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n
y
p
an
d
em
ic
s
o
th
at
th
ey
ca
n
b
e
aler
t in
a
d
v
an
ce
.
W
e
s
tu
d
y
r
ele
v
an
t
p
ap
er
s
f
r
o
m
v
ar
i
o
u
s
n
ati
o
n
al
a
n
d
in
ter
n
atio
n
al
jo
u
r
n
als,
as
well
as
p
ar
ts
f
r
o
m
p
er
io
d
icals,
n
ewsp
ap
er
s
,
web
s
ites
,
an
d
co
n
f
er
e
n
ce
p
r
o
ce
ed
in
g
s
.
Af
ter
th
at,
we
s
p
ea
k
with
s
tu
d
en
ts
to
lear
n
ab
o
u
t th
e
b
e
n
ef
its
an
d
d
r
awb
a
ck
s
o
f
s
tu
d
y
in
g
d
u
r
in
g
C
OVI
D
-
1
9
.
T
h
u
s
,
elev
en
f
ea
tu
r
es h
av
e
b
ee
n
s
elec
ted
f
o
r
th
e
p
r
o
p
o
s
ed
m
o
d
el
to
s
o
lv
e
th
e
p
r
o
b
lem
.
W
e
co
llect
th
o
u
s
an
d
an
d
n
i
n
etee
n
(
1
,
0
1
9
)
d
ata
f
r
o
m
p
r
iv
ate
u
n
iv
er
s
ity
s
tu
d
e
n
ts
in
B
an
g
la
d
esh
to
b
u
ild
th
e
en
tire
d
ata
s
et.
I
n
th
e
p
r
ep
ar
ed
d
ata
s
e
t,
th
er
e
ar
e
m
an
y
u
n
n
ec
ess
ar
y
d
ata
as
well
as
m
is
s
in
g
d
ata.
T
h
en
we
p
r
ep
r
o
c
e
s
s
th
e
d
ata
s
et
b
y
u
s
in
g
two
o
p
er
atio
n
s
f
illi
n
g
in
th
e
m
is
s
in
g
v
alu
e
an
d
n
o
is
e
r
em
o
v
al.
W
e
em
p
lo
y
ed
th
r
ee
s
ep
ar
ate
class
ica
l
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
n
am
ely
lin
ea
r
r
eg
r
ess
io
n
,
d
ec
is
io
n
tr
ee
,
an
d
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
k
-
NN)
to
b
u
ild
th
e
p
r
o
p
o
s
ed
m
o
d
el.
W
e
p
er
f
o
r
m
ex
ten
s
iv
e
an
d
in
-
d
ep
t
h
m
o
d
elin
g
s
ep
ar
ately
f
o
r
th
e
th
r
ee
s
ep
ar
ate
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
.
T
h
e
m
o
d
el
with
lin
ea
r
r
eg
r
ess
io
n
p
r
o
d
u
ce
s
th
e
b
est r
esu
lts
.
I
n
th
is
p
ap
er
,
m
ac
h
in
e
lea
r
n
in
g
m
o
d
elin
g
is
d
o
n
e
u
s
in
g
o
f
f
-
th
e
-
r
ac
k
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
to
p
r
ed
ict
s
tu
d
en
t
p
er
f
o
r
m
a
n
ce
d
u
e
to
a
p
an
d
em
ic
lik
e
C
OVI
D
-
1
9
,
w
h
ich
e
x
ten
d
s
th
e
s
tate
-
of
-
th
e
-
ar
t k
n
o
wled
g
e
in
s
o
lv
in
g
a
n
o
v
el
r
ea
l
-
life
p
r
o
b
lem
.
I
n
ess
en
ce
,
th
e
p
r
in
c
ip
al
co
n
tr
ib
u
tio
n
s
o
f
th
is
r
esear
ch
wo
r
k
to
th
e
cu
r
r
en
t state
o
f
liter
atu
r
e
ar
e:
−
A
d
em
o
n
s
tr
atio
n
o
f
th
e
d
e
p
lo
y
m
en
t
o
f
a
m
ac
h
in
e
lea
r
n
in
g
m
o
d
el
b
ased
o
n
s
tate
-
of
-
t
h
e
-
ar
t
r
eg
r
ess
io
n
tech
n
iq
u
es to
s
o
lv
e
a
n
o
v
el
r
e
al
-
life
p
r
o
b
lem
.
−
T
h
e
f
ir
s
t
attem
p
t
t
o
s
o
lv
e
th
e
p
r
o
b
lem
o
f
th
e
p
r
e
d
ictio
n
o
f
th
e
ch
an
g
e
in
s
tu
d
y
p
er
f
o
r
m
an
ce
d
u
e
to
a
p
an
d
em
ic
lik
e
C
OVI
D
-
1
9
.
−
Sy
s
tem
atica
lly
f
o
r
m
atio
n
o
f
a
s
tr
o
n
g
b
ase
o
f
d
is
tin
g
u
is
h
in
g
f
ea
tu
r
es
f
o
r
r
eg
r
ess
io
n
to
s
u
cc
ess
f
u
lly
p
r
ed
ict
th
e
ch
an
g
e
in
s
tu
d
y
p
er
f
o
r
m
an
ce
d
u
e
to
a
p
an
d
em
ic
lik
e
C
OVI
D
-
19.
−
An
in
-
d
ep
th
ex
p
lo
r
atio
n
o
f
d
i
f
f
er
en
t
r
eg
r
ess
io
n
alg
o
r
ith
m
s
in
th
e
co
n
tex
t
o
f
th
e
p
r
e
d
ictio
n
o
f
ch
a
n
g
e
in
s
tu
d
y
p
er
f
o
r
m
an
ce
to
f
ac
ilit
ate
f
u
tu
r
e
r
esear
ch
.
T
h
e
r
em
ain
in
g
s
ec
tio
n
s
o
f
th
e
p
ap
er
ar
e
ar
r
a
n
g
ed
as
f
o
llo
w
s
:
s
ec
tio
n
2
d
escr
ib
es
th
e
r
ec
e
n
t
s
tate
o
f
s
o
lu
tio
n
s
to
a
d
d
r
ess
th
e
p
r
o
b
l
em
o
f
s
tu
d
en
ts
’
p
er
f
o
r
m
a
n
ce
p
r
ed
ictio
n
an
d
its
v
a
r
ian
ts
.
Se
ctio
n
3
d
escr
ib
es
th
e
p
r
o
p
o
s
ed
m
o
d
el
a
n
d
th
e
ar
c
h
itectu
r
e
o
f
th
e
p
r
o
p
o
s
ed
p
r
ed
ictio
n
s
y
s
tem
.
Sectio
n
4
d
is
cu
s
s
e
s
th
e
en
tire
r
esear
ch
m
eth
o
d
o
lo
g
y
.
Sectio
n
5
d
escr
ib
es
th
e
e
x
p
er
im
e
n
tatio
n
p
r
o
ce
d
u
r
e
i
n
d
etail,
wh
ic
h
is
f
o
llo
wed
b
y
th
e
an
aly
s
is
o
f
r
esu
lts
b
y
c
o
m
p
a
r
in
g
th
em
with
o
th
er
wo
r
k
s
i
n
s
ec
tio
n
6
.
Fin
ally
,
a
s
u
m
m
a
r
y
,
lim
itatio
n
s
,
an
d
f
u
tu
r
e
s
co
p
e
ar
e
p
r
esen
ted
in
s
ec
tio
n
7
.
2.
RE
L
AT
E
D
WO
RK
S
T
h
er
e
h
av
e
b
ee
n
s
o
m
e
n
o
tice
ab
le
r
esear
ch
wo
r
k
s
o
n
s
tu
d
e
n
t
p
er
f
o
r
m
an
ce
p
r
e
d
ictio
n
as
well
as
it
s
v
ar
iatio
n
s
.
