I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
25
,
No
.
1
,
J
an
u
ar
y
2
0
2
2
,
p
p
.
2
1
4
~
2
2
2
I
SS
N:
2
5
0
2
-
4
7
5
2
,
DOI
: 1
0
.
1
1
5
9
1
/ijeecs.v
25
.i
1
.
pp
214
-
2
2
2
214
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
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e.
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m
An int
erne
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o
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thi
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s
-
ba
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18
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Acc
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No
v
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Du
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a
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tern
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(I
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fa
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lex
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t
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a
p
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a
c
o
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ffe
c
ti
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e
sto
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a
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th
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ti
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tu
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th
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.
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x
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K
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w
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s
:
B
r
ain
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m
o
r
Hea
lth
ca
r
e
I
n
ter
n
et
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f
th
in
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s
Ma
g
n
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eso
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an
ce
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a
g
in
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W
r
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t w
ea
r
ab
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T
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is i
s
a
n
o
p
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c
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a
rticle
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n
d
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e
CC B
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SA
li
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.
C
o
r
r
e
s
p
o
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A
uth
o
r
:
Ah
m
ed
W
asif
R
ez
a
Dep
ar
tm
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t o
f
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o
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p
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ter
Scie
n
ce
an
d
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g
in
ee
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in
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,
E
ast W
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s
ity
Dh
ak
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B
an
g
lad
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m
ail: w
asif@
ewu
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d
.
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u
1.
I
NT
RO
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O
N
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wad
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s
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th
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n
etwo
r
k
o
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p
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b
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with
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d
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v
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s
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s
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s
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ter
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I
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ld
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as
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with
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a
n
d
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h
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I
n
to
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ay
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m
o
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n
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e
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u
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r
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tly
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elate
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.
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ea
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ar
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to
m
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th
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p
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p
ar
am
eter
s
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p
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.
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n
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n
t
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s
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r
esear
ch
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s
h
av
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ield
to
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I
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I
n
t
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ical
en
v
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m
en
t,
th
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b
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ain
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m
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h
p
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itio
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as
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lead
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d
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[
1
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,
[
2
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.
I
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ated
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ately
7
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
E
lec
E
n
g
&
C
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m
p
Sci
I
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N:
2502
-
4
7
5
2
A
n
in
tern
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f
th
in
g
s
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b
a
s
ed
a
u
to
ma
tic
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r
a
in
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mo
r
d
etec
tio
n
s
ystem
(
Md
.
Liz
u
r
R
a
h
ma
n
)
215
T
h
e
I
o
T
a
n
d
in
f
o
r
m
atio
n
tech
n
o
lo
g
ies
n
o
w
r
ev
o
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tio
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ized
t
h
e
d
ev
elo
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m
en
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as
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o
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it
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n
d
ev
alu
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p
atien
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’
h
ea
lth
co
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d
itio
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s
.
Ma
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b
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a
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an
ce
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a
g
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(
MRIs)
a
r
e
p
r
o
p
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s
ed
in
[
5
]
.
T
o
test
if
y
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ac
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o
f
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ei
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3
0
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MRIs
o
f
1
4
p
a
tien
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p
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to
m
atic
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eg
m
en
tatio
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u
s
in
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th
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MRIs
tech
n
iq
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e
to
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s
eg
m
e
n
tatio
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o
f
b
r
ain
tu
m
o
r
wo
r
k
s
.
Usi
n
g
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
a
n
d
n
o
r
m
alize
d
h
i
s
to
g
r
am
s
eg
m
en
tatio
n
tech
n
iq
u
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a
b
r
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n
tu
m
o
r
d
etec
tio
n
tech
n
iq
u
e
is
d
ev
elo
p
ed
in
[
6
]
.
T
h
ey
u
s
ed
th
e
s
u
p
p
o
r
t
v
ec
t
o
r
m
ac
h
in
e
(
SVM)
an
d
Naïv
e
B
ay
es
class
if
ier
f
o
r
th
e
class
if
icatio
n
an
d
ac
cu
r
ac
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o
f
th
ei
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m
eth
o
d
.
Au
th
o
r
s
in
[
7
]
s
u
g
g
est
an
au
to
m
atic
d
etec
tio
n
an
d
s
eg
m
en
tatio
n
o
f
b
r
ain
tu
m
o
r
s
th
r
o
u
g
h
th
e
co
n
d
itio
n
al
r
an
d
o
m
f
ield
in
MRI
im
ag
es
an
d
o
b
tain
ed
8
9
%
ac
cu
r
ac
y
o
n
a
v
er
ag
e.
A
m
o
d
if
ied
m
ea
n
-
s
h
if
t
-
b
ased
f
u
zz
y
c
-
m
ea
n
s
eg
m
en
tatio
n
tech
n
iq
u
e
f
o
r
th
e
d
etec
tio
n
o
f
b
r
ain
tu
m
o
r
s
is
p
r
o
p
o
s
ed
in
[
8
]
wh
ich
is
f
ast
to
p
r
o
v
id
e
s
eg
m
en
tatio
n
r
esu
lts
.
I
n
[
9
]
,
th
e
a
u
th
o
r
s
p
r
o
p
o
s
ed
an
SVM
an
d
r
o
u
g
h
K
-
m
ea
n
s
-
b
a
s
ed
b
r
ain
tu
m
o
r
d
etec
tio
n
alg
o
r
ith
m
,
wh
ich
class
if
y
MRI
im
ag
es
an
d
claim
ed
alm
o
s
t
9
9
.
0
5
%
o
f
ac
cu
r
ac
y
.
A
u
th
o
r
s
in
[
1
0
]
c
o
n
v
e
r
t
MRI
im
ag
es
i
n
to
Ots
o
B
in
ar
izatio
n
f
o
l
lo
wed
b
y
K
-
m
ea
n
s
clu
s
ter
in
g
s
eg
m
en
tatio
n
in
b
r
a
in
tu
m
o
r
d
etec
tio
n
an
d
class
if
icatio
n
.
T
h
ey
u
s
ed
th
e
d
is
cr
ete
wav
elet
tr
an
s
f
o
r
m
tech
n
iq
u
e
to
e
x
tr
ac
t
th
e
f
ea
t
u
r
es
an
d
SVM
f
o
r
class
if
icatio
n
o
f
h
i
g
h
ac
cu
r
ac
y
.
