I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
.
10
, N
o.
1
,
M
a
r
c
h
202
1
, pp.
35
~
42
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
10
.i
1
.pp
35
-
42
35
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
ia
e
s
c
or
e
.c
om
Pr
e
d
i
c
t
i
on
of
t
h
e
e
f
f
e
c
t
s of
e
n
vi
r
on
m
e
n
t
al
f
ac
t
or
s t
o
w
ar
d
s
C
O
V
ID
-
19 ou
t
b
r
e
ak
u
si
n
g A
I
-
b
ase
d
m
od
e
l
s
K
h
al
id
M
ah
m
ou
d
1
, H
at
ic
e
B
e
b
iş
2
, A
. G
. U
s
m
an
3
, A
.N
. S
al
ih
u
4
, M
.S
G
aya
5
, U
m
ar
F
ar
ou
k
D
al
h
at
6
, R
.A
.
A
b
d
u
lk
ad
ir
7
, M
.B
. Ji
b
r
il
8
, S
.I
. A
b
b
a
9
1
Department of
P
ediatrics
Facult
y of Medi
cine, Near E
ast Uni
versity,
99138,
Nicosia,
Turkis
h Republ
ic of Nort
hern
Cyprus
2
,4
Department of Publ
ic health Nursing,
Faculty of Nursi
ng, Near East Universit
y, 99138 Nicosi
a, Turkish Republ
ic of
Northern Cyprus
3
De
partment of Analytical Chemistry, Faculty of Pharmacy, Ne
ar East University, 99138 Nicosia, Turkish Republic of
Northern Cyprus
5,7,8
Department of Electrical En
gineering, Kano Universi
ty of Science and Technol
ogy, Wudil, Ni
geria
6
Departments of Fami
ly medi
cine, Mu
rtala Muh
ammad Hos
pital K
ano, Nig
eria
9
Department of Civi
l Engineering Baze Uni
versity, Abuja, Ni
geria
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
J
un 27, 2020
R
e
vi
s
e
d D
e
c
20, 2020
A
c
c
e
pt
e
d J
a
n 20, 2021
The
need
for
elucidating
the
effects
of
environmental
factors
in
the
determination
of
the
novel
corona
virus
(COVID
-
19)
is
very
vital.
Thi
s
study
is
a
methodological
study
to
compare
three
different
test
models
(1.
A
rtificial
neural
networks
(ANN),
2.
Adaptive
neuro
fuzzy
inference
sy
stem
(A
NFIS),
3.
A
linear
classical
model
(MLR))
used
to
determine
the
relati
onship
between
COVID
-
19
spread
and
environmental
factors
(temperature,
humidity
and
wind).
These
data
were
obtained
from
the
studies
(
Pirouz,
Haghshenas,
Haghshenas,
&
Piro,
2020)
with
confirmed
COVID
-
19
patients
in
Wuhan,
China,
using
temperature,
humidity
and
wind
as
the
independent
variables.
The
measured
and
the
predicted
results
were
checked
ba
sed
on
three
different
performance
indices;
Root
mean
square
error
(
RMSE),
determina
tion
coefficient
(R
2
)
and
correlation
coefficient
(R).
The
results
showed
that
ANFIS
and
ANN
are
more
promising
over
the
classica
l
MLR
models
having
an
average
R
-
values
of
0.90
in
both
calibrati
on
and
verification
stages.
The
findings
indicated
that
ANFIS
outperforme
d
MLR
and
ANN.
In
addition,
their
performance
skills
boosted
up
to
25%
a
nd
9%
respectively
based
on
the
determinati
on
coefficient
for
the
predic
tion
of
confirmed
COVID
-
19
cases
in
Wuhan
city
of
China.
Overall,
the
results
depict
the
reliability
and
ability
of
AI
-
based
models
(ANFIS
and
A
NN)
for
the
simulation
of
COVID
-
19
using
the
effects
of
various
environ
mental
variables.
K
e
y
w
o
r
d
s
:
A
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
m
ode
ls
C
O
V
I
D
-
19
E
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
M
ul
ti
pl
e
l
in
e
a
r
r
e
gr
e
s
s
io
n
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
S
.I
. A
bba
D
e
pa
r
tm
e
nt
of
P
hys
ic
a
l
P
la
nni
ng D
e
ve
lo
pm
e
nt
M
a
it
a
m
a
S
ul
e
U
ni
ve
r
s
it
y
K
a
no S
ta
te
, N
ig
e
r
ia
E
m
a
il
:
s
a
n
ii
s
a
a
bba
86@
gm
a
il
.c
om
1.
I
N
T
R
O
D
U
C
T
I
O
N
T
he
nove
l
c
or
ona
vi
r
us
(
S
A
R
S
-
C
oV
I
I
)
a
ls
o
known
a
s
C
O
V
I
D
-
19
is
a
w
r
a
ppe
d
R
N
A
vi
r
us
th
a
t
is
s
pr
e
a
d
e
xt
e
n
s
iv
e
ly
a
m
ong
pe
opl
e
,
b
ir
ds
a
nd
di
f
f
e
r
e
nt
m
a
m
m
a
ls
,
it
c
a
us
e
s
r
e
s
pi
r
a
to
r
y,
e
nt
e
r
ic
,
he
pa
ti
c
,
a
nd
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
10
, N
o.
1
,
M
a
r
c
h
20
2
1
:
35
–
42
36
ne
ur
ol
ogi
c
il
ln
e
s
s
.
C
O
V
I
D
-
19
is
a
n
e
m
e
r
gi
ng
a
nd
r
e
-
e
m
e
r
gi
ng
pa
n
de
m
ic
in
f
e
c
ti
ou
s
di
s
e
a
s
e
,
w
hi
c
h
i
s
of
gl
oba
l
c
onc
e
r
n
to
th
e
publ
ic
he
a
lt
h
[
1
]
.
C
O
V
I
D
-
19
f
ir
s
t
a
ppe
a
r
e
d
in
D
e
c
e
m
be
r
2019
in
W
uha
n
c
it
y
of
C
hi
n
a
r
e
por
ti
ng
th
e
f
ir
s
t
4
c
a
s
e
s
in
th
e
w
or
ld
,
a
nd
th
is
m
ig
ht
be
c
onn
e
c
te
d
to
th
e
S
out
he
r
n
C
hi
na
(
H
ua
na
n)
s
e
a
f
ood
w
hol
e
s
a
le
m
a
r
ke
t.
M
or
e
ove
r
,
in
di
vi
dua
l
tr
a
ns
m
is
s
io
n
c
a
s
e
s
w
e
r
e
f
ound
to
be
r
a
pi
dl
y
in
c
r
e
a
s
in
g
a
s
ym
pt
om
a
ti
c
a
ll
y
[
2]
.
I
n
th
e
be
gi
nni
ng,
r
e
gi
ona
l
e
pi
d
e
m
ic
ha
s
s
in
c
e
qui
c
kl
y
e
nl
a
r
ge
d
in
to
gl
oba
l
p
a
nde
m
ic
.
T
he
C
O
V
I
D
-
19
a
f
f
e
c
te
d
212
c
ount
r
ie
s
a
r
ound
th
e
w
or
ld
w
it
h
h
uge
m
or
bi
di
ty
a
nd
m
or
ta
li
ty
r
a
te
,
w
it
h
m
or
e
th
a
n 3,700,00 pe
opl
e
in
f
e
c
te
d
w
it
h t
he
di
s
e
a
s
e
[
3]
.
T
he
m
ode
of
tr
a
ns
m
is
s
io
n
of
C
O
V
I
D
-
19
c
a
n
be
th
e
s
a
m
e
a
s
f
or
ot
he
r
r
e
s
pi
r
a
to
r
y
di
s
e
a
s
e
s
,
w
hi
c
h
c
a
n
be
tr
a
ns
m
it
te
d
th
r
ough
dr
opl
e
ts
of
va
r
io
us
s
iz
e
s
.
A
r
e
s
e
a
r
c
h
o
n
75,465
s
ubj
e
c
ts
w
hi
c
h
s
ho
w
s
th
a
t
C
O
V
I
D
-
19
in
f
e
c
ti
on
is
ba
s
ic
a
ll
y
tr
a
ns
m
it
te
d
a
m
ong
in
di
vi
dua
ls
th
r
ough
dr
opl
e
t
a
nd
c
ont
a
c
t
r
out
e
s
a
nd
not
th
r
ough
a
ir
bor
ne
tr
a
ns
m
is
s
io
n
[
4]
.
E
ve
n
th
ough,
ot
he
r
m
ode
s
o
f
tr
a
ns
m
is
s
io
n
m
ig
ht
b
e
po
s
s
ib
le
.
R
e
s
e
a
r
c
he
r
s
a
r
e
de
vot
in
g t
he
ir
t
im
e
a
nd
s
ki
ll
s
t
ow
a
r
ds
t
he
m
e
c
ha
ni
s
m
a
nd mode
s
of
t
r
a
ns
m
is
s
io
n of
t
hi
s
vi
r
us
[
5
]
. I
n
e
a
r
ly
2020,
th
is
di
s
e
a
s
e
ha
s
s
pr
e
a
d
qui
c
kl
y
a
r
ound
th
e
w
or
ld
.
I
n
th
e
hi
ghl
ig
ht
of
th
e
pot
e
nt
ia
l
da
nge
r
of
th
is
pa
nde
m
ic
,
r
e
s
e
a
r
c
h
e
r
s
a
nd
m
e
di
c
a
l
e
xpe
r
ts
ha
ve
be
e
n
doi
ng
th
e
ir
be
s
t
to
c
om
pr
e
he
nd
th
is
ne
w
in
f
e
c
ti
on
a
nd
th
e
pa
th
ophys
io
lo
gy
of
th
is
di
s
e
a
s
e
to
r
e
ve
a
l
li
ke
ly
tr
e
a
tm
e
n
t
r
e
gi
m
e
ns
a
nd
f
in
d
th
e
e
f
f
ic
ie
nt
th
e
r
a
pe
ut
ic
a
ge
nt
s
a
s
w
e
ll
a
s
t
he
v
a
c
c
in
e
[
6]
.