So
m
e
wo
r
k
h
as
b
e
en
d
o
n
e
o
n
th
e
p
r
ed
ictio
n
o
f
s
tu
d
en
t
p
er
f
o
r
m
an
ce
ir
r
esp
ec
ti
v
e
o
f
C
OVI
D
-
1
9
o
r
an
y
o
th
er
p
a
n
d
em
ic.
Ag
ain
,
s
o
m
e
p
r
ed
ictiv
e
r
esear
ch
wo
r
k
s
h
av
e
b
ee
n
p
er
f
o
r
m
ed
ta
k
in
g
th
e
C
OVI
D
-
1
9
p
an
d
em
ic
in
t
o
ac
co
u
n
t.
T
ar
ik
et
a
l
.
[
2
]
en
co
u
r
ag
e
th
e
d
ev
elo
p
m
e
n
t
o
f
p
ar
ticu
lar
tale
n
ts
.
B
y
d
o
in
g
b
o
th
in
d
iv
i
d
u
al
an
d
g
r
o
u
p
in
ter
v
iews
with
s
tu
d
en
ts
,
t
h
e
ed
u
ca
tio
n
al
g
u
id
an
ce
co
u
n
s
elo
r
b
u
ild
s
th
e
ed
u
ca
tio
n
al
s
u
p
p
o
r
t
s
er
v
ice.
Ma
n
d
ay
am
et
a
l
.
[
3
]
u
tili
ze
d
th
e
C
OVI
D
-
1
9
tim
e
-
s
er
ies
d
ataset
an
d
two
s
u
p
er
v
is
ed
lear
n
in
g
m
o
d
els
to
p
r
o
v
id
e
f
u
tu
r
e
p
r
ed
ictio
n
s
.
T
o
ev
alu
ate
th
e
ef
f
ec
tiv
e
n
ess
o
f
p
r
ed
ictio
n
,
lin
ea
r
r
eg
r
ess
io
n
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
r
eg
r
ess
io
n
ar
e
co
m
p
ar
ed
.
Pra
k
ash
et
a
l
.
[
4
]
f
o
u
n
d
th
at
a
r
t
if
icial
in
tellig
en
ce
is
th
e
m
o
s
t
ef
f
ec
tiv
e
wea
p
o
n
ag
ain
s
t
th
e
C
OVI
D
-
1
9
is
s
u
e.
T
wo
o
f
th
e
k
ey
tech
n
o
l
o
g
ies
i
n
ar
tific
ial
in
tellig
en
ce
ar
e
m
a
ch
in
e
lear
n
i
n
g
a
n
d
d
ee
p
lear
n
in
g
.
R
ai
et
a
l
.
[
5
]
f
o
u
n
d
th
at
u
n
d
e
r
s
tan
d
in
g
lev
els
an
d
g
iv
i
n
g
s
tu
d
e
n
ts
th
e
le
ar
n
in
g
th
ey
r
eq
u
ir
e
th
r
o
u
g
h
m
ac
h
i
n
e
lear
n
in
g
wer
e
em
p
lo
y
ed
.
T
o
g
et
th
e
b
est
p
r
ed
ictio
n
r
esu
lts
,
th
e
r
an
d
o
m
f
o
r
est
alg
o
r
ith
m
is
ap
p
lied
.
Ho
s
s
ain
et
a
l
.
[
6
]
d
escr
ib
e
th
e
im
p
o
r
tan
t
in
itiativ
es
th
at
th
e
g
o
v
er
n
m
en
t
co
u
ld
im
p
lem
en
t
to
f
u
r
th
e
r
d
ev
elo
p
o
n
lin
e
e
d
u
ca
tio
n
a
n
d
d
is
tan
ce
lear
n
in
g
(
DL
)
in
B
an
g
lad
esh
.
I
n
2
0
0
6
,
d
u
r
i
n
g
a
tim
e
o
f
s
ig
n
if
ican
t
ch
an
g
es
to
B
an
g
lad
esh
'
s
in
f
r
astru
ctu
r
e,
th
is
s
tu
d
y
was
co
n
d
u
cte
d
o
n
t
h
e
im
p
ac
t
p
r
ed
ictio
n
o
f
o
n
lin
e
ed
u
ca
tio
n
.
Su
ltan
a
an
d
Kh
an
[
7
]
d
is
cu
s
s
h
o
w
s
atis
f
i
ed
s
tu
d
en
ts
ar
e
with
th
eir
o
n
lin
e
ed
u
ca
tio
n
.
T
ab
ass
u
m
et
a
l
.
[
8
]
p
r
o
v
i
d
e
i
n
f
o
r
m
atio
n
o
n
t
h
e
s
itu
atio
n
in
v
o
lv
in
g
e
d
u
ca
to
r
s
a
n
d
o
n
lin
e
lear
n
in
g
f
ac
ilit
ies.
On
lin
e
ed
u
ca
tio
n
h
as
b
ee
n
p
r
o
v
en
to
b
e
r
elate
d
t
o
h
ap
p
in
es
s
an
d
r
esid
en
ce
ar
ea
s
.
Few
s
tu
d
ies
h
av
e
also
b
ee
n
d
o
n
e
f
r
o
m
th
e
p
er
s
p
ec
tiv
e
o
f
th
e
in
s
tr
u
cto
r
.
Acc
o
r
d
in
g
t
o
R
y
b
ar
cz
y
k
an
d
Z
alak
ev
ici
u
te
[
9
]
,
t
h
e
r
e
ar
e
2
4
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
2
,
Ap
r
il
2
0
2
5
:
1
1
3
0
-
1
1
3
9
1132
m
o
d
els
b
u
ilt
u
s
in
g
m
ac
h
in
e
lear
n
in
g
th
at
p
r
ed
ict
th
e
air
q
u
ality
as
if
th
e
C
OVI
D
-
1
9
q
u
ar
an
tin
e
h
ad
n
e
v
er
ex
is
ted
an
d
ar
e
b
ased
o
n
th
e
g
r
ad
ien
t
b
o
o
s
tin
g
m
ac
h
in
e
alg
o
r
ith
m
.
T
h
is
r
esear
ch
s
u
g
g
ests
a
d
if
f
er
en
t
s
tr
ateg
y
th
at
in
v
o
lv
es
teac
h
in
g
a
m
ac
h
in
e
lear
n
in
g
s
y
s
tem
to
r
ec
o
g
n
ize
h
o
w
tim
e
an
d
wea
th
er
af
f
ec
t
air
p
o
llu
tio
n
.
I
n
r
eg
io
n
s
with
d
if
f
icu
lt
ter
r
ai
n
,
it
also
ex
h
ib
its
d
ec
r
ea
s
ed
p
er
f
o
r
m
a
n
ce
.
Dec
r
ea
s
es
in
p
ar
ticu
lar
m
atter
PM
2.
5
co
n
ce
n
tr
atio
n
s
v
ar
y
s
im
ilar
ly
d
ep
en
d
in
g
o
n
th
e
k
in
d
o
f
m
o
n
ito
r
in
g
lo
ca
tio
n
.
Ach
ar
y
a
a
n
d
Sin
h
a
[
1
0
]
u
s
ed
d
ec
is
io
n
tr
ee
s
an
d
o
th
er
m
ac
h
i
n
e
-
lear
n
in
g
ap
p
r
o
ac
h
es
to
p
r
e
d
ict
s
tu
d
en
ts
'
p
er
f
o
r
m
an
ce
.
Va
n
d
am
m
e
et
a
l
.
[
1
1
]
em
p
lo
y
ed
m
ac
h
i
n
e
lear
n
in
g
m
eth
o
d
s
s
u
ch
as
d
ec
is
io
n
tr
ee
s
,
lin
ea
r
d
is
cr
im
in
ate
a
n
aly
s
is
,
a
n
d
n
eu
r
al
n
etwo
r
k
s
to
p
r
e
d
ict
s
tu
d
en
ts
'
ea
r
ly
p
er
f
o
r
m
an
ce
.
