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S
D)
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u
r
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d
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p
m
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tal
c
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d
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ti
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n
m
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d
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d
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c
ti
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c
h
a
ll
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s
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tera
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a
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ti
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h
a
v
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rs
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H
o
we
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e
r,
th
e
d
iag
n
o
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s
o
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ASD
u
su
a
ll
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o
c
c
u
rs
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h
in
a
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li
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l
s
e
tt
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g
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c
o
n
d
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c
ted
b
y
li
c
e
n
se
d
p
r
o
fe
ss
io
n
a
ls,
a
n
d
o
f
ten
i
n
v
o
lv
e
s
le
n
g
t
h
y
a
n
d
c
o
stl
y
p
r
o
c
e
d
u
re
s.
O
n
t
h
e
o
th
e
r
h
a
n
d
,
m
a
c
h
in
e
lea
rn
in
g
h
o
ld
s
si
g
n
ifi
c
a
n
t
p
r
o
m
ise
fo
r
imp
r
o
v
i
n
g
d
iag
n
o
stic
a
n
d
in
terv
e
n
t
io
n
re
se
a
rc
h
with
i
n
t
h
e
b
e
h
a
v
i
o
ra
l
sc
ien
c
e
s,
p
a
rti
c
u
larl
y
i
n
re
se
a
rc
h
c
o
n
c
e
rn
in
g
ASD
d
ise
a
se
.
He
n
c
e
,
a
d
e
e
p
in
v
e
sti
g
a
ti
o
n
o
f
a
m
a
c
h
i
n
e
lea
rn
in
g
a
lg
o
rit
h
m
fo
r
ASD
d
e
tec
ti
o
n
is
c
ru
c
ial.
Th
e
re
fo
re
,
t
h
is
p
a
p
e
r
p
r
e
se
n
ted
a
n
e
w
sy
ste
m
fo
r
d
iffere
n
ti
a
ti
n
g
th
e
ASD
sa
m
p
les
fro
m
n
o
n
-
ASD
(i.
e
.
,
h
e
a
lt
h
y
)
sa
m
p
les
.
Th
e
sa
m
p
les
o
f
ASD
h
a
v
e
b
e
e
n
c
o
m
p
il
e
d
fr
o
m
to
d
d
lers
.
Th
e
m
u
lt
il
a
y
e
r
p
e
rc
e
p
tro
n
(M
LP
)
a
lg
o
rit
h
m
is
u
se
d
to
c
las
sify
AS
D
sa
m
p
les
fro
m
n
o
n
-
ASD
sa
m
p
les
.
Th
e
p
r
o
p
o
se
d
M
LP
c
las
sifier
is
imp
lem
e
n
ted
b
a
se
d
o
n
d
iffere
n
t
n
u
m
b
e
rs
o
f
n
e
u
r
o
n
s
(i.
e
.
,
n
o
d
e
s).
I
n
o
t
h
e
r
wo
rd
s
,
th
e
p
ro
p
o
se
d
M
LP
c
las
sifier
sta
rted
with
1
0
n
e
u
ro
n
s
a
n
d
fi
n
ish
e
d
wit
h
5
0
n
e
u
ro
n
s
wit
h
a
n
in
c
re
m
e
n
t
ste
p
o
f
5
n
e
u
ro
n
s.
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e
o
u
tco
m
e
s
d
e
m
o
n
stra
te
th
a
t
th
e
M
LP
c
las
sifier
a
c
q
u
ired
d
iffere
n
t
re
su
l
ts
c
o
n
c
e
rn
in
g
th
e
n
u
m
b
e
r
o
f
n
e
u
ro
n
s.
T
h
e
M
LP
o
b
tain
e
d
th
e
b
e
st
p
e
rfo
rm
a
n
c
e
,
re
a
c
h
in
g
a
n
a
c
c
u
ra
c
y
ra
te
o
f
1
0
0
%
in
id
e
n
ti
f
y
in
g
ASD
c
a
se
s.
K
ey
w
o
r
d
s
:
Au
tis
m
s
p
ec
tr
u
m
d
is
o
r
d
er
E
v
alu
atio
n
m
ea
s
u
r
e
m
en
ts
Ma
ch
in
e
lear
n
in
g
Mu
ltil
ay
er
p
er
ce
p
tr
o
n
T
o
d
d
ler
a
u
tis
m
d
ataset
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
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s
p
o
nd
ing
A
uth
o
r
:
Fah
ad
T
ah
a
Al
-
Dh
ief
C
en
ter
f
o
r
So
f
twar
e
T
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h
n
o
l
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g
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d
Ma
n
ag
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n
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Facu
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o
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I
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f
o
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m
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Scien
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T
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lo
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Un
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s
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b
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B
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Selan
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ail: f
ah
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ed
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
Au
tis
m
s
p
ec
tr
u
m
d
is
o
r
d
er
(
A
SD
)
is
a
n
eu
r
o
lo
g
ical
co
n
d
itio
n
th
at
af
f
ec
ts
b
r
ain
d
ev
elo
p
m
en
t
an
d
is
ass
o
ciate
d
with
d
if
f
icu
lties
in
lan
g
u
a
g
e
u
s
e,
lim
ited
s
o
cial
en
g
ag
e
m
en
t,
im
p
air
ed
c
o
m
m
u
n
icatio
n
a
b
ilit
ies,
p
atter
n
s
o
f
r
e
p
ea
ted
b
e
h
av
io
r
,
an
d
d
ela
y
s
in
m
o
to
r
co
o
r
d
in
a
tio
n
[
1
]
.
T
y
p
ically
,
d
iag
n
o
s
is
ca
n
b
e
m
a
d
e
u
s
in
g
ex
is
tin
g
p
r
o
to
co
ls
f
r
o
m
th
e
ag
e
o
f
th
r
ee
o
n
war
d
s
[
2
]
.
ASD
af
f
ec
ts
v
ar
io
u
s
b
r
ain
r
eg
i
o
n
s
an
d
is
also
in
f
lu
en
ce
d
b
y
g
en
etic
f
ac
to
r
s
s
u
ch
as
g
en
e
in
ter
ac
tio
n
s
o
r
p
o
ly
m
o
r
p
h
is
m
s
[
3
]
.
Glo
b
ally
,
o
n
e
in
7
0
c
h
ild
r
en
is
af
f
ec
ted
b
y
au
tis
m
,
with
a
p
r
ev
alen
ce
r
ate
o
f
1
6
8
o
u
t o
f
1
0
,
0
0
0
ch
ild
r
e
n
esti
m
ated
in
th
e
Un
ited
States
in
2
0
1
8
,
o
n
e
o
f
th
e
h
ig
h
est
wo
r
ld
wid
e
[
4
]
.
Fu
r
t
h
er
m
o
r
e
,
ASD
is
m
o
r
e
co
m
m
o
n
ly
d
ia
g
n
o
s
ed
in
b
o
y
s
th
an
in
g
ir
ls
,
with
ap
p
r
o
x
im
ately
3
.
6
3
%
o
f
b
o
y
s
ag
ed
3
to
1
7
h
av
in
g
th
e
d
is
o
r
d
er
,
c
o
m
p
ar
e
d
to
ab
o
u
t
1
.
2
5
%
o
f
g
ir
ls
in
th
e
Un
ited
States
[
5
]
.
Diag
n
o
s
in
g
ASD
p
o
s
es
ch
alle
n
g
es
d
u
e
to
th
e
ab
s
en
ce
o
f
p
at
h
o
p
h
y
s
io
lo
g
ical
m
ar
k
e
r
s
,
r
ely
in
g
s
o
lely
o
n
p
s
y
ch
o
lo
g
i
ca
l
cr
iter
ia.
Ps
y
ch
o
lo
g
ical
ass
ess
m
en
ts
en
ab
le
th
e
id
en
tific
atio
n
o
f
s
p
ec
if
ic
Evaluation Warning : The document was created with Spire.PDF for Python.
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2
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b
eh
av
io
r
s
an
d
s
o
cial
in
ter
ac
tio
n
lev
els,
aid
in
g
in
ea
r
ly
d
ete
ctio
n
.
B
eh
av
io
r
al
ev
alu
atio
n
s
en
co
m
p
ass
a
r
an
g
e
o
f
to
o
ls
an
d
q
u
esti
o
n
n
air
es
t
h
at
h
elp
p
h
y
s
ician
s
p
in
p
o
in
t
d
ev
elo
p
m
e
n
tal
d
elay
s
(
DD)
in
ch
ild
r
en
.
T
h
ese
ev
alu
atio
n
s
m
a
y
in
cl
u
d
e
clin
i
ca
l
o
b
s
er
v
atio
n
s
,
m
e
d
ical
h
is
t
o
r
y
ass
ess
m
en
ts
,
au
tis
m
d
iag
n
o
s
tic
g
u
id
elin
es,
as
well
as a
s
s
e
s
s
m
en
ts
o
f
g
r
o
wth
an
d
in
tellig
en
ce
[
6
]
.
T
h
e
s
y
m
p
to
m
s
o
f
ASD
ex
h
ib
i
t
a
b
r
o
ad
s
p
ec
tr
u
m
,
wh
ich
p
r
o
m
p
ts
s
u
s
p
icio
n
s
o
f
au
tis
m
in
in
d
iv
id
u
als
d
u
e
to
th
e
p
er
s
is
ten
t
p
r
esen
c
e
o
f
d
iv
e
r
s
e
s
y
m
p
to
m
s
[
7
]
.
P
r
esen
tly
,
th
e
d
iag
n
o
s
is
o
f
ASD
r
elies
o
n
s
ev
er
al
f
ac
to
r
s
,
in
clu
d
in
g
i)
p
ar
en
tal
in
ter
v
iews,
ii)
u
tili
za
tio
n
o
f
s
c
ales
(
e.
g
.