F
or
in
s
ta
nc
e
Ç
ol
a
k
e
t
al
.
p
e
r
f
or
m
e
d
a
r
e
tr
os
pe
c
ti
ve
c
a
s
e
-
c
ont
r
ol
s
tu
dy.
124
pa
ti
e
nt
s
w
ho
h
a
d
be
e
n
id
e
nt
if
ie
d
to
ha
ve
C
A
D
by
c
or
ona
r
y
a
ngi
ogr
a
phy
(
in
a
ny
e
ve
nt
1
c
or
ona
r
y
s
te
nos
is
>
ha
lf
in
m
a
jo
r
e
pi
c
a
r
di
a
l
c
our
s
e
s
)
w
e
r
e
jo
in
e
d
up
w
it
h
th
e
w
or
k.
A
ngi
ogr
a
phi
c
a
ll
y,
th
e
113
-
s
oc
ia
l
or
de
r
(
2)
w
it
h
ty
pi
c
a
l
c
or
ona
r
y
a
r
t
e
r
ie
s
w
e
r
e
ta
ke
n
a
s
c
ont
r
ol
s
ubj
e
c
ts
.
M
ul
ti
-
la
ye
r
e
d
pe
r
c
e
pt
io
ns
a
r
ti
f
ic
ia
l
ne
ur
a
l
ne
twor
k
(
M
L
P
-
A
N
N
)
e
ngi
ne
e
r
in
g
w
e
r
e
a
ppl
ie
d.
T
he
A
N
N
m
ode
ls
pr
e
pa
r
e
d
w
it
h
va
r
io
us
le
a
r
ni
ng
a
lg
or
it
hm
s
w
e
r
e
a
c
te
d
in
23
7
r
e
c
or
ds
,
is
ol
a
te
d
in
to
pr
e
pa
r
in
g
(
n=
171)
a
nd
te
s
ti
ng
(
n
=
66)
d
a
ta
s
e
t.
T
he
pr
e
s
e
nt
a
ti
on
of
e
xpe
c
ta
ti
on
w
a
s
a
s
s
e
s
s
e
d
by
s
e
ns
it
iv
it
y,
s
pe
c
if
ic
it
y
a
nd
a
c
c
ur
a
c
y
va
lu
e
s
w
it
h
r
e
ga
r
ds
to
s
ta
nda
r
d
d
e
f
in
it
io
ns
.
I
n
a
ddi
ti
on,
th
e
out
c
om
e
s
ha
v
e
s
how
n
th
a
t
A
N
N
m
ode
ls
tr
a
in
e
d
w
it
h
e
ig
ht
v
a
r
io
us
le
a
r
ni
ng
a
lg
or
it
hm
s
a
r
e
pr
om
is
in
g
a
di
r
e
c
t
r
e
s
ul
t
of
hi
gh
(
gr
e
a
te
r
th
a
n
71%
)
s
e
ns
it
iv
it
y,
s
pe
c
if
ic
it
y
a
n
d
a
c
c
ur
a
c
y
va
lu
e
s
in
th
e
f
or
e
c
a
s
t
of
C
A
D
.
A
c
c
ur
a
c
y,
s
e
ns
it
iv
it
y
a
nd
s
pe
c
if
ic
it
y
va
lu
e
s
di
f
f
e
r
e
d
be
twe
e
n
83.63%
-
100%
,
86.46%
-
100
%
a
nd
74.67%
-
100%
f
or
pr
e
pa
r
in
g,
s
e
pa
r
a
te
ly
.
F
or
te
s
ti
n
g,
th
e
qua
li
ti
e
s
w
e
r
e
ove
r
71%
f
or
s
e
ns
it
iv
it
y,
76%
f
o
r
s
pe
c
if
ic
it
y
a
nd
81%
f
or
a
c
c
ur
a
c
y
[
7]
.
A
ls
o,
F
a
z
il
ic
e
t
al
.
r
e
por
te
d
th
e
a
pp
li
c
a
ti
on
of
A
N
F
I
S
in
a
r
e
s
e
a
r
c
h
a
nd
ha
s
be
e
n
te
s
te
d a
nd a
ppl
ie
d on
s
e
ve
r
a
l
s
tu
di
e
s
i
n pr
e
di
c
ti
ng a
di
s
e
a
s
e
f
or
pr
e
di
c
ti
on of
de
r
m
a
to
lo
gi
c
a
l
di
s
e
a
s
e
s
[
8]
.
T
he
e
nvi
r
onm
e
nt
i
n w
hi
c
h C
O
V
I
D
19 vir
us
i
s
s
us
pe
nde
d
c
a
n s
i
gni
f
ic
a
nt
ly
i
nf
lu
e
nc
e
t
he
s
ur
vi
va
l
a
nd
tr
a
ns
m
is
s
io
n
of
th
e
vi
r
us
.
A
lt
hough
it
ha
s
be
e
n
s
how
n
th
a
t
tr
a
n
s
m
is
s
io
n
of
th
e
r
e
s
pi
r
a
to
r
y
vi
r
us
is
by
hum
a
n
to
hum
a
n
r
out
e
,
e
it
h
e
r
by
in
ha
li
ng
th
e
a
e
r
os
ol
s
s
n
e
e
z
e
d
or
c
o
ughe
d
out
by
in
f
e
c
t
e
d
pe
r
s
on
or
by
to
uc
hi
ng
in
f
e
c
te
d
s
ur
f
a
c
e
s
a
nd
ge
tt
in
g
th
e
dr
opl
e
ts
pa
s
s
th
r
ough
e
ye
s
,
m
out
h
or
nos
e
(
th
e
T
z
one
)
.
I
t
is
s
ti
ll
im
pe
r
a
ti
ve
to
know
th
a
t
th
e
a
bi
li
ty
of
th
e
vi
r
us
to
s
ur
vi
ve
in
va
r
io
u
s
s
ur
f
a
c
e
s
di
f
f
e
r
s
gr
e
a
tl
y
[
9]
.
A
s
a
ge
ne
r
a
l
c
onc
e
pt
,
vi
r
us
e
s
in
c
lu
di
ng
th
e
c
or
ona
vi
r
us
ha
s
be
e
n
s
how
n
to
li
ve
f
or
a
lo
ng
pe
r
io
d
on
obj
e
c
ts
out
s
id
e
th
e
body
of
th
e
hos
t
or
ga
ni
s
m
.
A
t
r
oom
te
m
pe
r
a
tu
r
e
,
th
e
vi
r
us
c
a
n
s
ur
vi
ve
f
or
da
ys
w
hi
le
a
t
hi
ghe
r
te
m
p
e
r
a
tu
r
e
s
,
th
e
vi
r
us
c
a
n
s
ur
vi
ve
f
or
m
uc
h
l
e
s
s
pe
r
io
d.
C
O
V
I
D
-
19
c
a
n
s
ur
vi
ve
f
or
hour
s
on
s
te
r
il
e
s
ur
f
a
c
e
s
,
a
lu
m
in
um
or
s
ur
gi
c
a
l
gl
ove
s
w
hi
c
h
in
c
r
e
a
s
e
s
th
e
pr
oba
bi
li
ty
to
g
e
t
in
f
e
c
te
d
vi
a
c
ont
a
c
t,
e
xha
le
d
dr
opl
e
ts
c
a
n
s
ta
y
a
s
a
e
r
os
ol
s
f
or
s
om
e
ti
m
e
th
e
r
e
by
e
nha
nc
in
g
a
di
s
ta
nc
e
d
hum
a
n
to
hum
a
n
tr
a
ns
m
is
s
io
n
th
r
ough
th
e
m
ove
m
e
nt
of
a
ir
(
w
in
d
e
f
f
e
c
t)
.
S
ubs
e
que
nt
ly
,
tr
a
ns
m
is
s
io
n
by
f
e
c
a
l
r
out
e
m
ig
ht
be
pos
s
ib
le
a
s
it
is
s
how
n
th
a
t
s
om
e
f
e
c
a
l
s
a
m
pl
e
of
in
f
e
c
te
d
in
di
vi
dua
ls
ha
s
te
s
te
d
pos
it
iv
e
f
or
th
e
vi
r
us
.
A
s
tu
dy
s
how
s
th
a
t
th
e
vi
r
us
c
a
n
s
ur
vi
ve
f
or
a
pe
r
io
d
o
f
4
da
ys
in
s
to
ol
a
nd
th
e
vi
r
us
c
a
n
be
in
f
e
c
ti
ou
s
in
s
e
w
a
g
e
a
nd
w
a
te
r
f
or
w
e
e
ks
,
th
e
s
e
s
ugg
e
s
ts
th
a
t
th
e
r
e
is
ne
e
d f
or
f
ur
th
e
r
i
nve
s
ti
ga
ti
on on r
ol
e
of
c
ont
a
m
in
a
te
d s
e
w
a
ge
o
n t
r
a
ns
m
is
s
io
n of
t
he
di
s
e
a
s
e
[
10]
. F
or
a
bout
5
to
6
de
c
a
de
s
,
num
e
r
ous
a
r
ti
c
le
s
a
nd
publ
ic
a
ti
on
s
ha
ve
b
e
e
n
r
e
por
te
d
in
th
e
te
c
hni
c
a
l
li
te
r
a
tu
r
e
de
pi
c
ti
ng
th
e
e
f
f
e
c
t
of
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
s
uc
h
a
s
te
m
p
e
r
a
tu
r
e
,
r
e
la
ti
ve
hu
m
id
it
y
a
nd
w
in
d
on
th
e
s
ur
vi
va
l
a
nd
s
pr
e
a
d
of
vi
r
a
l
a
ge
nt
s
.
