I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
8
,
No
.
3
,
J
u
n
e
201
8
,
p
p
.
1
5
3
0
~
1
5
3
8
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v8
i
3
.
p
p
1
5
3
0
-
1538
1530
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
co
r
e
.
co
m/
jo
u
r
n
a
ls
/in
d
ex
.
p
h
p
/
I
JE
C
E
Cla
ss
ificatio
n of
No
r
m
a
l and
Crac
k
les
Respira
tory
So
unds
int
o
H
ea
lthy a
nd
Lun
g
Cancer
G
ro
ups
N.
Abdu
l M
a
lik
,
W.
I
dris
,
T
.
S.
G
un
a
w
a
n
,
R.
F
.
O
la
nrewa
j
u,
S.
No
o
rj
a
nn
a
h Ib
ra
hi
m
De
p
a
rte
m
e
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
Co
m
p
u
ter E
n
g
in
e
e
rin
g
,
In
ter
n
a
ti
o
n
a
l
Isla
m
i
c
Un
iv
e
rsit
y
M
a
la
y
si
a
,
M
a
la
y
sia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
1
9
,
2
0
1
8
R
ev
i
s
ed
A
p
r
2
,
2
0
1
8
A
cc
ep
ted
A
p
r
1
0
,
2
0
1
8
L
u
n
g
c
a
n
c
e
r
is
th
e
m
o
st
c
o
m
m
o
n
c
a
n
c
e
r
w
o
rld
w
id
e
a
n
d
t
h
e
t
h
ird
m
o
st
c
o
m
m
o
n
c
a
n
c
e
r
in
M
a
la
y
sia
.
Du
e
to
it
s
h
ig
h
p
re
v
a
len
c
e
w
o
rld
w
i
d
e
a
n
d
in
M
a
la
y
sia
,
it
is
a
n
u
t
m
o
st
im
p
o
rta
n
c
e
to
h
a
v
e
th
e
d
ise
a
se
d
e
tec
ted
a
t
a
n
e
a
rl
y
sta
g
e
w
h
ich
w
o
u
ld
re
su
lt
in
a
h
ig
h
e
r
c
h
a
n
c
e
o
f
c
u
re
a
n
d
p
o
ss
ib
ly
b
e
tt
e
r
su
rv
iv
a
l.
T
h
e
c
u
rre
n
t
m
e
th
o
d
s
u
s
e
d
f
o
r
lu
n
g
c
a
n
c
e
r
sc
re
e
n
in
g
m
i
g
h
t
n
o
t
b
e
sim
p
le,
in
e
x
p
e
n
siv
e
a
n
d
sa
f
e
a
n
d
n
o
t
re
a
d
il
y
a
c
c
e
s
sib
le
in
o
u
t
p
a
ti
e
n
t
c
li
n
ics
.
In
t
h
is
p
a
p
e
r,
w
e
p
re
se
n
t
t
h
e
c
l
a
ss
if
i
c
a
ti
o
n
o
f
n
o
rm
a
l
a
n
d
c
ra
c
k
les
so
u
n
d
s
a
c
q
u
ired
f
ro
m
2
0
h
e
a
lt
h
y
a
n
d
2
3
lu
n
g
c
a
n
c
e
r
p
a
ti
e
n
ts,
re
sp
e
c
ti
v
e
l
y
u
sin
g
A
rti
f
icia
l
Ne
u
ra
l
Ne
t
w
o
rk
.
F
irstl
y
,
th
e
so
u
n
d
s
sig
n
a
ls
w
e
re
d
e
c
o
m
p
o
se
d
in
to
se
v
e
n
d
iff
e
re
n
t
f
re
q
u
e
n
c
y
b
a
n
d
s
u
sin
g
Disc
re
te
W
a
v
e
l
e
t
T
ra
n
s
f
o
rm
(D
WT
)
b
a
se
d
o
n
tw
o
d
if
f
e
r
e
n
t
m
o
th
e
r
w
a
v
e
lets
n
a
m
e
l
y
Da
u
b
e
c
h
ies
7
(d
b
7
)
a
n
d
H
a
a
r.
S
e
c
o
n
d
ly
,
m
e
a
n
,
sta
n
d
a
rd
d
e
v
iatio
n
a
n
d
m
a
x
i
m
u
m
P
S
D
o
f
th
e
d
e
tail
c
o
e
ff
icie
n
ts
f
o
r
f
iv
e
f
re
q
u
e
n
c
y
b
a
n
d
s
(D3
,
D4
,
D5
,
D6
,
a
n
d
D7
)
w
e
r
e
c
a
lcu
late
d
a
s
f
e
a
tu
re
s.
F
if
tee
n
fe
a
tu
re
s
w
e
r
e
u
se
d
a
s
in
p
u
t
to
th
e
A
NN
c
las
si
f
ier.
T
h
e
re
su
lt
s
o
f
c
l
a
ss
i
f
ic
a
ti
o
n
sh
o
w
th
a
t
d
b
7
b
a
se
d
p
e
rf
o
rm
e
d
b
e
tt
e
r
th
a
n
Ha
a
r
w
it
h
p
e
rf
e
c
t
1
0
0
%
se
n
siti
v
it
y
,
sp
e
c
i
f
icit
y
a
n
d
a
c
c
u
ra
c
y
f
o
r
tes
ti
n
g
a
n
d
v
a
li
d
a
ti
o
n
sta
g
e
s
w
h
e
n
u
sin
g
1
5
n
o
d
e
s
a
t
th
e
h
id
d
e
n
lay
e
r.
W
h
il
e
f
o
r
Ha
a
r,
o
n
ly
te
stin
g
sta
g
e
sh
o
w
s
t
h
e
p
e
rf
e
c
t
1
0
0
%
f
o
r
s
e
n
siti
v
it
y
,
sp
e
c
if
ic
it
y
,
a
n
d
a
c
c
u
ra
c
y
w
h
e
n
u
sin
g
1
0
n
o
d
e
s at t
h
e
h
i
d
d
e
n
lay
e
r.
K
ey
w
o
r
d
:
A
N
N
C
r
ac
k
le
s
DW
T
L
u
n
g
ca
n
ce
r
R
esp
ir
ato
r
y
s
o
u
n
d
Co
p
y
rig
h
t
©
2
0
1
8
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
N.
A
b
d
u
l M
ali
k
,
Dep
ar
t
m
en
t o
f
E
lectr
ical
an
d
C
o
m
p
u
ter
E
n
g
in
ee
r
i
n
g
,
I
n
ter
n
atio
n
al
I
s
la
m
ic
U
n
iv
er
s
it
y
Ma
la
y
s
ia,
J
alan
Go
m
b
ak
,
5
3
1
0
0
Ku
ala
L
u
m
p
u
r
,
Ma
la
y
s
ia
.
E
m
ail:
n
o
r
eh
aa
@
iiu
m
.
ed
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
A
cc
o
r
d
in
g
to
I
n
ter
n
at
io
n
al
A
g
en
c
y
f
o
r
R
e
s
ea
r
ch
o
n
C
a
n
c
er
(
I
A
R
C
)
[
1
]
,
lu
n
g
ca
n
ce
r
i
s
t
h
e
m
o
s
t
co
m
m
o
n
ca
n
ce
r
w
o
r
ld
w
id
e
w
ith
m
o
r
e
th
a
n
1
.
8
m
illi
o
n
n
e
w
ca
s
es
an
d
1
.
6
m
il
lio
n
d
ea
th
s
esti
m
ated
in
2
0
1
2
.
W
h
ile
in
Ma
la
y
s
ia,
it
i
s
th
e
th
ir
d
m
o
s
t
co
m
m
o
n
ca
n
ce
r
af
ter
co
lo
r
ec
tal
an
d
b
r
ea
s
t
ca
n
ce
r
s
.
T
h
er
e
w
er
e
1
0
,
6
0
8
ca
s
es
r
ep
o
r
ted
b
y
Ma
la
y
s
ia
n
Natio
n
a
l
C
a
n
ce
r
R
eg
i
s
tr
y
b
et
w
ee
n
2
0
0
7
an
d
2
0
1
1
[
2
]
.
Ma
j
o
r
ity
o
f
th
e
p
atien
ts
p
r
ese
n
t
later
s
ta
g
e
o
f
th
e
d
is
ea
s
e
ie.
s
ta
g
e
3
o
r
4
d
is
ea
s
es
a
n
d
th
er
e
f
o
r
e
cu
r
ati
v
e
tr
ea
t
m
e
n
t
is
s
eld
o
m
an
o
p
tio
n
an
d
p
r
o
g
n
o
s
i
s
is
p
o
o
r
.
Du
e
to
its
h
ig
h
p
r
ev
ale
n
ce
in
Ma
la
y
s
ia
,
it
is
a
n
u
t
m
o
s
t
i
m
p
o
r
ta
n
ce
to
h
a
v
e
th
e
d
is
ea
s
e
d
etec
ted
at
an
ea
r
l
y
s
tag
e
w
h
ic
h
w
o
u
ld
r
esu
lt
in
a
h
ig
h
er
ch
an
ce
o
f
cu
r
e
an
d
p
o
s
s
ib
l
y
b
etter
s
u
r
v
iv
al.
Sp
u
t
u
m
c
y
to
lo
g
y
a
n
d
ch
est
X
-
r
a
y
(
C
XR
)
h
as
b
ee
n
u
s
ed
f
o
r
s
cr
ee
n
i
n
g
o
f
l
u
n
g
ca
n
ce
r
an
d
r
ec
en
tl
y
lo
w
r
ad
iat
io
n
-
d
o
s
e
h
e
lical
C
T
(
s
p
ir
al
C
T
)
h
as
b
ee
n
s
h
o
w
n
to
b
e
s
u
p
er
io
r
to
co
n
v
e
n
tio
n
al
C
XR
[
3
]
.
Au
to
f
lu
o
r
esce
n
ce
b
r
o
n
ch
o
s
co
p
y
is
al
s
o
o
n
e
o
f
th
e
p
o
ten
tial
s
cr
ee
n
in
g
to
o
ls
[
4
]
.
Ho
w
e
v
er
,
all
th
e
m
e
n
tio
n
ed
test
s
m
i
g
h
t
n
o
t b
e
s
i
m
p
le,
i
n
e
x
p
en
s
i
v
e
an
d
s
a
f
e
an
d
n
o
t r
ea
d
i
l
y
ac
ce
s
s
ib
le
in
o
u
tp
atie
n
t c
li
n
ics.
Au
s
c
u
ltat
io
n
is
a
n
o
n
-
i
n
v
asi
v
e,
s
af
e
a
n
d
in
e
x
p
en
s
iv
e
tec
h
n
iq
u
e
u
s
ed
to
lis
ten
to
l
u
n
g
an
d
h
ea
r
t
s
o
u
n
d
s
.
I
t
is
p
er
f
o
r
m
ed
as
clin
ical
e
x
a
m
in
a
tio
n
a
n
d
it
ca
n
p
r
o
v
id
e
u
s
e
f
u
l
in
f
o
r
m
at
i
o
n
r
eg
ar
d
in
g
lu
n
g
co
n
d
itio
n
.