C
o
r
tez
an
d
Sil
v
a
[
1
2
]
f
o
u
r
class
if
icatio
n
ap
p
r
o
a
ch
es
wer
e
u
s
ed
to
im
p
r
o
v
e
s
tu
d
en
t
p
er
f
o
r
m
a
n
ce
an
d
h
el
p
ac
h
iev
e
t
h
e
g
o
al:
d
ec
is
io
n
tr
ee
s
,
r
an
d
o
m
f
o
r
ests
,
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN)
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
.
Mia
et
a
l
.
[
1
3
]
u
s
in
g
a
v
ar
iety
o
f
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es,
it
is
p
o
s
s
ib
le
to
p
r
ed
ict
h
o
w
m
a
n
y
s
tu
d
en
ts
will
en
r
o
ll
in
a
s
em
ester
at
a
p
r
iv
ate
u
n
i
v
er
s
ity
.
T
h
ey
s
h
o
wed
t
h
at
SVM
h
ad
th
e
b
est ac
cu
r
ac
y
.
Ma
c
h
i
n
e
le
a
r
n
in
g
te
ch
n
i
q
u
es
a
r
e
u
t
ili
ze
d
in
f
iel
d
s
o
t
h
er
t
h
a
n
ed
u
c
ati
o
n
.
Ma
c
h
i
n
e
le
ar
n
i
n
g
t
ec
h
n
iq
u
es
ar
e
als
o
a
p
p
lie
d
i
n
t
h
e
m
e
d
i
ca
l
f
ie
ld
an
d
o
t
h
e
r
s
.
F
er
d
o
ws
y
e
t
a
l
.
[
1
4
]
w
o
r
k
e
d
o
n
t
h
e
r
is
k
o
f
o
b
es
it
y
p
r
e
d
i
cte
d
u
s
i
n
g
m
a
c
h
i
n
e
l
ea
r
n
in
g
m
et
h
o
d
s
.
T
h
ei
r
p
a
p
e
r
'
s
m
ai
n
a
r
g
u
m
e
n
t
is
t
h
at
i
n
d
iv
id
u
als
s
h
o
u
l
d
b
e
a
wa
r
e
o
f
th
ei
r
r
is
k
o
f
o
b
es
it
y
a
n
d
t
h
e
c
a
u
s
es
o
f
th
eir
o
b
es
it
y
.
A
r
i
f
e
t
a
l
.
[
1
5
]
a
ls
o
w
o
r
k
ed
o
n
t
h
e
r
is
k
o
f
b
e
c
o
m
in
g
d
r
u
g
o
r
alc
o
h
o
l
ad
d
i
cte
d
,
w
h
i
c
h
w
as
p
r
e
d
i
cte
d
u
s
in
g
a
v
ar
iet
y
o
f
m
ac
h
i
n
e
-
l
e
ar
n
i
n
g
ap
p
r
o
ac
h
es
.
T
o
d
ete
r
m
i
n
e
t
h
e
r
is
k
o
f
d
r
u
g
ad
d
i
cti
o
n
,
th
ey
em
p
l
o
y
e
d
n
i
n
e
m
a
c
h
i
n
e
l
ea
r
n
i
n
g
al
g
o
r
it
h
m
s
.
R
im
i
et
a
l
.
[
1
6
]
e
m
p
l
o
y
ed
a
m
a
c
h
i
n
e
l
ea
r
n
i
n
g
tec
h
n
i
q
u
e
t
o
f
o
r
ec
ast
d
e
n
t
al
d
is
e
ase
b
ase
d
o
n
h
o
w
a
n
ati
o
n
'
s
citi
ze
n
s
b
e
h
av
e
r
e
g
u
la
r
l
y
.
C
h
en
et
a
l
.
[
1
7
]
p
r
esen
ted
a
9
4
.
8
%
ac
cu
r
ate
m
ac
h
in
e
lear
n
in
g
m
et
h
o
d
f
o
r
p
r
ed
ictin
g
ch
r
o
n
ic
d
is
ea
s
e
o
u
tb
r
ea
k
s
u
s
in
g
h
o
s
p
ital
d
ata
an
d
a
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
-
b
ased
m
u
ltimo
d
al
alg
o
r
ith
m
.
Xu
et
a
l
.
[
1
8
]
d
escr
ib
e
d
a
u
n
iq
u
e
m
ac
h
in
e
lear
n
i
n
g
s
tr
ateg
y
f
o
r
p
r
ed
ictin
g
s
tu
d
e
n
t
p
er
f
o
r
m
a
n
ce
in
d
e
g
r
ee
p
r
o
g
r
a
m
s
th
at
ad
d
r
ess
es
b
ac
k
g
r
o
u
n
d
d
i
v
er
s
ity
,
c
o
u
r
s
e
in
f
o
r
m
ativ
e
n
ess
,
an
d
d
y
n
am
ic
p
r
o
g
r
ess
,
with
im
p
r
o
v
ed
r
esu
lts
o
n
a
Un
iv
er
s
ity
o
f
C
alif
o
r
n
ia
d
ataset.
Ab
d
u
llah
et
a
l
.
[
1
9
]
u
s
in
g
v
ar
iab
les
in
clu
d
in
g
m
a
k
e,
m
o
d
el,
ag
e,
m
ileag
e,
a
n
d
lo
ca
ti
o
n
,
r
an
d
o
m
f
o
r
est
m
ac
h
in
e
lear
n
i
n
g
p
r
e
d
icts
B
an
g
lad
esh
'
s
r
ec
o
n
d
itio
n
ed
ca
r
p
r
icin
g
with
9
9
.
5
9
%
ac
cu
r
ac
y
.
I
t
th
e
n
ev
alu
ates
m
ea
n
a
b
s
o
lu
te
er
r
o
r
(
MA
E
)
,
r
o
o
t
m
ea
n
s
q
u
a
r
e
er
r
o
r
(
R
MSE
)
,
m
ea
n
s
q
u
ar
e
d
er
r
o
r
(
MSE
)
,
an
d
R
2
,
s
u
p
p
o
r
tin
g
th
e
d
ec
is
io
n
s
o
f
s
tak
eh
o
ld
er
s
.
B
ar
h
m
i
an
d
E
l Fatn
i
[
2
0
]
p
r
esen
t f
o
u
r
h
y
b
r
id
m
o
d
els f
o
r
win
d
s
p
ee
d
f
o
r
ec
asti
n
g
th
at
co
m
b
in
e
n
eu
r
al
n
etwo
r
k
s
with
SVM.
T
h
e
n
eu
r
al
m
o
d
els
p
er
f
o
r
m
b
et
ter
th
an
th
e
SVM
m
o
d
els,
as
s
ee
n
b
y
th
eir
r
ed
u
ce
d
MSE
,
m
ea
n
e
r
r
o
r
(
ME
)
,
a
n
d
MA
E
v
alu
es.
Ab
d
u
llah
et
a
l
.
[
2
1
]
an
aly
s
is
o
f
th
e
ef
f
ec
ts
o
f
C
OVI
D
-
1
9
e
-
l
ea
r
n
in
g
o
n
s
tu
d
e
n
ts
at
J
o
r
d
a
n
Un
iv
er
s
ity
o
f
Scien
ce
an
d
T
ec
h
n
o
lo
g
y
(
J
UST)
s
h
o
ws
th
at
m
ac
h
in
e
lear
n
in
g
r
eg
r
ess
io
n
p
r
e
d
icts
ac
ad
em
ic
s
u
cc
ess
.
E
v
alu
atio
n
r
ev
ea
ls
th
e
lo
west
MA
E
an
d
R
MSE
f
o
r
ev
er
y
co
u
r
s
e.