T
he
s
ur
vi
va
l
a
nd
tr
a
ns
m
is
s
io
n
of
a
ir
bor
ne
in
f
e
c
ti
o
n
de
pe
nd
on
th
e
di
s
s
e
m
in
a
ti
on
of
th
e
vi
r
us
in
th
e
in
de
x
pe
r
s
on
a
nd
th
e
tr
a
ns
f
e
r
of
th
e
v
ir
us
to
a
s
e
c
onda
r
y
hos
t.
D
ur
in
g
th
is
jo
ur
ne
y,
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
pl
a
ys
a
m
a
jo
r
r
ol
e
in
th
e
tr
a
ns
m
is
s
io
n
c
ha
in
[
11]
.
F
or
in
s
ta
nc
e
,
P
ir
ouz
e
t
al
.
r
e
por
te
d
th
a
t
a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
a
nd
r
e
gr
e
s
s
io
n
a
na
ly
s
is
a
r
e
s
tr
ong
a
nd
r
e
li
a
bl
e
to
ol
s
in
pr
e
di
c
ti
ng
th
e
nove
l
C
O
V
I
D
-
19
out
br
e
a
k
us
in
g
va
r
io
us
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
[
1]
.
I
t
is
im
pe
r
a
ti
ve
to
not
e
th
a
t
s
in
c
e
th
e
c
r
e
a
ti
on
of
th
e
nove
l
A
I
-
ba
s
e
d
m
ode
ls
to
our
knowle
dge
th
is
i
s
th
e
f
ir
s
t
r
e
s
e
a
r
c
h
c
onduc
t
e
d
in
th
e
li
te
r
a
tu
r
e
,
s
how
in
g
th
e
c
om
bi
ne
d
a
ppl
ic
a
ti
ons
of
A
N
N
a
nd
A
N
F
I
S
in
th
e
pr
e
di
c
ti
on
of
C
O
V
I
D
-
19
out
br
e
a
k
us
in
g
va
r
io
us
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
.
O
ne
of
th
e
m
a
jo
r
r
e
a
s
on
s
of
a
ppl
yi
ng
th
e
s
e
m
ode
ls
is
due
to
th
e
f
a
c
t
th
a
t
in
or
de
r
to
g
e
ne
r
a
te
a
c
ons
is
te
nt
pr
e
di
c
ti
ng
a
ppr
oa
c
h
va
r
io
us
m
ode
ls
m
ig
ht
not
be
e
n
ough
due
to
th
e
na
tu
r
e
of
dyna
m
ic
pr
ope
r
t
ie
s
of
th
e
m
e
a
s
ur
e
d
da
ta
.
T
he
r
e
f
or
e
,
th
is
m
a
ke
s
it
ne
c
e
s
s
a
r
y
f
or
m
ode
ll
e
r
s
to
de
ve
lo
p
a
nd
c
ons
tr
uc
t
e
f
f
ic
ie
nt
a
n
d
s
tr
onge
r
m
ode
ls
w
it
h t
he
he
lp
of
t
he
c
ur
r
e
nt
a
nd e
xi
s
ti
ng da
ta
i
n
ha
nd.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
P
r
e
di
c
ti
on of t
he
e
ff
e
c
ts
of
e
nv
i
r
onm
e
nt
al
f
ac
to
r
s
t
ow
ar
d
s
C
O
V
I
D
-
19 outbr
e
ak
…
(
K
hal
id
M
ahm
oud
)
37
A
c
c
or
di
ng
to
th
e
s
tu
di
e
d
e
s
ta
bl
is
h
e
d
in
th
e
li
te
r
a
tu
r
e
,
th
e
tr
a
di
ona
l
r
e
gr
e
s
s
io
n
m
e
th
od
w
e
r
e
th
e
w
id
e
ly
e
m
pl
oye
d
a
ppr
oa
c
he
s
,
w
hi
c
h
h
a
ve
lo
w
e
r
pr
e
c
i
s
on
a
n
d
s
e
ns
it
iv
it
y.
T
h
e
r
e
f
or
e
,
th
is
br
in
gs
a
bout
th
e
ne
e
d f
or
t
he
de
ve
lo
pm
e
nt
of
t
he
r
obus
t
non
-
li
ne
a
r
A
I
-
ba
s
e
d t
e
c
hni
que
s
[
12]
.
T
hi
s
w
or
k
i
s
a
im
e
d
to
de
te
r
m
in
e
th
e
a
ppl
ic
a
ti
ons
of
two
di
f
f
e
r
e
nt
non
-
li
ne
a
r
m
ode
ls
(
A
N
N
a
nd
A
N
F
I
S
)
w
it
h
a
li
ne
a
r
c
la
s
s
ic
a
l
m
ode
l
M
L
R
to
pr
e
di
c
t
th
e
out
br
e
a
k
of
C
O
V
I
D
-
19
in
W
uha
n
c
it
y,
C
hi
na
u
s
in
g
va
r
io
us
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
s
uc
h a
s
w
in
d, t
e
m
pe
r
a
tu
r
e
a
nd hu
m
id
it
y a
s
t
he
i
nput
va
r
ia
bl
e
2.
M
A
T
E
R
I
A
L
A
N
D
M
E
T
H
O
D
2.1. I
n
s
t
r
u
m
e
n
t
at
io
n
2.1.1. Hu
m
id
it
y
T
he
h
um
id
it
y
is
m
e
a
s
ur
e
d
by
d
ig
it
a
l
hygr
om
e
te
r
a
nd
a
b
ui
lt
-
in
pos
it
io
n
s
e
ns
or
.
T
he
s
e
ns
or
c
om
pr
is
e
s
of
a
pol
yi
m
id
e
f
il
m
s
pi
n
-
c
oa
te
d
ont
o a
S
i
s
ub
s
tr
a
te
. T
he
s
e
ns
or
m
ode
l
f
or
s
ig
na
l
pr
oc
e
s
s
in
g,
w
hi
c
h
por
tr
a
ys
t
he
s
e
ns
or
c
a
p
a
c
it
a
nc
e
w
it
h r
e
ga
r
d t
o r
e
la
ti
ve
humi
di
ty
a
nd t
e
m
pe
r
a
tu
r
e
c
lo
s
e
d
-
pa
th
[
13]
.
2.1.2. T
e
m
p
e
r
at
u
r
e
A
th
e
r
m
om
e
te
r
is
a
n
in
s
tr
um
e
nt
us
e
d
in
m
e
a
s
ur
in
g
te
m
pe
r
a
tu
r
e
;
a
th
e
r
m
om
e
te
r
c
a
n
be
us
e
d
in
m
e
a
s
ur
in
g
th
e
te
m
pe
r
a
tu
r
e
of
li
qui
ds
,
s
ol
id
s
a
nd
ga
s
e
s
.
I
t
is
e
q
ua
ll
y
us
e
d
in
m
e
a
s
ur
in
g
te
m
pe
r
a
tu
r
e
o
f
a
ir
a
s
us
e
d i
n t
hi
s
s
tu
dy [
14]
.
2.1.3. Win
d
T
he
in
s
tr
um
e
nt
us
e
d
in
m
e
a
s
ur
in
g
w
in
d
s
pe
e
d
in
th
is
e
xpe
r
im
e
nt
is
a
n
a
ne
m
om
e
te
r
(
a
ty
pe
of
w
e
a
th
e
r
i
ns
tr
um
e
nt
us
e
d t
o m
e
a
s
ur
e
w
in
d s
pe
e
d a
nd dir
e
c
ti
on [
15]
.
2.2. P
r
op
os
e
d
m
e
t
h
od
ol
ogy
I
n
th
is
s
tu
dy,
th
e
da
t
a
w
a
s
ta
ke
n
f
r
om
hi
s
to
r
ic
a
l
e
xpe
r
im
e
nt
a
l
r
e
s
ul
ts
f
r
om
a
s
tu
dy
c
onduc
te
d
by
[
1]
to
pr
e
di
c
t
th
e
out
b
r
e
a
k
of
nove
l
C
O
V
I
D
-
19
di
s
e
a
s
e
us
in
g
di
f
f
e
r
e
nt
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
by
a
ppl
yi
ng
li
ne
a
r
a
nd
non
-
li
ne
a
r
m
ode
ls
A
N
N
,
A
N
F
I
S
,
a
nd
M
L
R
. T
he
da
ta
w
a
s
s
e
pa
r
a
te
ly
pr
opos
e
d
in
or
de
r
to
in
ve
s
ti
ga
te
th
e
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
th
a
t
a
f
f
e
c
t
th
e
c
onf
ir
m
e
d
c
a
s
e
s
of
c
or
on
a
vi
r
us
;
th
e
da
ta
of
pr
e
vi
ous
r
e
s
e
a
r
c
h
[
1]
w
a
s
c
ol
le
c
te
d
to
id
e
nt
if
y
th
e
r
e
la
ti
ons
hi
p
be
twe
e
n
te
m
pe
r
a
tu
r
e
,
hum
id
it
y
a
nd
w
in
d
s
pe
e
d
w
it
h
c
onf
i
r
m
e
d
c
a
s
e
s
of
c
or
ona
vi
r
us
.
F
iv
e
va
r
ia
bl
e
s
w
e
r
e
us
e
d
a
s
in
put
s
va
r
ia
bl
e
s
;
m
a
xi
m
um
te
m
pe
r
a
tu
r
e
,
m
in
im
um
te
m
pe
r
a
tu
r
e
,
a
ve
r
a
ge
te
m
pe
r
a
tu
r
e
,
a
n
a
ve
r
a
ge
hum
id
it
y
a
nd
w
in
d
s
pe
e
d
ki
lo
m
e
te
r
p
e
r
hour
.
T
he
C
onf
ir
m
e
d
c
a
s
e
is
c
on
s
id
e
r
e
d
a
s
a
n
out
put
pa
r
a
m
e
te
r
.