A
b
et
ter
tech
n
iq
u
e
th
a
n
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
AN
N)
an
d
SVM
tech
n
iq
u
es
is
p
r
o
p
o
s
ed
in
[
1
1
]
wh
ich
in
cl
u
d
es
k
-
m
ea
n
s
clu
s
ter
in
g
s
eg
m
en
tatio
n
,
h
ig
h
co
n
ce
n
tr
a
tio
n
s
lu
r
r
y
d
is
p
o
s
al
(
HC
SD)
m
eth
o
d
,
e
x
tr
ac
tio
n
o
f
f
ea
t
u
r
es,
an
d
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
class
if
ier
.
Au
th
o
r
s
in
[
1
2
]
p
r
o
p
o
s
e
d
I
o
T
b
ased
m
alig
n
an
t
tu
m
o
r
p
r
e
d
ictio
n
s
y
s
tem
,
wh
er
e
th
ey
u
s
ed
o
n
ly
th
r
ee
p
h
y
s
ical
s
y
m
p
to
m
s
an
d
th
eir
ac
cu
r
ac
y
is
n
o
t
g
o
o
d
.
A
h
y
b
r
id
f
ea
tu
r
e
ex
tr
ac
tio
n
m
eth
o
d
was
u
s
ed
b
ased
o
n
d
is
cr
ete
w
av
elet
tr
an
s
f
o
r
m
an
d
p
r
in
cip
al
co
m
p
o
n
en
t
an
aly
s
is
to
id
e
n
ti
f
y
th
e
b
r
a
in
tu
m
o
r
[
1
3
]
.
B
ased
o
n
th
e
c
o
m
p
r
ess
io
n
o
f
MRI
b
r
ain
im
ag
es
an
au
to
m
atic
tu
m
o
r
r
eg
io
n
ex
tr
ac
tio
n
s
y
s
tem
wa
s
p
r
o
p
o
s
ed
in
[
1
4
]
.
Prin
cip
le
co
m
p
o
n
e
n
t
an
al
y
s
is
an
d
ANN
tech
n
iq
u
es
wer
e
u
s
ed
to
d
etec
t
an
d
r
ec
o
g
n
ize
th
e
b
r
ain
tu
m
o
r
[
1
5
]
,
b
u
t
th
at
s
y
s
tem
u
s
ed
o
n
ly
2
0
MRI
im
ag
e
s
f
o
r
tr
ain
in
g
p
u
r
p
o
s
es
an
d
4
5
MRI
im
ag
es
f
o
r
test
in
g
p
u
r
p
o
s
es.
I
n
th
is
p
ap
er
,
an
I
o
T
-
b
ased
au
to
m
atic
b
r
ain
tu
m
o
r
d
etec
t
io
n
s
y
s
tem
is
d
esig
n
ed
an
d
d
ev
elo
p
e
d
.
Dif
f
er
en
t
s
y
m
p
to
m
s
o
f
b
r
ain
tu
m
o
r
s
ar
e
class
if
ied
to
e
x
tr
a
ct
th
eir
in
ter
n
al
ch
a
r
ac
ter
i
s
tics
an
d
m
ea
s
u
r
ed
b
y
u
s
in
g
s
en
s
o
r
s
.
Po
r
tab
le
wr
is
tb
an
d
Mi
B
an
d
2
,
tem
p
er
atu
r
e,
a
n
d
b
lo
o
d
p
r
ess
u
r
e
m
o
n
ito
r
in
g
s
en
s
o
r
s
ar
e
u
s
ed
in
th
e
ex
p
er
im
e
n
ts
to
m
o
n
ito
r
t
h
e
d
if
f
er
e
n
t
s
y
m
p
to
m
s
o
f
p
at
ien
ts
f
r
o
m
tim
e
to
tim
e.
Patien
ts
’
in
f
o
r
m
atio
n
is
s
to
r
ed
in
a
d
ev
elo
p
e
d
m
o
b
ile
ap
p
licatio
n
v
ia
a
th
ir
d
-
p
ar
ty
s
er
v
er
.
A
co
m
p
a
r
is
o
n
s
tu
d
y
h
as b
ee
n
m
ad
e
b
etwe
e
n
s
ick
an
d
h
ea
lth
y
p
eo
p
le
b
ased
o
n
th
eir
ex
tr
ac
ted
p
h
y
s
io
lo
g
y
in
f
o
r
m
atio
n
t
o
test
if
y
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
d
ev
elo
p
e
d
b
r
ain
tu
m
o
r
d
etec
ti
o
n
s
y
s
tem
.
T
h
e
p
ap
e
r
is
o
r
g
a
n
iz
ed
as
f
o
llo
ws.
I
n
s
ec
tio
n
2
,
th
e
m
e
th
o
d
o
lo
g
y
o
f
th
e
p
r
o
p
o
s
ed
b
r
ain
tu
m
o
r
d
etec
tio
n
tech
n
i
q
u
e
is
d
is
cu
s
s
ed
.
T
h
e
class
if
icatio
n
s
an
d
m
e
asu
r
em
en
t
o
f
d
if
f
e
r
en
t
s
y
m
p
t
o
m
s
r
elate
d
to
b
r
ain
tu
m
o
r
s
ar
e
ex
p
lain
ed
in
th
is
s
ec
tio
n
.
T
h
e
ex
p
e
r
im
en
tal
d
ata
co
llectio
n
an
d
d
ata
tr
an
s
f
er
tech
n
iq
u
es
ar
e
d
is
cu
s
s
ed
in
s
ec
tio
n
3
.
Sectio
n
4
s
h
o
ws
th
e
r
esu
lt
a
n
d
d
is
cu
s
s
io
n
p
ar
t
o
f
th
is
s
y
s
tem
in
cl
u
d
in
g
th
e
e
x
tr
ac
ted
ex
p
er
im
en
tal
d
ataset.
Sectio
n
5
s
h
o
ws
th
e
ac
cu
r
ac
y
an
d
co
m
p
ar
is
o
n
p
a
r
t
o
f
th
e
s
y
s
tem
wh
ile
s
ec
tio
n
6
p
r
o
v
id
es th
e
co
n
clu
s
io
n
.
2.