Gr
ar
i
et
a
l
.
[
2
2
]
ev
alu
ated
th
em
u
s
in
g
th
e
R
2
s
co
r
e,
MA
E
,
an
d
MSE
to
ass
ess
th
eir
p
r
ed
ictiv
e
p
e
r
f
o
r
m
an
ce
.
C
o
n
s
id
er
in
g
th
e
d
is
cu
s
s
io
n
a
b
o
v
e
m
eticu
lo
u
s
ly
,
we
ca
n
c
o
m
e
u
p
with
th
e
co
n
cl
u
s
io
n
th
at
n
o
t
a
s
in
g
le
s
ig
n
if
ican
t
r
esear
ch
wo
r
k
h
as
b
ee
n
p
er
f
o
r
m
e
d
o
n
th
e
p
r
ed
ictio
n
o
f
s
tu
d
e
n
t
p
er
f
o
r
m
an
ce
o
win
g
to
th
e
C
OVI
D
-
1
9
p
an
d
e
m
ic.
T
h
is
r
ev
ea
ls
a
clea
r
r
esear
c
h
g
a
p
in
th
is
r
esp
ec
t.
T
h
is
is
q
u
ite
in
ter
esti
n
g
f
o
r
r
esear
ch
er
s
in
E
DM
.
3.
P
RO
P
O
SE
D
M
O
D
E
L
I
n
itially
,
it
is
ass
u
m
ed
th
at
a
u
s
er
m
u
s
t
an
s
wer
a
f
ew
q
u
esti
o
n
s
o
n
s
tu
d
en
t
p
er
f
o
r
m
a
n
ce
.
T
h
r
o
u
g
h
th
e
web
-
b
ased
ap
p
licatio
n
,
u
s
er
s
m
ay
q
u
ick
ly
p
r
o
v
i
d
e
an
s
wer
s
to
q
u
esti
o
n
s
.
T
h
e
u
s
er
'
s
i
n
f
o
r
m
atio
n
will
b
e
f
o
r
war
d
e
d
to
t
h
e
s
er
v
er
,
an
d
s
u
b
s
eq
u
en
tly
to
th
e
ex
p
e
r
t
s
y
s
tem
.
Data
will
b
e
co
llected
v
ia
th
e
ex
p
er
t
s
y
s
tem
in
ter
f
ac
e
af
ter
th
is
ex
p
er
t
s
y
s
tem
h
as
b
ee
n
p
r
o
ce
s
s
ed
.
T
h
e
o
b
tain
ed
d
ata
will
b
e
co
n
n
ec
ted
to
th
e
p
r
ev
io
u
s
ly
b
u
ilt
f
o
r
ec
asti
n
g
m
o
d
el
b
y
th
e
in
ter
f
ac
e
en
g
in
e
a
n
d
tr
an
s
m
itted
to
th
e
c
o
llected
k
n
o
wl
ed
g
e
-
b
ased
s
y
s
tem
.
Af
ter
th
e
p
r
e
d
ictio
n
p
r
o
ce
s
s
is
co
m
p
leted
,
it
will
r
etu
r
n
th
e
r
esu
lt
to
th
e
ex
p
er
t
in
ter
f
ac
e.
B
ased
o
n
th
e
in
p
u
t
p
r
o
v
id
e
d
,
th
e
r
esu
lts
will
b
e
g
en
er
ated
b
y
ap
p
ly
i
n
g
th
e
p
r
o
p
er
m
eth
o
d
s
to
th
e
r
ef
in
e
d
d
ata
.
T
h
e
u
s
er
will
th
en
g
et
th
e
o
u
tco
m
es
v
ia
th
e
s
er
v
er
,
p
r
o
v
id
i
n
g
in
s
ig
h
t
in
t
o
th
e
p
r
o
b
a
b
ilit
y
o
f
s
tu
d
y
p
e
r
f
o
r
m
a
n
ce
ch
a
n
g
es
d
u
e
t
o
C
OVI
D
-
1
9
.
T
h
e
en
tire
p
r
o
ce
s
s
f
o
r
p
r
ed
ictin
g
s
tu
d
y
p
er
f
o
r
m
an
ce
ch
an
g
e
d
u
e
t
o
C
OVI
D
-
1
9
is
d
ep
icted
in
Fig
u
r
e
1
in
te
r
m
s
o
f
s
y
s
tem
ar
ch
itectu
r
e.
3
.
1
.
Descript
io
n o
f
f
e
a
t
ures
T
h
e
d
ata
was
co
llected
f
r
o
m
t
h
e
s
tu
d
en
ts
th
r
o
u
g
h
v
ar
io
u
s
q
u
esti
o
n
s
.
T
h
ese
d
ata
ca
m
e
f
r
o
m
s
tu
d
en
ts
cu
r
r
en
tly
s
tu
d
y
in
g
in
d
if
f
er
en
t
s
em
ester
s
.
T
ab
le
s
1
an
d
2
s
h
o
w
th
e
attr
ib
u
tes,
i.e
.
f
ea
tu
r
es
o
f
th
e
d
ata
s
ets
alo
n
g
with
s
o
m
e
r
elev
an
t
in
f
o
r
m
atio
n
.
T
ab
le
1
s
h
o
ws
th
e
f
e
atu
r
es
u
tili
ze
d
with
o
u
t
th
e
C
O
VI
D
-
1
9
p
an
d
em
ic,
wh
er
ea
s
T
ab
le
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s
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as
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e
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ester
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Ma
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1133
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leep
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th
e
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D
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itu
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ti
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e
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en
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itu
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h
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s
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t
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OVI
D
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19
(
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b
ec
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s
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s
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3
.
2
.
P
er
f
o
r
m
a
nce
m
e
a
s
ures
R
eg
r
ess
io
n
alg
o
r
ith
m
s
p
r
ed
ict
o
u
tp
u
t
v
alu
es
b
ased
o
n
in
p
u
t
ch
ar
ac
ter
is
tics
f
r
o
m
d
ata
p
r
o
v
i
d
ed
to
th
e
s
y
s
tem
.
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eg
r
ess
io
n
alg
o
r
ith
m
s
u
tili
ze
th
e
ch
ar
ac
ter
is
tics
o
f
th
e
tr
ain
in
g
d
ata
to
g
en
er
ate
a
m
o
d
el
.
T
h
at
an
aly
s
ts
th
en
u
s
e
to
f
o
r
ec
ast n
ew
v
alu
es.
T
h
e
ac
tu
al
p
r
ed
ictio
n
p
r
o
ce
s
s
in
v
o
lv
es
f
ee
d
i
n
g
n
ew
d
ata
to
th
e
tr
ain
ed
m
o
d
el
a
n
d
u
s
in
g
th
e
m
o
d
el'
s
alg
o
r
ith
m
s
to
g
en
er
ate
p
r
e
d
i
ctio
n
s
.
I
n
th
is
ca
s
e,
two
ac
t
u
al
p
r
ed
ictio
n
s
h
av
e
b
ee
n
ex
tr
ac
ted
.
On
e
with
C
OVI
D
-
1
9
ac
tu
al
p
r
ed
ictio
n
m
etr
ic
(
)
an
d
th
e
o
th
e
r
with
o
u
t
C
OVI
D
-
1
9
ac
tu
al
p
r
ed
ictio
n
m
etr
ic
(
̅
)
.
T
h
en
th
e
d
i
f
f
er
en
ce
b
etwe
en
with
C
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D
-
1
9
ac
tu
al
p
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ic
tio
n
m
etr
ic
(
)
an
d
with
o
u
t
C
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D
-
1
9
ac
tu
al
p
r
ed
ictio
n
m
et
r
ic
(
̅
)
is
r
ep
r
esen
ted
b
y
AP
an
d
ca
lcu
lated
in
t
h
e
f
o
llo
win
g
way
:
=
−
̅
(
3
)
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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,
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2
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3
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9
1134
I
n
th
is
ca
s
e,
in
p
u
t
all
th
e
te
s
t
d
ata
in
to
th
e
m
o
d
el
an
d
g
et
t
h
e
o
u
tp
u
t.