T
he
da
ta
w
a
s
c
ol
le
c
te
d
f
or
a
pe
r
io
d
of
30
da
ys
,
w
hi
c
h
is
c
om
pos
e
d
of
64
in
s
ta
nc
e
s
f
or
e
a
c
h
of
th
e
va
r
ia
bl
e
s
.
I
n
th
e
de
ve
lo
pm
e
nt
of
th
is
r
e
s
e
a
r
c
h,
F
ig
ur
e
1
s
how
s
th
e
f
lo
w
c
ha
r
t
of
th
e
A
I
-
ba
s
e
d
m
ode
ls
a
nd e
xpe
r
im
e
nt
a
l
m
e
th
ods
a
ppl
ie
d.
F
ig
ur
e
1. S
how
s
t
he
f
lo
w
c
ha
r
t
of
t
he
A
I
-
ba
s
e
d m
ode
ls
a
nd e
xpe
r
im
e
nt
a
l
m
e
th
ods
a
ppl
ie
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
10
, N
o.
1
,
M
a
r
c
h
20
2
1
:
35
–
42
38
W
he
r
e
by,
t
he
f
lo
w
c
ha
r
t
de
s
c
r
ib
e
s
a
nd
s
um
m
a
r
iz
e
th
e
ov
e
r
a
ll
s
tu
dy,
s
ta
r
ti
ng
w
it
h
th
e
e
xpe
r
im
e
nt
a
l
a
na
ly
s
is
to
de
te
r
m
in
e
th
e
va
lu
e
s
of
th
e
te
m
pe
r
a
tu
r
e
,
hum
id
it
y
a
nd
w
in
d
a
s
th
e
c
or
r
e
s
ponding
in
put
v
a
r
ia
bl
e
s
.
A
n
in
s
tr
um
e
nt
a
li
s
t
w
il
l
de
te
r
m
in
e
w
he
th
e
r
th
e
r
e
is
a
n
in
s
tr
um
e
nt
a
l
e
r
r
or
o
r
not
a
s
s
how
n
in
th
e
f
lo
w
c
ha
r
t
by
c
he
c
ki
ng
th
e
r
e
s
ul
ts
.
T
he
s
tu
dy
pr
oc
e
e
ds
w
it
h
th
e
da
ta
dr
iv
e
n
a
ppr
oa
c
h
th
r
ough
pr
e
-
pr
oc
e
s
s
in
g
m
e
th
od,
w
hi
c
h
in
vol
ve
s
pr
e
li
m
in
a
r
y
da
ta
a
na
ly
s
is
s
uc
h
a
s
c
or
r
e
la
ti
on
a
na
ly
s
is
a
nd
s
ta
ti
s
ti
c
a
l
a
na
ly
s
is
.
T
h
e
m
od
e
ls
a
r
e
f
ur
th
e
r
e
m
pl
oye
d, a
nd t
he
ir
pr
e
di
c
ti
ve
pe
r
f
or
m
a
nc
e
w
a
s
e
va
lu
a
t
e
d a
s
s
how
n i
n
F
ig
ur
e
1.
2.3
.
A
r
t
if
ic
ia
l
n
e
u
r
al
n
e
t
w
or
k
s
(
A
N
N
)
A
r
ti
f
ic
ia
l
ne
ur
a
l
ne
twor
ks
(
A
N
N
s
)
a
r
e
ge
ne
r
a
ll
y
c
om
put
a
ti
ona
l
da
ta
dr
iv
e
n
m
ode
ls
us
e
d
in
e
m
ul
a
ti
ng
a
nd
m
im
ic
ki
ng
how
th
e
hum
a
n
br
a
in
in
te
r
pr
e
t
a
nd
tr
a
ns
la
te
in
f
or
m
a
ti
on.
T
he
y
e
qua
ll
y
c
om
po
s
e
d
of
va
r
io
us
ne
ur
ons
a
s
w
e
ll
a
s
uni
ts
f
or
pr
oc
e
s
s
in
g
th
a
t
a
r
e
in
te
r
c
onne
c
te
d
w
it
h
a
da
pt
a
bl
e
bi
a
s
e
s
a
nd
w
e
ig
ht
s
[
16]
.
T
hi
s
c
ur
r
e
nt
s
tu
dy
e
m
pl
oys
th
e
us
e
d
of
ba
c
kpr
opa
ga
ti
on
a
lg
or
it
hm
s
.
B
a
s
e
d
on
th
e
te
c
hni
c
a
l
li
te
r
a
tu
r
e
,
A
N
N
s
a
r
e
s
y
s
te
m
s
a
ppl
ie
d
to
pr
oc
e
s
s
in
f
or
m
a
ti
on,
w
hi
c
h
a
r
e
d
e
s
ig
ne
d
li
ke
hum
a
n
br
a
in
c
ons
i
s
ti
ng
of
a
ba
s
ic
uni
t
known a
s
node
(
ne
ur
on)
[
17]
.
T
he
r
e
f
or
e
,
ba
c
kpr
opa
ga
ti
on
is
e
m
pl
oye
d
in
de
te
r
m
in
in
g
th
e
e
r
r
or
,
w
hi
c
h
is
c
a
lc
ul
a
t
e
d
by
ta
ki
ng
th
e
di
f
f
e
r
e
nc
e
of
t
he
s
im
ul
a
te
d va
lu
e
s
a
nd t
he
m
e
a
s
ur
e
d va
lu
e
s
.
T
h
e
ge
ne
r
a
l
e
qua
ti
on c
a
n b
e
e
xpr
e
s
s
e
d a
s
(
1)
:
=
−
(
1)
2.4
.
A
d
ap
t
iv
e
n
e
u
r
o
-
f
u
z
z
y i
n
f
e
r
e
n
c
e
s
ys
t
e
m
(
A
N
F
I
S
)
A
N
F
I
S
w
a
s
pr
ove
d
a
s
a
s
uc
c
e
s
s
f
ul
s
of
twa
r
e
th
a
t
in
c
or
por
a
te
s
th
e
a
ppr
oa
c
h
of
f
uz
z
y
s
ur
ge
on
m
ode
l
th
a
t
s
how
s
th
e
be
ne
f
it
of
bot
h
f
uz
z
y
lo
gi
c
a
nd
A
N
N
in
one
s
ys
te
m
.
A
N
F
I
S
is
us
e
d
r
e
c
e
nt
ly
in
p
r
e
di
c
ti
ng
a
nd
m
ode
ll
in
g
c
om
pl
e
x
da
ta
s
e
t
[
18]
.
A
N
F
I
S
is
a
ls
o
a
r
e
a
l
-
w
or
ld
e
s
ti
m
a
to
r
be
c
a
us
e
of
it
s
c
a
pa
c
it
y
to
a
ppr
oxi
m
a
te
th
e
r
e
a
l
f
unc
ti
ons
.
F
uz
z
y
lo
gi
c
c
onv
e
r
ts
th
e
in
put
d
a
ta
in
to
f
u
z
z
y
va
lu
e
s
vi
a
th
e
a
ppl
ic
a
ti
on
of
m
e
m
b
e
r
s
hi
p
f
unc
ti
ons
.
T
he
num
be
r
s
r
a
nge
b
e
twe
e
n
0
-
1
[
19]
.
F
ur
th
e
r
m
or
e
,
in
A
N
F
I
S
m
ode
l
node
s
w
or
ks
a
s
m
e
m
be
r
s
hi
p
f
unc
ti
on (
M
F
s
)
a
nd a
ls
o a
ll
ow
s
t
he
m
ode
ll
in
g be
twe
e
n t
he
r
e
la
t
io
ns
of
t
he
i
nput
w
it
h t
he
out
put
.
A
s
s
um
e
th
e
F
I
S
c
ont
a
in
s
two
in
put
s
‘
x’
a
nd
‘
y’
a
nd
one
out
p
ut
‘
f
’
,
a
f
ir
s
t
-
o
r
de
r
S
uge
no
f
uz
z
y
ha
s
th
e
f
ol
lo
w
in
g r
ul
e
s
.
R
ul
e
1:
i
f
μ
(
x
)
is
A
1
a
n
d
μ
(
y
)
B
1
t
he
n
f
1
=
p
1
x+
q
1
y+
r
1
(
2)
R
ul
e
2:
i
f
μ
(
x
)
is
A
2
a
n
d
μ
(
y
)
is
B
2
t
he
n
f
2
=
p
2
x+
q
2
y+
r
2
(
3)
1
,
1
,
2
,
2
V
a
r
ia
bl
e
s
a
r
e
m
e
m
b
e
r
s
hi
p
f
unc
ti
ons
f
or
x
a
nd
y
in
put
s
1
,
1
,
1
,
2
,
2
,
2
,
a
r
e
out
le
t
f
unc
ti
on va
r
ia
bl
e
s
. T
he
s
tr
uc
tu
r
e
a
nd f
or
m
ul
a
ti
on of
A
N
F
I
S
f
ol
l
ow
s
a
f
iv
e
-
la
ye
r
ne
ur
a
l
ne
twor
k a
r
r
a
nge
m
e
nt
.
2.
5
.
M
u
lt
ip
le
li
n
e
ar
r
e
gr
e
s
s
io
n
(
M
L
R
)
T
hi
s
is
one
of
th
e
tr
iv
ia
l
a
nd
c
la
s
s
ic
a
l
m
e
th
od
us
e
d
in
pr
e
di
c
ti
on
in
e
ngi
ne
e
r
in
g,
s
c
ie
nc
e
,
he
a
lt
h
s
c
ie
nc
e
a
nd
s
oc
ia
l
s
c
ie
nc
e
s
.
I
t
i
s
ge
ne
r
a
ll
y
c
la
s
s
if
ie
d
in
to
two
m
a
in
gr
oups
;
th
e
s
im
pl
e
a
nd
m
ul
ti
pl
e
li
ne
a
r
r
e
gr
e
s
s
io
n.