P
RO
P
O
SE
D
M
E
T
H
O
D
Fo
r
d
etec
tio
n
o
f
b
r
ain
tu
m
o
r
,
s
ev
en
c
o
m
m
o
n
s
y
m
p
to
m
s
i
n
clu
d
in
g
-
h
ea
d
ac
h
e,
v
o
m
itin
g
o
r
n
au
s
ea
,
v
is
io
n
ch
an
g
e,
s
eizu
r
es,
walk
in
g
p
r
o
b
lem
(
co
n
s
id
er
n
o
r
m
a
l
p
eo
p
le
wh
o
ca
n
walk
)
,
d
r
o
wsi
n
ess
o
r
s
leep
in
g
p
r
o
b
lem
s
ar
e
f
atig
u
e
co
n
s
id
er
ed
.
Firstl
y
,
we
wil
l
clas
s
if
y
t
h
o
s
e
s
y
m
p
to
m
s
an
d
co
r
r
esp
o
n
d
in
g
in
f
o
r
m
atio
n
.
T
h
en
we
will sen
s
e
th
at
in
f
o
r
m
atio
n
u
s
in
g
s
en
s
o
r
s
.
2
.
1
.
S
y
m
pto
m
s
a
na
ly
s
is
Sin
ce
th
er
e
is
n
o
wea
r
ab
le
s
en
s
o
r
f
o
r
ca
p
t
u
r
in
g
all
th
e
s
y
m
p
t
o
m
s
d
ata
co
r
r
ec
tly
,
we
u
s
e
class
if
icatio
n
in
th
o
s
e
s
y
m
p
to
m
s
.
B
ased
o
n
th
e
class
if
icatio
n
in
f
o
r
m
atio
n
,
we
will
co
llect
d
ata
b
y
u
s
in
g
o
u
r
p
r
o
p
o
s
ed
d
ev
ice.
T
h
e
class
if
icatio
n
s
o
f
s
y
m
p
to
m
s
ar
e
s
h
o
wn
i
n
T
ab
le
1
.
2
.
2
.
M
ea
s
urem
ent
s
o
f
cla
s
s
if
ica
t
io
n sy
m
pto
m
s
Fo
r
ev
er
y
class
if
icatio
n
s
y
m
p
to
m
(
C
S),
a
d
ef
in
ed
v
alu
e
is
s
et
to
co
m
p
a
r
e
with
th
e
o
b
s
er
v
ed
v
al
u
e.
Fo
r
ex
am
p
le,
1
4
0
/9
0
is
th
e
d
e
f
in
ed
v
alu
e
o
f
b
l
o
o
d
p
r
ess
u
r
e.
T
o
m
ea
s
u
r
e
th
e
C
S,
in
(
1
)
is
p
r
o
p
o
s
ed
wh
ich
ca
n
b
e
s
tated
.
=
{
1
,
≥
ℎ
ℎ
1
,
<
−
1
,
ℎ
(
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
25
,
No
.
1
,
J
an
u
ar
y
20
22
:
214
-
2
2
2
216
I
n
(
1
)
,
o
b
s
er
v
e
v
alu
e
is
th
e
s
en
s
o
r
’
s
s
en
s
ed
v
alu
e,
an
d
th
e
h
ig
h
d
ef
in
ed
v
al
u
e
(
HDV)
is
s
u
ch
k
in
d
o
f
s
y
m
p
to
m
s
v
alu
es
th
at
will
b
e
alwa
y
s
g
r
ea
ter
th
an
o
r
eq
u
al
to
th
e
d
ef
in
ed
v
alu
e
if
a
p
er
s
o
n
h
as
th
at
s
y
m
p
to
m
.
Si
m
ilar
ly
,
a
lo
w
d
ef
in
ed
v
alu
e
(
L
DV)
is
s
u
ch
a
k
in
d
o
f
s
y
m
p
to
m
s
v
alu
e
th
at
will
b
e
alwa
y
s
less
th
an
th
e
d
ef
in
ed
v
alu
e
if
a
p
er
s
o
n
h
as
th
at
s
y
m
p
to
m
.
I
n
th
is
r
esear
c
h
,
HDVs
ar
e
h
ig
h
b
lo
o
d
p
r
ess
u
r
e,
in
c
r
ea
s
ed
b
o
d
y
tem
p
er
atu
r
e,
h
ig
h
h
ea
r
t
r
ate,
a
n
d
a
lar
g
e
am
o
u
n
t
o
f
awa
k
e
ti
m
e
in
b
etwe
en
s
leep
.
Similar
ly
,
L
DVs
ar
e
a
lo
w
h
ea
r
t r
ate,
a
f
ewe
r
n
u
m
b
er
o
f
s
tep
s
,
lo
wer
d
ee
p
s
leep
,
a
n
d
in
s
o
m
n
ia.
B
lo
o
d
p
r
ess
u
r
e
h
as
two
v
alu
es
-
s
y
s
to
lic
an
d
d
iast
o
lic.
T
ab
l
e
2
s
h
o
ws
th
e
ch
ar
t
o
f
b
lo
o
d
p
r
ess
u
r
e.
I
f
th
e
m
ea
s
u
r
ed
b
lo
o
d
p
r
ess
u
r
e
b
ec
o
m
es
h
ig
h
e
r
th
an
HVD,
th
e
co
m
p
u
ter
s
cien
ce
(
C
S)
v
alu
e
b
ec
o
m
es
eq
u
al
to
1
ac
co
r
d
in
g
to
(
1
)
,
o
th
er
wis
e,
C
S
b
ec
o
m
es
-
1
.
T
ab
le
3
s
h
o
ws
th
e
n
o
r
m
al
b
o
d
y
tem
p
er
atu
r
e
f
o
r
p
eo
p
le
ag
e
d
3
o
r
ab
o
v
e.
B
o
d
y
tem
p
er
atu
r
e
9
8
°
Fah
r
en
h
eit
is
co
n
s
id
er
ed
a
s
th
e
HDV
f
o
r
‘
in
cr
ea
s
ed
b
o
d
y
tem
p
er
atu
r
e
’
s
y
m
p
to
m
s
[
1
6
]
.
On
th
e
o
th
er
h
an
d
,
HDV
f
o
r
h
ig
h
h
ea
r
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6
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Du
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d
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[
1
7
]
-
[
1
9
]
.