Af
ter
th
at,
all
th
e
o
u
tp
u
ts
ar
e
s
u
m
s
.
Her
e,
two
m
o
d
el
p
r
ed
ic
tio
n
s
h
av
e
b
ee
n
e
x
tr
ac
ted
.
On
e
with
C
OVI
D
-
1
9
m
o
d
el
p
r
ed
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n
m
etr
ic
(
)
an
d
th
e
o
th
e
r
with
o
u
t
th
e
C
OVI
D
-
1
9
m
o
d
el
p
r
ed
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n
m
etr
ic
(
̅
)
.
T
h
e
n
th
e
d
if
f
e
r
en
c
e
b
etwe
en
with
C
OVI
D
-
1
9
m
o
d
el
p
r
ed
ictio
n
m
etr
ic
(
)
an
d
with
o
u
t
th
e
C
OVI
D
-
1
9
m
o
d
el
p
r
ed
ictio
n
m
etr
ic
(
̅
)
is
r
ep
r
esen
ted
b
y
MP
an
d
ca
lcu
l
ated
in
th
e
f
o
llo
win
g
way
:
=
−
̅
(
4
)
Fin
ally
,
th
e
in
ten
d
e
d
p
er
f
o
r
m
a
n
ce
m
etr
ic
(
PM
)
is
ca
lcu
lated
as sh
o
wn
in
(
5
)
.
=
{
(
1
−
∑
=
1
−
|
|
)
×
100%
(
∑
=
1
−
|
|
)
×
100%
,
(
5
)
wh
er
e
PM
is
o
n
e
o
f
th
e
f
iv
e
m
etr
ics ac
cu
r
ac
y
,
MA
E
,
R
2
,
MS
E
,
an
d
R
MSE
.
4.
RE
S
E
ARCH
M
E
T
H
O
DO
L
O
G
Y
T
h
ese
d
ata
ca
m
e
f
r
o
m
s
tu
d
e
n
ts
cu
r
r
en
tly
s
tu
d
y
in
g
in
d
if
f
er
en
t
s
em
ester
s
.
T
h
e
n
e
x
t
p
a
r
t
will
g
o
th
r
o
u
g
h
d
ata
co
llectio
n
an
d
p
r
e
-
p
r
o
ce
s
s
in
g
p
r
o
ce
d
u
r
es
in
d
etail.
T
h
e
d
ata
co
llected
was
in
tex
t
f
o
r
m
at
an
d
th
er
ef
o
r
e
u
n
s
u
itab
le
f
o
r
tr
ain
i
n
g
.
T
h
e
d
ata
is
s
u
b
s
eq
u
en
tly
p
r
esen
ted
in
a
m
ac
h
i
n
e
lear
n
i
n
g
alg
o
r
ith
m
u
s
in
g
th
e
d
ata
clea
n
in
g
ap
p
r
o
ac
h
.
I
n
th
r
ee
p
h
ases
,
we
em
p
lo
y
e
d
th
r
ee
m
ac
h
in
e
-
lear
n
in
g
alg
o
r
it
h
m
s
.
T
h
e
ac
cu
r
ac
y
was f
ir
s
t
ca
lcu
lated
o
n
th
e
p
r
o
ce
s
s
ed
d
ata
b
ef
o
r
e
th
e
p
r
in
cip
al
co
m
p
o
n
en
t a
n
aly
s
is
(
PC
A)
,
th
en
ag
ain
af
ter
th
e
PC
A,
an
d
f
in
ally
o
n
t
h
e
u
n
r
ef
in
ed
d
ata
u
s
in
g
th
e
alg
o
r
ith
m
.
W
e
h
av
e
an
aly
ze
d
t
h
e
class
if
ier
s
u
s
in
g
ac
c
u
r
ac
y
as
well
as
o
th
er
m
etr
ics,
in
clu
d
in
g
ac
cu
r
ac
y
,
MA
E
,
R
2
,
MSE
,
an
d
R
MSE
.
T
h
ese
wo
r
k
p
r
o
ce
s
s
es
ar
e
s
h
o
wn
in
th
e
f
lo
w
d
iag
r
am
in
Fig
u
r
e
2
.
T
h
e
m
eth
o
d
f
o
r
g
ettin
g
th
e
p
r
o
ce
s
s
d
ata
s
et
i
s
s
h
o
wn
in
Fig
u
r
e
2
,
s
tar
tin
g
with
d
ata
co
llectio
n
o
n
th
e
lef
t.
O
n
th
e
o
th
er
s
id
e,
th
e
th
r
ee
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
u
tili
ze
d
f
o
r
d
ec
is
io
n
m
ak
in
g
ar
e
lis
ted
at
th
e
b
o
tto
m
r
ig
h
t.
Fig
u
r
e
2
.
T
h
e
m
eth
o
d
o
lo
g
y
a
p
p
lied
f
o
r
p
r
ed
ictin
g
th
e
ch
an
g
e
in
s
tu
d
y
p
e
r
f
o
r
m
an
ce
d
u
e
to
C
OVI
D
-
19
4
.
1
.
Descript
io
n o
f
t
he
da
t
a
s
et
W
e
c
o
l
l
e
ct
e
d
d
a
ta
f
r
o
m
s
e
v
e
r
a
l
u
n
i
v
e
r
s
i
t
ie
s
i
n
B
a
n
g
l
a
d
es
h
.
W
e
c
o
l
l
e
ct
e
d
d
a
t
a
t
h
r
o
u
g
h
v
a
r
i
o
u
s
q
u
e
s
t
i
o
n
s
a
n
d
a
n
s
w
e
r
s
.
W
e
c
o
l
l
e
c
t
e
d
d
a
t
a
f
r
o
m
s
t
u
d
e
n
ts
o
f
s
o
m
e
p
r
i
v
at
e
u
n
i
v
e
r
s
i
ti
e
s
w
i
t
h
a
n
d
w
it
h
o
u
t
C
O
V
I
D
-
1
9
a
b
o
u
t
t
h
e
s
it
u
a
t
i
o
n
.
W
e
c
o
l
l
e
ct
e
d
d
a
t
a
b
a
s
e
d
o
n
t
h
e
t
w
el
v
e
f
e
a
t
u
r
es
as
s
h
o
w
n
i
n
T
a
b
l
e
s
1
a
n
d
2
.
T
h
e
o
u
t
p
u
t
f
e
a
t
u
r
e
i
s
t
h
e
SG
P
A
.
T
h
u
s
,
t
h
e
o
u
t
p
u
t
f
e
a
t
u
r
e
y
w
a
s
a
s
f
o
l
l
o
w
s
i
f
t
h
e
i
n
p
u
t
f
e
at
u
r
e
s
we
r
e
x
1
,
x
2
,
x
3
,
…
,
x
11
:
=
(
1
,
2
,
3
,
…
,
11
)
(
6
)
A
p
p
l
y
i
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
s
D
e
c
i
s
i
o
n
-
ma
k
i
n
g
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
Ma
ch
in
e
lea
r
n
in
g
b
a
s
ed
C
OV
I
D
-
1
9
s
tu
d
y
p
erfo
r
ma
n
ce
p
r
ed
ictio
n
(
Md
.
A
ta
u
r
R
a
h
ma
n
)
1135
W
e
c
o
l
l
ec
t
e
d
d
a
t
a
f
r
o
m
1
0
1
9
s
t
u
d
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n
t
s
i
n
t
o
t
a
l
f
r
o
m
s
o
m
e
p
r
i
v
a
t
e
u
n
i
v
e
r
s
i
ti
es
i
n
B
a
n
g
l
a
d
es
h
.