E
a
c
h
of
th
e
s
e
c
la
s
s
e
s
c
a
n
b
e
us
e
d
d
e
pe
ndi
ng
on
t
he
a
im
.
F
or
e
xa
m
pl
e
,
if
th
e
s
tu
dy
in
vol
ve
s
a
s
in
gl
e
out
put
a
nd
s
in
gl
e
in
put
va
r
ia
bl
e
,
it
is
s
a
id
to
be
known a
s
s
im
pl
e
li
ne
a
r
r
e
gr
e
s
s
io
n
(
S
L
R
)
.
F
ur
th
e
r
m
or
e
,
if
w
e
w
a
nt
to
c
he
c
k
th
e
r
e
la
ti
on
be
twe
e
n
m
or
e
th
a
n
two
in
put
s
w
it
h
a
s
in
gl
e
out
put
li
ne
a
r
,
th
e
r
e
f
or
e
s
uc
h
is
a
m
ul
ti
pl
e
li
ne
a
r
r
e
gr
e
s
s
io
n
(
M
L
R
)
[
20]
.
U
s
ua
ll
y,
M
L
R
is
th
e
li
ne
a
r
r
e
gr
e
s
s
io
n
ty
pe
th
a
t
is
u
s
e
d
uni
ve
r
s
a
ll
y,
a
nd
it
in
vol
ve
s
a
na
ly
s
is
in
th
e
f
or
m
th
a
t
e
ve
r
y
va
lu
e
f
r
om
th
e
in
put
in
put
pa
r
a
m
e
te
r
to
be
r
e
la
te
d
w
it
h
th
e
out
put
[
21]
.
G
e
ne
r
a
ll
y,
th
is
te
c
hni
que
c
ons
is
ts
of
e
s
ti
m
a
ti
ng
th
e
le
ve
l
of
c
or
r
e
la
ti
on
th
a
t
is
be
twe
e
n
a
s
in
gl
e
r
e
s
pons
e
va
r
ia
bl
e
t
ha
t
i
s
t
he
de
pe
nde
nt
a
nd t
w
o or
m
or
e
pr
e
di
c
to
r
s
t
ha
t
is
i
nde
pe
nde
nt
va
r
ia
bl
e
s
[
22]
.
T
he
ge
ne
r
a
l
e
qu
a
ti
on
of
M
L
R
c
a
n be
s
how
n i
n
(
4
)
.
=
0
+
1
1
+
2
2
+
⋯
(
4)
W
he
r
e
x_1, is
t
he
va
lu
e
of
t
he
th
pr
e
di
c
to
r
, b_0 is
t
he
r
e
gr
e
s
s
io
n c
ons
ta
nt
, a
nd b_ii
s
t
he
c
oe
f
f
ic
ie
nt
of
t
he
th
pr
e
di
c
to
r
.
2.6
.
M
od
e
l
v
al
id
at
io
n
I
n
a
ny
c
om
put
a
ti
ona
l
da
ta
-
dr
iv
e
n
a
ppr
oa
c
h,
th
e
ba
s
ic
a
im
is
to
f
it
th
e
m
ode
ls
to
a
gi
ve
n
da
ta
s
e
ts
ba
s
e
d
on
th
e
e
m
pl
oye
d
in
di
c
a
to
r
s
in
or
de
r
to
pr
oduc
e
a
s
a
ti
s
f
a
c
to
r
y
pr
e
di
c
ti
on
of
th
e
unknown
da
ta
s
e
t
[
1]
.
C
ons
id
e
r
in
g
is
s
u
e
s
s
uc
h a
s
ov
e
r
f
it
ti
ng,
r
e
li
a
bl
e
c
a
li
br
a
ti
on
pe
r
f
or
m
a
nc
e
is
not
a
gr
e
e
m
e
nt
w
it
h
th
e
ve
r
if
ic
a
ti
on
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
P
r
e
di
c
ti
on of t
he
e
ff
e
c
ts
of
e
nv
i
r
onm
e
nt
al
f
ac
to
r
s
t
ow
ar
d
s
C
O
V
I
D
-
19 outbr
e
ak
…
(
K
hal
id
M
ahm
oud
)
39
pe
r
f
or
m
a
nc
e
a
lwa
ys
.
G
e
ne
r
a
ll
y,
w
e
ha
ve
di
f
f
e
r
e
nt
c
la
s
s
e
s
of
va
li
da
ti
on
c
ons
i
s
ti
ng
of
;
th
e
popula
r
c
r
os
s
-
va
li
da
ti
on.
T
he
K
-
f
ol
d
is
a
n
e
x
a
m
pl
e
o
f
th
e
c
r
os
s
-
va
li
da
ti
on,
w
hi
c
h
i
s
e
m
pl
oye
d
in
th
is
s
tu
dy.
I
n
th
is
va
li
da
ti
on
m
e
th
od,
th
e
da
ta
is
c
la
s
s
if
ie
d
r
a
ndoml
y
to
two
di
f
f
e
r
e
nt
s
s
e
ts
c
a
ll
e
d,
th
e
ve
r
if
ic
a
ti
on
a
nd
th
e
c
a
li
br
a
ti
on
pha
s
e
s
[
2]
.
A
m
ong
th
e
a
dva
nt
a
ge
s
of
th
is
va
li
da
ti
on
a
ppr
oa
c
h
is
th
a
t
in
e
a
c
h
r
ound,
th
e
tr
a
in
in
g
a
nd
va
li
da
ti
on
da
ta
s
e
ts
a
r
e
not
de
p
e
nde
nt
upon
e
a
c
h
ot
h
e
r
.
W
hi
c
h
le
a
d
s
to
th
e
pr
ovi
s
io
n
of
hi
gh
e
r
pe
r
f
or
m
a
nc
e
a
c
c
ua
r
a
c
y
[
23]
.
A
s
s
ta
t
e
d
a
bove
,
th
e
da
ta
i
s
f
ur
th
e
r
di
vi
de
d
in
to
c
a
te
gor
ie
s
75%
f
or
th
e
c
a
li
br
a
ti
on (
tr
a
in
in
g)
a
nd 25%
f
or
t
he
t
e
s
t
in
g (
ve
r
if
ic
a
ti
on)
s
ta
ge
. C
ons
id
e
r
in
g t
he
4
-
f
ol
d c
r
os
s
-
va
li
da
ti
on. I
t
is
ve
r
y i
m
por
ta
nt
t
o note
t
ha
t
ot
he
r
va
li
da
ti
on me
th
ods
c
a
n be
a
pp
li
e
d t
o t
he
da
ta
s
e
t
[
24]
.
3.
R
E
S
U
L
T
S
AND
D
I
S
C
U
S
S
I
O
N
3.1
.
M
od
e
ls
d
e
ve
lo
p
m
e
n
t
I
n
th
e
de
ve
lo
pm
e
nt
of
th
e
s
e
m
ode
l
s
,
th
e
s
im
ul
a
ti
on
w
a
s
done
i
n
M
A
T
L
A
B
9.3
(
R
2019a
)
.
F
or
A
N
N
m
ode
l,
a
s
p
e
c
ia
l
a
lg
or
it
hm
known
a
s
L
e
ve
nb
e
r
g
-
M
a
r
qua
r
dt
w
a
s
u
s
e
d
by
e
m
pl
oyi
ng
1,000
it
e
r
a
ti
ons
,
c
oe
f
f
ic
ie
nt
of
m
om
e
nt
um
o
f
0.9, le
a
r
ni
ng s
pe
e
d of
0.01
a
s
w
e
ll
a
s
a
n M
S
E
of
0.0001. T
he
be
s
t
a
r
c
hi
te
c
tu
r
e
of
th
e
m
ode
l
w
a
s
opt
im
iz
e
d
a
nd
s
e
l
e
c
te
d
th
r
ough
th
e
us
e
of
tr
ia
l
by
e
r
r
or
m
e
th
od.
I
n
m
ode
ll
in
g
of
A
N
F
I
S
,
di
f
f
e
r
e
nt
ki
nds
of
e
poc
h
it
e
r
e
a
ti
on
s
a
s
w
e
ll
a
s
m
e
m
be
r
s
hi
p
f
u
nc
ti
on
(
M
F
s
)
w
e
r
e
u
s
e
d
in
or
de
r
to
r
e
c
ogni
z
e
th
e
s
ui
ta
bl
e
m
ode
l
a
r
c
hi
te
c
tu
r
e
.
W
hi
le
,
th
e
de
te
r
m
in
is
ti
c
li
ne
a
r
M
L
R
m
ode
l
w
a
s
de
v
e
lo
pe
d
u
s
in
g
th
e
s
im
ul
a
ti
on t
ool
i
n t
he
E
V
ie
w
s
s
of
twa
r
e
9.5.
3.2
.
A
p
p
li
c
at
io
n
s
of
t
h
e
d
at
a d
r
iv
e
n
ap
p
r
oac
h
e
s
r
e
s
u
lt
AI
-
ba
s
e
d
m
ode
ls
(
A
N
F
I
S
a
nd
A
N
N
)
w
it
h
a
li
ne
a
r
m
ode
l
M
L
R
w
e
r
e
e
m
pl
oye
d
to
pr
e
di
c
t
th
e
e
f
f
e
c
ts
of
e
nvi
r
onm
e
n
ta
l
f
a
c
to
r
s
on
C
O
V
I
D
-
19
out
br
e
a
k
in
W
uha
n
C
it
y
of
C
hi
na
ba
s
e
d
on
hi
s
to
r
ic
a
l
da
ta
.
P
r
io
r
to
th
e
m
ode
ll
in
g,
s
ta
ti
s
ti
c
a
l
a
nd
c
or
r
e
la
ti
on
da
ta
a
na
ly
s
is
w
a
s
c
onduc
te
d
a
s
s
how
n
in
T
a
bl
e
1.