C
o
m
p
u
ter
ized
a
u
s
cu
ltatio
n
h
a
s
o
v
er
co
m
e
li
m
it
atio
n
s
o
f
t
h
e
tr
ad
itio
n
al
tech
n
iq
u
e
th
at
u
s
es
a
n
an
alo
g
s
teth
o
s
co
p
e.
C
lass
i
f
ic
atio
n
s
o
f
r
e
s
p
ir
ato
r
y
s
o
u
n
d
t
h
r
o
u
g
h
co
m
p
u
ter
ized
au
s
cu
l
t
atio
n
h
a
v
e
s
h
o
w
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
C
la
s
s
i
fica
tio
n
o
f No
r
ma
l a
n
d
C
r
a
ck
les R
esp
i
r
a
to
r
y
S
o
u
n
d
s
i
n
t
o
Hea
lth
y
a
n
d
Lu
n
g
…
(
N
.
A
b
d
u
l Ma
lik
)
1531
p
r
o
m
i
s
in
g
r
esu
lts
f
o
r
th
e
d
iag
n
o
s
is
o
f
v
ar
io
u
s
lu
n
g
d
is
ea
s
es
[
5
]
.
B
ased
o
n
th
e
r
ev
ie
w
p
ap
er
,
lu
n
g
s
o
u
n
d
an
al
y
s
es
u
s
in
g
co
m
p
u
ter
ized
au
s
c
u
ltatio
n
g
a
v
e
g
o
o
d
r
esu
lts
o
f
s
e
n
s
i
tiv
it
y
a
n
d
s
p
ec
i
f
icit
y
.
Mo
s
t
o
f
t
h
e
an
al
y
s
es
p
r
e
-
p
r
o
ce
s
s
ed
t
h
e
s
o
u
n
d
s
i
g
n
al
to
r
ed
u
ce
n
o
is
e
s
a
n
d
ex
tr
ac
t
u
s
e
f
u
l
f
ea
t
u
r
es a
n
d
u
s
e
m
ac
h
i
n
e
lear
n
i
n
g
f
o
r
class
i
f
i
ca
tio
n
.
T
h
er
e
w
er
e
d
if
f
er
en
t
f
ilter
i
n
g
tech
n
iq
u
e
s
u
s
ed
to
r
ed
u
ce
h
ea
r
t
s
o
u
n
d
s
f
r
o
m
lu
n
g
s
o
u
n
d
s
s
u
c
h
as
w
a
v
elet
tr
an
s
f
o
r
m
[
6
]
,
ad
ap
tiv
e
f
ilter
in
g
[
7
]
,
[
8
]
an
d
b
an
d
p
ass
f
ilter
in
g
[
8
]
.
Fo
r
f
u
r
th
er
an
al
y
s
is
,
f
a
s
t
Fo
u
r
ier
tr
an
s
f
o
r
m
(
FF
T
)
[
9
]
,
s
h
o
r
t
ti
m
e
Fo
u
r
ier
tr
an
s
f
o
r
m
(
ST
FT)
[
10
]
o
r
d
is
cr
ete
w
av
el
et
tr
an
s
f
o
r
m
(
DW
T
)
[1
1
]
h
as
b
ee
n
ap
p
lied
to
tr
an
s
f
o
r
m
t
h
e
s
ig
n
al
s
i
n
to
d
i
f
f
er
en
t
d
o
m
a
in
s
u
ch
as
f
r
eq
u
en
c
y
o
r
ti
m
e
-
f
r
eq
u
e
n
c
y
d
o
m
ai
n
.
Sp
ec
tr
al
r
ep
r
esen
tati
o
n
o
f
t
h
e
s
ig
n
al
m
a
k
es
i
t
m
o
r
e
u
s
e
f
u
l
to
ex
tr
ac
t
f
ea
t
u
r
es
r
eq
u
i
r
ed
b
y
lear
n
i
n
g
alg
o
r
ith
m
s
.
R
esp
ir
ato
r
y
s
o
u
n
d
s
ca
n
b
e
cl
ass
i
f
ied
i
n
to
n
o
r
m
al
a
n
d
ad
v
e
n
titi
o
u
s
s
o
u
n
d
s
.
T
h
e
ad
v
e
n
titi
o
u
s
s
o
u
n
d
ca
n
b
e
f
u
r
t
h
er
class
if
ied
as
d
is
co
n
ti
n
u
o
u
s
a
n
d
co
n
t
in
u
o
u
s
b
ased
o
n
th
eir
c
h
ar
ac
ter
is
tic
s
[
1
2
]
.
Fo
r
in
s
tan
ce
,
cr
ac
k
les
eit
h
er
f
i
n
e
o
r
co
ar
s
e
ar
e
class
i
f
ied
as
d
is
co
n
ti
n
u
o
u
s
ad
v
en
tit
io
u
s
s
o
u
n
d
s
w
h
ile
w
h
ee
ze
s
an
d
r
h
o
n
c
h
u
s
b
elo
n
g
to
co
n
ti
n
u
o
u
s
ad
v
e
n
tit
io
u
s
s
o
u
n
d
s
[
1
3
]
.
Sev
er
al
s
t
u
d
ies
h
av
e
a
n
al
y
s
ed
r
esp
ir
ato
r
y
s
o
u
n
d
s
i
n
ast
h
m
a,
p
n
eu
m
o
n
ia,
c
h
r
o
n
ic
o
b
s
tr
u
cti
v
e
p
u
l
m
o
n
ar
y
d
i
s
ea
s
e
(
C
OP
D)
an
d
i
d
io
p
ath
ic
p
u
l
m
o
n
ar
y
f
ib
r
o
s
is
p
atien
t
s
to
ch
ar
ac
ter
ize
an
d
clas
s
i
f
y
as
n
o
r
m
al,
w
h
ee
ze
,
r
h
o
n
ch
i,
co
ar
s
e
cr
ac
k
les
a
n
d
f
i
n
e
cr
ac
k
les
[
1
1]
,
[
1
3
]
.
Dif
f
er
en
t
t
y
p
es
o
f
cla
s
s
i
f
icatio
n
m
et
h
o
d
s
h
a
v
e
b
ee
n
e
m
p
lo
y
ed
to
class
if
y
cr
ac
k
le
s
f
o
r
ex
a
m
p
les
u
s
i
n
g
T
s
allis
E
n
tr
o
p
y
an
d
Mu
lti
la
y
er
P
er
ce
p
tr
o
n
[
1
4
]
,
K
-
n
ea
r
est Ne
ig
h
b
o
r
[
1
5
]
an
d
Su
p
p
o
r
t V
ec
to
r
Ma
ch
in
e
(
SVM)
[1
6
].
A
lt
h
o
u
g
h
m
an
y
r
esear
c
h
er
s
h
a
v
e
clas
s
i
f
ied
cr
ac
k
les
s
o
u
n
d
,
n
o
n
e
h
as
tak
e
n
s
a
m
p
les
f
r
o
m
l
u
n
g
ca
n
ce
r
p
atien
ts
f
r
o
m
th
e
b
est
k
n
o
w
l
ed
g
e
o
f
t
h
e
a
u
t
h
o
r
s
.
On
l
y
[
1
3
]
h
a
v
e
u
s
ed
s
a
m
p
le
s
f
r
o
m
l
u
n
g
ca
n
ce
r
b
u
t
th
e
s
a
m
p
les
w
er
e
tak
e
n
r
an
d
o
m
l
y
f
r
o
m
v
ar
io
u
s
p
u
l
m
o
n
ar
y
d
is
e
ases
f
o
r
th
e
a
n
al
y
s
is
.
I
n
t
h
is
s
tu
d
y
,
th
e
cr
ac
k
le
s
s
o
u
n
d
s
ar
e
ex
tr
ac
ted
f
r
o
m
l
u
n
g
ca
n
ce
r
p
atien
ts
o
n
l
y
.
2.
M
E
T
H
O
DO
L
O
G
Y
T
h
is
s
ec
tio
n
p
r
esen
ts
t
h
e
m
e
t
h
o
d
o
lo
g
y
u
s
ed
in
t
h
is
s
t
u
d
y
t
o
class
if
y
b
et
w
ee
n
n
o
r
m
a
l
an
d
cr
ac
k
les
s
o
u
n
d
s
in
h
ea
lt
h
y
a
n
d
lu
n
g
ca
n
ce
r
p
atien
ts
.
T
h
e
p
r
o
p
o
s
ed
al
g
o
r
ith
m
is
s
h
o
w
n
in
Fig
u
r
e
1.
Fig
u
r
e
1
.
P
r
o
p
o
s
ed
A
lg
o
r
ith
m
u
s
i
n
g
DW
T
to
I
d
en
tify
No
r
m
a
l a
n
d
C
r
ac
k
le
s
in
Hea
lth
y
a
n
d
L
u
n
g
C
a
n
ce
r
P
atien
ts
2.
1.
Da
t
a
co
llect
io
n
a
nd
pre
-
pro
ce
s
s
ing
o
f
re
s
pira
t
o
ry
s
o
un
ds
Data
co
llectio
n
f
o
r
th
i
s
s
t
u
d
y
w
a
s
ap
p
r
o
v
ed
b
y
m
ed
ical
eth
ics
co
m
m
it
tee
o
f
U
n
i
v
er
s
i
t
y
Ma
la
y
a
Me
d
ical
C
en
tr
e
(
UM
MC)
w
i
th
r
e
f
er
en
ce
n
u
m
b
er
(
MRE
C
I
D
NO:
2
0
1
6
9
8
-
4
2
4
2
)
.
T
h
er
e
w
er
e
2
0
n
o
r
m
al
s
u
b
j
ec
t
s
an
d
2
3
l
u
n
g
ca
n
ce
r
p
atien
t
s
p
ar
ticip
ated
in
t
h
e
d
ata
co
llectio
n
w
h
ic
h
to
o
k
p
lace
a
t
C
li
n
ical
O
n
co
lo
g
y
Un
it,
U
n
i
v
er
s
it
y
Ma
la
y
a
Me
d
ical
C
en
tr
e.
T
h
e
p
ar
ticip
ated
p
atien
ts
h
av
e
n
o
co
-
e
x
is
ti
n
g
r
esp
ir
ato
r
y
r
elate
d
d
is
ea
s
es.
A
l
l
t
h
e
h
ea
l
th
y
s
u
b
j
ec
ts
r
ec
r
u
ited
a
r
e
t
h
e
n
o
n
-
s
m
o
k
er
.
A
l
l
t
h
e
s
u
b
j
ec
ts
w
er
e
g
i
v
en
in
f
o
r
m
co
n
s
e
n
t
f
o
r
m
a
n
d
b
r
ief
ed
o
n
t
h
e
s
t
u
d
y
p
r
o
to
co
l.