T
a
b
le
4
s
h
o
w
s
th
e
h
o
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s
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f
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f
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x
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m
al
p
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p
le.
T
h
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e
f
o
r
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th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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d
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J
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n
g
&
C
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m
p
Sci
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N:
2502
-
4
7
5
2
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g
s
-
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a
s
ed
a
u
to
ma
tic
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r
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mo
r
d
etec
tio
n
s
ystem
(
Md
.
Liz
u
r
R
a
h
ma
n
)
217
L
DV
f
o
r
‘
in
s
o
m
n
ia’
is
co
n
s
id
er
ed
as
4
h
o
u
r
s
o
r
2
4
0
m
in
.
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f
an
y
o
n
e
s
leep
s
less
th
an
th
is
L
DV
v
alu
e
f
o
r
a
lo
n
g
tim
e,
th
en
h
e/sh
e
m
ay
h
av
e
in
s
o
m
n
ia.
T
h
e
s
leep
s
tag
es
f
o
r
ad
u
lts
ar
e
g
iv
en
in
T
ab
le
5
w
h
er
e
th
e
am
o
u
n
t
o
f
d
ee
p
s
leep
is
o
b
s
er
v
ed
to
b
e
a
s
ar
o
u
n
d
5
0
to
6
0
m
in
u
tes.
T
h
er
ef
o
r
e,
f
o
r
th
e
C
S
“L
ess
am
o
u
n
t
o
f
d
ee
p
s
leep
,
”
th
e
L
VD
is
s
et
at
4
0
m
in
u
tes.
As
o
b
s
er
v
ed
,
th
e
av
er
ag
e
aw
ak
e
tim
e
f
o
r
a
d
u
lts
is
2
5
m
in
u
tes.
T
h
er
ef
o
r
e,
t
h
e
HDV
is
co
n
s
id
er
ed
as 3
5
m
in
u
tes f
o
r
th
e
C
S “
L
ar
g
e
a
m
o
u
n
t o
f
Awa
k
e
tim
e
in
b
etwe
en
Sleep
”.
T
ab
le
5
.
Sleep
s
tag
es f
o
r
ad
u
lt
s
S
l
e
e
p
S
t
a
g
e
s
M
i
n
u
t
e
s
Li
g
h
t
sl
e
e
p
2
5
2
t
o
3
2
4
R
E
M
sl
e
e
p
8
4
t
o
1
0
8
D
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e
p
sl
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e
p
5
0
t
o
6
5
A
w
a
k
e
25
Fo
r
d
etec
tin
g
th
e
C
S “
L
es
s
n
u
m
b
er
o
f
s
tep
s
”,
its
p
r
ev
io
u
s
d
ata
ar
e
u
s
ed
as L
D
V
an
d
co
m
p
ar
ed
to
th
e
p
r
esen
t
d
ata
as
o
b
s
er
v
e
v
alu
e
an
d
ex
tr
ac
t
th
e
r
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lt.
Similar
s
tep
s
ar
e
also
f
o
llo
wed
to
f
in
d
“
lack
co
o
r
d
in
atio
n
in
th
e
ar
m
o
r
leg
s
”
wh
ich
is
p
r
esen
ted
in
(
2
)
.
ℎ
=
(
2
)
Ar
o
u
n
d
3
0
m
in
u
tes
o
f
a
v
er
ag
e
awa
k
e
tim
e
in
b
etwe
en
s
lee
p
is
co
n
s
id
er
ed
n
o
r
m
al.
B
u
t
if
th
e
awa
k
e
tim
e
in
b
etwe
en
s
leep
is
g
r
ea
t
er
th
an
o
r
eq
u
al
to
3
5
m
i
n
u
tes,
it
will
b
e
co
n
s
id
er
ed
HDV
f
o
r
a
la
r
g
e
a
m
o
u
n
t
o
f
awa
k
e
tim
e
in
b
etwe
en
s
leep
.
T
ab
le
6
s
h
o
ws th
e
HDV
an
d
L
DV
v
alu
es f
o
r
C
S.
T
ab
le
6
.
HDV
an
d
L
DV
v
alu
e
s
f
o
r
C
S
CS
HDV
LD
V
H
i
g
h
B
l
o
o
d
P
r
e
s
su
r
e
1
4
0
&
9
0
-
I
n
c
r
e
a
se
B
o
d
y
T
e
m
p
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r
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t
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r
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h
H
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r
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1
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w
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a
r
t
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a
t
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60
I
n
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mn
i
a
-
2
4
0
Le
ss
a
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o
f
D
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p
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p
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p
35
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2
.
3
.
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ea
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s
y
m
p
to
m
s
(
SS
)
m
en
tio
n
ed
in
T
ab
le
1
,
(
3
)
is
p
r
o
p
o
s
ed
b
ased
o
n
th
e
r
elate
d
C
S v
alu
es.
=
{
1
,
∑
≥
0
1
−
1
,
ℎ
(
3
)
I
n
(
3
)
,
‘
p
’
is
th
e
to
tal
C
S
r
ela
ted
to
SS
.
Usi
n
g
(
3
)
,
it
is
p
o
s
s
ib
le
to
id
en
tify
th
e
s
y
m
p
to
m
o
f
a
b
r
ai
n
tu
m
o
r
o
f
an
y
r
an
d
o
m
p
e
r
s
o
n
o
r
p
atien
t.
Fo
r
ex
am
p
le,
v
o
m
itin
g
o
r
n
au
s
ea
(
VN)
r
elate
d
C
Ss
ar
e
in
cr
ea
s
ed
b
o
d
y
tem
p
er
atu
r
e,
h
ig
h
h
ea
r
t
r
ate,
an
d
h
ig
h
b
lo
o
d
p
r
ess
u
r
e
w
h
o
s
e
C
S
v
alu
es
co
u
ld
b
e
eith
e
r
1
o
r
-
1
ac
c
o
r
d
in
g
to
(
1
)
.
I
f
th
e
s
u
m
m
atio
n
r
elat
ed
th
r
ee
C
S’s
v
alu
e
o
f
VN
s
y
m
p
to
m
s
is
g
r
ea
ter
th
an
o
r
eq
u
al
to
ze
r
o
th
e
n
SS
b
ec
o
m
es e
q
u
al
to
1
(
m
ea
n
s
th
e
s
elec
ted
p
er
s
o
n
h
as VN
s
y
m
p
to
m
s
)
,
o
th
e
r
wis
e
b
ec
o
m
es
-
1.