I
n
or
de
r
to
unde
r
s
ta
nd
th
e
be
ha
vi
or
a
nd
s
c
ie
nc
e
of
th
e
hi
s
to
r
ic
a
l
da
ta
,
t
he
r
e
la
ti
ons
hi
p
th
a
t
e
xi
s
ts
a
m
ong
a
ll
th
e
va
r
ia
bl
e
s
in
vol
ve
d
m
or
e
e
s
pe
c
ia
ll
y
th
e
de
pe
nde
nt
(
out
put
)
a
nd
th
e
in
de
p
e
nde
nt
(
in
put
)
va
r
ia
bl
e
s
.
F
r
om
th
e
c
or
r
e
la
ti
on
a
nd
s
ta
ti
s
ti
c
a
l
a
n
a
ly
s
is
,
th
e
s
c
ie
n
c
e
of
th
e
da
ta
c
a
n
b
e
w
e
ll
und
e
r
s
to
od
pr
io
r
to
na
vi
ga
ti
ng
in
t
o
th
e
s
im
ul
a
ti
on.
T
he
s
ta
ti
s
c
a
l
a
na
ly
s
i
s
w
a
s
de
m
on
s
tr
a
te
d
ba
s
e
d
on
th
e
m
e
a
n,
m
e
a
di
a
n,
s
ta
nd
a
r
d
de
vi
a
ti
on,
th
e
m
in
im
um
a
s
w
e
ll
a
s
t
he
m
a
xi
m
um
numbe
r
of
e
a
c
h of
t
he
va
r
ia
bl
e
s
i
nvol
ve
i
n t
hi
s
s
tu
dy a
s
s
how
n i
n
T
a
bl
e
1.
I
t
c
a
n
be
obs
e
r
ve
d
a
s
s
how
n
in
T
a
bl
e
1
t
ha
t
th
e
r
e
i
s
a
s
tr
ong
i
nve
r
s
e
c
or
r
e
la
ti
on
th
a
t
e
xi
s
t
s
be
twe
e
n
M
a
x
T
°C
a
nd
th
e
c
onf
ir
m
e
d
c
a
s
e
s
ha
vi
ng
a
n
R
-
va
lu
e
=
-
0.534
01.
T
he
r
e
f
or
e
,
th
is
c
a
n
va
li
da
te
th
e
hypothe
s
is
pr
opos
e
d by va
r
io
us
s
c
ie
nt
is
t
a
nd me
di
c
a
l
e
xpe
r
ts
t
ha
t
a
t
hi
ghe
r
t
e
m
pe
r
a
tu
r
e
or
a
t
te
m
pe
r
a
te
r
e
gi
ons
t
hi
s
vi
r
us
m
a
y
be
e
xpos
e
to
de
a
th
.
T
a
bl
e
1
e
qua
ll
y
s
how
s
m
ode
r
a
te
a
nd
di
r
e
c
t
r
e
la
ti
ons
hi
p
be
twe
e
n
a
ve
r
a
g
e
hum
id
it
y
a
nd
th
e
c
onf
ir
m
e
d
c
a
s
e
w
it
h
R
=
0.382997.
T
he
w
e
a
ke
s
t
c
or
r
e
la
ti
on
e
xi
s
ts
b
e
twe
e
n
w
in
d
a
nd
c
onf
ir
m
e
d
c
a
s
e
s
ha
vi
ng R
=
0.072453.
B
a
s
e
d
on
th
e
c
om
pa
r
a
ti
ve
pr
e
di
c
ti
on
r
e
s
ul
ts
of
th
e
m
ode
ls
,
it
c
a
n
be
obs
e
r
ve
d
c
le
a
r
ly
th
a
t
th
e
A
I
-
ba
s
e
d
m
ode
ls
i.
e
.
A
N
N
a
nd
A
N
F
I
S
s
how
s
upe
r
io
r
it
y
ove
r
th
e
tr
a
di
ti
ona
l
li
ne
a
r
r
e
gr
e
s
s
io
n
m
ode
l
M
L
R
w
it
h
c
ons
id
e
r
a
bl
e
pe
r
f
or
m
a
nc
e
. T
a
bl
e
2
e
qu
a
ll
y
in
di
c
a
te
s
th
a
t
a
ll
th
e
th
r
e
e
m
ode
ls
s
how
good
r
e
s
ul
t
in
th
e
tr
a
in
in
g
pha
s
e
w
it
h
a
lo
w
e
r
R
2
-
va
lu
e
of
0.9076
d
e
s
pi
te
th
e
f
a
c
t
th
a
t
th
e
M
L
R
pe
r
f
or
m
a
nc
e
in
th
e
te
s
ti
ng
pha
s
e
is
not
r
e
li
a
bl
e
due
to
it
s
lo
w
e
r
de
te
r
m
in
a
ti
on
of
c
o
-
e
f
f
ic
ie
nt
va
lu
e
a
s
w
e
ll
a
s
th
e
hi
ghe
r
r
oot
m
e
a
n
s
qua
r
e
e
r
r
or
va
lu
e
. F
ur
th
e
r
de
s
c
r
ip
ti
ve
of
t
he
r
e
s
ul
t
s
how
s
t
ha
t
A
N
F
I
S
out
pe
r
f
or
m
e
d a
ll
t
he
t
hr
e
e
m
ode
ls
a
nd i
nc
r
e
a
s
e
d t
he
pe
r
f
or
m
a
nc
e
a
c
c
ur
a
c
y
of
M
L
R
a
nd
A
N
N
up
to
25%
a
nd
9%
r
e
s
pe
c
ti
ve
ly
ba
s
e
d
on
th
e
ir
r
oot
m
e
a
n
s
qua
r
e
e
r
r
or
a
s
s
how
n i
n F
ig
ur
e
2.
T
a
bl
e
1.
S
ta
ti
s
ti
c
a
l
a
nd
s
pe
a
r
m
a
n pe
a
r
s
on c
or
r
e
la
ti
on a
na
ly
s
i
s
S
t
a
t
i
s
t
i
c
a
l
A
na
l
ys
i
s
V
a
r
i
a
bl
e
s
M
A
X
T
°
C
M
I
N
T
°
C
A
V
G
T
°
C
H
R
A
V
G
(
%
)
W
I
N
D
K
M
/
H
C
onf
i
r
m
e
d C
a
s
e
s
M
e
a
n
15.38
1.39
8.30
77.90
5.37
1604.97
M
e
di
a
n
14.70
0.30
7.35
78.15
4.10
1710.50
S
t
a
nda
r
d
D
e
vi
a
t
i
on
4.31
4.03
4.19
8.69
3.26
832.56
M
i
ni
m
um
8.20
-
2.70
2.20
55.50
2.30
349.00
M
a
xi
m
um
24.90
10.60
18.10
91.30
17.60
3156.00
C
or
r
e
l
a
t
i
on A
na
l
ys
i
s
V
a
r
i
a
bl
e
s
M
A
X
T
°
C
M
I
N
T
°
C
A
V
G
T
°
C
H
R
A
V
G
(
%
)
W
I
N
D
K
M
/
H
C
onf
i
r
m
e
d C
a
s
e
s
M
a
x T
°
C
1
M
i
n T
°C
0.634798
1
A
vg T
°
C
0.835269
0.781245
1
H
r
A
vg (
%
)
0.11413
0.243575
0.13875
1
W
i
nd K
m
/
H
0.345658
0.282814
0.149187
0.332678
1
C
onf
i
r
m
e
d C
a
s
e
s
0.53401
0.35121
0.49456
0.382997
0.072453
1
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
10
, N
o.
1
,
M
a
r
c
h
20
2
1
:
35
–
42
40
T
a
bl
e
2. R
e
s
ul
ts
of
t
he
m
ode
ls
T
r
a
i
ni
ng
T
e
s
t
i
ng
R2
R
M
S
E
R
R2
R
M
S
E
R
M
L
R
0.9076
630.2573
0.9527
0.74337
235.3869
0.862189
ANN
0.9559
435.4262
0.9777
0.907096
141.6266
0.952416
A
N
F
I
S
0.9997
34.1108
0.9999
0.998279
19.27619
0.999139
F
ig
ur
e
2
.
R
oot
m
e
a
n s
qua
r
e
e
r
r
or
of
t
he
m
ode
ls
i
n both t
r
a
in
in
g a
nd t
e
s
ti
ng pha
s
e
s
M
or
e
ove
r
,
th
e
pr
e
di
c
ti
ve
r
e
s
ul
t
s
w
e
r
e
e
qua
ll
y
de
m
ons
tr
a
te
d
us
in
g
a
gr
a
phi
c
a
l
il
lu
s
tr
a
ti
on
(
s
c
a
tt
e
r
pl
ot
)
to
di
s
pl
a
y
th
e
r
e
la
ti
ons
hi
p
th
a
t
e
xi
s
t
s
be
twe
e
n
th
e
m
e
a
s
ur
e
d
a
nd
th
e
s
im
ul
a
te
d
va
lu
e
s
a
s
s
how
n
in
F
ig
ur
e
3.
I
t
is
c
le
a
r
f
r
om
th
e
il
lu
s
tr
a
ti
on
th
a
t
A
N
F
I
S
a
nd
A
N
N
s
how
hi
gh
e
r
f
it
ti
ng
a
gr
e
e
m
e
nt
be
twe
e
n
th
e
m
e
a
s
ur
e
d
a
nd
th
e
s
im
ul
a
te
d
va
lu
e
s
.
T
he
m
or
e
r
obus
t
pr
e
di
c
ti
on
a
c
c
ur
a
c
y
of
th
e
c
onf
ir
m
e
d
C
O
V
I
D
-
19
c
a
s
e
s
is
r
e
la
te
d t
o t
he
hi
ghe
r
c
or
r
e
la
ti
on t
ha
t
e
xi
s
ts
be
twe
e
n t
he
va
r
ia
bl
e
s
, a
s
s
how
n i
n T
a
bl
e
1.
(
a
)
(
b)
(
c
)
F
ig
ur
e
3.