T
h
e
r
esp
ir
ato
r
y
s
o
u
n
d
o
f
n
o
r
m
al
s
u
b
j
ec
ts
an
d
l
u
n
g
ca
n
ce
r
p
atie
n
ts
w
a
s
ac
q
u
ir
ed
u
s
i
n
g
d
ig
ital
s
te
th
o
s
co
p
e
b
y
T
h
i
n
k
lab
s
a
n
d
s
a
v
ed
as
.
au
in
a
co
m
p
u
ter
lap
to
p
u
s
in
g
T
h
in
k
lab
s
P
h
o
n
o
ca
r
d
io
g
r
ap
h
y
b
y
Au
d
ac
it
y
.
T
h
e
s
teth
o
s
co
p
e
w
a
s
co
n
n
ec
ted
to
th
e
co
m
p
u
ter
lap
to
p
v
ia
a
s
o
u
n
d
ca
r
d
(
Xo
n
ar
U3
)
an
d
t
h
e
co
m
p
u
te
r
w
a
s
d
is
co
n
n
ec
ted
f
r
o
m
th
e
m
ai
n
p
o
w
er
s
u
p
p
l
y
d
u
r
in
g
t
h
e
r
ec
o
r
d
in
g
.
T
h
e
s
a
m
p
li
n
g
r
ate
u
s
ed
w
a
s
1
1
0
2
5
Hz.
A
ll
th
e
s
u
b
j
ec
ts
w
er
e
ask
ed
to
b
r
ea
th
e
n
o
r
m
a
ll
y
a
n
d
th
e
r
esp
ir
ato
r
y
s
o
u
n
d
w
a
s
r
ec
o
r
d
ed
f
o
r
a
b
o
u
t
2
0
s
e
co
n
d
s
f
o
r
ea
ch
au
s
c
u
ltatio
n
p
o
in
t.
I
n
to
tal,
th
er
e
w
er
e
t
w
e
n
t
y
-
t
w
o
au
s
cu
lta
tio
n
p
o
in
ts
,
ele
v
en
p
o
in
ts
ea
ch
a
t a
n
ter
io
r
an
d
p
o
s
ter
io
r
o
f
t
h
e
c
h
est
w
all
in
c
lu
d
i
n
g
tr
ac
h
ea
a
s
s
h
o
w
n
in
Fi
g
u
r
e
2
(
a)
an
d
Fig
u
r
e
2
(
b
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
5
3
0
–
1538
1532
(
a)
(
b
)
Fig
u
r
e
2
.
Au
s
c
u
ltatio
n
p
o
in
ts
(
a)
elev
en
p
o
in
t
s
an
ter
io
r
(
b
)
elev
en
p
o
in
t
s
p
o
s
ter
io
r
Nex
t,
a
b
an
d
p
ass
f
ilter
w
it
h
cu
t
-
o
f
f
f
r
eq
u
e
n
cie
s
o
f
1
0
0
an
d
2
0
0
0
Hz
w
a
s
ap
p
lied
to
th
e
r
a
w
r
esp
ir
ato
r
y
s
o
u
n
d
s
ig
n
al
to
en
h
an
ce
t
h
e
lu
n
g
s
o
u
n
d
u
s
in
g
T
h
in
k
lab
s
P
h
o
n
o
ca
r
d
io
g
r
ap
h
y
s
o
f
t
w
ar
e.
Fil
ter
i
n
g
p
r
o
ce
s
s
is
n
ee
d
ed
to
r
ed
u
ce
n
o
is
es c
o
m
in
g
f
r
o
m
th
e
h
ea
r
t,
m
u
s
cle
o
r
a
m
b
ie
n
t
w
h
ich
ar
e
n
o
t r
elate
d
to
th
e
lu
n
g
s
o
u
n
d
.
C
r
ac
k
les
s
o
u
n
d
s
p
r
esen
t
in
th
e
l
u
n
g
ca
n
ce
r
p
atien
t
‟
s
r
esp
ir
ato
r
y
s
o
u
n
d
w
er
e
id
en
ti
f
ied
m
a
n
u
al
l
y
.
T
h
e
r
esp
ir
ato
r
y
s
o
u
n
d
c
y
c
le
co
n
s
is
ts
o
f
cr
ac
k
le
s
i
s
e
x
tr
ac
ted
an
d
ex
p
o
r
ted
as
.
w
a
v
to
b
e
r
ea
d
l
ater
b
y
M
A
T
L
A
B
f
o
r
s
i
g
n
a
l
d
ec
o
m
p
o
s
itio
n
a
n
d
f
ea
t
u
r
e
ex
tr
ac
t
io
n
p
r
o
ce
s
s
es
.
T
h
er
e
w
er
e
6
0
s
a
m
p
les
co
n
s
i
s
t
o
f
cr
ac
k
les
s
o
u
n
d
an
d
6
0
s
a
m
p
les
o
f
n
o
r
m
al
s
o
u
n
d
s
.
Fig
u
r
e
3
(
a)
an
d
Fi
g
u
r
e
3
(
c)
s
h
o
w
t
h
e
r
esp
ir
ato
r
y
s
o
u
n
d
s
c
y
cle
o
f
l
u
n
g
ca
n
ce
r
p
atien
t
an
d
h
ea
lt
h
y
s
u
b
j
ec
t
b
ef
o
r
e
f
ilter
i
n
g
w
h
il
e
F
ig
u
r
e
3
(
b
)
an
d
Fi
g
u
r
e
3
(
d
)
af
ter
f
ilter
i
n
g
,
r
es
p
ec
tiv
el
y
.
Fig
u
r
e
3
.
R
esp
ir
ato
r
y
s
o
u
n
d
cy
cle
(
in
h
ale
a
n
d
ex
h
ale)
f
o
r
ab
n
o
r
m
al
s
o
u
n
d
(
a)
b
ef
o
r
e
an
d
(
b
)
af
ter
f
ilter
i
n
g
an
d
f
o
r
n
o
r
m
al
(
c)
b
ef
o
r
e
an
d
(
d
)
af
ter
f
ilter
in
g
2.
2.
Sig
na
l dec
o
m
po
s
it
io
n u
s
ing
dis
cr
et
e
w
a
v
elet
t
ra
ns
f
o
r
m
s
a
nd
f
ea
t
ure
ex
t
ra
ct
i
on
W
av
elet
tr
an
s
f
o
r
m
p
r
o
v
id
es ti
m
e
-
f
r
eq
u
e
n
c
y
r
ep
r
esen
tatio
n
o
f
a
s
i
g
n
al.
Di
s
cr
ete
w
av
e
let
tr
a
n
s
f
o
r
m
ca
n
b
e
w
r
itte
n
as [
1
7
],
(
)
(
)
(
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
C
la
s
s
i
fica
tio
n
o
f No
r
ma
l a
n
d
C
r
a
ck
les R
esp
i
r
a
to
r
y
S
o
u
n
d
s
i
n
t
o
Hea
lth
y
a
n
d
Lu
n
g
…
(
N
.
A
b
d
u
l Ma
lik
)
1533
w
h
er
e
ψ
is
t
h
e
w
a
v
elet
f
u
n
ct
io
n
o
r
th
e
m
o
th
er
w
a
v
elet.
j
is
a
p
o
s
itiv
e
v
alu
e
d
e
f
i
n
es
t
h
e
s
ca
lin
g
an
d
b
is
a
r
ea
l
n
u
m
b
er
t
h
at
d
e
f
i
n
es
th
e
s
h
i
f
ti
n
g
.
T
w
o
m
o
t
h
er
w
a
v
elet
s
n
a
m
el
y
Haa
r
a
n
d
d
b
7
h
a
v
e
b
ee
n
u
s
ed
f
o
r
t
h
e
s
ig
n
al
d
ec
o
m
p
o
s
itio
n
.
T
h
e
d
ec
o
m
p
o
s
itio
n
o
f
t
h
e
s
i
g
n
a
l
u
s
i
n
g
d
is
cr
ete
w
av
e
let
tr
a
n
s
f
o
r
m
i
n
v
o
l
v
e
co
n
v
o
l
u
tio
n
o
p
er
atio
n
g
iv
e
n
as,
[
]
∑
[
]
[
]
(
2
)
[
]
∑
[
]
[
]
(
3
)
w
h
er
e
[
]
is
th
e
d
is
cr
ete
m
o
t
h
er
w
a
v
elet
an
d
in
th
is
ca
s
e,
it
is
th
e
h
i
g
h
p
ass
f
ilter
an
d
[
]
f
o
r
lo
w
p
ass
f
ilter
.
Af
ter
th
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
s
ta
g
e
,
th
e
s
ig
n
al
w
a
s
d
ec
o
m
p
o
s
ed
at
s
ev
en
le
v
els
u
s
i
n
g
d
is
cr
ete
wav
elet
tr
an
s
f
o
r
m
to
o
b
tain
p
r
o
m
in
e
n
t
i
n
f
o
r
m
atio
n
at
d
if
f
er
en
t
f
r
eq
u
e
n
c
y
b
an
d
s
.
T
h
e
s
ig
n
al
w
as
p
ass
ed
t
h
r
o
u
g
h
a
h
ig
h
p
as
s
f
ilter
an
d
a
lo
w
p
ass
f
ilter
f
o
llo
w
e
d
b
y
s
u
b
s
a
m
p
li
n
g
b
y
2
.
T
h
e
d
etail,
(
)
an
d
ap
p
r
o
x
i
m
atio
n
,
(
)
co
ef
f
icien
ts
w
er
e
o
b
tain
ed
af
ter
th
e
s
u
b
s
a
m
p
li
n
g
t
h
r
o
u
g
h
h
ig
h
p
ass
a
n
d
lo
w
p
as
s
f
i
lter
s
,
r
esp
ec
tiv
el
y
.
T
h
e
p
r
o
ce
s
s
is
r
ep
ea
ted
f
o
r
s
u
cc
es
s
i
v
e
le
v
el
u
n
til
t
h
e
d
esire
d
le
v
el
as
s
h
o
w
n
i
n
Fi
g
u
r
e
4
.
T
h
er
e
ar
e
th
r
ee
f
r
eq
u
e
n
c
y
b
an
d
s
(
)
th
at
h
av
e
h
i
g
h
a
m
p
lit
u
d
e
o
f
d
etail
co
ef
f
icie
n
ts
g
r
ea
ter
th
a
n
1
.
T
h
ese
b
an
d
s
co
n
tai
n
m
o
s
t
in
f
o
r
m
at
i
o
n
ab
o
u
t
th
e
s
ig
n
al.
A
lt
h
o
u
g
h
th
e
a
m
p
lit
u
d
e
f
o
r
an
d
D
7
w
a
s
n
o
t
s
o
h
ig
h
co
m
p
ar
ed
to
,
an
d
,
th
e
y
will
b
e
in
clu
d
ed
in
th
e
f
ea
t
u
r
es
ex
tr
ac
tio
n
as
s
o
m
e
in
f
o
r
m
a
tio
n
o
f
th
e
cr
ac
k
les
m
a
y
co
n
tai
n
in
t
h
e
s
e
f
r
eq
u
en
c
y
b
an
d
s
.
T
h
e
f
r
eq
u
en
c
y
co
n
te
n
t
f
o
r
cr
ac
k
les
i
s
1
0
0
to
2
0
0
0
Hz
o
r
h
ig
h
er
[
5
]
.