2
.
4
.
B
ra
in t
um
o
r
predict
io
n
A
f
t
e
r
p
r
e
d
i
c
t
i
n
g
a
l
l
t
h
e
S
S
v
a
l
u
e
s
,
t
h
e
d
e
t
e
c
t
i
o
n
o
f
a
b
r
a
i
n
t
u
m
o
r
c
a
n
b
e
d
o
n
e
b
y
u
s
i
n
g
t
h
e
p
r
o
p
o
s
e
d
(
4
)
.
=
1
1
+
−
(
4
)
I
n
(
4
)
,
L
is
th
e
s
u
m
o
f
all
SS
v
alu
es
(
HA
+
VN
+
VC
+
SZ
+
WP
+
DS
+
FG
)
.
T
h
e
p
r
o
p
o
s
ed
(
4
)
will
s
h
o
w
th
e
p
r
o
b
ab
ilit
y
o
f
b
r
ain
tu
m
o
r
s
b
etwe
en
0
an
d
1
.
T
h
e
p
er
c
e
n
tag
e
o
f
t
h
is
p
r
o
b
a
b
ilit
y
o
f
b
r
ain
tu
m
o
r
is
d
i
v
id
ed
in
to
a
d
if
f
e
r
en
t c
lass
to
m
ak
e
t
h
e
d
ec
is
io
n
s
h
o
wn
i
n
T
ab
le
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
25
,
No
.
1
,
J
an
u
ar
y
20
22
:
214
-
2
2
2
218
T
ab
le
7
.
Dec
is
io
n
tab
le
o
f
b
r
ain
tu
m
o
r
P
e
r
c
e
n
t
a
g
e
p
r
o
b
a
b
i
l
i
t
y
o
f
b
r
a
i
n
t
u
m
o
r
D
e
c
i
s
i
o
n
7
0
%
o
r
a
b
o
v
e
B
r
a
i
n
T
u
m
o
r
3
0
%
≤
p
e
r
c
e
n
t
a
g
e
p
r
o
b
a
b
i
l
i
t
y
<
7
0
%
B
r
a
i
n
T
u
m
o
r
C
a
n
d
i
d
a
t
e
B
e
l
o
w
3
0
%
N
o
r
mal
3.
E
XP
E
R
I
M
E
N
T
A
L
SE
T
UP
3
.
1
.
Da
t
a
c
o
llect
io
n f
ro
m
s
ens
o
rs
I
n
th
e
ex
p
er
im
en
t,
Xiao
m
i
“
Mi
B
an
d
2
”
wr
is
t
b
an
d
as
s
h
o
wn
in
Fig
u
r
e
1
is
u
s
ed
wh
ich
in
clu
d
es
an
ac
ce
ler
o
m
eter
,
o
p
tical
h
ea
r
t
r
ate
m
o
n
ito
r
,
v
ib
r
atio
n
e
n
g
in
e,
g
y
r
o
s
co
p
e,
am
b
ien
t
lig
h
t,
an
d
altim
eter
s
en
s
o
r
s
[
2
0
]
,
[
2
1
]
.
T
h
e
p
ed
o
m
eter
o
f
MI
B
an
d
2
u
s
ed
an
im
p
r
o
v
ed
alg
o
r
ith
m
to
m
ea
s
u
r
e
s
te
p
s
m
o
r
e
ac
cu
r
ately
.
T
h
e
h
ig
h
-
p
r
ec
is
io
n
ac
ce
ler
o
m
eter
m
ea
s
u
r
es
th
e
n
u
m
b
e
r
o
f
s
tep
s
an
d
t
r
ac
k
s
th
e
to
tal
ac
tiv
it
y
tim
e
f
o
r
a
t
o
tal
n
u
m
b
er
o
f
s
tep
s
.
T
h
is
d
e
v
ice
m
ea
s
u
r
es
th
e
h
ea
r
t
r
ate
b
y
u
s
in
g
an
o
p
tical
h
ea
r
t
r
ate
m
o
n
ito
r
s
en
s
o
r
a
n
d
tr
ac
k
s
d
ee
p
s
leep
r
ec
o
r
d
s
.
T
h
is
d
ev
i
ce
tr
ac
k
s
th
e
s
leep
p
atter
n
(
d
e
ep
an
d
lig
h
t
s
leep
)
o
f
h
u
m
an
an
d
awa
k
e
tim
e
in
b
etwe
en
s
leep
b
y
u
s
in
g
a
h
ea
r
t
r
ate
s
leep
ass
is
tan
t,
wh
ich
m
ea
s
u
r
es
th
e
h
ea
r
t
r
ate
wh
e
n
a
h
u
m
an
is
asleep
.
Usi
n
g
th
is
wea
r
ab
le
wr
is
tb
an
d
,
m
o
s
t
o
f
th
e
C
S
s
y
m
p
to
m
s
ca
n
b
e
m
ea
s
u
r
ed
.
An
o
t
h
er
tw
o
in
d
iv
i
d
u
al
s
en
s
o
r
s
ar
e
u
s
ed
to
g
et
th
e
b
o
d
y
tem
p
er
atu
r
e
an
d
b
lo
o
d
p
r
ess
u
r
e.
A
ll
th
ese
s
en
s
o
r
s
an
d
th
e
wr
is
t
b
an
d
ar
e
ass
o
ciate
d
with
Ar
d
u
in
o
Un
o
to
g
et
th
e
r
esu
lts
.
T
h
e
wr
is
tb
an
d
c
o
n
n
ec
ts
with
th
e
an
d
r
o
id
s
m
ar
tp
h
o
n
e
u
s
in
g
th
e
Mi
Fit
ap
p
to
co
llect
d
ata
f
r
o
m
d
ev
i
ce
s
as
s
h
o
wn
in
Fig
u
r
e
2
(
a)
.
Mi
Fit
s
to
r
es
th
o
s
e
d
ata
an
d
s
h
o
ws
th
e
av
er
ag
e
s
tatis
t
ics
an
d
o
th
er
in
f
o
r
m
atio
n
in
ter
m
s
o
f
tim
e
(
e.
g
.