,
au
tis
m
d
iag
n
o
s
tic
o
b
s
er
v
atio
n
s
ch
e
d
u
le
(
ADOS
)
,
au
tis
m
o
b
s
er
v
atio
n
s
in
in
f
an
ts
(
AOSI
)
,
s
o
cial
-
co
m
m
u
n
icatio
n
r
atin
g
s
ca
le
(
SOC
-
RS
)
,
an
d
ea
r
ly
s
o
cial
co
m
m
u
n
icatio
n
s
ca
les
(
E
SC
S
)
)
,
an
d
iii)
clin
ical
o
b
s
er
v
atio
n
s
[
8
]
.
T
h
is
d
iag
n
o
s
tic
p
r
o
ce
s
s
,
b
ein
g
h
ig
h
l
y
s
u
b
jectiv
e,
m
ay
v
ar
y
d
ep
en
d
in
g
o
n
f
ac
t
o
r
s
s
u
ch
as
th
e
p
h
y
s
ician
'
s
ex
p
er
ien
ce
,
p
atie
n
t
b
eh
a
v
io
r
,
p
ar
e
n
tal
k
n
o
wled
g
e,
an
d
r
esp
o
n
s
es
o
n
th
e
s
ca
les
[
9
]
.
C
o
n
s
eq
u
e
n
tly
,
th
e
d
iag
n
o
s
tic
jo
u
r
n
e
y
m
ay
b
e
p
r
o
lo
n
g
ed
,
in
f
lu
en
ce
d
b
y
b
o
th
th
e
p
h
y
s
ician
an
d
p
ar
en
tal
f
ac
to
r
s
,
an
d
m
ay
ca
r
r
y
th
e
r
is
k
o
f
m
is
d
iag
n
o
s
is
d
u
e
to
th
ese
d
is
cr
ep
an
cies
[
1
0
]
.
T
h
is
in
d
i
ca
tes
th
e
o
n
g
o
i
n
g
n
ec
ess
ity
f
o
r
s
cien
tific
ad
v
a
n
ce
m
en
ts
a
n
d
s
u
p
p
o
r
t
in
ASD
d
iag
n
o
s
is
.
W
h
ile
clin
ical
f
in
d
in
g
s
o
r
b
io
m
a
r
k
er
s
ar
e
s
o
u
g
h
t
f
o
r
ea
r
ly
ASD
d
etec
tio
n
,
it'
s
a
ck
n
o
wled
g
e
d
th
at
r
ely
in
g
s
o
lely
o
n
clin
ical
b
io
m
ar
k
er
s
m
ay
n
o
t su
f
f
ice
f
o
r
ea
r
ly
d
iag
n
o
s
is
.
On
t
h
e
o
th
er
s
id
e,
th
e
m
ac
h
in
e
l
ea
r
n
in
g
d
o
m
a
in
h
a
s
d
em
o
n
s
t
r
a
ted
it
s
ef
f
ic
ac
y
in
ter
m
s
o
f
d
et
ec
tio
n
an
d
p
r
ed
i
ct
io
n
s
u
b
j
ec
t
s
in
v
ar
io
u
s
ap
p
l
ica
ti
o
n
s
b
y
u
s
in
g
d
if
f
er
en
t
ty
p
e
s
o
f
m
eth
o
d
s
an
d
ap
p
r
o
ac
h
es
.
Fo
r
i
n
s
tan
ce
,
m
a
ch
in
e
l
ea
r
n
i
n
g
m
eth
o
d
s
h
av
e
b
ee
n
a
p
p
l
i
ed
in
e
m
o
t
io
n
r
e
co
g
n
it
io
n
[
1
1
]
,
[
1
2
]
,
ce
llu
lar
n
et
wo
r
k
[
1
3
]
,
v
o
ic
e
p
a
th
o
lo
g
y
d
e
te
ct
io
n
[
1
4
]
–
[
1
7
]
,
l
ic
en
s
e
p
la
te
id
en
t
if
ic
at
io
n
[
1
8
]
,
d
iab
et
es
p
r
ed
i
ct
io
n
[
1
9
]
,
cr
im
e
a
ct
iv
ity
d
e
tec
t
io
n
[
2
0
]
,
an
d
in
tr
u
s
io
n
d
et
ec
tio
n
[
2
1
]
.
I
n
ad
d
i
t
io
n
,
th
e
m
o
d
e
l
s
o
f
m
ac
h
in
e
le
ar
n
in
g
h
a
v
e
b
ee
n
u
s
ed
in
th
e
A
S
D
r
ec
o
g
n
it
io
n
[
2
2
]
–
[
2
4
]
.
Ho
wev
e
r
,
t
h
es
e
te
ch
n
iq
u
e
s
n
ee
d
f
u
r
th
er
in
v
es
t
ig
a
tio
n
an
d
ex
am
in
a
tio
n
in
th
e
d
et
ec
tio
n
o
f
A
S
D
in
t
er
m
s
o
f
u
s
in
g
h
y
p
er
p
ar
am
e
te
r
m
e
th
o
d
s
,
s
u
ch
a
s
u
s
in
g
d
if
f
e
r
en
t
n
u
m
b
er
s
o
f
n
eu
r
o
n
s
o
r
n
o
d
es
.
T
h
er
e
f
o
r
e
,
th
e
p
r
im
ar
y
c
o
n
tr
i
b
u
t
io
n
s
o
f
th
i
s
s
tu
d
y
c
an
b
e
s
u
m
m
ar
ize
d
as
f
o
l
lo
ws
:
−
T
h
is
p
ap
er
p
r
o
p
o
s
es
a
n
ew
m
eth
o
d
f
o
r
d
etec
tin
g
ASD
d
is
ea
s
e
b
ased
o
n
a
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
(
ML
P)
class
if
ier
.
−
T
h
e
p
r
o
p
o
s
ed
ML
P
m
o
d
el
is
tr
ain
ed
an
d
ass
ess
ed
b
ased
o
n
ASD
d
atab
ase
u
s
in
g
d
if
f
er
e
n
t
n
u
m
b
e
r
s
o
f
n
eu
r
o
n
s
(
i.e
.
,
n
o
d
es).
−
T
h
e
m
eth
o
d
is
ev
alu
ated
u
ti
lizin
g
s
ev
er
al
p
er
f
o
r
m
a
n
ce
m
ea
s
u
r
em
en
ts
s
u
ch
as
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e,
s
p
ec
if
icity
,
a
n
d
G
-
m
ea
n
.
−
T
h
e
p
r
o
p
o
s
ed
ML
P
ac
h
iev
e
s
h
ig
h
r
esu
lts
f
o
r
d
etec
tin
g
ASD
d
is
ea
s
e,
o
u
tp
er
f
o
r
m
in
g
m
an
y
o
f
its
co
m
p
ar
ativ
e
in
ter
m
s
o
f
d
etec
tio
n
ac
cu
r
ac
y
.
T
h
e
r
est
o
f
th
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws
:
s
ec
tio
n
2
p
r
esen
ts
th
e
p
r
ev
io
u
s
s
tu
d
ies
o
f
ASD
r
ec
o
g
n
itio
n
u
tili
zin
g
m
o
d
els
o
f
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
l
ea
r
n
in
g
.
Sectio
n
3
p
r
esen
ts
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
wh
ich
co
n
s
is
ts
o
f
two
s
u
b
s
ec
tio
n
s
.
T
h
e
f
ir
s
t
s
u
b
s
ec
tio
n
r
ef
er
s
to
th
e
ASD
d
atab
ase
f
o
r
to
d
d
ler
s
,
wh
ich
is
u
s
ed
to
tr
ain
a
n
d
ass
ess
th
e
ML
P
m
o
d
el.
Me
an
wh
ile,
t
h
e
s
ec
o
n
d
s
u
b
s
ec
tio
n
r
ef
er
s
to
th
e
p
r
o
p
o
s
ed
ML
P
class
if
ier
th
at
i
s
u
s
ed
to
r
ec
o
g
n
ize
th
e
ASD
s
am
p
les
f
r
o
m
th
e
n
o
n
-
ASD
s
am
p
les.
Secti
o
n
4
p
r
o
v
id
es
an
d
d
is
cu
s
s
es
th
e
ex
p
er
im
en
tal
r
e
s
u
lts
o
f
th
e
p
r
o
p
o
s
ed
ML
P
al
g
o
r
ith
m
.
L
astl
y
,
th
e
co
n
clu
s
io
n
o
f
t
h
is
s
tu
d
y
is
p
r
o
v
id
e
d
in
s
ec
tio
n
5
.
2.
RE
L
AT
E
D
WO
RK
T
h
e
u
s
e
o
f
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
es
to
id
en
tif
y
ASD
d
is
ea
s
e
h
as
b
ee
n
wid
ely
s
tu
d
ied
a
n
d
in
v
esti
g
ated
.
I
n
o
th
e
r
wo
r
d
s
,
th
e
p
r
ev
io
u
s
r
esear
ch
s
tu
d
ie
s
h
av
e
an
al
y
ze
d
d
if
f
er
e
n
t
A
SD
d
atab
ases
,
u
s
ed
v
ar
io
u
s
class
if
ier
s
,
an
d
ass
ess
ed
th
e
m
o
d
el
u
s
in
g
ac
c
u
r
ac
y
a
n
d
o
th
er
m
ea
s
u
r
em
e
n
ts
.
Hen
c
e,
m
an
y
r
esear
ch
er
s
an
d
s
ch
o
lar
s
h
a
v
e
attain
ed
h
u
g
e
atten
tio
n
to
t
h
is
f
ield
o
f
s
y
s
tem
s
.
