
ist
ic
s
an
d
th
e
ob
je
ct
iv
e
va
r
ia
b
le
.
W
e
ha
ve
in
s
ta
ll
ed
l
i
br
a
r
ie
s
s
uc
h
as
S
eab
or
n
an
d
Matp
lo
t
li
b
f
r
om
th
e
P
yt
ho
n
p
r
og
r
am
m
in
g
l
an
gu
ag
e
to
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am
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ne
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d
d
isp
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y
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e
va
r
ia
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le
s
.
F
ig
ur
e
1
i
l
lu
s
t
r
at
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th
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to
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l
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m
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of
m
in
u
te
s
th
at
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ur
s
tu
d
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ts
s
pe
n
t
na
pp
i
ng
.
T
he
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o
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ow
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g
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ne
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t
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s
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t
19
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n
ts
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le
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42
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1
42
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or
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t
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s
.
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i
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e
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e
nt
s
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h
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t
m
ap
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al
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pe
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t
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bl
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s
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ev
e
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er
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e
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t
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or
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el
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e
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.
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or
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a
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ce,
w
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k
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u
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e
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s
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t
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m
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i
s
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s
it
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or
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el
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te
d
w
it
h
ti
m
e
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pe
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t
in
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ol
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e
g
e.
A
v
al
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of
1
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nd
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ca
t
e
s
a
p
er
f
e
ct
c
or
r
el
at
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p
o
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v
e
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n
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lu
e
s
r
ef
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or
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el
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u
r
e
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.
No
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p
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f
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o
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ith
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s
:
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a
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e
1
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ar
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,
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r
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ig
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th
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s
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f
te
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gh
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s
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nc
e
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at
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,
m
ak
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it
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k.
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a
bl
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i
s
a
pr
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di
ct
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d
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o
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th
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m
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e
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e
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te
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.
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s
in
g
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s
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pr
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at
a
f
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T
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bl
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2,
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e
pr
ed
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ct
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e
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f
or
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gr
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a
n
d
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h
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u
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t
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o
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ud
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e,
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nd
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h
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bl
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t
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e
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l
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ti
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al
l
o
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at
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t
o
di
f
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er
e
n
t
a
ct
iv
it
ie
s
.
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h
e
s
e
p
ar
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m
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er
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w
er
e
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m
p
l
oy
e
d
a
s
in
p
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o
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p
r
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ct
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v
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m
od
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l
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te
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nt
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s
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s
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7
3
f
or
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d
8.
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f
or
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3.
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h
e
s
e
pr
e
d
i
ct
i
o
n
s
pr
o
vi
d
e
a
n
a
c
c
ur
at
e
f
or
e
c
a
s
t
of
t
h
e
s
tu
d
e
nt
s
’
po
t
en
ti
al
pe
r
f
or
m
a
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e
i
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f
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ur
e
s
e
m
e
s
te
r
s
b
a
s
e
d
on
t
he
ir
c
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r
en
t
ti
m
e
m
a
n
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nt
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er
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.
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g
t
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d
a
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de
m
i
c
p
e
r
f
or
m
a
n
ce,
s
t
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nt
s
c
a
n
ga
in
v
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u
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le
i
n
s
i
gh
t
s
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nt
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h
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w
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h
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s
m
o
d
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m
p
a
ct
o
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ir
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.
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m
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gr
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de
s
in
s
u
b
s
eq
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e
nt
s
e
m
e
s
t
er
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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2
2
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I
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Vo
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15
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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… (
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71
5.
CO
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RE
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73
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:
sv
th
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jas
win
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su
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m
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c
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m
.
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