,
d
aily
,
wee
k
ly
an
d
m
o
n
th
ly
)
.
Als
o
,
it
ca
n
m
ea
s
u
r
e
s
tep
s
,
d
is
tan
ce
s
,
an
d
d
if
f
e
r
en
t
p
h
y
s
i
ca
l
ac
t
iv
ities
.
Fig
u
r
e
2
(
b
)
s
h
o
ws
th
e
b
lo
ck
d
iag
r
am
o
f
t
h
e
d
ata
tr
an
s
m
is
s
io
n
f
r
o
m
s
en
s
o
r
s
.
(
a
)
(
b
)
Fig
u
r
e
1
.
C
S sy
m
p
to
m
s
m
ea
s
u
r
ed
b
y
MI
B
an
d
2
Fig
u
r
e
2
.
Data
tr
a
n
s
m
is
s
io
n
th
r
o
u
g
h
,
(
a)
Mi
b
an
d
to
Mi
Fit
ap
p
an
d
(
b
)
s
en
s
o
r
s
to
a
n
d
r
o
id
ap
p
3
.
2
.
Da
t
a
t
r
a
ns
f
er
a
nd
s
t
o
re
in t
he
s
er
v
er
An
in
d
ir
ec
t
ac
ce
s
s
tech
n
iq
u
e
wh
er
e
an
in
ter
m
ed
iate
s
y
s
tem
wo
r
k
s
to
co
llect
d
ata
f
r
o
m
th
e
s
o
u
r
ce
to
th
e
th
ir
d
p
ar
ty
is
u
s
ed
to
tr
a
n
s
f
er
d
ata
f
r
o
m
s
m
ar
t
p
h
o
n
e
to
s
er
v
er
.
Fig
u
r
e
2
illu
s
tr
ates
th
e
d
ata
f
l
o
w
o
f
wea
r
ab
le
b
a
n
d
s
an
d
s
en
s
o
r
s
v
i
a
in
d
ir
ec
t
ac
ce
s
s
.
As
s
h
o
wn
,
wea
r
ab
le
MI
B
an
d
2
ca
p
tu
r
es
d
ata
clo
ck
wis
e
a
n
d
s
en
d
s
th
o
s
e
ca
p
tu
r
ed
d
ata
to
s
m
ar
tp
h
o
n
es
u
s
in
g
th
e
Mi
Fit
a
p
p
ter
m
ed
as
Sen
d
d
ata
1
.
Ar
d
u
in
o
Un
o
tr
an
s
f
er
s
th
e
ca
p
tu
r
e
d
d
ata
f
r
o
m
s
en
s
o
r
s
th
r
o
u
g
h
t
h
e
g
lo
b
al
s
y
s
tem
f
o
r
m
o
b
ile
c
o
m
m
u
n
icatio
n
(
GSM)
m
o
d
u
le
to
an
d
r
o
id
p
h
o
n
es.
T
h
is
d
ata
s
e
n
d
in
g
p
r
o
ce
s
s
is
ter
m
ed
h
er
e
as
Sen
d
d
ata
2
.
Fin
ally
,
th
e
s
to
r
ed
d
ata
in
th
ese
s
m
ar
tp
h
o
n
es a
r
e
tr
a
n
s
f
er
r
ed
to
th
e
th
ir
d
-
p
ar
ty
s
er
v
e
r
.
4.
E
XP
E
R
I
M
E
N
T
A
L
RE
SUL
T
S
4
.
1
.
Da
t
a
s
et
s
co
llect
io
ns
T
h
e
ex
p
er
im
en
tal
d
ata
wer
e
e
x
tr
ac
ted
f
r
o
m
two
g
r
o
u
p
s
o
f
p
eo
p
le:
o
n
e
is
f
r
o
m
b
r
ain
tu
m
o
r
p
atien
ts
an
d
th
e
o
th
er
is
f
r
o
m
n
o
r
m
al
p
eo
p
le.
B
r
ain
tu
m
o
r
p
atien
t
d
ata
wer
e
co
llected
f
r
o
m
a
r
e
n
o
wn
ed
h
o
s
p
ital
in
B
an
g
lad
esh
th
r
o
u
g
h
th
e
clin
ic
al
tr
ial
m
eth
o
d
.
T
o
tal
3
7
5
b
r
ai
n
tu
m
o
r
p
atien
t d
ata
ar
e
co
llec
ted
an
d
u
s
ed
in
th
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
A
n
in
tern
et
o
f
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in
g
s
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b
a
s
ed
a
u
to
ma
tic
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r
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in
tu
mo
r
d
etec
tio
n
s
ystem
(
Md
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u
r
R
a
h
ma
n
)
219
s
y
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tem
.
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r
m
al
p
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p
le’
s
d
at
a
wer
e
co
llected
f
r
o
m
u
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i
v
e
r
s
ity
an
d
co
lleg
e
s
tu
d
e
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ts
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an
d
s
taf
f
.
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o
tal
6
2
5
n
o
r
m
al
p
er
s
o
n
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ata
wer
e
co
llected
an
d
u
s
ed
in
th
is
s
y
s
tem
f
o
r
v
alid
atio
n
.
I
n
th
is
p
ap
er
,
r
a
n
d
o
m
ly
s
elec
ted
1
0
d
ata
f
r
o
m
ea
ch
g
r
o
u
p
ar
e
u
s
ed
.
T
ab
les 8
an
d
9
s
h
o
w
th
e
d
ata
o
f
th
e
b
r
ain
tu
m
o
r
p
atien
t a
n
d
th
e
n
o
r
m
al
p
er
s
o
n
u
s
ed
in
th
is
p
ap
er
,
r
esp
ec
tiv
el
y
.
T
ab
le
8
.
E
x
p
er
im
en
tal
d
ata
o
f
b
r
ain
tu
m
o
r
p
atien
t
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a
mp
l
e
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n
f
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l
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r
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a
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me
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t
a
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s
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5.
RE
SU
L
T
ANAL
YSI
S AN
D
DIS
CU
SS
I
O
N
I
n
o
u
r
e
x
p
er
im
e
n
t,
we
d
iv
id
e
o
u
r
d
ataset
in
to
two
s
ce
n
ar
io
s
an
d
u
s
e
1
0
r
a
n
d
o
m
l
y
tak
e
n
s
am
p
les
f
o
r
ea
ch
s
ce
n
ar
io
.