J
ay
an
th
i
et
a
l.
[
2
5
]
p
r
o
p
o
s
ed
an
ass
is
tiv
e
tech
n
o
lo
g
y
aim
ed
at
m
o
n
ito
r
in
g
s
tr
ess
lev
els
in
in
d
iv
id
u
a
ls
af
f
ec
ted
b
y
ASD
d
is
ea
s
e.
ASD
o
f
ten
af
f
ec
ts
lin
g
u
is
tic
an
d
m
o
to
r
s
k
ills
,
an
d
s
tr
ess
ca
n
ex
ac
er
b
ate
b
o
th
p
h
y
s
ical
an
d
m
e
n
tal
h
ea
lth
is
s
u
es
in
th
es
e
in
d
iv
id
u
als.
R
ed
u
ce
d
h
ea
r
t
r
a
te
v
ar
iab
ilit
y
(
HR
V)
is
o
b
s
er
v
ed
in
th
o
s
e
with
ASD,
wh
ich
is
co
n
s
id
er
ed
a
s
ig
n
if
ican
t
f
ac
to
r
.
Acc
o
r
d
i
n
g
l
y
,
p
ar
a
m
eter
s
s
u
ch
as
b
o
d
y
m
o
v
em
en
ts
,
b
o
d
y
tem
p
er
at
u
r
e,
an
d
h
ea
r
t
r
ate
ar
e
m
o
n
ito
r
ed
.
T
h
e
h
a
r
d
war
e
m
o
d
u
le
in
te
g
r
ated
v
ar
io
u
s
s
en
s
o
r
s
lik
e
p
u
ls
e
s
en
s
o
r
,
tem
p
er
atu
r
e
s
en
s
o
r
,
an
d
ac
ce
ler
o
m
eter
s
,
ad
ap
tab
le
to
p
er
s
o
n
alize
d
wea
r
ab
les
f
o
r
u
s
e
r
s
.
I
n
th
is
s
tu
d
y
,
th
e
co
llected
d
ata
is
tr
an
s
m
itted
to
a
n
o
n
r
elatio
n
al
d
atab
ase
a
n
d
th
e
n
p
r
o
ce
s
s
ed
u
s
in
g
m
a
ch
in
e
lear
n
in
g
m
o
d
els
lik
e
r
an
d
o
m
f
o
r
est
(
R
F)
class
if
ier
s
an
d
lin
ea
r
r
eg
r
ess
io
n
.
Sh
r
iv
astav
a
et
a
l.
[
2
6
]
h
as
ap
p
lied
v
a
r
io
u
s
m
a
ch
in
e
lear
n
in
g
class
if
ier
s
to
d
is
tin
g
u
is
h
b
et
wee
n
d
ata
r
ep
r
esen
tin
g
ASD
an
d
ty
p
ica
lly
d
ev
elo
p
e
d
(
T
D)
in
d
iv
id
u
a
ls
.
Var
io
u
s
ASD
d
atasets
en
c
o
m
p
ass
in
g
all
ag
e
g
r
o
u
p
s
(
to
d
d
ler
s
,
ch
ild
r
en
,
ad
o
lescen
ts
,
an
d
ad
u
lts
)
wer
e
e
m
p
lo
y
ed
f
o
r
th
is
p
u
r
p
o
s
e.
C
ateg
o
r
ical
d
ata
wer
e
co
n
v
er
ted
in
t
o
n
u
m
er
ical
d
ata
u
s
in
g
one
-
h
o
t
en
c
o
d
in
g
d
u
r
i
n
g
p
r
ep
r
o
ce
s
s
in
g
.
T
o
ad
d
r
ess
m
is
s
in
g
v
alu
es
an
d
n
o
r
m
alize
d
ata,
th
e
au
th
o
r
s
h
av
e
u
tili
ze
d
k
-
n
ea
r
est
n
e
ig
h
b
o
r
s
(
KNN)
im
p
u
te
r
in
co
n
ju
n
ctio
n
wit
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Dete
ctio
n
o
f a
u
tis
m
s
p
ec
tr
u
m
d
is
o
r
d
er u
s
in
g
mu
ltil
a
ye
r
p
ercep
tr
o
n
cla
s
s
ifier
(
A
h
med
Q.
Ha
d
i
)
2105
Min
Ma
x
Scaler
f
ea
tu
r
e
tr
an
s
f
o
r
m
atio
n
.
C
lass
if
icatio
n
o
f
ASD
an
d
T
D
d
ata
was
p
er
f
o
r
m
ed
u
s
in
g
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
(
SVM)
,
KNN,
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN)
,
an
d
R
F
class
if
ier
s
.
T
h
e
R
F
class
if
ier
ex
h
ib
ited
th
e
h
ig
h
est ac
cu
r
ac
y
r
esu
lt,
wh
er
e
it is
ac
h
iev
ed
1
0
0
% a
cc
u
r
ac
y
ac
r
o
s
s
d
if
f
er
en
t t
r
ain
in
g
an
d
test
in
g
d
ata
s
p
lits
f
o
r
all
ag
e
g
r
o
u
p
s
,
with
o
u
t o
cc
u
r
r
in
g
o
v
er
f
itti
n
g
i
s
s
u
es.
An
o
th
er
s
tu
d
y
h
as
b
ee
n
p
r
ese
n
ted
f
o
r
th
e
d
etec
tio
n
o
f
ASD
d
is
ea
s
e
[
2
7
]
.
I
n
th
is
s
tu
d
y
,
t
h
e
f
ed
er
ated
lear
n
in
g
(
FL)
ap
p
r
o
ac
h
is
u
ti
lized
an
d
in
v
esti
g
ated
f
o
r
th
e
p
u
r
p
o
s
e
o
f
ASD
d
etec
tio
n
.
Fu
r
th
er
m
o
r
e
,
th
er
e
wer
e
m
ac
h
in
e
lear
n
in
g
class
if
ier
s
,
wh
ich
ar
e
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
,
an
d
SVM
th
at
wer
e
tr
ain
ed
to
class
if
y
ASD
f
ac
to
r
s
an
d
d
etec
t
ASD
i
n
b
o
th
ad
u
lts
an
d
ch
ild
r
e
n
.
L
e
v
er
ag
in
g
FL,
th
e
r
esu
lts
f
r
o
m
th
ese
m
o
d
els
wer
e
tr
an
s
m
itted
to
a
ce
n
tr
al
s
er
v
er
,
wh
er
e
a
m
eta
-
ap
p
r
o
ac
h
was
tr
ain
ed
to
d
eter
m
in
e
th
e
m
o
s
t
ac
cu
r
ate
ap
p
r
o
ac
h
f
o
r
ASD
d
etec
tio
n
ac
r
o
s
s
d
if
f
er
en
t
ag
e
g
r
o
u
p
s
.
Fo
u
r
d
iv
e
r
s
e
ASD
p
atien
t
d
ata
b
ases
,
ea
ch
co
m
p
r
is
in
g
o
v
e
r
6
0
0
s
am
p
les
o
f
af
f
ec
ted
a
d
u
lt
s
an
d
ch
ild
r
e
n
,
wer
e
s
o
u
r
ce
d
f
r
o
m
v
ar
io
u
s
r
e
p
o
s
ito
r
ies
f
o
r
f
ea
tu
r
e
ex
tr
ac
tio
n
.
T
h
e
m
o
d
el
d
e
m
o
n
s
tr
ated
t
h
a
t
th
e
SVM
alg
o
r
ith
m
o
b
tain
ed
th
e
h
ig
h
est
p
e
r
f
o
r
m
an
ce
,
wh
er
e
it
h
as
b
ee
n
ac
h
iev
ed
a
d
etec
tio
n
ac
cu
r
ac
y
o
f
9
9
% f
o
r
ASD
in
ch
ild
r
en
a
n
d
8
1
% f
o
r
ASD
in
ad
u
lts
.
T
h
e
A
SD
d
is
ea
s
e
h
a
s
b
e
en
a
n
aly
ze
d
an
d
s
tu
d
ied
b
a
s
ed
o
n
v
ar
io
u
s
te
ch
n
i
q
u
e
s
o
f
m
ac
h
i
n
e
lea
r
n
in
g
[
2
8
]
.
T
h
i
s
s
t
u
d
y
h
a
s
d
e
lv
ed
in
to
th
e
p
r
ed
ict
io
n
o
f
AS
D
an
d
co
m
p
ar
ed
v
ar
io
u
s
a
s
p
e
ct
s
,
in
c
lu
d
in
g
ap
p
l
ic
at
io
n
k
in
d
,
s
im
u
la
tio
n
m
e
th
o
d
,
m
e
th
o
d
co
m
p
a
r
i
s
o
n
,
an
d
d
a
ta
t
y
p
e.
I
t
s
m
ain
o
b
jec
ti
v
e
i
s
to
o
f
f
er
d
ev
elo
p
er
s
a
ce
n
t
r
a
li
ze
d
f
r
a
m
e
wo
r
k
en
g
ag
ed
in
A
SD
p
r
ed
i
ct
io
n
.
T
h
e
p
r
o
p
o
s
ed
al
g
o
r
i
th
m
s
w
er
e
e
x
am
in
ed
b
y
u
s
in
g
d
if
f
e
r
en
t
m
ea
s
u
r
em
en
t
s
wh
ic
h
ar
e
p
r
ec
i
s
i
o
n
,
ac
cu
r
a
cy
,
an
d
r
e
ca
ll
.
T
h
e
s
am
p
le
s
w
er
e
g
ath
er
ed
f
r
o
m
th
e
au
t
is
m
b
r
a
in
im
a
g
in
g
d
a
ta
e
x
ch
an
g
e
(
A
B
I
DE
)
d
a
ta
s
e
t.