A
l
l
o
f
t
h
e
d
a
t
a
w
e
g
a
t
h
e
r
e
d
h
a
d
s
o
m
e
n
u
m
e
r
i
c
d
a
ta
,
m
i
s
s
i
n
g
d
a
t
a
,
n
o
is
y
v
a
l
u
e
s
,
an
d
s
o
m
e
t
e
x
t
d
a
t
a
,
w
h
i
c
h
n
e
e
d
ed
t
o
b
e
d
e
a
l
t
w
it
h
.
4
.
2
.
Da
t
a
p
re
pro
ce
s
s
ing
W
e
in
itially
ch
ec
k
ed
th
e
d
ata
s
et
f
o
r
m
is
s
in
g
v
alu
es
b
ef
o
r
e
p
er
f
o
r
m
in
g
lev
el
en
co
d
in
g
to
tr
an
s
f
o
r
m
th
e
tex
t
d
ata
to
n
u
m
er
ical.
T
h
e
im
p
u
ter
an
d
m
ed
ian
wer
e
u
s
ed
to
f
ill
in
th
e
m
is
s
in
g
d
ata.
T
h
e
d
ata
s
et'
s
n
o
is
y
v
alu
es
wer
e
th
en
e
v
alu
ated
a
n
d
s
o
r
ted
u
s
in
g
th
e
b
o
x
p
lo
t.
Af
ter
th
at,
th
e
n
o
r
m
alize
d
d
at
a
s
et
was
p
r
o
ce
s
s
ed
b
y
ap
p
l
y
in
g
al
g
o
r
ith
m
s
to
it.
T
h
is
p
r
o
ce
d
u
r
e
is
d
em
o
n
s
tr
ated
in
Fig
u
r
e
3
.
Fig
u
r
e
3
.
Pre
p
r
o
ce
s
s
in
g
s
tep
s
f
o
r
th
e
d
ata
th
at
was c
o
llected
4
.
3
.
Descript
io
n o
f
t
he
cla
s
if
ica
t
io
n m
o
dels
us
ed
A
s
im
p
le
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
e
is
k
n
o
wn
as
k
-
NN.
T
h
e
k
-
NN
alg
o
r
ith
m
ca
n
b
e
u
s
ed
to
r
eso
lv
e
r
eg
r
ess
io
n
an
d
class
if
icatio
n
is
s
u
es.
T
h
e
k
-
NN
alg
o
r
ith
m
c
h
ec
k
s
if
n
ea
r
b
y
n
eig
h
b
o
r
h
o
o
d
s
in
clu
d
e
co
m
p
a
r
ab
le
o
b
jects
[
2
3
]
.
T
h
e
f
o
llo
win
g
(
7
)
is
u
s
ed
to
ca
lcu
late
th
e
Min
k
o
wsk
i d
is
tan
ce
(
D
M
)
b
etwe
en
th
e
q
u
er
y
p
o
i
n
ts
an
d
o
t
h
er
p
o
in
ts
.
D
M
=
(
∑
(
|
−
|
)
=
1
)
1
(
7
)
R
eg
r
ess
io
n
an
d
class
if
icatio
n
ar
e
b
o
th
p
o
s
s
ib
le
u
s
in
g
lin
ea
r
f
u
n
ctio
n
s
.
As
d
em
o
n
s
tr
ated
,
a
lin
ea
r
class
if
ier
is
p
r
o
d
u
ce
d
b
y
p
ass
in
g
a
lin
ea
r
f
u
n
ctio
n
'
s
o
u
tp
u
t
th
r
o
u
g
h
a
th
r
esh
o
ld
f
u
n
ctio
n
[
2
3
]
.
T
h
e
lin
ea
r
r
eg
r
ess
io
n
f
o
r
m
u
la
p
r
o
v
id
ed
is
g
iv
e
n
in
(
8
)
.
Y
=
a
+
bX
(
8
)
A
d
ec
is
io
n
tr
ee
r
ep
r
esen
ts
a
f
u
n
ctio
n
t
h
at
"d
ec
id
es"
a
s
in
g
le
o
u
tp
u
t
v
alu
e
f
r
o
m
a
v
ec
to
r
o
f
i
n
p
u
t
attr
ib
u
te
v
alu
es.
Dis
cr
ete
o
r
c
o
n
tin
u
o
u
s
v
alu
es
ca
n
b
e
u
s
ed
f
o
r
in
p
u
t
an
d
o
u
tp
u
t
[
2
3
]
.
T
h
e
d
ec
is
io
n
tr
ee
is
a
class
ical
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
u
s
ed
to
m
o
d
el
t
h
e
c
o
n
n
ec
tio
n
b
etwe
en
d
ep
en
d
en
t
v
a
r
iab
les
an
d
s
ev
er
al
in
d
ep
en
d
en
t
v
ar
iab
les.
Fo
r
n
o
w,
we
will
f
o
cu
s
o
n
s
itu
atio
n
s
wh
er
e
th
e
in
p
u
ts
h
av
e
d
is
cr
ete
v
alu
es
an
d
th
e
r
esu
lt
h
as
ex
ac
tly
two
p
o
te
n
tial
v
alu
es
.
T
h
is
is
a
B
o
o
lean
class
if
icatio
n
,
wh
er
e
ea
ch
in
p
u
t
ex
am
p
le
will
b
e
class
if
ied
as tr
u
e
o
r
f
alse
[
2
3
]
.
Fig
u
r
e
4
p
r
o
v
i
d
es th
e
d
ec
is
io
n
tr
ee
alg
o
r
ith
m
'
s
wo
r
k
in
g
p
r
i
n
cip
le.
Fig
u
r
e
4
.
T
h
e
wo
r
k
in
g
p
r
in
cip
le
o
f
th
e
d
ec
is
io
n
tr
ee
class
if
icatio
n
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
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tell
,
Vo
l.
14
,
No
.
2
,
Ap
r
il
2
0
2
5
:
1
1
3
0
-
1
1
3
9
1136
5.
E
XP
RE
RI
M
E
NT
A
L
E
V
AL
UATI
O
N
W
e
d
iv
id
ed
t
h
e
en
tire
s
am
p
le
d
ata
s
et,
as
p
er
th
e
h
o
ld
o
u
t
ap
p
r
o
ac
h
[
2
4
]
,
in
to
tr
ain
in
g
a
n
d
test
in
g
p
ar
ts
.
W
e
ap
p
ly
to
d
eter
m
in
e
h
o
w
m
u
c
h
o
f
th
e
d
ata
is
s
et
asid
e
f
o
r
tr
ai
n
in
g
a
n
d
h
o
w
m
u
ch
f
o
r
v
ali
d
atio
n
.
T
h
ese
d
ata
ar
e
co
llected
f
r
o
m
s
tu
d
en
ts
s
tu
d
y
in
g
in
d
if
f
e
r
en
t
s
em
ester
s
.
T
h
e
d
ata
o
f
1
0
1
9
s
tu
d
en
ts
ar
e
co
llected
to
cr
ea
te
a
d
ata
s
et.
Acc
o
r
d
in
g
to
th
e
h
o
ld
o
u
t
ap
p
r
o
ac
h
,
we
g
o
t
th
e
f
o
llo
win
g
r
at
io
s
:
tr
ain
6
0
%
an
d
test
4
0
%;
tr
ain
7
0
%
an
d
test
3
0
%;
tr
ain
8
0
%
an
d
test
2
0
%;
tr
ain
9
0
%
a
n
d
test
1
0
%.
W
e
ex
am
in
e
all
r
atio
s
an
d
f
i
n
d
t
h
at,
o
f
th
ese
r
atio
s
,
tr
ain
in
g
at
8
0
%
an
d
test
in
g
at
2
0
%
g
a
v
e
b
etter
p
e
r
f
o
r
m
a
n
ce
.