T
h
er
ef
o
r
e,
f
iv
e
f
r
eq
u
en
c
y
b
an
d
s
(
)
w
er
e
s
elec
t
ed
f
o
r
f
ea
tu
r
e
e
x
tr
ac
tio
n
.
Me
an
,
s
tan
d
ar
d
d
ev
iatio
n
an
d
m
ax
i
m
u
m
p
o
w
er
s
p
ec
tr
al
d
en
s
it
y
(
P
S
D)
o
f
d
etail
co
ef
f
icie
n
ts
o
f
t
h
e
s
e
f
i
v
e
b
an
d
s
w
er
e
ca
lcu
lated
u
s
i
n
g
M
A
T
L
A
B
.
T
h
ese
b
an
d
s
h
a
v
e
f
r
eq
u
e
n
c
y
r
a
n
g
e
f
r
o
m
8
6
.
1
3
Hz
to
2
7
5
6
.
2
5
Hz.
Fig
u
r
e
4
.
Sig
n
al
d
ec
o
m
p
o
s
itio
n
p
r
o
ce
s
s
u
s
in
g
DW
T
to
o
b
tain
d
et
ail
co
ef
f
icie
n
ts
at
s
e
v
e
n
l
ev
el
2.
3.
Cla
s
s
if
ica
t
io
n us
i
ng
ANN
a
n
d per
f
o
r
m
a
nce
ev
a
lua
t
io
n
Neu
r
al
n
et
w
o
r
k
s
co
n
s
is
t
o
f
n
o
d
es
w
h
ic
h
i
n
s
p
ir
ed
b
y
th
e
n
eu
r
o
n
s
in
t
h
e
h
u
m
a
n
b
r
ain
o
f
n
er
v
o
u
s
s
y
s
te
m
[
1
8
]
.
T
h
e
n
et
w
o
r
k
is
b
u
ilt
b
ased
o
n
th
r
ee
la
y
er
s
n
a
m
el
y
in
p
u
t,
h
id
d
en
an
d
o
u
tp
u
t
c
o
n
n
ec
ted
v
ia
n
o
d
es.
T
h
e
h
id
d
en
la
y
er
ca
n
b
e
m
o
r
e
th
a
n
o
n
e.
E
v
er
y
n
o
d
e
in
th
e
h
id
d
en
la
y
er
i
s
co
n
n
ec
ted
to
all
in
p
u
t
(
f
ea
tu
r
es
)
an
d
o
n
th
e
o
t
h
er
s
id
e
o
f
t
h
e
n
o
d
e
is
co
n
n
ec
ted
to
all
o
u
tp
u
t (
class
)
.
E
ac
h
co
n
n
ec
tio
n
b
et
w
e
en
th
e
in
p
u
t a
n
d
t
h
e
n
o
d
e
ca
r
r
y
a
w
e
ig
h
ta
g
e.
A
g
g
r
eg
atio
n
o
f
t
h
e
in
p
u
t
m
u
ltip
lied
w
i
th
t
h
e
w
ei
g
h
ta
g
e
is
f
ed
in
to
ac
tiv
atio
n
f
u
n
ctio
n
to
o
b
tain
a
n
e
w
v
al
u
e
as
in
p
u
t
to
n
ex
t
la
y
er
.
T
h
e
m
o
s
t
c
o
m
m
o
n
ac
ti
v
atio
n
f
u
n
ctio
n
is
s
ig
m
o
d
al
f
u
n
ctio
n
.
I
n
th
is
s
tu
d
y
,
A
NN
h
as
b
ee
n
e
m
p
lo
y
ed
as
class
i
f
ier
u
s
i
n
g
M
AT
L
A
B
to
class
if
y
in
p
u
ts
i
n
to
a
s
et
o
f
tar
g
et
o
u
tp
u
t
th
at
w
er
e
in
itial
ized
as
m
atr
i
x
[
1
0
]
f
o
r
n
o
r
m
al
an
d
[
0
1
]
f
o
r
cr
ac
k
les.
I
n
th
e
tr
ain
i
n
g
s
tag
e
o
f
t
h
e
n
e
u
r
al
n
et
w
o
r
k
,
b
ac
k
p
r
o
p
ag
atio
n
al
g
o
r
ith
m
w
as
u
s
ed
to
ad
j
u
s
t
weig
h
ts
i
n
t
h
e
n
et
w
o
r
k
if
t
h
e
p
r
ed
icted
o
u
tp
u
t
d
o
es
no
t
m
atc
h
w
it
h
t
h
e
tar
g
et
o
u
tp
u
t.
T
h
e
Neu
r
al
Net
w
o
r
k
u
s
ed
in
t
h
is
s
t
u
d
y
i
s
m
u
ltil
a
y
er
f
ee
d
f
o
r
w
ar
d
n
eu
r
al
n
e
t
w
o
r
k
(
ML
FN
N)
tr
ain
ed
w
it
h
B
a
ck
p
r
o
p
ag
atio
n
(
B
P
)
.
T
h
e
o
p
ti
m
al
g
o
al
o
f
b
ac
k
p
r
o
p
ag
atio
n
is
to
h
av
e
m
i
n
i
m
al
er
r
o
r
th
at
is
r
elativ
e
to
h
av
i
n
g
o
u
tp
u
t
s
clo
s
er
to
t
h
e
t
ar
g
et.
A
s
s
u
m
in
g
th
e
d
ata
i
n
p
u
ts
ar
e
r
ep
r
esen
ted
b
y
Xi
a
n
d
w
eig
h
t
s
b
y
W
;
t
h
e
d
etail
ed
ex
p
lan
atio
n
i
s
b
ased
o
n
th
e
Fig
u
r
e
5
.
Th
e
n
eu
r
o
n
s
i
n
ea
ch
la
y
er
ar
e
f
u
ll
y
co
n
n
ec
ted
to
th
e
n
e
u
r
o
n
s
in
th
e
n
e
x
t
la
y
er
,
f
r
o
m
la
y
er
i
to
j
to
k
.
Su
p
p
o
s
e
th
at
th
e
n
et
w
o
r
k
i
s
d
e
s
ig
n
ed
w
it
h
o
n
l
y
o
n
e
h
id
d
en
l
a
y
er
n
e
u
r
o
n
s
a
n
d
g
e
n
er
ate
o
n
l
y
o
n
e
o
u
tp
u
t.
W
i
j
is
th
e
w
ei
g
h
t
t
h
at
co
n
n
ec
t
s
t
h
e
i
th
n
eu
r
o
n
f
r
o
m
i
n
p
u
t
la
y
er
to
t
h
e
j
t
h
n
eu
r
o
n
i
n
t
h
e
o
u
tp
u
t
la
y
er
,
w
h
er
ea
s
W
jk
i
s
th
e
w
ei
g
h
t
th
a
t
co
n
n
ec
ts
t
h
e
j
th
n
e
u
r
o
n
f
r
o
m
h
id
d
en
la
y
er
to
th
e
k
th
n
e
u
r
o
n
i
n
t
h
e
o
u
tp
u
t
la
y
er
.
I
n
th
e
B
P
alg
o
r
ith
m
,
t
h
e
g
e
n
er
al
izatio
n
o
f
d
elta
r
u
le
i
n
v
o
lv
es
t
w
o
p
h
ases
,
w
h
ich
ar
e
th
e
f
o
r
w
ar
d
p
h
a
s
e
a
n
d
th
e
b
ac
k
w
ar
d
p
h
a
s
e
[
1
9
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
5
3
0
–
1538
1534
b
i
b
k
W
ij
W
jk
j
O
j
N
et
j
O
i
X
i
N
et
k
O
k
t
k
E
k
In
p
u
t
L
a
y
er
Hi
d
d
en
L
a
y
er
O
u
t
p
u
t
l
a
y
er
i
Fig
u
r
e
5
.
Sig
n
al
Mu
ltil
a
y
er
P
er
ce
p
tr
o
n
w
it
h
B
ac
k
p
r
o
p
ag
atio
n
T
h
e
f
o
r
w
ar
d
p
h
ase: Fo
r
h
id
d
e
n
la
y
er
o
u
tp
u
t,
co
n
s
id
e
r
(
)
(
4
)
w
h
er
e
∑
(
5
)
w
h
er
e
b
j
is
t
h
e
b
ias o
f
t
h
e
h
id
d
en
n
o
d
e
an
d
ca
n
b
e
s
et
to
ze
r
o
,
Φ
is
t
h
e
s
i
g
m
o
id
ac
ti
v
atio
n
f
u
n
ct
io
n
.
Fo
r
o
u
tp
u
t
la
y
er
k
,
th
e
n
e
t
w
o
r
k
o
u
tp
u
t is
g
iv
e
n
as
;
(
)
(
6
)
w
h
er
e
∑
(
7
)
T
h
e
b
ac
k
w
ar
d
p
h
ase
b
et
w
ee
n
o
u
tp
u
t
an
d
h
id
d
en
la
y
er
:
T
h
e
b
ac
k
w
ar
d
p
h
ase
i
n
cl
u
d
es
th
e
ca
lcu
latio
n
o
f
th
e
s
ig
n
al
er
r
o
r
an
d
th
e
w
eig
h
t
u
p
d
ate
o
f
th
e
n
et
w
o
r
k
.
T
h
e
n
et
wo
r
k
er
r
o
r
„
E
‟
is
d
ev
elo
p
ed
as f
o
llo
w
:
(
8
)
w
h
er
e
t
k
i
s
t
h
e
d
esire
d
o
u
tp
u
t
an
d
O
k
i
s
t
h
e
o
u
tp
u
t
o
f
n
et
w
o
r
k
w
h
ic
h
t
h
e
o
u
tp
u
t
o
f
t
h
e
o
u
tp
u
t
la
y
er
.
T
h
e
o
b
j
ec
tiv
e
is
to
f
i
n
d
t
h
e
s
e
t o
f
p
ar
a
m
eter
s
t
h
at
m
i
n
i
m
ize
t
h
e
s
u
m
o
f
th
e
s
q
u
ar
ed
o
f
th
e
er
r
o
r
f
u
n
ctio
n
,
w
h
er
e
th
e
av
er
ag
e
s
u
m
s
q
u
ar
ed
er
r
o
r
o
f
th
e
n
et
w
o
r
k
is
d
ef
i
n
ed
as;
∑
(
)
∑
(
9
)
w
h
er
e
N
is
t
h
e
to
tal
n
u
m
b
er
o
f
tr
ain
in
g
p
atter
n
,
E
is
t
h
e
er
r
o
r
f
u
n
ctio
n
to
b
e
m
in
i
m
ized
.
T
h
e
n
et
w
o
r
k
w
eig
h
t
u
p
d
ate
b
et
w
ee
n
t
h
e
h
id
d
en
la
y
er
j
an
d
o
u
tp
u
t la
y
er
k
is
g
iv
e
n
b
y
;
(
1
0
)
w
h
er
e
(
1
1
)
η
is
th
e
lear
n
i
n
g
r
ate,
is
th
e
g
r
ad
ien
t o
f
t
h
e
co
s
f
u
n
c
tio
n
.