T
h
e
R
F
alg
o
r
i
th
m
em
er
g
ed
as
th
e
m
o
s
t
ef
f
ec
tiv
e
,
o
u
tp
er
f
o
r
m
in
g
o
th
er
tr
ad
it
io
n
al
a
lg
o
r
ith
m
s
.
T
h
e
R
F
a
lg
o
r
i
t
h
m
h
as
at
ta
in
ed
a
d
et
ec
tio
n
a
cc
u
r
a
cy
o
f
8
9
.
2
3
%.
Ho
w
ev
er
,
t
h
e
o
b
ta
in
ed
ac
cu
r
ac
ie
s
o
f
n
a
ïv
e
B
ay
e
s
(
N
B
)
,
S
VM
,
an
d
ML
P
wer
e
8
5
.
4
3
%,
8
0
.
4
3
%,
an
d
8
1
.
8
4
%
,
r
esp
ec
ti
v
el
y
.
Alv
es
et
a
l.
[
2
9
]
h
as
b
ee
n
in
t
r
o
d
u
ce
d
an
a
p
p
r
o
ac
h
f
o
r
a
u
to
m
atica
lly
d
iag
n
o
s
in
g
ASD
d
is
ea
s
e
u
s
in
g
f
u
n
ctio
n
al
b
r
ain
im
ag
in
g
d
ata
o
b
tain
ed
f
r
o
m
a
c
o
h
o
r
t
o
f
5
0
0
s
u
b
jects,
am
o
n
g
wh
o
m
2
4
2
h
a
v
e
b
ee
n
d
iag
n
o
s
ed
with
ASD
d
i
s
ea
s
e,
u
til
izin
g
b
o
o
ts
tr
ap
an
aly
s
is
o
f
s
tab
le
c
lu
s
ter
m
ap
s
to
id
en
tify
r
eg
io
n
s
o
f
in
ter
est.
T
h
is
m
eth
o
d
h
as
b
ee
n
ex
h
i
b
ited
a
h
ig
h
ac
cu
r
ac
y
in
d
i
f
f
er
en
tiatin
g
am
o
n
g
th
e
co
n
tr
o
l
g
r
o
u
p
a
n
d
ASD
p
atien
ts
.
I
n
o
th
er
wo
r
d
s
,
it
h
as
ac
h
iev
ed
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
with
a
n
a
r
ea
u
n
d
er
th
e
cu
r
v
e
(
AUC
)
ap
p
r
o
ac
h
in
g
1
.
0
.
T
h
e
an
aly
s
is
r
ev
ea
led
a
r
e
d
u
ce
d
c
o
n
n
ec
tiv
ity
b
etwe
en
th
e
lef
t
v
en
tr
al
p
o
s
ter
io
r
ci
n
g
u
late
c
o
r
t
ex
an
d
a
r
eg
io
n
in
th
e
ce
r
eb
ellu
m
am
o
n
g
in
d
iv
i
d
u
als
with
th
is
n
eu
r
o
d
ev
elo
p
m
en
tal
d
is
o
r
d
er
.
Ad
d
itio
n
ally
,
f
u
n
ctio
n
al
b
r
ai
n
n
etwo
r
k
s
in
ASD
p
atien
ts
e
x
h
ib
ited
g
r
ea
ter
s
eg
r
eg
atio
n
,
r
ed
u
ce
d
i
n
f
o
r
m
atio
n
d
is
s
em
in
atio
n
ac
r
o
s
s
th
e
n
etwo
r
k
,
a
n
d
d
im
in
is
h
ed
c
o
n
n
ec
tiv
ity
co
m
p
ar
ed
to
co
n
tr
o
l
s
u
b
jects.
I
n
t
h
is
wo
r
k
,
a
m
o
n
g
all
tech
n
iq
u
es
u
s
ed
f
o
r
th
e
d
etec
tio
n
o
f
ASD,
th
e
R
F
,
an
d
L
R
alg
o
r
ith
m
s
attai
n
ed
th
e
b
est
d
etec
tio
n
ac
cu
r
a
cy
,
wh
er
e
b
o
th
h
av
e
b
ee
n
ac
h
iev
e
d
1
0
0
%.
A
m
u
l
tim
o
d
a
l
s
y
s
tem
r
e
la
y
o
n
m
a
ch
i
n
e
le
ar
n
in
g
f
o
r
ea
r
ly
s
cr
ee
n
in
g
o
f
A
S
D
i
s
p
r
e
s
en
te
d
[
3
0
]
.
I
n
th
e
s
tu
d
y
,
th
er
e
wer
e
1
2
5
to
d
d
ler
s
p
ar
tic
ip
at
ed
,
co
m
p
r
is
i
n
g
in
d
iv
id
u
al
s
d
ia
g
n
o
s
ed
wi
t
h
A
SD
d
i
s
e
as
e.
I
n
o
th
er
wo
r
d
s
,
th
e
n
u
m
b
er
o
f
AS
D
p
at
ien
t
s
w
a
s
6
1
s
am
p
l
e
s
,
th
e
n
u
m
b
er
o
f
DD
wa
s
3
1
,
an
d
th
e
n
u
m
b
er
o
f
ty
p
i
ca
lly
d
ev
elo
p
m
en
ta
l
(
T
D)
wa
s
3
3
ch
i
ld
r
e
n
.
T
h
e
r
e
s
p
o
n
s
e
to
n
am
e
(
R
T
N)
p
r
o
ce
d
u
r
e
s
wa
s
ad
m
in
is
te
r
ed
s
ep
ar
a
te
ly
b
y
b
o
th
th
e
a
s
s
es
s
o
r
an
d
th
e
c
ar
e
tak
er
.
I
n
ad
d
it
io
n
,
th
e
d
at
a
r
e
la
ted
to
b
eh
a
v
io
r
wer
e
ca
p
tu
r
ed
u
s
in
g
8
-
d
ef
in
i
tio
n
tr
ip
o
d
-
m
o
u
n
ted
ca
m
er
a
s
an
d
an
aly
ze
d
u
s
in
g
a
m
u
l
ti
m
o
d
a
l
m
ac
h
in
e
l
ea
r
n
in
g
s
y
s
te
m
(
MM
L
S)
.
B
e
s
id
e
s
,
th
e
r
e
s
p
o
n
s
e
p
er
io
d
,
s
co
r
e
o
f
r
e
s
p
o
n
s
e,
an
d
t
im
e
o
f
r
esp
o
n
s
e
wer
e
p
r
ec
i
s
ely
m
ea
s
u
r
ed
to
as
s
es
s
R
T
N.
T
h
e
f
in
d
in
g
s
i
n
d
ic
at
ed
a
t
o
t
al
a
cc
u
r
ac
y
o
f
0
.
9
2
f
o
r
R
T
N
s
co
r
es
g
en
e
r
ated
b
y
co
m
p
u
te
r
s
.
T
o
d
d
l
er
s
w
ith
AS
D
in
d
ic
at
e
d
n
o
tab
le
d
i
s
ti
n
c
tio
n
s
i
n
th
e
r
es
p
o
n
s
e
p
er
io
d
,
s
co
r
e
o
f
r
e
s
p
o
n
s
e,
an
d
t
im
e
o
f
r
es
p
o
n
s
e
co
m
p
a
r
ed
to
th
o
s
e
wi
th
DD
an
d
T
D
in
b
o
th
a
s
s
e
s
s
o
r
an
d
ca
r
et
ak
er
p
r
o
ce
s
s
es
.
T
h
e
AU
C
wa
s
0
.
8
1
f
o
r
co
m
p
u
ter
-
g
en
er
a
ted
o
u
tco
m
e
s
,
an
d
th
e
h
u
m
an
-
r
at
ed
o
u
t
co
m
es
w
er
e
0
.
9
1
.
Me
an
wh
i
le,
th
e
a
cc
u
r
ac
y
r
at
es
f
o
r
id
en
t
if
y
in
g
A
SD
b
a
s
ed
o
n
h
u
m
an
-
r
at
ed
r
e
s
u
l
t
s
a
n
d
co
m
p
u
t
er
-
g
en
er
at
ed
r
e
s
u
lt
s
w
er
e
8
2
.
9
%
an
d
7
4
.
8
%
,
r
e
s
p
ec
tiv
ely
.
3.
P
RO
P
O
SE
D
M
E
T
H
O
D
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
aim
s
to
d
is
tin
g
u
is
h
th
e
ASD
s
am
p
les
f
r
o
m
th
e
h
ea
lth
y
o
n
es.
I
n
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
th
er
e
ar
e
two
p
h
ases
,
th
e
f
ir
s
t
o
n
e
r
ef
e
r
s
to
th
e
AS
D
d
atab
ase
,
wh
ic
h
co
n
tain
s
th
e
ex
tr
ac
ted
f
ea
tu
r
es
f
r
o
m
th
e
ASD
s
am
p
les
an
d
h
ea
lth
y
s
am
p
les.
I
n
ad
d
itio
n
,
th
e
ex
tr
ac
ted
attr
ib
u
tes
will
b
e
p
r
o
v
id
ed
to
th
e
m
o
d
el.
T
h
e
s
ec
o
n
d
p
h
ase
r
ef
e
r
s
to
th
e
class
if
icatio
n
p
ar
t,
w
h
er
e
th
e
ML
P
alg
o
r
ith
m
p
er
f
o
r
m
s
to
class
if
y
th
e
s
am
p
les o
f
in
d
iv
id
u
als wh
o
s
u
f
f
er
f
r
o
m
ASD
d
is
ea
s
e
f
r
o
m
th
e
h
ea
lth
y
o
n
es.