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r
ea
c
h
s
ce
n
ar
io
,
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
p
er
f
o
r
m
s
v
er
y
ef
f
icien
tly
.
W
e
h
av
e
u
s
ed
th
e
m
o
s
t
p
o
p
u
lar
a
n
d
co
m
m
o
n
m
etr
ics
av
ailab
le
to
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alu
ate
th
e
r
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lts
.
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e
h
av
e
also
m
ea
s
u
r
ed
th
e
ac
cu
r
ac
y
u
s
in
g
th
e
f
o
ll
o
win
g
eq
u
atio
n
.
=
+
+
+
+
(
5
)
wh
er
e
T
P
=
T
r
u
e
Po
s
itiv
e
(
ac
tu
ally
p
o
s
itiv
e
an
d
p
r
e
d
icted
p
o
s
itiv
e)
,
FP
=
f
alse
p
o
s
itiv
e
(
a
ctu
ally
n
eg
ativ
e
b
u
t
p
r
ed
icted
p
o
s
itiv
e)
,
T
N
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tr
u
e
n
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ativ
e
(
ac
tu
ally
n
e
g
ativ
e
a
n
d
p
r
e
d
icted
n
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ativ
e)
,
an
d
F
N
=
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alse
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eg
ativ
e
(
ac
tu
ally
p
o
s
itiv
e
b
u
t
p
r
ed
icte
d
n
eg
ativ
e
)
.
T
h
e
ac
cu
r
ac
y
o
f
th
e
d
esig
n
ed
s
y
s
tem
with
th
e
p
r
o
p
o
s
ed
m
o
d
el
is
g
iv
en
in
T
a
b
le
1
2
f
o
r
b
o
th
s
ce
n
ar
io
s
1
an
d
2
.
Fig
u
r
e
3
s
h
o
ws th
e
g
r
ap
h
ical
r
ep
r
esen
tatio
n
o
f
o
u
r
r
esu
lts
.
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io
u
s
tech
n
iq
u
es
h
av
e
b
e
en
p
r
o
p
o
s
ed
to
d
etec
t
b
r
ai
n
tu
m
o
r
s
.
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s
t
o
f
th
e
m
a
r
e
im
ag
e
class
if
icatio
n
-
b
ased
.
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o
f
in
d
t
h
e
ef
f
icie
n
cy
o
f
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r
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et
h
o
d
,
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av
e
co
m
p
ar
ed
o
u
r
ac
c
u
r
a
cy
with
o
th
er
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tate
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of
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th
e
-
a
r
t m
eth
o
d
o
lo
g
ies.
T
a
b
le
1
3
s
h
o
ws th
e
ac
cu
r
ac
y
tab
le
f
o
r
v
a
r
io
u
s
tech
n
i
q
u
es.
T
ab
le
1
2
.
Acc
u
r
ac
y
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
f
o
r
b
o
th
s
ce
n
ar
i
o
s
D
a
t
a
s
e
t
#
o
f
sam
p
l
e
I
d
e
n
t
i
f
y
c
o
r
r
e
c
t
l
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c
c
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r
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c
y
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c
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n
a
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o
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1
(
B
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[
1
]
C
.
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J.
L.
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3
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B
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4
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[
5
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C
o
mp
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r
-
a
i
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d
d
i
a
g
n
o
si
s
o
f
h
u
ma
n
b
r
a
i
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t
u
mo
r
t
h
r
o
u
g
h
M
R
I
:
A
s
u
r
v
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y
a
n
d
a
n
e
w
a
l
g
o
r
i
t
h
m
,
”
Ex
p
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r
t
S
y
s
t
A
p
p
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,
v
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.
4
,
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1
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p
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[
2
5
]
M
.
P
.
A
r
a
k
e
r
i
a
n
d
G
.
R
.
M
.
R
e
d
d
y
,
“
C
o
mp
u
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e
r
-
a
i
d
e
d
d
i
a
g
n
o
s
i
s
s
y
st
e
m
f
o
r
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ssu
e
c
h
a
r
a
c
t
e
r
i
z
a
t
i
o
n
o
f
b
r
a
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n
t
u
mo
r
o
n
ma
g
n
e
t
i
c
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e
so
n
a
n
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e
i
m
a
g
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s,
”
S
i
g
n
a
l
I
m
a
g
e
Vi
d
e
o
P.
,
v
o
l
.
9
,
p
p
.
4
0
9
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2
5
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0
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:
1
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-
z
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Md
.
Lizur
Ra
h
m
a
n
re
c
e
iv
e
d
t
h
e
B.
S
c
.
d
e
g
re
e
in
c
o
m
p
u
ter
sc
ien
c
e
a
n
d
e
n
g
in
e
e
rin
g
fro
m
th
e
Eas
t
Wes
t
Un
iv
e
rsity
,
Ba
n
g
lad
e
sh
,
fr
o
m
2
0
14
to
2
0
1
8
.
He
h
a
s
re
c
e
iv
e
d
S
u
m
m
a
Cu
m
Lau
d
e
Aw
a
rd
fro
m
Eas
t
Wes
t
Un
iv
e
rsity
.
H
e
is
c
u
rre
n
tl
y
t
h
e
lea
d
e
r
o
f
th
e
so
ftwa
re
d
e
p
a
rtme
n
t
o
f
a
so
ftwa
r
e
c
o
m
p
a
n
y
n
a
m
e
d
Ultra
-
X
BD
Lt
d
.
in
D
h
a
k
a
,
Ba
n
g
lad
e
sh
.
H
e
h
a
s
a
u
th
o
re
d
o
r
c
o
a
u
th
o
re
d
m
o
re
th
a
n
15
p
u
b
li
c
a
ti
o
n
s:
4
p
r
o
c
e
e
d
in
g
s
a
n
d
11
j
o
u
r
n
a
ls,
wit
h
4
H
-
in
d
e
x
a
n
d
m
o
re
th
a
n
70
c
i
tatio
n
s.