W
e
d
iv
i
d
ed
t
h
e
to
tal
am
o
u
n
t
o
f
d
ata
we
o
b
tai
n
ed
in
two
,
u
s
in
g
8
0
%
f
o
r
tr
a
in
in
g
a
n
d
2
0
%
f
o
r
test
in
g
.
I
n
p
r
ev
i
o
u
s
s
ec
tio
n
s
,
d
etailed
d
is
cu
s
s
io
n
s
o
f
d
ata
co
llectin
g
an
d
p
r
e
-
p
r
o
ce
s
s
in
g
m
eth
o
d
s
h
av
e
b
ee
n
p
r
o
v
id
ed
.
C
las
s
ical
m
ac
h
in
e
lear
n
in
g
class
if
icatio
n
m
o
d
els’
p
er
f
o
r
m
an
ce
is
in
f
lu
en
ce
d
b
y
th
e
v
alu
es
o
f
s
ev
er
al
p
a
r
am
e
ter
s
.
T
ab
le
3
s
h
o
ws
th
e
v
alu
es o
f
s
o
m
e
o
f
t
h
e
th
r
ee
alg
o
r
ith
m
p
ar
am
eter
s
u
s
ed
in
o
u
r
r
esear
c
h
.
T
ab
le
3
.
Sp
ec
if
icatio
n
s
o
f
th
e
alg
o
r
ith
m
s
u
s
ed
in
d
etail
C
l
a
s
si
f
i
c
a
t
i
o
n
m
o
d
e
l
S
p
e
c
i
f
i
c
a
t
i
o
n
k
-
NN
N
u
mb
e
r
o
f
n
e
i
g
h
b
o
r
s =
1
W
e
i
g
h
t
f
u
n
c
t
i
o
n
u
se
d
f
o
r
p
r
e
d
i
c
t
i
o
n
,
w
(
x
)
=
c
,
w
h
e
r
e
c
i
s
a
c
o
n
s
t
a
n
t
D
i
st
a
n
c
e
m
e
t
r
i
c
:
M
i
n
k
o
w
sk
i
d
i
s
t
a
n
c
e
=
(
∑
(
|
−
|
)
=
1
)
1
D
e
c
i
s
i
o
n
t
r
e
e
D
i
st
r
i
b
u
t
i
o
n
m
e
a
s
u
r
e
:
G
i
n
i
i
n
d
e
x
M
a
x
i
m
u
m
d
e
p
t
h
=
0
M
i
n
i
m
u
m
sam
p
l
e
s s
p
l
i
t
=
2
Li
n
e
a
r
r
e
g
r
e
s
si
o
n
F
i
t
i
n
t
e
r
c
e
p
t
=
1
Th
e
n
u
m
b
e
r
o
f
i
n
p
u
t
f
e
a
t
u
r
e
s
=
0
C
o
p
y
st
a
t
e
o
f
t
h
e
i
n
p
u
t
f
e
a
t
u
r
e
s
=
T
r
u
e
T
ab
le
4
d
escr
ib
es
th
e
p
er
f
o
r
m
an
ce
o
f
ea
ch
alg
o
r
ith
m
.
T
h
e
alg
o
r
ith
m
s
'
ac
cu
r
ac
y
,
MA
E
,
R
2
,
MSE
,
an
d
R
MSE
p
er
f
o
r
m
a
n
ce
ar
e
ev
alu
ated
.
W
e
wo
u
ld
c
h
o
o
s
e
th
e
m
o
s
t
ef
f
ec
tiv
e
al
g
o
r
ith
m
f
o
r
o
u
r
is
s
u
e
ar
ea
b
ased
o
n
th
e
way
th
e
a
p
p
r
o
a
ch
es
wo
r
k
ed
.
W
e
f
o
u
n
d
th
at
th
e
m
ax
im
u
m
ac
cu
r
ac
y
o
f
l
in
ea
r
r
eg
r
ess
io
n
is
9
9
.
8
4
%.
Acc
o
r
d
in
g
ly
,
th
e
b
e
s
t
r
esu
lts
in
ter
m
s
o
f
MA
E
,
R
2
,
MSE
,
an
d
R
MSE
wer
e
lik
ewise
s
u
cc
ess
f
u
lly
p
r
o
d
u
ce
d
th
r
o
u
g
h
lin
ea
r
r
e
g
r
ess
io
n
.
Go
o
d
p
r
e
d
ictio
n
s
m
ay
b
e
o
b
tain
ed
th
r
o
u
g
h
lin
ea
r
r
eg
r
ess
io
n
.
T
h
e
ch
a
r
ac
ter
is
tics
o
f
lin
ea
r
r
eg
r
ess
io
n
m
o
d
els
ar
e
well
-
k
n
o
wn
an
d
s
im
p
le
to
tr
ain
.
Go
o
d
r
esu
lts
ar
e
s
h
o
w
n
b
y
its
p
er
f
o
r
m
an
ce
o
n
o
u
r
d
at
aset.
L
in
ea
r
r
eg
r
ess
io
n
wo
r
k
s
well
f
o
r
p
r
e
d
ictio
n
m
o
d
els
th
a
t
h
av
e
a
n
u
m
er
ical
o
b
jectiv
e.
T
ab
le
4
.
Per
f
o
r
m
an
ce
ev
alu
ati
o
n
o
f
th
e
class
if
icatio
n
m
o
d
els
u
s
ed
C
l
a
s
si
f
i
c
a
t
i
o
n
M
o
d
e
l
A
c
c
u
r
a
c
y
(
%)
M
A
E
(
%)
R
2
(
%)
M
S
E
(
%)
R
M
S
E
(
%)
k
-
NN
9
5
.
3
5
0
.
0
7
0
.
2
1
0
.
0
6
0
.
0
8
D
e
c
i
s
i
o
n
t
r
e
e
9
8
.
7
8
0
.
0
2
0
.
0
1
0
.
0
2
0
.
0
2
Li
n
e
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r
r
e
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r
e
s
si
o
n
9
9
.
8
4
0
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0
1
0
.
0
7
0
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0
1
0
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1
B
ased
o
n
th
e
m
eth
o
d
'
s
p
er
f
o
r
m
an
ce
,
we
will
ch
o
o
s
e
th
e
alg
o
r
ith
m
th
at
is
m
o
s
t
s
u
itab
le
f
o
r
o
u
r
p
r
o
b
lem
ar
ea
.
I
n
co
m
p
a
r
is
o
n
to
all
r
eg
r
ess
io
n
s
,
it
h
as
b
ee
n
f
o
u
n
d
th
at
lin
ea
r
r
eg
r
ess
io
n
p
er
f
o
r
m
ed
th
e
b
est.
I
n
co
n
cl
u
s
io
n
,
th
e
lin
ea
r
r
eg
r
e
s
s
io
n
alg
o
r
ith
m
was u
s
ed
to
id
en
tify
th
e
m
o
d
el
th
at
p
er
f
o
r
m
e
d
b
etter
o
v
er
all.
6.
CO
M
P
ARA
T
I
V
E
ANA
L
YS
I
S O
F
RE
SU
L
T
S
W
e
n
ee
d
to
co
m
p
a
r
e
o
u
r
wo
r
k
with
s
ev
er
al
cu
r
r
e
n
t
an
d
p
er
tin
en
t
r
esear
ch
ef
f
o
r
ts
to
ev
alu
ate
th
e
q
u
ality
o
f
o
u
r
p
r
o
p
o
s
ed
r
esear
ch
p
er
f
o
r
m
a
n
ce
p
r
e
d
ictio
n
s
y
s
tem
b
ased
o
n
C
OVI
D
-
1
9
.