T
h
e
alg
o
r
ith
m
s
h
av
e
b
ee
n
d
ee
p
ly
in
v
esti
g
ated
b
ased
o
n
d
if
f
er
e
n
t
n
u
m
b
er
s
o
f
n
eu
r
o
n
s
,
s
tar
t
in
g
at
1
0
n
eu
r
o
n
s
a
n
d
f
in
is
h
in
g
at
5
0
n
eu
r
o
n
s
with
an
in
cr
em
en
t
s
tep
o
f
5
n
eu
r
o
n
s
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
h
as
b
ee
n
ass
es
s
ed
b
ased
o
n
wid
ely
k
n
o
w
n
m
ea
s
u
r
em
e
n
ts
.
Fig
u
r
e
1
s
h
o
ws
th
e
f
lo
wch
ar
t
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
f
o
r
t
h
e
class
if
icatio
n
o
f
ASD.
I
n
ad
d
itio
n
,
th
ese
two
s
tag
es
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
will
b
e
ex
p
lain
e
d
f
u
r
th
e
r
in
th
e
f
o
llo
win
g
s
u
b
s
ec
tio
n
s
,
r
esp
ec
tiv
ely
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
5
,
No
.
3
,
J
u
n
e
2
0
2
6
:
2
1
0
3
-
2
1
1
2
2106
Fig
u
r
e
1
.
T
h
e
f
lo
wch
a
r
t o
f
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
3
.
1
.
Aut
is
m
s
pect
rum
dis
o
rder
da
t
a
ba
s
e
T
h
e
s
am
p
les
o
r
f
ea
tu
r
es
o
f
th
e
ASD
d
atab
ase
ar
e
ty
p
ically
u
tili
ze
d
to
tr
ain
an
d
ass
es
s
th
e
m
o
d
els
o
f
m
ac
h
in
e
lear
n
in
g
.
I
n
th
is
s
tu
d
y
,
we
h
av
e
co
llected
th
e
A
SD
d
atab
ase
f
r
o
m
Kag
g
le
r
e
s
p
ir
ato
r
y
[
3
1
]
.
T
h
is
d
atab
ase
h
as
b
ee
n
cr
ea
te
d
by
th
e
Dep
ar
tm
en
t
o
f
Dig
ital
T
ec
h
n
o
lo
g
y
,
New
Z
ea
lan
d
.
T
h
e
s
am
p
les
in
th
is
d
atab
ase
h
av
e
b
ee
n
g
ath
e
r
ed
b
y
u
s
in
g
a
m
o
b
ile
ap
p
n
am
e
d
A
SDTe
s
ts
.
Fu
r
th
er
m
o
r
e
,
th
is
d
atab
ase
is
r
elate
d
to
au
tis
m
s
cr
ee
n
in
g
o
f
to
d
d
ler
s
,
an
d
it
co
m
p
r
is
es
in
f
lu
en
tial
f
ea
tu
r
es
to
b
e
u
tili
ze
d
f
o
r
ad
d
itio
n
al
an
aly
s
is
,
p
ar
ticu
la
r
ly
in
d
eter
m
in
in
g
au
tis
tic
tr
aits
an
d
im
p
r
o
v
in
g
th
e
id
en
tific
atio
n
o
f
ASD
ca
s
es.
I
n
th
is
d
atab
ase,
th
er
e
ar
e
1
0
b
eh
av
io
r
al
f
ea
tu
r
es
(
Q
-
C
h
at
-
1
0
)
th
at
h
av
e
b
ee
n
r
ec
o
r
d
e
d
,
as
well
as
o
th
er
attr
ib
u
tes
th
at
ca
n
b
e
u
s
ed
f
o
r
d
e
tectin
g
th
e
ASD
ca
s
es.
T
h
er
e
ar
e
two
class
es
in
th
is
d
atab
ase.
T
h
e
f
ir
s
t
o
n
e
w
as
lab
elled
as
“y
e
s
”
(
i.e
.
,
it
r
e
f
er
s
to
th
e
p
ar
ticip
a
n
ts
h
av
in
g
ASD
d
is
ea
s
e)
an
d
th
e
s
ec
o
n
d
o
n
e
was
lab
ell
ed
as
“n
o
”
(
i.e
.
,
n
o
n
-
ASD
d
is
ea
s
e)
.
T
h
e
d
ata
b
ase
co
m
p
r
is
es
a
to
tal
o
f
1
,
0
5
4
s
am
p
les,
wh
e
r
e
7
2
8
s
am
p
les
wer
e
lab
elled
as
"y
es",
an
d
3
2
6
s
am
p
les
wer
e
lab
ell
ed
as
“n
o
”.
I
n
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
t
h
e
d
atab
a
s
e
h
as
b
ee
n
s
ep
ar
ate
d
in
to
7
0
%
an
d
3
0
%
f
o
r
t
h
e
tr
ain
i
n
g
an
d
ass
ess
m
en
t
p
r
o
ce
d
u
r
es,
r
esp
ec
tiv
ely
.
T
h
e
r
ef
o
r
e,
th
e
tr
ain
in
g
s
tep
in
clu
d
es
7
3
7
s
am
p
les
,
an
d
th
e
tes
tin
g
s
tep
co
n
tain
s
3
1
7
s
am
p
les.
I
n
ad
d
itio
n
,
T
ab
le
1
s
h
o
ws
th
e
n
u
m
b
er
o
f
s
am
p
les
u
s
ed
in
th
e
p
r
o
p
o
s
ed
m
o
d
el
f
o
r
th
e
ASD
d
etec
tio
n
.
T
ab
le
1
.
T
h
e
ASD
d
atab
ase
C
l
a
s
ses
N
u
mb
e
r
o
f
c
a
ses
Tr
a
i
n
i
n
g
Te
st
i
n
g
A
S
D
7
2
8
7
3
7
3
1
7
N
o
n
-
A
S
D
3
2
6
To
t
a
l
1
0
5
4
3
.
2
.
Cla
s
s
if
ica
t
io
n
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
u
s
es
th
e
ML
P a
lg
o
r
ith
m
f
o
r
th
e
class
if
icatio
n
p
ar
t to
class
if
y
th
e
ASD
s
am
p
les
f
r
o
m
n
o
n
-
ASD
s
am
p
les.
T
h
e
ML
P
is
a
ty
p
e
o
f
ANN
th
a
t
h
as
b
ee
n
wid
el
y
u
s
ed
f
o
r
c
lass
if
icatio
n
task
s
,
in
clu
d
in
g
m
ed
ical
d
iag
n
o
s
is
,
s
u
ch
as
th
y
r
o
id
d
is
ea
s
e
d
etec
t
io
n
[
3
2
]
.
T
h
is
s
tu
d
y
em
p
lo
y
e
d
an
ML
P
class
if
ier
to
d
is
tin
g
u
is
h
am
o
n
g
in
d
iv
i
d
u
als
with
ASD
an
d
th
o
s
e
with
o
u
t,
with
class
e
s
lab
e
lled
as
"
y
es
"
f
o
r
ASD
-
p
o
s
itiv
e
ca
s
es
an
d
"
no
"
f
o
r
n
o
n
-
ASD
ca
s
es.
T
h
e
ML
P
class
if
ier
co
n
s
is
t
s
o
f
an
in
p
u
t
lay
er
,
o
n
e
o
r
m
o
r
e
h
id
d
en
lay
e
r
s
,
an
d
an
o
u
tp
u
t
l
ay
er
.
E
ac
h
lay
e
r
co
m
p
r
is
es
m
u
ltip
le
n
eu
r
o
n
s
,
an
d
co
n
n
ec
tio
n
s
b
etwe
en
n
eu
r
o
n
s
ar
e
weig
h
ted
.
T
h
e
ML
P
lear
n
s
f
r
o
m
tr
ain
in
g
d
ata
b
y
ad
j
u
s
tin
g
th
ese
weig
h
ts
to
m
in
im
ize
c
lass
if
icatio
n
er
r
o
r
s
an
d
im
p
r
o
v
e
its
p
er
f
o
r
m
a
n
ce
i
n
th
e
class
if
icatio
n
b
etwe
en
s
u
b
jects.
I
n
th
e
ML
P
alg
o
r
ith
m
,
th
er
e
a
r
e
th
r
ee
m
ain
lay
e
r
s
,
n
am
ely
i
n
p
u
t,
h
i
d
d
en
,
a
n
d
o
u
tp
u
t.
T
h
e
in
p
u
t
lay
er
co
m
p
r
is
es
n
eu
r
o
n
s
wh
ich
ar
e
r
ep
r
esen
tin
g
th
e
f
ea
tu
r
es
o
f
th
e
in
p
u
t
d
ata
(
i.e
.
,
th
e
f
e
atu
r
es
o
f
th
e
ASD
d
atab
ase)
.
I
n
th
e
h
i
d
d
en
lay
er
,
th
e
ML
Ps
ca
n
i
n
clu
d
e
o
n
e
o
r
m
o
r
e
o
f
s
u
ch
la
y
er
s
,
ea
ch
en
c
o
m
p
ass
in
g
n
u
m
er
o
u
s
n
eu
r
o
n
s
.
T
h
ese
h
id
d
en
lay
er
s
allo
w
th
e
n
etwo
r
k
to
ac
q
u
ir
e
in
tr
icate
r
elatio
n
s
h
i
p
s
with
in
th
e
d
ata.