T
h
e
b
ac
k
w
ar
d
p
h
ase
b
et
w
ee
n
h
id
d
en
la
y
er
a
n
d
in
p
u
t la
y
er
:
A
d
j
u
s
ti
n
g
b
et
w
ee
n
h
id
d
en
la
y
er
an
d
th
e
in
p
u
t
la
y
er
b
y
:
(
1
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
C
la
s
s
i
fica
tio
n
o
f No
r
ma
l a
n
d
C
r
a
ck
les R
esp
i
r
a
to
r
y
S
o
u
n
d
s
i
n
t
o
Hea
lth
y
a
n
d
Lu
n
g
…
(
N
.
A
b
d
u
l Ma
lik
)
1535
T
h
er
ef
o
r
e,
th
e
n
e
w
w
eig
h
t
u
p
d
ate
is
;
(
1
3
)
2.
4
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n
T
h
e
o
u
tp
u
t
o
f
th
e
alg
o
r
ith
m
was
ev
alu
a
ted
u
s
i
n
g
t
h
e
v
al
u
e
o
f
tr
u
e
p
o
s
itiv
e
(
T
P
)
,
tr
u
e
n
eg
at
iv
e
(
T
N)
,
f
alse
p
o
s
it
iv
e
(
FP
)
an
d
f
alse
n
eg
at
iv
e
(
F
N)
to
d
eter
m
i
n
e
t
h
e
s
e
n
s
i
tiv
it
y
,
s
p
ec
if
ic
it
y
a
n
d
ac
cu
r
ac
y
u
s
i
n
g
a
s
u
itab
le
s
t
atis
tical
an
al
y
s
is
as
s
h
o
w
n
in
E
q
u
a
tio
n
s
(
1
4
)
,
(
1
5
)
an
d
(
1
6)
[
20
].
(
1
4)
(
1
5)
(
1
6)
3.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
I
O
N
Mo
s
t
class
if
ica
tio
n
p
r
o
b
le
m
s
ca
n
b
e
s
o
lv
ed
b
y
o
n
l
y
t
w
o
h
id
d
en
la
y
er
s
i
n
A
N
N
ar
ch
itec
tu
r
e
[
1
8
]
,
[
2
2
]
.
I
n
th
is
s
t
u
d
y
,
t
w
o
h
id
d
en
la
y
er
s
w
er
e
e
m
p
lo
y
ed
i
n
th
e
ar
ch
itect
u
r
e
to
c
lass
if
y
b
et
w
ee
n
cr
ac
k
les
an
d
n
o
r
m
al
r
esp
ir
ato
r
y
s
o
u
n
d
s
.
T
h
er
e
w
er
e
s
i
x
t
y
cr
ac
k
les
a
n
d
s
i
x
t
y
n
o
r
m
al
s
o
u
n
d
s
u
s
ed
as
s
a
m
p
les.
T
h
e
s
a
m
p
le
s
ar
e
r
an
d
o
m
l
y
d
i
v
id
ed
in
to
7
0
%
f
o
r
tr
ai
n
in
g
,
1
5
%
f
o
r
test
i
n
g
a
n
d
1
5
%
f
o
r
v
alid
atio
n
.
E
lev
en
A
NN
m
o
d
els
w
it
h
a
d
if
f
er
e
n
t
n
u
m
b
er
o
f
n
o
d
es
w
er
e
ch
o
s
e
n
to
b
e
u
s
ed
in
t
h
e
tr
ain
i
n
g
,
v
alid
ati
n
g
an
d
test
in
g
.
Fo
r
ev
er
y
ch
o
s
en
n
o
d
e,
th
e
d
ata
w
a
s
r
etr
ain
ed
f
i
v
e
ti
m
e
s
an
d
t
h
e
c
lass
i
f
icatio
n
r
es
u
lts
f
o
r
tr
ain
i
n
g
,
v
al
id
atio
n
,
a
n
d
test
i
n
g
ar
e
tab
u
lated
i
n
T
ab
le
1
.
T
h
is
is
b
ased
o
n
th
e
b
est r
es
u
lt o
b
tain
ed
f
o
r
test
in
g
.
As
ca
n
b
e
s
ee
n
in
T
ab
le
1
an
d
T
a
b
le
2
,
th
e
b
est
class
if
icati
o
n
p
er
ce
n
tag
e
w
as
o
b
tain
ed
w
h
e
n
u
s
i
n
g
1
5
n
o
d
es
an
d
1
0
n
o
d
es
at
th
e
h
id
d
en
la
y
er
f
o
r
d
b
7
an
d
H
a
ar
,
r
esp
ec
tiv
el
y
.
Fro
m
th
e
o
b
tain
ed
r
esu
lt
s
,
d
b
7
m
an
a
g
e
to
g
et
1
0
0
%
co
r
r
ec
t
class
if
icatio
n
f
o
r
b
o
th
test
a
n
d
v
alid
atio
n
s
ta
g
e
w
h
ic
h
was
v
er
y
g
o
o
d
as
it
ac
h
iev
ed
t
h
e
p
er
f
ec
t
o
p
tim
iza
tio
n
.
W
h
ile
f
o
r
Haa
r
,
it
o
n
ly
ca
n
o
b
tain
1
0
0
%
co
r
r
ec
t
clas
s
if
ica
tio
n
at
ei
th
er
v
alid
atio
n
o
r
test
s
ta
g
e.
Fro
m
th
e
p
er
ce
n
ta
g
e
o
b
tai
n
ed
,
test
s
tag
e
w
a
s
t
h
e
m
o
s
t
i
m
p
o
r
tan
t
c
r
iter
ia
th
at
n
ee
d
to
b
e
a
f
o
c
u
s
o
n
b
ec
a
u
s
e
it
h
elp
s
i
n
a
s
s
es
s
i
n
g
t
h
e
p
er
f
o
r
m
a
n
c
e
b
ased
o
n
g
e
n
er
aliza
tio
n
an
d
p
r
ed
ictiv
e
p
o
w
er
.
T
h
e
n
u
m
b
er
o
f
ep
o
ch
f
o
r
b
o
th
d
b
7
an
d
Haa
r
w
a
s
n
o
t
m
o
r
e
t
h
an
2
0
w
h
ic
h
m
ea
n
s
it
s
h
o
w
s
a
g
o
o
d
p
er
f
o
r
m
a
n
ce
.
T
h
e
lo
w
er
t
h
e
n
u
m
b
er
o
f
ep
o
ch
,
th
e
b
etter
t
h
e
p
er
f
o
r
m
a
n
ce
a
n
d
t
h
e
q
u
ick
e
r
it
ca
n
ac
h
ie
v
e
t
h
e
b
est o
p
ti
m
izatio
n
.
Fo
r
ev
alu
at
io
n
o
n
cla
s
s
i
f
icat
io
n
p
er
f
o
r
m
a
n
ce
,
th
e
p
er
ce
n
tag
e
o
f
s
e
n
s
iti
v
i
t
y
,
s
p
ec
i
f
icit
y
a
n
d
ac
cu
r
ac
y
w
a
s
ca
lcu
la
ted
b
ased
o
n
E
q
u
atio
n
s
(
1
4
)
,
(
1
5
)
an
d
(
1
6
)
u
s
in
g
th
e
v
al
u
e
o
f
T
P
(
co
r
r
ec
tly
cla
s
s
if
ied
as
cr
ac
k
les),
T
N
(
co
r
r
ec
tly
cla
s
s
i
f
ied
as
n
o
r
m
al)
,
FP
(
in
co
r
r
ec
tl
y
clas
s
i
f
i
ed
as
cr
ac
k
les)
a
n
d
FN
(
in
co
r
r
ec
tl
y
clas
s
i
f
ied
as
n
o
r
m
al)
a
s
tab
u
la
ted
in
T
ab
le
3
an
d
T
ab
le
4
f
o
r
d
b
7
an
d
Haa
r
,
r
esp
ec
tiv
el
y
.
Fro
m
th
e
s
e
tab
les,
d
b
7
b
ased
s
h
o
w
s
a
b
etter
p
er
f
o
r
m
a
n
ce
th
a
n
Haa
r
w
i
th
1
0
0
%
s
en
s
itiv
it
y
,
s
p
ec
if
ici
t
y
an
d
ac
c
u
r
ac
y
f
o
r
test
i
n
g
an
d
v
alid
atio
n
s
tag
e
s
.
As
f
o
r
Haa
r
,
o
n
l
y
test
i
n
g
s
tag
e
s
h
o
w
s
t
h
e
p
er
f
ec
t
1
0
0
%
f
o
r
all
s
en
s
itiv
i
t
y
,
s
p
ec
if
ic
it
y
a
n
d
ac
cu
r
ac
y
.
Ho
w
e
v
er
,
th
e
p
er
c
en
tag
e
o
f
ac
c
u
r
ac
y
f
o
r
Haa
r
w
a
s
s
ti
ll
g
o
o
d
f
o
r
clas
s
i
f
icati
o
n
o
f
cr
ac
k
le
s
a
n
d
n
o
r
m
al
s
o
u
n
d
s
.
T
ab
le
1
.
P
er
f
o
r
m
a
n
ce
o
f
V
ar
io
u
s
A
NN
M
o
d
el
f
o
r
d
b
7
A
N
N
mo
d
e
l
(
I
n
p
u
t
-
N
o
d
e
s
-
O
u
t
p
u
t
)
N
o
.
e
p
o
c
h
(
b
e
st
v
a
l
i
d
a
t
i
o
n
)
T
r
a
i
n
i
n
g
(
%)
V
a
l
i
d
a
t
i
o
n
(
%)
T
e
st
i
n
g
(
%)
15
-
10
-
2
14
9
0
.
5
9
4
.
4
1
0
0
15
-
11
-
2
12
9
2
.
9
8
8
.
9
9
4
.
4
15
-
12
-
2
9
8
9
.
3
8
3
.
3
9
4
.
4
15
-
13
-
2
18
9
1
.
7
9
4
.
4
9
4
.
4
15
-
14
-
2
14
9
1
.
7
9
4
.
4
1
0
0
.
0
15
-
15
-
2
5
9
0
.
5
1
0
0
.
0
1
0
0
.
0
15
-
35
-
2
2
9
0
.
5
8
3
.
3
9
4
.
4
15
-
55
-
2
8
9
1
.
7
8
8
.
9
1
0
0
.
0
15
-
75
-
2
3
9
1
.
7
9
4
.
4
8
8
.
9
15
-
95
-
2
8
9
0
.
5
9
4
.
4
1
0
0
15
-
1
1
5
-
2
13
9
4
.
0
8
8
.
9
9
4
.
4
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
5
3
0
–
1538
1536
T
ab
le
2
.
P
er
f
o
r
m
a
n
ce
o
f
V
ar
io
u
s
A
NN
M
o
d
el
f
o
r
Haa
r
A
N
N
mo
d
e
l
(
I
n
p
u
t
-
N
o
d
e
s
-
O
u
t
p
u
t
)
N
o
.
e
p
o
c
h
(
b
e
st
v
a
l
i
d
a
t
i
o
n
)
T
r
a
i
n
i
n
g
(
%)
V
a
l
i
d
a
t
i
o
n
(
%)
T
e
st
i
n
g
(
%)
15
-
10
-
2
7
8
9
.