S
c
a
tt
e
r
pl
ot
s
f
or
(
a
)
M
L
R
(
b
)
A
N
N
a
nd (
c
)
A
N
F
I
S
0
100
200
300
400
500
600
700
M
L
R
ANN
A
N
F
I
S
R
M
S
E
T
e
s
t
i
ng
T
r
a
i
n
i
n
g
R
²
= 0.6521
300
1300
2300
3300
300
800
1300
1800
2300
2800
3300
S
i
m
u
l
a
t
e
d
M
L
R
-
c
on
f
ir
m
e
d
C
a
s
e
s
M
e
s
u
r
e
d
C
on
f
i
r
m
e
d
c
a
s
e
s
R
²
= 0.8034
300
1300
2300
3300
300
800
1300
1800
2300
2800
3300
S
i
m
u
l
a
t
e
d
A
N
N
-
C
on
f
i
r
m
e
d
c
a
s
e
s
M
e
a
s
u
r
e
d
c
on
f
i
r
m
e
d
c
a
s
e
s
R
²
= 0.9986
300
800
1300
1800
2300
2800
3300
300
800
1300
1800
2300
2800
3300
S
i
m
u
l
a
t
e
d
-
A
N
F
I
S
c
on
f
i
r
m
e
d
c
a
s
e
s
M
e
a
s
u
r
e
d
C
on
f
i
r
m
e
d
c
a
s
e
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
P
r
e
di
c
ti
on of t
he
e
ff
e
c
ts
of
e
nv
i
r
onm
e
nt
al
f
ac
to
r
s
t
ow
ar
d
s
C
O
V
I
D
-
19 outbr
e
ak
…
(
K
hal
id
M
ahm
oud
)
41
A
pa
r
t
f
r
om
th
e
de
te
r
m
in
a
ti
on
c
oe
f
f
ic
ie
nt
(
R
2)
s
how
n
in
T
a
bl
e
2
th
e
a
dva
nt
a
ge
s
of
A
N
F
I
S
a
nd
A
N
N
in
c
om
pa
r
is
on
w
it
h
th
e
tr
a
di
ti
ona
l
M
L
R
m
ode
l
is
th
a
t
m
os
tl
y
M
L
R
f
a
il
s
a
t
a
c
e
r
ta
in
poi
nt
e
s
pe
c
ia
ll
y
w
he
n
it
e
nc
ount
e
r
e
d
a
hi
ghl
y
c
om
pl
e
x
a
nd
s
ophi
s
ti
c
a
te
d
non
-
li
ne
a
r
d
a
ta
,
w
hi
c
h
is
s
in
c
e
M
L
R
m
ode
l
f
ol
lo
w
s
th
e
m
e
c
ha
ni
s
m
of
le
a
s
t
-
s
qua
r
e
s
m
e
th
od.
B
e
s
id
e
s
,
th
e
m
a
jo
r
r
e
a
s
on
is
th
e
ge
n
e
r
a
ti
on
of
ne
ga
ti
ve
r
e
s
ul
ts
th
a
t
c
a
n
hi
nde
r
th
e
pe
r
f
or
m
a
nc
e
of
th
e
m
ode
l.
F
ig
ur
e
4
d
e
pi
c
ts
th
e
p
e
r
f
or
m
a
nc
e
of
th
e
m
ode
ls
f
or
th
e
c
onf
ir
m
e
d
C
O
V
I
D
-
19
c
a
s
e
s
in
W
uha
n
c
it
y
of
C
hi
na
us
in
g
a
r
a
da
r
c
ha
r
t
th
a
t
s
how
s
th
e
s
c
a
le
of
R
in
bot
h
th
e
tr
a
in
in
g
a
nd t
e
s
ti
ng s
ta
ge
s
.
F
ig
ur
e
4.
R
a
da
r
c
ha
r
t
of
t
he
c
onf
ir
m
e
d C
O
V
I
D
-
19 c
a
s
e
s
i
n W
u
ha
n C
it
y of
C
hi
na
T
he
pr
e
di
c
ti
ve
c
om
pa
r
is
on
of
th
e
r
e
s
ul
ts
c
a
n
be
a
r
r
a
nge
d
a
s
A
N
F
I
S
>
A
N
N
>
M
L
R
f
or
th
e
pr
e
di
c
ti
on
of
c
onf
ir
m
e
d
c
a
s
e
s
of
C
O
V
I
D
-
19
in
th
e
W
uh
a
n
C
it
y
of
C
hi
na
us
in
g
va
r
io
us
e
nvi
r
onm
e
nt
a
l
va
r
ia
bl
e
s
.
F
ig
ur
e
5
de
m
ons
tr
a
te
d
th
e
r
e
s
pons
e
of
th
e
m
od
e
ls
ba
s
e
d
on
ti
m
e
s
e
r
ie
s
pl
ot
s
.
A
c
c
or
di
ng
to
th
is
pl
ot
,
th
e
e
xt
e
n
t
by
w
hi
c
h
th
e
v
a
lu
e
s
w
e
r
e
s
pr
e
a
d
b
e
twe
e
n
th
e
m
e
a
s
ur
e
d a
nd
th
e
pr
e
di
c
te
d
m
ode
ls
pr
ove
d T
a
bl
e
2. T
hi
s
r
e
s
ul
t
is
i
n l
in
e
w
it
h [
25
-
27]
.
F
ig
ur
e
5.
T
im
e
s
e
r
ie
s
pl
ot
s
of
t
he
m
e
a
s
ur
e
d a
nd
s
im
ul
a
te
d c
onf
ir
m
e
d C
O
V
I
D
-
19 c
a
s
e
s
4.
C
O
N
C
L
U
S
I
O
N
T
he
th
r
e
a
t
of
C
O
V
I
D
-
19
is
of
gl
oba
l
c
onc
e
r
n
th
a
t
ne
e
d
t
o
be
a
ddr
e
s
s
e
d
qui
c
kl
y
a
nd
r
a
pi
dl
y
c
onc
e
r
ni
ng
it
s
ne
ga
ti
ve
im
pa
c
t
w
or
ld
w
id
e
.
T
he
ne
e
d
f
o
r
pr
e
di
c
ti
ng
a
nd
s
im
ul
a
ti
ng
th
e
e
f
f
e
c
ts
of
th
e
s
e
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
f
or
th
e
e
lu
c
id
a
ti
on
of
nove
l
C
O
V
I
D
-
19
out
b
r
e
a
k
us
in
g
a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
(
A
I
)
is
of
pa
r
a
m
ount
im
por
ta
nc
e
.
I
t
is
th
e
r
e
f
or
e
,
s
ig
ni
f
ic
a
nt
to
s
im
ul
a
t
e
th
e
e
f
f
e
c
ts
of
th
e
s
e
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
a
ga
in
s
t
th
e
c
onf
ir
m
e
d c
a
s
e
s
of
C
O
V
I
D
-
19 dis
e
a
s
e
us
in
g di
f
f
e
r
e
nt
m
ode
ls
. I
n t
hi
s
w
or
k, t
hr
e
e
di
f
f
e
r
e
nt
m
o
de
ls
(
A
N
F
I
S
,
A
N
N
a
nd
M
L
R
)
w
e
r
e
e
m
pl
oye
d.
I
n
th
e
da
ta
-
dr
iv
e
n
m
e
th
od,
th
e
da
ta
w
a
s
c
ol
le
c
te
d
f
r
om
a
pr
e
vi
ous
hi
s
to
r
ic
a
l
s
tu
dy
publ
is
h
e
d
in
th
e
li
te
r
a
tu
r
e
,
r
e
f
e
r
e
nc
e
d
i
n
th
e
m
a
te
r
ia
l
a
nd
m
e
th
od
s
e
c
ti
on.
T
he
c
om
pa
r
a
ti
ve
r
e
s
ul
ts
pr
ove
d
th
e
a
bi
li
ty
of
th
e
A
I
-
ba
s
e
d
m
ode
ls
(
A
N
F
I
S
a
nd
A
N
N
)
ove
r
th
e
tr
a
di
ti
ona
l
r
e
gr
e
s
s
io
n
m
ode
l
(
M
L
R
)
in
pr
e
di
c
ti
ng
th
e
c
onf
ir
m
e
d
c
a
s
e
s
.
T
he
r
e
s
ul
ts
e
qua
ll
y
s
how
e
d
th
e
s
tr
e
ngt
h
of
A
N
F
I
S
a
s
a
hybr
id
m
ode
l
in
out
pe
r
f
or
m
in
g
th
e
ot
he
r
two
m
od
e
ls
a
nd
in
c
r
e
a
s
e
d
th
e
ir
pr
e
di
c
ti
ve
pe
r
f
or
m
a
nc
e
a
c
c
ur
a
c
y
up
to
25%
in
th
e
te
s
ti
ng
ph
a
s
e
.
M
o
s
tl
y,
th
e
non
-
li
ne
a
r
m
ode
l
s
di
s
pl
a
ye
d
hi
ghe
r
pr
e
di
c
ti
on
a
c
c
ur
a
c
y
th
a
n
th
e
c
la
s
s
ic
a
l
li
ne
a
r
m
ode
ls
a
nd
he
nc
e
r
e
ga
r
de
d
a
s
r
e
li
a
bl
e
f
or
pr
e
di
c
ti
ng
th
e
e
f
f
e
c
ts
of
e
nvi
r
onm
e
nt
a
l
f
a
c
to
r
s
to
w
a
r
ds
C
O
V
I
D
-
19
out
br
e
a
k.
O
th
e
r
no
n
-
li
ne
a
r
m
ode
ls
s
uc
h
as
s
uppor
t
ve
c
to
r
m
a
c
hi
ne
(
S
V
M
)
,
ha
m
m
e
r
s
te
in
-
w
e
in
e
r
0
1000
2000
3000
4000
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
S
I
M
U
L
A
T
E
D
V
A
L
U
E
S
T
I
M
E
S
S
E
R
I
E
S
M
e
a
s
ur
e
d
M
L
R
ANN
A
N
F
I
S
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
10
, N
o.