I
n
th
e
p
r
o
p
o
s
ed
m
o
d
el
u
s
in
g
th
e
ML
P
alg
o
r
ith
m
,
th
e
n
u
m
b
er
o
f
h
id
d
en
n
o
d
es
u
s
ed
f
o
r
tr
ain
in
g
th
e
ML
P
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Dete
ctio
n
o
f a
u
tis
m
s
p
ec
tr
u
m
d
is
o
r
d
er u
s
in
g
mu
ltil
a
ye
r
p
ercep
tr
o
n
cla
s
s
ifier
(
A
h
med
Q.
Ha
d
i
)
2107
class
if
ier
v
ar
ies.
I
n
o
th
er
wo
r
d
s
,
th
e
h
id
d
en
n
o
d
es
(
i.e
.
,
n
e
u
r
o
n
s
)
in
th
e
p
r
o
p
o
s
ed
ML
P
alg
o
r
ith
m
a
r
e
s
et
to
s
tar
t
f
r
o
m
1
0
an
d
f
in
is
h
at
5
0
h
id
d
en
n
o
d
es
with
an
in
cr
e
m
en
t
b
y
5
h
id
d
en
n
o
d
es.
Me
an
wh
ile,
th
e
o
u
tp
u
t
lay
er
g
iv
es
th
e
f
in
al
class
if
icatio
n
r
esu
lts
.
I
n
b
in
a
r
y
class
if
icatio
n
task
s
,
th
e
m
o
d
el
u
s
u
ally
h
as
a
s
in
g
le
o
u
tp
u
t
n
eu
r
o
n
,
wh
er
ea
s
in
m
u
lti
-
class
class
if
icatio
n
,
it e
m
p
lo
y
s
m
u
l
tip
le
o
u
tp
u
t
n
eu
r
o
n
s
co
r
r
esp
o
n
d
in
g
to
ea
c
h
class
.
I
n
th
e
tr
ain
in
g
p
r
o
ce
s
s
o
f
th
e
ML
P a
lg
o
r
ith
m
,
an
d
d
u
r
in
g
f
o
r
war
d
p
r
o
p
ag
atio
n
,
in
p
u
t
d
ata
is
p
r
o
v
id
ed
in
to
th
e
n
etwo
r
k
.
T
h
e
weig
h
ted
s
u
m
o
f
i
n
p
u
ts
an
d
b
iases
is
ca
lcu
lated
at
ea
ch
n
e
u
r
o
n
,
f
o
llo
wed
b
y
th
e
ap
p
licatio
n
o
f
ac
tiv
atio
n
f
u
n
ctio
n
s
.
T
h
is
p
r
o
ce
s
s
g
en
er
ates
p
r
ed
ictio
n
s
.
Af
ter
p
r
ed
ictio
n
s
ar
e
m
ad
e,
th
e
alg
o
r
ith
m
c
o
m
p
u
tes
th
e
er
r
o
r
am
o
n
g
f
o
r
ec
asted
an
d
r
ea
l
o
u
tp
u
ts
.
Usi
n
g
g
r
ad
ien
t
d
esc
en
t
o
p
tim
izatio
n
,
it
f
in
e
-
tu
n
es
th
e
weig
h
ts
an
d
b
ia
s
es
b
ac
k
war
d
th
r
o
u
g
h
th
e
n
et
wo
r
k
to
m
in
im
ize
th
is
er
r
o
r
.
T
h
is
iter
ativ
e
p
r
o
ce
s
s
is
r
ec
o
g
n
ized
as
b
ac
k
p
r
o
p
ag
at
io
n
.
I
n
th
e
ML
P
class
if
ier
,
ea
ch
n
eu
r
o
n
ty
p
ically
ap
p
lies
an
ac
tiv
atio
n
f
u
n
ctio
n
to
p
r
esen
t
n
o
n
lin
ea
r
ity
in
to
th
e
m
o
d
el.
Fam
iliar
ac
tiv
atio
n
f
u
n
ctio
n
s
lik
e
s
ig
m
o
id
,
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
eL
U
)
,
an
d
o
th
er
s
.
I
n
th
e
p
r
o
p
o
s
ed
m
o
d
el,
th
e
lo
g
is
tic
s
ig
m
o
id
f
u
n
ctio
n
h
as
b
ee
n
u
s
ed
as
an
ac
tiv
atio
n
f
u
n
ctio
n
.
T
h
e
m
ain
p
r
o
ce
s
s
f
o
r
th
e
class
if
icatio
n
p
ar
t
in
th
e
ML
P
alg
o
r
ith
m
in
v
o
lv
es
f
o
r
war
d
p
r
o
p
ag
atio
n
,
wh
er
e
in
p
u
t
f
ea
tu
r
es
ar
e
p
r
o
ce
s
s
ed
th
r
o
u
g
h
th
e
n
etwo
r
k
to
p
r
o
d
u
ce
o
u
t
p
u
t
p
r
e
d
ictio
n
s
.
Fu
r
th
er
m
o
r
e
,
th
e
ML
P
alg
o
r
ith
m
is
tr
ain
ed
b
ased
o
n
(
1
)
an
d
(
2
)
.
=
⋅
+
(
1
)
W
h
er
e
=
a
c
tiva
tion
(
)
.
̃
=
⋅
̃
+
̃
(
2
)
W
h
er
e
̂
=
s
oft
ma
x
(
̃
)
.
I
n
(
1
)
an
d
(
2
)
,
r
ep
r
esen
ts
th
e
in
p
u
t
d
ata
(
i.e
.
,
f
ea
tu
r
es),
an
d
̃
d
en
o
te
th
e
weig
h
t
v
alu
es
o
f
th
e
1
s
t
an
d
2
n
d
la
y
er
s
,
r
esp
ec
tiv
e
ly
.
I
n
ad
d
itio
n
,
an
d
̃
r
ep
r
ese
n
t
th
e
b
ias
v
ec
t
o
r
s
o
f
th
e
1
s
t
an
d
2
n
d
lay
er
s
,
r
esp
ec
tiv
ely
.
an
d
̃
r
ep
r
esen
t
t
h
e
weig
h
ted
s
u
m
s
o
f
in
p
u
ts
in
t
h
e
h
id
d
en
a
n
d
o
u
tp
u
t
la
y
er
s
,
r
esp
ec
tiv
ely
.
is
th
e
ac
tiv
atio
n
o
f
th
e
h
i
d
d
en
lay
er
.
L
astl
y
,
̂
is
th
e
o
u
tp
u
t
o
f
th
e
n
etwo
r
k
,
r
ep
r
esen
tin
g
th
e
p
r
ed
icted
p
r
o
b
a
b
ilit
ies f
o
r
ea
ch
class
af
ter
ap
p
ly
in
g
th
e
s
o
f
tm
a
x
f
u
n
cti
o
n
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
r
im
a
r
y
aim
o
f
th
is
m
e
th
o
d
is
to
r
ec
o
g
n
ize
th
e
ASD
d
is
ea
s
e
f
ea
tu
r
es
u
tili
zin
g
a
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
.
Data
s
am
p
les
f
o
r
au
tis
m
s
cr
ee
n
in
g
d
at
a
f
o
r
to
d
d
ler
s
en
co
m
p
ass
in
g
b
o
th
ASD
in
s
tan
ce
s
an
d
n
o
n
-
ASD
(
i.e
.
,
n
o
r
m
al)
wer
e
s
o
u
r
ce
d
f
r
o
m
th
e
Ka
g
g
l
e
r
ep
o
s
ito
r
y
.
W
ith
in
th
is
d
ata
b
ase,
th
er
e
ar
e
ten
b
eh
av
io
r
al
f
ea
tu
r
es
o
f
ea
ch
p
a
r
ticip
an
t
th
at
h
av
e
b
ee
n
r
ec
o
r
d
ed
.
B
esid
es,
th
er
e
ar
e
two
cla
s
s
es
th
at
h
av
e
b
ee
n
lab
eled
as
Yes
(
i.e
.
,
AS
D
cl
ass
)
an
d
No
(
i.e
.
,
n
o
n
-
ASD
class
)
.
T
h
e
d
ataset
wa
s
p
ar
tit
io
n
ed
in
to
7
0
%
f
o
r
tr
ain
in
g
an
d
3
0
% f
o
r
ass
ess
m
en
t p
u
r
p
o
s
es.
T
h
e
f
ea
tu
r
es
ch
a
r
ac
ter
izin
g
e
ac
h
class
wer
e
u
tili
ze
d
as
in
p
u
ts
f
o
r
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
el.
Sp
ec
if
ically
,
th
e
ML
P
class
if
ier
was
em
p
lo
y
e
d
f
o
r
th
e
ASD
d
etec
tio
n
.
T
h
e
ML
P
cl
ass
if
ier
co
n
s
is
ts
o
f
d
if
f
er
en
t
n
eu
r
o
n
s
(
i.e
.
,
n
o
d
es).
I
n
th
e
p
r
o
p
o
s
ed
m
o
d
el,
th
e
ML
P
class
if
ier
h
as
a
d
if
f
er
en
t
n
u
m
b
er
o
f
n
e
u
r
o
n
s
,
wh
er
e
th
e
ML
P
class
if
ier
s
t
ar
ts
with
1
0
n
eu
r
o
n
s
an
d
f
i
n
is
h
es
at
5
0
n
eu
r
o
n
s
with
an
in
cr
em
en
t
s
tep
o
f
5
n
eu
r
o
n
s
.
T
h
u
s
,
th
e
to
tal
n
u
m
b
er
o
f
ex
p
er
im
e
n
ts
is
9
.
Fu
r
th
er
,
th
e
e
x
p
er
im
e
n
ts
o
f
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
wer
e
im
p
lem
en
ted
u
s
in
g
Py
th
o
n
3
.
1
0
o
n
a
lap
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p
eq
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ip
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e
d
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I
n
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C
o
r
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r
o
ce
s
s
o
r
(
2
.