3
9
4
.
4
1
0
0
.
0
15
-
11
-
2
4
9
0
.
5
8
3
.
3
1
0
0
.
0
15
-
12
-
2
9
8
9
.
3
8
8
.
9
1
0
0
.
0
15
-
13
-
2
6
9
0
.
5
9
4
.
4
9
4
.
4
15
-
14
-
2
13
8
9
.
3
9
4
.
4
9
4
.
4
15
-
15
-
2
5
9
0
.
5
8
3
.
3
9
4
.
4
15
-
35
-
2
4
8
9
.
3
9
4
.
4
9
4
.
4
15
-
55
-
2
6
9
0
.
5
8
8
.
9
1
0
0
.
0
15
-
75
-
2
12
8
6
.
9
8
8
.
9
1
0
0
.
0
15
-
95
-
2
8
9
0
.
5
1
0
0
.
0
8
8
.
9
15
-
1
1
5
-
2
7
9
1
.
7
8
8
.
9
9
4
.
4
T
ab
le
3
.
E
v
alu
atio
n
P
er
f
o
r
m
a
n
ce
o
f
A
NN
C
las
s
i
f
icatio
n
o
f
C
r
ac
k
le
s
an
d
N
o
r
m
al
S
o
u
n
d
s
u
s
i
n
g
db7
S
t
a
g
e
TP
TN
FP
FN
S
e
n
si
t
i
v
i
t
y
(
%)
S
p
e
c
i
f
i
c
i
t
y
(
%)
A
c
c
u
r
a
c
y
(
%)
T
r
a
i
n
i
n
g
38
38
4
4
9
0
.
5
9
0
.
5
9
0
.
5
T
e
st
9
9
0
0
1
0
0
.
0
1
0
0
.
0
1
0
0
.
0
V
a
l
i
d
a
t
i
o
n
9
9
0
0
1
0
0
.
0
1
0
0
.
0
1
0
0
.
0
A
l
l
56
56
4
4
9
3
.
3
9
3
.
3
9
3
.
3
T
ab
le
4
.
E
v
alu
atio
n
P
er
f
o
r
m
a
n
ce
o
f
A
NN
C
las
s
i
f
icatio
n
o
f
C
r
ac
k
le
s
an
d
N
o
r
m
al
S
o
u
n
d
s
u
s
i
n
g
Haar
S
t
a
g
e
TP
TN
FP
FN
S
e
n
si
t
i
v
i
t
y
(
%)
S
p
e
c
i
f
i
c
i
t
y
(
%)
A
c
c
u
r
a
c
y
(
%)
T
r
a
i
n
i
n
g
39
36
4
5
8
8
.
6
9
0
.
0
8
9
.
3
T
e
st
11
7
0
0
1
0
0
.
0
1
0
0
.
0
1
0
0
.
0
V
a
l
i
d
a
t
i
o
n
6
11
0
1
8
5
.
7
1
0
0
.
0
9
4
.
4
A
l
l
56
54
4
6
9
0
.
3
9
6
.
6
9
1
.
7
4.
CO
NCLU
SI
O
N
T
h
ese
p
r
elim
in
ar
y
r
esu
lts
to
w
ar
d
s
t
h
e
d
ev
elo
p
m
e
n
t
o
f
s
cr
ee
n
in
g
m
eth
o
d
f
o
r
lu
n
g
ca
n
ce
r
u
s
in
g
co
m
p
u
ter
ized
h
a
v
e
r
es
u
lted
with
p
o
s
iti
v
e
o
u
tco
m
e
s
.
Nev
er
t
h
eles
s
,
o
th
er
f
ac
to
r
s
s
u
c
h
as
a
g
e,
s
m
o
k
i
n
g
h
ab
it
,
a
m
b
ien
t
air
p
o
llu
tio
n
an
d
o
cc
u
p
atio
n
al
ex
p
o
s
u
r
e
n
ee
d
to
b
e
c
o
n
s
id
er
ed
w
h
e
n
in
ter
p
r
eti
n
g
t
h
e
r
esu
lts
in
f
u
t
u
r
e.
T
h
ese
f
ac
to
r
s
ca
n
b
e
ad
d
ed
as
f
e
at
u
r
es
to
th
e
c
lass
if
ier
.
I
n
t
h
is
s
t
u
d
y
,
n
o
r
m
al
an
d
cr
ac
k
le
s
r
esp
ir
ato
r
y
s
o
u
n
d
s
h
av
e
s
u
cc
e
s
s
f
u
ll
y
b
ee
n
clas
s
i
f
ied
u
s
in
g
A
N
N
w
it
h
b
ac
k
p
r
o
p
ag
atio
n
co
n
s
is
t
s
o
f
t
w
o
h
i
d
d
en
la
y
er
s
.
B
o
th
m
o
th
er
w
a
v
elet
s
,
Haa
r
,
an
d
d
b
7
ca
n
p
r
o
v
id
e
d
is
tin
ctiv
e
p
att
er
n
n
ee
d
ed
as
f
e
at
u
r
es
i
n
lear
n
in
g
al
g
o
r
ith
m
w
it
h
s
o
m
e
s
tat
is
tica
l a
n
d
s
i
g
n
a
l stre
n
g
t
h
f
o
r
m
u
latio
n
s
u
c
h
as
m
ea
n
,
s
ta
n
d
ar
d
d
ev
iatio
n
,
an
d
P
SD.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
th
o
r
s
w
o
u
ld
li
k
e
to
ac
k
n
o
w
led
g
e
t
h
e
Mi
n
is
tr
y
o
f
H
ig
h
er
E
d
u
ca
tio
n
Ma
la
y
s
ia
(
M
O
H
E
)
f
o
r
f
u
n
d
i
n
g
t
h
i
s
r
esear
c
h
p
r
o
j
ec
t
th
r
o
u
g
h
F
u
n
d
a
m
e
n
tal
s
R
e
s
e
ar
ch
Gr
an
t
Sc
h
e
m
e
(
FR
GS)
[
R
ef
.
:F
R
G
S1
6
-
067
-
0
5
6
6
]
.
W
e
also
w
o
u
ld
li
k
e
t
o
th
an
k
Dr
.
R
o
zita
A
b
d
u
l
M
alik
a
n
d
Dr
.
A
d
lin
d
a
Alip
o
f
UM
MC
f
o
r
th
e
ir
co
n
s
u
ltatio
n
a
n
d
ad
v
ice
o
n
l
u
n
g
ca
n
ce
r
r
elate
d
is
s
u
es
RE
F
E
R
E
NC
E
S
[1
]
S
tew
a
rd
BW
,
W
il
d
C
P
.
W
o
rl
d
c
a
n
c
e
r
re
p
o
rt
2
0
1
4
.
He
a
lt
h
.
2
0
1
7
Oc
t
2
4
.
[2
]
A
z
i
z
a
h
A
M
,
No
r
S
a
leh
a
IT
,
No
o
r
H
a
sh
ima
h
A
,
As
m
a
h
Z
A
,
M
a
stu
lu
W
,
“
M
a
la
y
sia
n
n
a
ti
o
n
a
l
c
a
n
c
e
r
re
g
istr
y
re
p
o
rt
2
0
0
7
-
2
0
1
1
”
,
M
in
istry
o
f
He
a
lt
h
M
a
la
y
s
ia,
2
0
1
6
.
[3
]
Ho
ffm
a
n
RM
,
S
a
n
c
h
e
z
R,
“
L
u
n
g
Ca
n
c
e
r
S
c
re
e
n
in
g
”
,
M
e
d
ica
l
Cli
n
i
c
s
,
2
0
1
7
J
u
l
1
,
v
o
l.
1
0
1
,
n
o
.
4
,
p
p
.
7
6
9
-
8
5
.
[4
]
A
n
d
o
lf
i
M
,
P
o
ten
z
a
R,
Ca
p
o
z
z
i
R
,
L
ip
a
ru
lo
V
,
P
u
m
a
F
,
Ya
su
f
u
k
u
K,
“
T
h
e
ro
le
o
f
b
r
o
n
c
h
o
sc
o
p
y
in
t
h
e
d
iag
n
o
sis
o
f
e
a
rl
y
lu
n
g
c
a
n
c
e
r:
a
re
v
ie
w
”
,
J
o
u
rn
a
l
o
f
t
h
o
r
a
c
ic d
ise
a
se
,
2
0
1
6
No
v
,
v
o
l.
8
,
n
o
.
1
1
,
3
3
2
9
.
[5
]
G
u
ru
n
g
A
,
S
c
r
a
ff
o
rd
CG
,
T
ie
lsc
h
JM,
L
e
v
in
e
OS,
Ch
e
c
k
le
y
W
,
“
Co
m
p
u
teriz
e
d
lu
n
g
so
u
n
d
a
n
a
ly
sis
a
s
d
iag
n
o
stic
a
id
f
o
r
th
e
d
e
tec
ti
o
n
o
f
a
b
n
o
rm
a
l
lu
n
g
so
u
n
d
s: A
s
y
st
e
m
a
ti
c
re
v
ie
w
a
n
d
m
e
ta
-
a
n
a
l
y
sis
”
,
Res
p
ira
to
ry
me
d
icin
e
,
2
0
1
1
S
e
p
1
,
v
o
l.
1
0
5
,
n
o
.
9
,
p
p
.
1
3
9
6
-
4
0
3
.
[6
]
G
n
it
e
c
k
i
J,
M
o
u
ss
a
v
i
ZM
,
“
S
e
p
a
ra
ti
n
g
h
e
a
rt
so
u
n
d
s
f
ro
m
lu
n
g
so
u
n
d
s”
,
IEE
E
E
n
g
i
n
e
e
rin
g
i
n
me
d
ic
in
e
a
n
d
b
i
o
lo
g
y
ma
g
a
zi
n
e
,
2
0
0
7
Ja
n
1
,
v
o
l.
2
6
,
n
o
.
1
,
20.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
C
la
s
s
i
fica
tio
n
o
f No
r
ma
l a
n
d
C
r
a
ck
les R
esp
i
r
a
to
r
y
S
o
u
n
d
s
i
n
t
o
Hea
lth
y
a
n
d
Lu
n
g
…
(
N
.
A
b
d
u
l Ma
lik
)
1537
[7
]
Ho
ss
a
in
I,
M
o
u
ss
a
v
i
Z,
“
A
n
o
v
e
rv
ie
w
o
f
h
e
a
rt
-
n
o
ise
re
d
u
c
ti
o
n
o
f
lu
n
g
so
u
n
d
u
sin
g
w
a
v
e
let
tran
sf
o
r
m
b
a
se
d
f
il
ter”
,
In
En
g
in
e
e
rin
g
i
n
M
e
d
icin
e
a
n
d
Bi
o
lo
g
y
S
o
c
iety
,
2
0
0
3
,
Pro
c
e
e
d
i
n
g
s o
f
th
e
2
5
th
A
n
n
u
a
l
In
ter
n
a
ti
o
n
a
l
Co
n
fe
re
n
c
e
o
f
th
e
IEE
E
2
0
0
3
S
e
p
1
7
(v
o
l.