W
e
s
h
o
u
ld
k
ee
p
i
n
m
in
d
th
at
o
u
r
ef
f
o
r
ts
to
ev
alu
a
te
p
er
f
o
r
m
a
n
ce
will
b
e
s
ig
n
if
ican
tly
in
f
lu
en
ce
d
b
y
th
e
ass
u
m
p
tio
n
s
r
esear
ch
er
s
m
ak
e
wh
en
co
llectin
g
d
ata
an
d
p
r
esen
tin
g
th
e
r
esu
lts
o
f
th
eir
r
esear
ch
af
ter
p
r
o
ce
s
s
in
g
th
ese
s
am
p
les.
W
e
attem
p
ted
to
co
m
p
a
r
e
o
u
r
wo
r
k
to
t
h
at
o
f
o
th
er
s
u
s
in
g
s
ev
er
al
cr
iter
ia,
in
clu
d
in
g
s
am
p
le
s
ize,
f
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e
s
et
s
ize,
class
if
icat
io
n
m
o
d
el,
a
n
d
ac
cu
r
ac
y
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a
b
le
5
d
is
p
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s
a
co
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p
ar
is
o
n
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etwe
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o
u
r
wo
r
k
an
d
o
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e
r
wo
r
k
s
.
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k
ash
[
4
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wo
r
k
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d
atasets
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%.
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5
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lin
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eg
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r
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r
f
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in
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a
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k
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le
ac
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f
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9
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8
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ed
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in
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h
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ir
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f
9
9
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8
4
%
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m
s
to
b
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r
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g
o
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d
.
T
h
e
f
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r
es
u
s
ed
in
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r
s
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g
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ested
m
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d
p
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ely
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tr
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d
is
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im
in
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g
in
f
o
r
m
ati
o
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to
p
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th
e
ch
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g
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in
s
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p
e
r
f
o
r
m
an
ce
d
u
e
to
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D
-
1
9
,
wh
ich
is
co
m
p
u
tatio
n
ally
ea
s
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to
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lcu
late
an
d
en
a
b
les
u
s
to
o
b
tain
v
e
r
y
h
ig
h
ac
cu
r
ac
y
.
Mo
s
t
o
f
th
e
o
th
er
wo
r
k
s
a
r
e
n
o
t
p
ar
ticu
lar
ly
s
im
ilar
to
o
u
r
wo
r
k
.
T
h
er
ef
o
r
e,
th
e
m
er
i
ts
o
f
o
u
r
tech
n
iq
u
e
s
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o
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ld
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t b
e
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en
ly
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o
m
p
a
r
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d
to
o
th
e
r
wo
r
k
s
.
7.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
WO
RK
I
n
th
is
p
ap
er
,
we
p
r
o
v
id
e
ex
te
n
s
iv
e
ex
p
lo
r
ato
r
y
wo
r
k
to
p
r
e
d
ict
ch
an
g
es in
r
esear
ch
p
er
f
o
r
m
an
ce
d
u
e
to
C
OVI
D
-
1
9
u
s
in
g
v
ar
io
u
s
ty
p
es
o
f
m
ac
h
in
e
-
lear
n
in
g
ap
p
r
o
ac
h
es.
Firstl
y
,
with
t
h
e
s
tu
d
en
t'
s
in
p
u
t,
we
cr
ea
ted
th
e
f
ea
tu
r
e
s
et
th
at
s
er
v
es
as
th
e
f
o
u
n
d
atio
n
f
o
r
t
h
is
p
r
ed
ictiv
e
wo
r
k
.
Pre
p
r
o
ce
s
s
in
g
h
as
b
ee
n
d
o
n
e
af
ter
th
e
d
ata
h
av
e
b
ee
n
p
r
o
p
er
ly
ev
alu
ated
.
T
h
r
ee
d
if
f
er
en
t
class
if
ier
s
h
av
e
b
ee
n
u
s
ed
to
p
r
ed
ict
th
e
ch
a
n
g
e
in
s
tu
d
y
p
er
f
o
r
m
a
n
ce
ca
u
s
ed
b
y
C
OVI
D
-
1
9
.
Fo
u
r
o
b
v
i
o
u
s
p
er
f
o
r
m
an
ce
m
etr
ics
h
av
e
b
ee
n
u
s
ed
to
ev
alu
ate
th
e
b
en
ef
its
o
f
th
o
s
e
class
if
ier
s
.
T
h
e
r
elativ
e
q
u
alities
o
f
th
e
r
esu
lts
wer
e
th
en
ass
ess
ed
b
y
co
m
p
ar
in
g
th
em
t
o
th
o
s
e
o
f
r
elate
d
r
esear
ch
.
Ou
r
p
r
o
p
o
s
ed
m
et
h
o
d
h
as
a
v
er
y
g
o
o
d
an
d
p
r
o
m
is
in
g
ac
cu
r
ac
y
o
f
9
9
.
8
4
%
u
s
in
g
lin
ea
r
r
eg
r
ess
io
n
.
E
d
u
ca
tio
n
al
in
s
titu
tio
n
s
,
s
tu
d
en
ts
,
an
d
th
eir
g
u
ar
d
ian
s
ca
n
u
s
e
th
is
ap
p
licatio
n
to
g
et
aler
t
an
d
ad
o
p
t
p
r
ec
au
tio
n
ar
y
m
ea
s
u
r
es
d
u
e
to
th
e
h
in
d
r
an
ce
o
f
a
n
y
p
an
d
em
ic
lik
e
C
OVI
D
-
1
9
.
A
p
o
s
s
ib
le
f
u
tu
r
e
ch
allen
g
e
is
to
wo
r
k
with
a
m
u
ch
g
r
ea
ter
d
ataset
o
f
s
tu
d
en
t
s
to
co
v
er
a
b
r
o
ad
er
s
p
ec
tr
u
m
o
f
s
tu
d
e
n
t
lear
n
in
g
p
er
f
o
r
m
an
ce
r
e
q
u
ir
ed
in
B
an
g
lad
esh
.
RE
F
E
R
E
NC
E
S
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1
]
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.
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mer
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3
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6
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M
.
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.
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h
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l
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s
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f
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l
i
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d
u
c
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t
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st
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r
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7
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l
t
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n
d
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.
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h
a
n
,
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I
n
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s’
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ter
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n
d
En
g
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n
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g
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t
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n
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e
p
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n
d
e
n
t
Un
i
v
e
rsity
,
Ba
n
g
lad
e
sh
.
Earlier,
h
e
wa
s
a
n
As
so
c
iate
P
ro
fe
ss
o
r
a
t
th
e
De
p
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rtme
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t
o
f
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m
p
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ter
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c
ien
c
e
a
n
d
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g
i
n
e
e
rin
g
a
t
Da
ffo
d
il
In
ter
n
a
ti
o
n
a
l
Un
iv
e
rsity
.
He
is
m
u
c
h
fo
n
d
o
f
re
se
a
rc
h
.
He
h
a
s
p
u
b
li
sh
e
d
m
a
n
y
a
rti
c
les
i
n
in
ter
n
a
ti
o
n
a
l
j
o
u
r
n
a
ls
a
n
d
c
o
n
fe
re
n
c
e
p
r
o
c
e
e
d
in
g
s
.
His
re
se
a
rc
h
in
tere
st
is
in
a
rti
ficia
l
i
n
telli
g
e
n
c
e
,
e
sp
e
c
ially
m
a
c
h
in
e
lea
rn
in
g
,
a
rti
ficia
l
n
e
u
ra
l
n
e
two
r
k
s,
c
o
m
p
u
ter
v
isio
n
,
a
n
d
n
a
t
u
ra
l
lan
g
u
a
g
e
p
ro
c
e
ss
in
g
.
Re
c
e
n
tl
y
,
h
e
h
a
s
sta
rted
wo
rk
i
n
g
o
n
Io
T
to
o
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
d
.
tare
k
h
a
b
ib
@
y
a
h
o
o
.
c
o
m
.
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