5
0
GHz
)
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d
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GB
o
f
R
AM
.
T
o
ass
ess
th
e
e
f
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el,
a
r
an
g
e
o
f
ev
alu
atio
n
m
ea
s
u
r
em
en
ts
in
clu
d
in
g
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
a
ll
(
i.e
.
,
s
en
s
itiv
ity
)
,
F1
-
s
co
r
e
(
i.e
.
,
F
-
m
ea
s
u
r
e)
,
s
p
ec
if
icity
,
an
d
G
-
m
ea
n
wer
e
u
t
ilized
.
T
h
ese
m
ea
s
u
r
es
ar
e
ca
lcu
lated
em
p
lo
y
in
g
in
(
3
)
-
(
8
)
[
3
3
]
,
[
3
4
]
.
T
h
e
ex
p
er
im
en
tal
r
esu
lts
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
u
s
in
g
th
e
ML
P
class
if
ier
f
o
r
th
e
d
etec
tio
n
o
f
ASD
d
is
ea
s
e
ar
e
d
is
p
lay
ed
in
T
a
b
le
2
.
=
+
+
+
+
(
3
)
=
+
(
4
)
=
+
(
5
)
1
−
=
(
2
×
×
)
(
+
)
(
6
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
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8
9
3
8
I
n
t J Ar
tif
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n
tell
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5
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3
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u
n
e
2
0
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6
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2
2108
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+
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7
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le
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h
e
ex
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e
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en
tal
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lts
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ith
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r
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Acc
o
r
d
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g
to
th
e
ex
p
er
im
e
n
t
al
r
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lts
,
th
e
ML
P
class
if
ier
h
as
o
b
tain
e
d
d
if
f
er
en
t
r
esu
lts
b
ased
o
n
v
ar
io
u
s
n
u
m
b
e
r
s
o
f
n
eu
r
o
n
s
.
I
n
o
th
e
r
wo
r
d
s
,
wh
en
th
e
n
u
m
b
er
o
f
n
e
u
r
o
n
s
is
1
0
,
th
e
p
r
o
p
o
s
ed
ML
P
class
if
ier
h
as
ac
h
iev
ed
8
5
.
4
9
%
ac
cu
r
a
cy
,
8
2
.
3
7
%
p
r
ec
is
io
n
,
an
d
5
4
.
9
0
%
r
ec
all.
Fu
r
th
e
r
m
o
r
e
,
th
e
r
esu
lts
o
f
th
e
p
r
o
p
o
s
ed
ML
P
alg
o
r
ith
m
f
o
r
F1
-
s
co
r
e,
s
p
ec
if
icity
,
an
d
G
-
m
ea
n
wer
e
7
0
.
8
8
%,
5
4
.
9
0
%,
an
d
7
4
.
1
0
%,
r
esp
ec
tiv
ely
.
T
h
e
p
r
o
p
o
s
ed
ML
P
class
if
ier
h
as
ac
h
iev
ed
th
e
b
est
o
u
tc
o
m
es
wh
en
th
e
n
u
m
b
er
o
f
n
eu
r
o
n
s
r
ea
ch
ed
4
5
an
d
5
0
,
wh
e
r
ea
s
th
e
ML
P
class
if
ier
h
as
ac
h
iev
ed
1
0
0
%
f
o
r
ea
ch
e
v
alu
a
tio
n
m
ea
s
u
r
em
en
t.
Fig
u
r
e
2
d
is
p
lay
s
th
e
b
est o
u
tc
o
m
es o
f
th
e
ML
P c
lass
if
ier
f
o
r
th
e
d
etec
tio
n
o
f
ASD
d
is
ea
s
e.
Fig
u
r
e
2
.
T
h
e
h
ig
h
est o
b
tain
ed
r
esu
lts
o
f
th
e
ML
P c
lass
if
ier
f
o
r
ASD
d
etec
tio
n
Ho
wev
er
,
th
e
ML
P
class
if
ier
h
as
b
ee
n
o
b
tain
e
d
th
e
lo
west
r
esu
lts
wh
en
th
e
n
u
m
b
e
r
o
f
n
eu
r
o
n
s
was
1
5
,
wh
e
r
e
th
e
ML
P
class
if
ier
o
b
tain
ed
8
3
.
9
1
%
ac
cu
r
ac
y
,
8
0
.
8
2
%
p
r
ec
is
io
n
,
an
d
5
0
.
0
0
%
r
ec
all.
Mo
r
eo
v
e
r
,
t
h
e
lo
west
o
b
tain
ed
r
esu
lts
o
f
th
e
ML
P
class
if
ier
f
o
r
F1
-
s
co
r
e,
s
p
ec
if
icity
,
an
d
G
-
m
ea
n
wer
e
6
6
.
6
6
%,
5
0
.
0
0
%,
an
d
7
0
.
7
1
%,
r
esp
ec
tiv
ely
.
I
n
a
d
d
itio
n
,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
u
s
in
g
th
e
ML
P c
las
s
if
ier
h
as b
ee
n
co
m
p
ar
ed
with
o
th
er
m
eth
o
d
s
an
d
tech
n
iq
u
es
f
o
r
th
e
r
ec
o
g
n
itio
n
o
f
ASD
i
lln
ess
in
ter
m
s
o
f
ac
cu
r
ac
y
.
T
ab
le
3
d
is
p
lay
s
th
e
co
m
p
ar
is
o
n
b
etwe
en
th
e
p
r
o
p
o
s
ed
ML
P
class
if
ier
an
d
o
th
er
ap
p
r
o
ac
h
es
[
3
5
]
–
[
4
0
]
.
T
h
ese
m
eth
o
d
s
h
av
e
b
ee
n
u
s
ed
th
e
s
am
e
d
ataset
,
wh
ich
is
ASD
d
ata
f
o
r
to
d
d
ler
s
.
A
d
d
itio
n
ally
,
t
h
ese
m
eth
o
d
s
h
a
v
e
b
ee
n
u
s
ed
with
d
if
f
er
en
t
tech
n
iq
u
es
f
o
r
th
e
d
etec
tio
n
o
f
ASD
d
is
ea
s
e.
Fo
r
ex
am
p
le,
t
h
e
m
et
h
o
d
in
[
3
5
]
,
KNN,
R
F,
L
R
,
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Dete
ctio
n
o
f a
u
tis
m
s
p
ec
tr
u
m
d
is
o
r
d
er u
s
in
g
mu
ltil
a
ye
r
p
ercep
tr
o
n
cla
s
s
ifier
(
A
h
med
Q.
Ha
d
i
)
2109
d
ec
is
io
n
tr
ee
(
DT
)
alg
o
r
ith
m
s
wer
e
u
s
ed
,
C
4
.
5
tr
ee
,
R
F,
an
d
n
e
u
r
al
n
etwo
r
k
(
NN)
[
3
6
]
,
SVM
[
3
7
]
,
NB
,
L
R
,
SVM,
an
d
R
F
[
3
8
]
,
a
d
ap
tiv
e
b
o
o
s
tin
g
(
AB
)
[
3
9
]
,
an
d
lin
ea
r
d
is
cr
im
in
an
t a
n
aly
s
is
(
L
DA)
[
4
0
]
.
B
ased
o
n
th
e
o
b
tain
ed
r
esu
lts
f
o
r
ea
ch
m
eth
o
d
,
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
ML
P
alg
o
r
ith
m
h
as
b
ee
n
s
h
o
wed
th
e
h
ig
h
est
ac
h
iev
ed
ac
cu
r
ac
y
co
m
p
ar
ed
to
o
t
h
er
m
eth
o
d
s
.
T
h
is
d
em
o
n
s
tr
at
es
th
e
s
ig
n
if
ican
ce
o
f
d
ee
p
in
v
esti
g
atio
n
o
f
class
if
ier
s
in
th
e
d
etec
tio
n
o
f
AS
D
d
is
ea
s
e,
wh
er
e
it
h
a
s
b
ee
n
in
d
icate
d
th
at
th
e
p
r
o
p
o
s
ed
ML
P
class
if
ier
o
b
t
ain
ed
th
e
h
ig
h
est
r
esu
lt
wh
en
th
e
n
u
m
b
e
r
o
f
n
e
u
r
o
n
s
r
e
ac
h
ed
4
5
an
d
5
0
,
r
esp
ec
tiv
ely
.
Ho
wev
er
,
th
e
r
e
ar
e
s
o
m
e
lim
itatio
n
s
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
th
at
ca
n
b
e
c
o
n
s
id
er
ed
as
f
u
tu
r
e
wo
r
k
s
.
T
h
ese
lim
itatio
n
s
ca
n
b
e
lis
ted
as f
o
llo
ws:
−
T
h
is
m
eth
o
d
is
lim
ited
to
d
etec
tin
g
ASD
d
ata
f
o
r
to
d
d
ler
s
.
Me
an
wh
ile,
th
e
ASD
d
ata
f
o
r
ad
u
lts
is
s
ig
n
if
ican
t.
−
T
h
e
s
tatis
tical
an
aly
s
is
f
o
r
f
u
r
th
er
an
aly
zin
g
th
e
e
x
p
er
im
e
n
tal
r
esu
lt is
ig
n
o
r
ed
in
th
is
m
eth
o
d
.
−
I
n
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
o
n
e
d
ataset
an
d
o
n
e
class
if
ier
h
av
e
b
ee
n
u
s
ed
.
T
ab
le
3
.
C
o
m
p
a
r
is
o
n
b
etwe
en
m
eth
o
d
s
M
e
t
h
o
d
s
A
c
c
u
r
a
c
y
(
%)
Th
e
p
r
o
p
o
s
e
d
M
LP
c
l
a
ssi
f
i
e
r
1
0
0
KNN
[
3
5
]
7
8
.