1
,
p
p
.
4
5
8
-
4
6
1
)
,
IEE
E
.
[8
]
L
a
k
he
A
,
S
o
d
h
i
I,
W
a
rrier
J,
S
in
h
a
V
,
“
De
v
e
lo
p
m
e
n
t
o
f
d
ig
it
a
l
st
e
th
o
sc
o
p
e
f
o
r
te
lem
e
d
icin
e
”
,
J
o
u
rn
a
l
o
f
me
d
ic
a
l
e
n
g
in
e
e
rin
g
&
te
c
h
n
o
l
o
g
y
,
2
0
1
6
Ja
n
2
,
v
o
l.
4
0
,
n
o
.
1
,
p
p
.
20
-
2
4
.
[9
]
X
ie
S
,
Ji
n
F
,
Kris
h
n
a
n
S
,
S
a
tt
a
r
F
,
“
S
ig
n
a
l
f
e
a
tu
re
e
x
tr
a
c
ti
o
n
b
y
m
u
lt
i
-
sc
a
le
P
CA
a
n
d
it
s
a
p
p
li
c
a
ti
o
n
t
o
r
e
sp
irato
ry
so
u
n
d
c
las
sif
ica
ti
o
n
”
,
M
e
d
ica
l
&
b
io
l
o
g
ic
a
l
e
n
g
in
e
e
rin
g
&
c
o
mp
u
ti
n
g
,
2
0
1
2
Ju
l
1
,
v
o
l.
5
0
,
n
o
.
7
,
p
p
.
759
-
7
6
8
.
[1
0
]
Jin
F
,
S
a
tt
a
r
F
,
G
o
h
DY
,
“
Ne
w
a
p
p
r
o
a
c
h
e
s
f
o
r
sp
e
c
tro
-
te
m
p
o
ra
l
fe
a
tu
re
e
x
tra
c
ti
o
n
w
it
h
a
p
p
li
c
a
ti
o
n
s
to
r
e
sp
irato
ry
so
u
n
d
c
las
sif
ica
ti
o
n
”
,
Ne
u
ro
c
o
m
p
u
ti
n
g
,
2
0
1
4
Ja
n
1
0
,
1
2
3
,
p
p
.
3
6
2
-
3
7
1
.
[1
1
]
G
ö
ğ
ü
ş
F
Z,
Ka
rlı
k
B,
Ha
rm
a
n
G
,
“
Clas
si
f
ica
ti
o
n
o
f
a
sth
m
a
ti
c
b
re
a
th
so
u
n
d
s
b
y
u
sin
g
w
a
v
e
let
tran
sfo
rm
s
a
n
d
n
e
u
ra
l
n
e
tw
o
rk
s”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
S
ig
n
a
l
Pro
c
e
ss
in
g
S
y
ste
ms
,
2
0
1
5
De
c
,
v
o
l.
3
,
n
o
.
2
,
p
p
.
1
0
6
-
1
1
1.
[1
2
]
P
a
st
e
rk
a
m
p
H,
Kra
m
a
n
S
S
,
W
o
d
ick
a
GR,
“
Re
sp
irato
ry
so
u
n
d
s:
a
d
v
a
n
c
e
s
b
e
y
o
n
d
th
e
ste
th
o
sc
o
p
e
”
,
Ame
ric
a
n
jo
u
rn
a
l
o
f
re
sp
ir
a
to
ry
a
n
d
c
riti
c
a
l
c
a
re
me
d
icin
e
,
1
9
9
7
S
e
p
1
,
v
o
l.
1
5
6
,
n
o
.
3
,
p
p
.
9
7
4
-
9
8
7
.
[1
3
]
İç
e
r
S
,
G
e
n
g
e
ç
Ş
,
“
Cla
ss
i
f
ica
ti
o
n
a
n
d
a
n
a
ly
sis
o
f
n
o
n
-
sta
ti
o
n
a
r
y
c
h
a
ra
c
teristics
o
f
c
ra
c
k
le
a
n
d
rh
o
n
c
h
u
s
l
u
n
g
a
d
v
e
n
ti
ti
o
u
s so
u
n
d
s”
,
Di
g
it
a
l
S
ig
n
a
l
Pro
c
e
ss
in
g
,
2
0
1
4
M
a
y
1
,
v
o
l.
2
8
,
n
o
.
18
-
2
7
.
[1
4
]
Riza
l
A
,
Hid
a
y
a
t
R,
Nu
g
ro
h
o
H
A
,
“
P
u
lm
o
n
a
ry
c
ra
c
k
le
fe
a
tu
re
e
x
trac
ti
o
n
u
si
n
g
tsa
ll
is
e
n
t
ro
p
y
f
o
r
a
u
to
m
a
ti
c
lu
n
g
so
u
n
d
c
las
sif
ica
ti
o
n
”
,
In
Bi
o
me
d
i
c
a
l
En
g
in
e
e
rin
g
(
IBI
OM
ED),
In
t
e
rn
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
2
0
1
6
Oc
t
5
(p
p
.
1
-
4
)
,
IEE
E.
[1
5
]
Ch
e
n
CH,
Hu
a
n
g
WT
,
T
a
n
T
H
,
Ch
a
n
g
CC,
Ch
a
n
g
YJ
,
“
Us
in
g
k
-
n
e
a
r
e
st
n
e
ig
h
b
o
r
c
las
si
f
ica
ti
o
n
to
d
iag
n
o
se
a
b
n
o
rm
a
l
lu
n
g
so
u
n
d
s”
,
S
e
n
s
o
rs
,
2
0
1
5
J
u
n
4
,
v
o
l
.
1
5
,
n
o
.
6
,
p
p
.
1
3
1
3
2
-
1
3
1
5
8
.
[1
6
]
L
i
J,
Ho
n
g
Y,
“
Cra
c
k
les
d
e
te
c
t
io
n
m
e
th
o
d
b
a
se
d
o
n
ti
m
e
-
f
re
q
u
e
n
c
y
fe
a
tu
re
s
a
n
a
l
y
sis
a
n
d
S
V
M
”
,
In
S
ig
n
a
l
Pro
c
e
ss
in
g
(
ICS
P),
2
0
1
6
I
EE
E
1
3
th
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
2
0
1
6
No
v
6
(p
p
.
1
4
1
2
-
1
4
1
6
)
,
IEE
E
.
[1
7
]
Co
h
e
n
A
,
Ko
v
a
c
e
v
ic
J.
W
a
v
e
lets
,
“
T
h
e
m
a
th
e
m
a
ti
c
a
l
b
a
c
k
g
ro
u
n
d
”
,
Pro
c
e
e
d
in
g
s
o
f
th
e
IE
EE
,
1
9
9
6
A
p
r,
v
o
l.
8
4
,
n
o
.
4
,
p
p
.
5
1
4
-
5
2
2
.
[1
8
]
L
a
p
e
d
e
s
A
S
,
F
a
rb
e
r
RM
,
“
Ho
w
n
e
u
ra
l
n
e
ts
w
o
rk
”
,
In
Ne
u
ra
l
in
f
o
r
ma
ti
o
n
p
r
o
c
e
ss
in
g
sy
ste
ms
1
9
8
8
(
p
p
.
4
4
2
-
4
5
6
).
[1
9
]
R.
F
.
Ola
n
re
w
a
ju
,
O.
Kh
a
li
fa
,
a
n
d
K.
N.
A
.
L
a
ti
f
,
“
Co
m
p
u
tatio
n
a
l
In
tell
ig
e
n
c
e
:
It‟s
A
p
p
l
ica
ti
o
n
i
n
Dig
it
a
l
W
a
ter
m
a
rk
in
g
”
,
M
id
d
le
-
Ea
st J.
S
c
i.
Res
.
,
v
o
l.
1
3
,
p
p
.
25
-
3
0
,
2
0
1
3
.
[2
0
]
Ba
ra
tl
o
o
,
A
li
re
z
a
,
M
o
sta
f
a
Ho
ss
e
in
i,
A
h
m
e
d
N
e
g
id
a
,
a
n
d
G
e
h
a
d
El
A
sh
a
l,
“
P
a
rt
1
:
si
m
p
le
d
e
f
in
it
io
n
a
n
d
c
a
lcu
latio
n
o
f
a
c
c
u
ra
c
y
,
se
n
siti
v
it
y
a
n
d
sp
e
c
if
icit
y
”
,
2
0
1
5
,
p
p
.
48
-
4
9
.
[2
1
]
G
u
n
a
w
a
n
,
T
.
S
.
,
a
n
d
Ka
rti
w
i,
M
.
,
“
n
t
h
e
Co
m
p
a
riso
n
o
f
L
in
e
S
p
e
c
tral
F
re
q
u
e
n
c
ies
a
n
d
M
e
l
-
F
re
q
u
e
n
c
y
Ce
p
stra
l
Co
e
ff
icie
n
ts
Us
in
g
F
e
e
d
f
o
r
w
a
rd
Ne
u
ra
l
Ne
tw
o
rk
f
o
r
L
a
n
g
u
a
g
e
I
d
e
n
t
if
ica
ti
o
n
”
,
I
n
d
o
n
e
sia
n
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
,
v
o
l.
1
0
,
n
o
.
1
,
p
p
.
1
6
8
-
1
7
5
,
Ap
ril
2
0
1
8
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
No
r
e
h
a
A
b
d
u
l
M
a
li
k
re
c
e
iv
e
d
h
e
r
BEn
g
in
M
e
d
ica
l
El
e
c
tro
n
ics
f
ro
m
Un
iv
e
rsit
y
o
f
Tec
h
n
o
lo
g
y
M
a
la
y
sia
(2
0
0
1
)
a
n
d
late
r
p
u
rsu
e
d
h
e
r
M
En
g
in
C
o
m
m
u
n
ica
ti
o
n
a
n
d
Co
m
p
u
ter
En
g
in
e
e
rin
g
a
t
Na
ti
o
n
a
l
Un
iv
e
rsity
o
f
M
a
la
y
si
a
(2
0
0
4
).
S
h
e
late
r
re
c
e
iv
e
d
h
e
r
P
h
D
in
El
e
c
tro
n
ics
a
n
d
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
f
ro
m
Un
iv
e
r
sit
y
o
f
S
o
u
th
a
m
p
to
n
,
U
n
it
e
d
Ki
n
g
d
o
m
(2
0
1
1
).
S
h
e
is
c
u
rre
n
t
ly
a
n
a
ss
istan
t
p
ro
f
e
ss
o
r
a
t
In
tern
a
ti
o
n
a
l
Isla
m
ic
Un
iv
e
rsit
y
M
a
la
y
sia
(II
UM).
He
r
re
se
a
r
c
h
in
tere
sts
a
re
in
b
i
o
m
e
d
ica
l
sig
n
a
l
p
ro
c
e
ss
in
g
a
n
d
b
i
o
m
e
d
ica
l
a
p
p
li
c
a
ti
o
n
s.