His
re
se
a
rc
h
a
re
a
s
a
re
t
h
e
in
tern
e
t
o
f
t
h
in
g
s
,
d
e
e
p
lea
rn
in
g
,
u
n
iv
e
rsa
l
n
e
two
r
k
in
g
la
n
g
u
a
g
e
,
n
a
t
u
ra
l
lan
g
u
a
g
e
p
r
o
c
e
ss
in
g
,
a
n
d
i
n
telli
g
e
n
t
sy
ste
m
s.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
l
izu
r.
sk
y
@
g
m
a
il
.
c
o
m
.
Ahm
e
d
W
a
sif
Re
z
a
o
b
tain
e
d
B.
S
c
(Ho
n
s.)
in
Co
m
p
u
ter S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
fro
m
Kh
u
l
n
a
Un
i
v
e
rsity
(Ba
n
g
l
a
d
e
sh
),
M
a
ste
r
o
f
En
g
i
n
e
e
rin
g
S
c
ien
c
e
(M
.
En
g
.
S
c
.
)
fro
m
M
u
lt
ime
d
ia
Un
i
v
e
rsity
(M
a
lay
si
a
),
a
n
d
Do
c
t
o
r
o
f
P
h
il
o
so
p
h
y
(
P
h
.
D.)
fro
m
Un
iv
e
rsit
y
o
f
M
a
lay
a
(M
a
lay
sia
).
S
in
c
e
Au
g
u
st
2
0
1
6
,
h
e
is
wo
r
k
i
n
g
a
s
a
n
As
so
c
iate
P
ro
fe
ss
o
r
a
t
t
h
e
D
e
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
E
n
g
i
n
e
e
rin
g
(CS
E),
Eas
t
W
e
st
Un
iv
e
rsity
,
Ba
n
g
la
d
e
sh
.
He
wa
s
a
lso
a
p
p
o
i
n
ted
a
s
th
e
Ch
a
irp
e
rso
n
o
f
th
e
CS
E
d
e
p
a
rtme
n
t.
P
re
v
io
u
sly
,
h
e
wa
s
a
tt
a
c
h
e
d
with
t
h
e
Un
i
v
e
rsity
o
f
M
a
lay
a
,
D
e
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
ri
n
g
,
F
a
c
u
lt
y
o
f
E
n
g
i
n
e
e
ri
n
g
,
M
a
lay
sia
fo
r
a
lmo
st
8
y
e
a
rs.
He
is
se
rv
i
n
g
a
s
a
m
e
m
b
e
r
o
f
th
e
Ev
a
lu
a
ti
o
n
Tea
m
(ET
)
f
o
r
Ac
c
re
d
it
a
ti
o
n
o
f
d
iffere
n
t
p
r
o
g
r
a
m
s
o
f
v
a
ri
o
u
s
u
n
i
v
e
rsiti
e
s,
a
p
p
o
i
n
ted
b
y
t
h
e
Bo
a
r
d
o
f
Ac
c
re
d
it
a
ti
o
n
fo
r
En
g
in
e
e
rin
g
a
n
d
Tec
h
n
ica
l
E
d
u
c
a
ti
o
n
(BAETE
)
,
Ba
n
g
lad
e
sh
.
He
a
lso
h
a
s
v
a
st
e
x
p
e
rien
c
e
i
n
su
p
e
rv
isi
n
g
P
h
.
D.,
M
a
ste
rs,
a
n
d
Un
d
e
rg
ra
d
u
a
te
stu
d
e
n
ts.
He
h
a
s
b
e
e
n
p
lac
e
d
in
t
h
e
“
Wo
rl
d
S
c
ien
ti
st
a
n
d
Un
i
v
e
rsity
Ra
n
k
i
n
g
s
2
0
2
1
”
ra
n
k
e
d
b
y
“
AD
S
c
ien
t
ifi
c
In
d
e
x
”
.
He
h
a
s
b
e
e
n
wo
rk
in
g
i
n
th
e
field
o
f
ra
d
i
o
fre
q
u
e
n
c
y
i
d
e
n
ti
fica
ti
o
n
(R
F
ID),
wire
les
s
c
o
m
m
u
n
ica
ti
o
n
s,
b
io
m
e
d
ica
l
ima
g
e
p
r
o
c
e
ss
in
g
,
b
io
i
n
fo
rm
a
ti
c
s,
d
a
ta
sc
ien
c
e
,
th
e
in
ter
n
e
t
o
f
th
in
g
s,
m
a
c
h
in
e
lea
rn
in
g
,
a
n
d
d
e
e
p
lea
rn
in
g
.
He
h
a
s
a
u
th
o
re
d
a
n
d
c
o
-
a
u
th
o
re
d
se
v
e
ra
l
jo
u
rn
a
ls
a
n
d
c
o
n
fe
re
n
c
e
p
a
p
e
rs
(
a
b
o
u
t
1
30
p
a
p
e
rs;
h
-
in
d
e
x
:
2
1
;
c
it
a
ti
o
n
s: 1
7
3
9
).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
wa
sif@e
wu
b
d
.
e
d
u
.
S
h
a
if
u
l
Is
la
m
S
h
a
b
u
j
c
o
m
p
lete
d
h
is
B.
S
c
.
d
e
g
re
e
in
c
o
m
p
u
ter
sc
ien
c
e
a
n
d
e
n
g
in
e
e
rin
g
fr
o
m
th
e
Eas
t
Wes
t
Un
iv
e
rsity
,
Ba
n
g
lad
e
sh
,
fr
o
m
2
0
1
4
t
o
2
0
1
8
.
He
is
c
u
rre
n
t
ly
wo
rk
i
n
g
a
s
a
so
ftwa
re
e
n
g
i
n
e
e
r
o
n
Ultra
-
X
As
ia
P
a
c
ifi
c
Lt
d
.
i
n
To
k
y
o
,
Ja
p
a
n
.
His
re
se
a
rc
h
in
tere
st
i
n
c
lu
d
e
s
m
a
c
h
in
e
lea
rn
in
g
,
t
h
e
i
n
tern
e
t
o
f
th
in
g
s,
n
a
t
u
ra
l
lan
g
u
a
g
e
p
r
o
c
e
ss
in
g
,
a
rti
ficia
l
in
telli
g
e
n
c
e
,
a
n
d
p
a
tt
e
rn
lea
rn
in
g
.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sh
a
ifu
lsh
a
b
u
j@g
m
a
il
.
c
o
m
.
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