1
,
M
a
r
c
h
20
2
1
:
35
–
42
42
(
H
W
)
,
f
uz
z
y
lo
gi
c
(
F
L
)
a
s
w
e
ll
a
s
di
f
f
e
r
e
nt
opt
im
iz
a
ti
on
a
lg
or
it
hm
s
s
uc
h
a
s
g
e
ne
ti
c
a
lg
or
it
hm
s
(
G
A
)
a
r
e
r
e
c
om
m
e
nde
d
in
or
de
r
to
im
pr
ove
th
e
pe
r
f
o
r
m
a
nc
e
a
c
c
ur
a
c
y
of
th
e
m
ode
ll
in
g.
T
hi
s
w
or
k
is
not
onl
y
r
e
s
tr
ic
te
d
to
c
hi
na
in
f
a
c
t
th
e
s
im
ul
a
ti
on
o
f
va
r
io
us
e
nvi
r
on
m
e
nt
a
l
f
a
c
to
r
s
to
w
a
r
ds
de
te
r
m
in
in
g
num
be
r
of
C
O
V
I
D
-
19 c
a
s
e
s
i
s
hi
ghl
y r
e
c
om
m
e
nde
d i
n ot
he
r
pa
r
ts
of
t
he
w
or
ld
.
R
E
F
E
R
E
N
C
E
S
[1]
B.
Pirouz,
S.
S.
Haghshenas,
S.
S.
Haghshenas,
and
P.
Piro,
“Investi
gating
a
serious
challenge
in
the
sustainable
development
process:
Analysis
of
confirmed
cases
of
COVID
-
19
(
new
type
of
Coronavirus)
through
a
binary
classifi
cation u
sing art
ificial
intell
igence and
regression
analysi
s,” Sus
tain. (U
nited S
ta
tes), vol. 12
, no. 6, 2020.
[2]
N.
Zhu
et
al
.,
“A
novel
coronavirus
from
patients
with
pneumonia
in
China,
2019,”
N.
Engl.
J.
Med
.,
vol.
382,
no.
8, pp. 727
-
733, 2020.
[3]
E.
Dong,
H.
Du,
and
L.
Gardner,
“An
interactive
web
-
based
dashbo
ard
to
track
COVID
-
19
in
rea
l
time,”
Lance
t
Infect. Dis
., vol. 20, no. 5, pp. 533
-
534, 2020.
[4]
World Healt
h Organizat
ion, “Mo
des of t
ransmiss
ion of v
irus caus
ing COV
ID
-
19,” pp. 19
-
21, March 2020.
[5]
X.
Wang,
Z.
Zhou,
J.
Zhang,
F.
Zhu,
Y.
Tang,
and
X.
Shen,
“A
ca
se
of
2019
Novel
Coronaviru
s
in
a
pregnant
woman with preterm d
elivery,”
Clin. Infe
ct. Dis
., 2020.
[6]
C.
Liu
et
al
.,
“Research
and
Development
on
Therapeutic
Agents
and Vaccines
for
COVID
-
19
and
Related
Human
Coronavir
us Disea
ses,”
ACS Cent. Sci
., vol. 6, no. 3, pp. 315
-
331, 2020.
[7]
M.
C.
Çolak,
C.
Çolak,
H.
Kocatürk,
Ş.
Saǧiroglu,
and
I.
Barutçu
,
“Predicting
coronary
artery
disease
using
different artificial neur
al network models,”
Anadolu Kardiyol. Derg
., vol. 8, no. 4, pp. 249
-
254, 2008.
[8]
L.
Begic
Fazlic,
K.
Avdagic,
and
S.
Omanovic
,
“GA
-
ANFIS
Expert
System
Prototype
for
Prediction
of
Dermatological Diseases,
”
Stud. Health Technol. Inform
., vol. 210, pp. 622
-
626, 2015.
[9]
Y.
Ma
et
al
.,
“Effects
of
temperature
variation
and
humidity
on
the
death
of
COVID
-
19
in
Wuhan,
China,”
Sci.
Total E
nviron
., vol. 724, p. 138226, 2020.
[10]
N. C. Peeri
et al
., “The SARS, MER
S and novel coronavirus (COVID
-
19) epidemics, the newest and biggest globa
l
health threats: what lessons have we le
arned?,”
Int. J. Epi
demiol
., pp. 1
-
10, 2020.
[11]
J.
W.
Tang,
“The
effect
of
environm
ental
p
arameters
on
the
surviv
al
of
airborne
infectio
us
agents,”
J.
R.
Soc.
Interface
, vol. 6, no. SUPPL. 6, 2009.
[12]
V.
Nourani,
H.
Hakim
zadeh,
and
A.
B.
Amini,
“Implementation
o
f
artificial
neural
network
technique
in
the
simulation of dam brea
ch h
ydrograph,”
J. Hydroi
nformat
ics
, vol. 14, no. 2, p. 478, 2012.
[13]
H.
Shibata,
M.
Ito,
M.
Asakursa,
and
K.
Watanabe,
“A
digital
hygrom
eter
using
a
polyimide
film
relative
humidity
sensor,”
IEEE Trans
. Instrum.
Meas
., vol. 45, no. 2, pp. 564
-
569, 1996.
[14]
D.
S.
Mo
ran
,
L.
Mendal,
“Core
temperature
measurement:
Methods
a
nd
current
insights,”
Sport.
Med
.,
vol.
32,
no
.
14, pp. 879
-
885, 2002.
[15]
E.
I.
Kaganov
,
A.
M.
Yaglom,
“Errors
in
wind
-
speed
measurements
by
rotation
anemometers,”
Boundary
-
Layer
Meteorol
., vol. 10, no. 1, pp. 15
-
34, 1976.
[16]
M.
Mohammadhassani,
H.
Nezamabadi
-
Pour,
M.
Z.
Jumaat,
M.
Jame
el,
and
A.
M.
S.
Arumugam,
“Application
of
artificial
neural
networks
(ANNs)
and
linear
regressions
(LR)
to
pre
dict
the
deflection
of
concrete
deep
beams,”
Comput. Conc
r
., vol. 11, no. 3, pp. 237
-
252, 2013.
[17]
G.
Zhang,
B.
Eddy
Patuwo,
and
M.
Y.
Hu,
“Forecasting
with
artificial
neural
networks:
The
state
of
the
art,”
Int.
J.
Forecast
., vol. 14, no. 1, pp. 35
-
62, 1998.
[18]
S.
I.
Abba,
A.
G.
Usman,
and
S.
IŞIK,
“Simulation
for
response
surface
in
the
HPLC
optimization
method
development using artificial intelligence models:
A data
-
driven
approa
ch,”
Chemom.
Intell. Lab
.
Syst
.
, vol. 201,
no.
August 2019, 2
020.
[19]
S.
I.
Abba
et
al
.,
“Emerging
evolutiona
ry
algorithm
integrated
wi
th
kernel
principal
component
analysis
for
modeling the p
erforma
nce of a
water tr
eatment pla
nt,”
J. Water
Process E
ng
., vol. 33, no. October 2019, p. 101081
,
2020.
[20]
F.
Khademi,
S.
M.
Jamal,
N.
Deshpande,
and
S.
Londhe,
“Predicting
strength
of
recycled
aggregate
concrete
using
Artificial
Neural
Network,
Adaptive
Neuro
-
Fuzzy
Inference
Syste
m
and
Multiple
Linear
Regression,”
Int.
J.
Sustain. Built Environ
., vol.
5, no. 2, pp. 355
-
369, 2016.
[21]
J.
Tomi
et
al
.,
“Chemometrically
Assisted
RP
-
HPLC
Method
Develo
pment
for
Efficient
Separation
of
Ivabradine
and its
Eleven Im
purities
,” vol.
32, no.
June 20
19, pp.
53
-
63, 2020.
[22]
Q.
B.
Pham
et
al
.,
“Potential
of
Hybrid
Data
-
Intelligen
ce
Algorith
ms
for
Multi
-
Station
Modellin
g
of
Rainfa
ll,”
Water Resour
. Manag
., vol. 33, no. 15, 2019.
[23]
A.
G.
U.
Selin
,
I.
S.
I.
Abba,
“A
Novel
Multi
-
model
Data
-
Drive
n
Ensemble
Technique
for
the
Prediction
of
Retenti
on Factor in HPLC Method Development,”
Chromatogra
phia
, no. 0123456789, 2020.
[24]
Z.
M.
Yaseen
et
al
.,
“A
novel
hybrid
evolutionary
data
-
intelligence
a
lgorithm
for
irrigation
and
power
production
manageme
nt: Application
to multi
-
purpose reservoir systems,” Sus
tain., vol. 11,
no. 7, 2019.
[25]
U.
M.
Ghali
et
al.
,
“Applications
of
Artificial
Intelligence
-
Based
Models
and
Multi
-
Linear
Regression
for
the
Predic
tion of
Thyro
id Stimula
ting Hor
mone L
evel i
n the H
uman Bo
dy,”
IJAS
T
vol. 29, no. 4, pp. 3690
-
3699, 2020.
[26]
H.
U.
Abdullahi,
A.
G.
Usman,
and
S.
I.
Abba,
“Modelling
the
Absorbance
of
a
Bioactive
Compound
in
HPLC
Method using Artificia
l Neural Netw
ork and Multilinear
Regression Methods,”
vol. 6, no. 2, pp. 362
-
371, 2020.
[27]
Als
harksi,
Ahmed
Nouri,
Y.
A.
Danmaraya,
Hadiz
a
Usman
Abdullahi
,
Umar
Muhammad
Ghali,
and
A.
G.
Usman.
"
Potential of Hybrid Adaptive Neuro Fuzzy Model in Simulating Clostridium Difficile Infection Status. 2020"
.
Evaluation Warning : The document was created with Spire.PDF for Python.