S
h
e
is
a
m
e
m
b
e
r
o
f
In
stit
u
te
o
f
En
g
in
e
e
rs M
a
lay
si
a
(IE
M
)
a
n
d
B
o
a
rd
o
f
En
g
i
n
e
e
r
M
a
lay
sia
(BEM
).
Wa
r
d
a
ti
I
d
r
is
r
e
c
e
i
v
e
d
h
e
r
BEn
g
d
e
g
re
e
in
Co
m
m
u
n
ica
ti
o
n
En
g
in
e
e
rin
g
in
2
0
1
8
f
ro
m
th
e
In
tern
a
ti
o
n
a
l
Isla
m
ic
Un
iv
e
rsit
y
M
a
lay
si
a
.
He
r
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
sig
n
a
l
p
ro
c
e
ss
in
g
i
n
b
io
m
e
d
ica
l
f
ield
.
Te
d
d
y
S
u
r
y
a
G
u
n
a
w
a
n
re
c
e
i
v
e
d
h
is
BEn
g
d
e
g
re
e
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
w
it
h
c
u
m
lau
d
e
a
wa
rd
f
ro
m
In
stit
u
t
T
e
k
n
o
lo
g
i
Ba
n
d
u
n
g
(IT
B),
In
d
o
n
e
sia
in
1
9
9
8
.
He
o
b
tain
e
d
h
is
M
.
E
n
g
d
e
g
re
e
in
2
0
0
1
f
ro
m
th
e
S
c
h
o
o
l
o
f
Co
m
p
u
ter
En
g
in
e
e
rin
g
a
t
Na
n
y
a
n
g
Tec
h
n
o
lo
g
ica
l
Un
iv
e
rsit
y
,
S
in
g
a
p
o
re
,
a
n
d
P
h
D
d
e
g
re
e
in
2
0
0
7
f
ro
m
th
e
S
c
h
o
o
l
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
n
d
T
e
le
c
o
m
m
u
n
ica
ti
o
n
s,
T
h
e
Un
iv
e
r
sity
o
f
Ne
w
S
o
u
th
W
a
les
,
A
u
stra
li
a
.
His
re
se
a
rc
h
in
tere
sts
a
re
in
sp
e
e
c
h
a
n
d
a
u
d
io
p
ro
c
e
ss
in
g
,
b
io
m
e
d
ica
l
sig
n
a
l
p
ro
c
e
ss
in
g
a
n
d
in
stru
m
e
n
tatio
n
,
im
a
g
e
a
n
d
v
i
d
e
o
p
ro
c
e
ss
in
g
,
a
n
d
p
a
ra
ll
e
l
c
o
m
p
u
ti
n
g
.
He
is
c
u
rre
n
tl
y
a
n
IEE
E
S
e
n
io
r
M
e
m
b
e
r
(sin
c
e
2
0
1
2
)
,
w
a
s
c
h
a
irma
n
o
f
IEE
E
I
n
stru
m
e
n
tatio
n
a
n
d
M
e
a
su
re
m
e
n
t
S
o
c
iety
–
M
a
la
y
sia
S
e
c
ti
o
n
(2
0
1
3
a
n
d
2
0
1
4
),
A
s
so
c
iate
P
r
o
f
e
ss
o
r
(sin
c
e
2
0
1
2
)
,
He
a
d
o
f
De
p
a
rtm
e
n
t
(2
0
1
5
-
2
0
1
6
)
a
t
De
p
a
rtm
e
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
Co
m
p
u
ter
En
g
in
e
e
rin
g
,
a
n
d
He
a
d
o
f
P
ro
g
ra
m
m
e
Ac
c
re
d
it
a
t
io
n
a
n
d
Qu
a
li
ty
As
su
ra
n
c
e
f
o
r
F
a
c
u
lt
y
o
f
En
g
in
e
e
rin
g
(sin
c
e
2
0
1
7
),
I
n
tern
a
ti
o
n
a
l
Isla
m
ic
Un
iv
e
r
sity
M
a
la
y
sia
.
He
is
Ch
a
rtere
d
En
g
in
e
e
r
(IE
T
,
UK
)
a
n
d
I
n
sin
y
u
r
P
r
o
f
e
sio
n
a
l
M
a
d
y
a
(P
II,
I
n
d
o
n
e
si
a
)
sin
c
e
2
0
1
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
5
3
0
–
1538
1538
Ra
s
h
id
a
h
F
u
n
k
e
O
la
n
r
e
w
a
ju
re
c
e
iv
e
d
h
e
r
BS
c
.
Ho
n
s.
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Un
iv
e
rsit
y
P
u
tra
M
a
la
y
sia
,
m
a
jo
rin
g
in
S
o
f
tw
a
r
e
En
g
in
e
e
rin
g
i
n
2
0
0
2
,
M
.
S
c
.
in
Co
m
p
u
ter
a
n
d
I
n
f
o
rm
a
ti
o
n
En
g
in
e
e
rin
g
,
m
a
jo
rin
g
in
In
f
o
r
m
a
ti
o
n
E
n
g
in
e
e
r
in
g
f
ro
m
In
tern
a
ti
o
n
a
l
Isla
m
ic
Un
iv
e
r
sit
y
M
a
la
y
sia
(IIUM
)
in
2
0
0
6
.
S
h
e
late
r
re
c
e
iv
e
d
h
e
r
P
h
D
(E
n
g
in
e
e
rin
g
)
in
A
u
g
u
st
2
0
1
1
w
it
h
sp
e
c
ializa
ti
o
n
in
In
f
o
rm
a
ti
o
n
S
e
c
u
rit
y
in
Dig
it
a
l
I
m
a
g
e
P
ro
c
e
ss
in
g
.
S
h
e
a
lso
re
c
e
iv
e
d
a
P
o
stg
ra
d
u
a
te
Dip
lo
m
a
in
Isla
m
ic
S
tu
d
ies
(DIS)
f
ro
m
IIUM
in
2
0
0
1
.
S
h
e
is
c
u
rre
n
t
ly
a
n
A
ss
istan
t
p
r
o
f
e
ss
o
r
a
t
In
ter
n
a
ti
o
n
a
l
Isla
m
ic
Un
iv
e
rsit
y
M
a
la
y
sia
(IIU
M
).
He
r
re
s
e
a
rc
h
in
tere
sts
a
re
In
f
o
r
m
a
ti
o
n
S
e
c
u
rit
y
,
A
p
p
li
c
a
ti
o
n
o
f
A
rti
f
icia
l
In
telli
g
e
n
c
e
in
Bio
e
n
v
iro
n
m
e
n
tal
S
y
ste
m
s,
Co
m
p
u
ter
A
rc
h
it
e
c
tu
r
e
a
n
d
De
sig
n
,
T
e
le
m
e
d
icin
e
S
y
ste
m
s,
I
m
a
g
e
P
ro
c
e
ss
in
g
a
n
d
Clo
u
d
Co
m
p
u
ti
n
g
.
S
h
e
is
a
S
e
n
io
r
m
e
m
b
e
r
o
f
In
stit
u
te
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
r
(
SM
I
EE
E),
Co
m
p
u
ter
S
o
c
iety
,
W
o
m
e
n
in
En
g
in
e
e
rin
g
(W
IE)
,
M
e
m
b
e
r
(
IET
,
UK
)
A
ra
b
Re
s
e
a
rc
h
In
stit
u
te
in
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
(A
RIS
E),
Nig
e
ria
Co
m
p
u
ter
S
o
c
iet
y
(NCS)
M
a
la
y
si
a
n
S
o
c
iety
f
o
r
Cr
y
p
to
lo
g
y
Re
s
e
a
rc
h
(M
S
CR),
Clo
u
d
Co
m
p
u
ti
n
g
,
V
irt
u
a
l
iza
ti
o
n
a
n
d
Disa
ste
r
Re
c
o
v
e
r
y
in
Nig
e
ria Ne
tw
o
rk
.
S
.
No
o
r
ja
n
n
a
h
Ib
r
a
h
i
m
h
a
s
a
P
h
D.
i
n
El
e
c
tri
c
a
l
a
n
d
C
o
m
p
u
ter
En
g
in
e
e
rin
g
f
ro
m
th
e
Un
iv
e
rsity
o
f
Ca
n
terb
u
ry
,
Ne
w
Z
e
a
lan
d
.
S
h
e
sp
e
c
ialize
s
in
m
icro
-
n
a
n
o
f
a
b
rica
ti
o
n
tec
h
n
o
lo
g
y
p
a
rti
c
u
larl
y
in
p
a
tt
e
rn
tran
sf
e
r
tec
h
n
iq
u
e
,
m
e
t
a
l
d
e
p
o
siti
o
n
,
m
icro
f
lu
id
ic
d
e
sig
n
,
BIOME
M
S
,
M
E
M
S
a
n
d
b
io
m
e
d
ica
l
a
p
p
li
c
a
ti
o
n
.
C
u
rre
n
tl
y
,
h
e
r
re
se
a
rc
h
in
te
re
st
is
in
th
e
a
re
a
o
f
re
sp
irato
r
y
(b
io
m
e
d
ica
l)
se
n
so
r
a
n
d
Io
T
a
p
p
li
c
a
ti
o
n
s.
S
h
e
h
a
s
b
e
e
n
a
n
a
c
a
d
e
m
ic
sin
c
e
2
0
0
1
a
n
d
h
a
s
c
o
n
sid
e
ra
b
le
tea
c
h
in
g
e
x
p
e
rien
c
e
in
u
n
d
e
rg
ra
d
u
a
te
le
v
e
l
a
n
d
p
o
stg
ra
d
u
a
te,
ra
n
g
in
g
f
ro
m
th
e
f
u
n
d
a
m
e
n
tals
c
o
u
rse
(e
lec
tro
n
ics
)
to
t
h
e
m
o
r
e
sp
e
c
iali
st
to
p
ics
su
c
h
a
s
w
irele
ss
te
c
h
n
o
lo
g
y
a
n
d
M
EM
S
.
T
o
d
a
te,
s
h
e
w
o
rk
s
a
s
a
n
A
ss
ist
a
n
t
P
ro
f
e
ss
o
r
a
t
th
e
De
p
a
rtm
e
n
t
o
f
El
e
c
tri
c
a
l
&
Co
m
p
u
ter
En
g
in
e
e
rin
g
,
Ku
ll
iy
y
a
h
O
f
En
g
in
e
e
rin
g
,
In
ter
n
a
ti
o
n
a
l
Isla
m
ic
Un
iv
e
rsit
y
M
a
l
a
y
sia
(IIUM
).
S
h
e
is
a
se
n
io
r
m
e
m
b
e
r
o
f
IEE
E,
S
e
c
re
tar
y
o
f
I
EE
E
El
e
c
tro
n
De
v
ice
s
S
o
c
iety
(
EDS
)
M
a
lay
sia
Ch
a
p
ter
a
n
d
a
m
e
m
b
e
r
o
f
In
stit
u
te o
f
En
g
in
e
e
rs
M
a
la
y
sia
(IE
M
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.