1
2
R
F
[
3
5
]
9
0
.
8
8
LR
[
3
5
]
8
5
.
3
8
D
T
[
3
5
]
9
1
.
0
2
C
4
.
5
tr
e
e
[
3
6
]
98
R
F
[
3
6
]
96
NN
[
3
6
]
99
S
V
M
[
3
7
]
98
N
B
[
3
8
]
4
3
.
6
LR
[
3
8
]
6
8
.
4
8
S
V
M
[
3
8
]
7
6
.
7
7
R
F
[
3
8
]
9
2
.
6
5
A
B
[
3
9
]
9
9
.
2
5
LD
A
[
4
0
]
9
3
.
8
4
5.
CO
NCLU
SI
O
N
ASD
is
a
co
n
d
itio
n
im
p
ac
ted
b
y
g
en
etic
an
d
n
eu
r
o
lo
g
ical
f
ac
to
r
s
,
r
esu
ltin
g
in
ch
allen
g
es
r
elate
d
to
s
o
cial
in
ter
ac
tio
n
an
d
co
m
m
u
n
icatio
n
.
Ho
wev
er
,
d
ia
g
n
o
s
i
n
g
ASD
ty
p
ically
in
v
o
lv
es
s
p
ec
ialized
clin
ical
ass
es
s
m
en
ts
,
wh
ich
ca
n
b
e
tim
e
-
co
n
s
u
m
in
g
an
d
ex
p
e
n
s
iv
e.
C
o
n
v
er
s
ely
,
m
ac
h
i
n
e
l
ea
r
n
in
g
p
r
esen
ts
a
p
r
o
m
is
in
g
av
en
u
e
f
o
r
ad
v
a
n
cin
g
d
iag
n
o
s
tic
a
n
d
i
n
ter
v
en
tio
n
r
esear
ch
in
b
eh
a
v
io
r
al
s
cien
ce
s
,
p
ar
ticu
lar
ly
i
n
th
e
co
n
tex
t
o
f
ASD.
Mo
r
e
o
v
e
r
,
m
o
s
t
s
tu
d
ies
th
at
u
s
ed
s
ev
er
al
alg
o
r
ith
m
s
f
o
r
th
e
ASD
r
ec
o
g
n
itio
n
h
a
v
e
b
ee
n
u
s
ed
f
ix
ed
p
ar
a
m
eter
s
,
s
u
ch
as
a
f
ix
ed
n
u
m
b
er
o
f
h
id
d
en
n
o
d
es.
T
h
er
ef
o
r
e,
in
v
esti
g
atin
g
a
m
o
d
el
f
o
r
ASD
r
ec
o
g
n
itio
n
b
ased
o
n
d
if
f
er
en
t
n
u
m
b
er
s
o
f
h
id
d
e
n
n
o
d
es
is
i
m
p
er
ativ
e.
T
h
is
p
ap
e
r
p
r
esen
t
ed
a
n
o
v
el
s
y
s
tem
d
esig
n
ed
to
d
is
tin
g
u
is
h
ASD
s
am
p
les
f
r
o
m
n
o
n
-
ASD
(
h
e
alth
y
)
s
am
p
les,
f
o
cu
s
in
g
o
n
d
ata
co
llected
f
r
o
m
to
d
d
ler
s
.
T
h
e
s
y
s
tem
em
p
lo
y
e
d
th
e
ML
P
alg
o
r
it
h
m
f
o
r
class
if
icatio
n
,
v
ar
y
in
g
th
e
n
u
m
b
e
r
o
f
n
eu
r
o
n
s
in
th
e
ML
P
class
if
ier
f
r
o
m
1
0
to
5
0
in
in
cr
em
en
ts
o
f
5
.
Per
f
o
r
m
an
ce
ev
alu
atio
n
o
f
th
e
ML
P
class
if
ier
en
co
m
p
ass
es
m
u
ltip
le
m
etr
ics s
u
ch
as a
cc
u
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e,
s
p
ec
if
icity
,
an
d
G
-
m
ea
n
.
E
x
p
er
im
en
tal
f
in
d
in
g
s
r
ev
ea
l
th
at
th
e
p
r
o
p
o
s
ed
ML
P
class
if
ier
ac
h
iev
es
v
ar
y
in
g
r
e
s
u
lts
b
ased
o
n
th
e
n
u
m
b
er
o
f
n
eu
r
o
n
s
,
with
th
e
h
ig
h
est
r
esu
lt
o
f
1
0
0
%
f
o
r
e
ac
h
ev
alu
atio
n
m
ea
s
u
r
e
.
T
h
is
r
esu
lt
is
ac
h
iev
ed
wh
en
th
e
n
u
m
b
er
o
f
n
eu
r
o
n
s
r
ea
ch
es
4
5
an
d
5
0
.
Ho
wev
er
,
th
e
lo
west
r
esu
lts
o
f
ea
ch
ev
alu
atio
n
m
ea
s
u
r
e
wer
e
ac
h
iev
ed
wh
en
th
e
n
u
m
b
e
r
o
f
n
e
u
r
o
n
s
r
ea
ch
e
d
1
5
.
T
h
e
o
u
tco
m
es
d
em
o
n
s
tr
ate
a
p
r
o
m
i
s
in
g
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
ML
P
class
if
ier
,
s
u
r
p
ass
in
g
its
co
u
n
ter
p
ar
ts
in
ter
m
s
o
f
d
etec
tio
n
ac
cu
r
ac
y
.
Fu
tu
r
e
wo
r
k
in
cl
u
d
es
u
s
in
g
m
an
y
d
atab
ases
an
d
class
if
ier
s
f
o
r
ASD
d
etec
tio
n
,
wh
er
e
th
e
r
esu
lts
ca
n
b
e
s
tatis
t
ically
an
aly
ze
d
.
ACK
NO
WL
E
DG
M
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N
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S
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h
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au
th
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r
s
wo
u
ld
lik
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to
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Facu
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r
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Scien
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T
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h
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Un
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Keb
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Ma
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f
o
r
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F
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Gr
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FTM
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f
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Scien
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an
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lo
g
y
,
Un
iv
er
s
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b
an
g
s
aa
n
Ma
lay
s
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a
re
a
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c
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a
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ti
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n
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Ov
e
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h
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y
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rs,
h
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h
a
s
c
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b
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te
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ra
l
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ic
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li
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ti
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ts
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n
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ra
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ti
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l
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t
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n
s
wit
h
in
h
is
fiel
d
s
o
f
e
x
p
e
rti
s
e
.
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wo
rk
c
o
n
ti
n
u
e
s
t
o
fo
c
u
s
o
n
a
d
v
a
n
c
i
n
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th
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n
teg
ra
ti
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n
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f
m
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n
tec
h
n
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l
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ies
to
a
d
d
r
e
ss
re
a
l
-
wo
rld
c
h
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ll
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n
g
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s,
with
a
p
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c
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lar
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m
p
h
a
sis
o
n
fo
ste
rin
g
i
n
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a
ti
o
n
in
th
e
a
c
a
d
e
m
ic
a
n
d
sc
ien
ti
fic
c
o
m
m
u
n
it
ies
o
f
Ira
q
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
h
m
e
d
.
q
.
h
a
d
i
@u
o
tec
h
n
o
l
o
g
y
.
e
d
u
.
i
q
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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in
2
0
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s
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n
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ss
o
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iate
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tu
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h
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g
h
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se
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rc
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te
re
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in
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lu
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icle
s
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c
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n
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c
o
n
tac
ted
a
t
e
m
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il
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if.
h
.
k
a
m
il
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o
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h
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e
d
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.
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a
h
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d
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in
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rsity
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P
h
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i
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a
c
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lt
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e
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tri
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p
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rtme
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lay
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3
.
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tl
y
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h
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rm
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ti
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n
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d
Tec
h
n
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l
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g
y
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T
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M
),
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,
M
a
lay
sia
.
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e
is
a
sc
ien
ti
fic
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rc
h
e
r
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d
iffere
n
t
h
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lt
h
c
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re
a
p
p
li
c
a
ti
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n
s.
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is
a
n
a
c
ti
v
e
stu
d
e
n
t
m
e
m
b
e
r
o
f
IE
EE
,
a
n
d
a
m
e
m
b
e
r
o
f
IEE
E
Co
m
m
u
n
ica
ti
o
n
s
S
o
c
iety
.
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re
se
a
rc
h
in
tere
sts
a
re
m
a
c
h
in
e
lea
rn
in
g
,
a
rti
ficia
l
n
e
u
ra
l
n
e
two
r
k
s,
d
e
e
p
lea
rn
i
n
g
,
se
n
so
r
n
e
two
r
k
s,
r
o
u
t
in
g
p
r
o
to
c
o
ls
,
m
o
b
il
e
a
d
-
h
o
c
n
e
two
r
k
s,
so
c
ial
n
e
two
r
k
s,
Io
T
,
a
n
d
lo
c
a
ti
o
n
-
b
a
se
d
se
rv
ice
s
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
fa
h
a
d
@u
k
m
.
e
d
u
.
m
y
.
Am
m
a
r
Ab
d
Ra
b
a
S
a
k
r
a
n
is
a
re
se
a
r
c
h
e
r
a
t
th
e
Un
iv
e
rsity
o
f
In
f
o
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
a
n
d
Co
m
m
u
n
ica
ti
o
n
s,
Ba
g
h
d
a
d
,
Ira
q
.
His
re
se
a
rc
h
fo
c
u
se
s
o
n
a
rti
f
icia
l
in
telli
g
e
n
c
e
,
m
a
c
h
in
e
lea
rn
in
g
,
b
i
o
in
f
o
rm
a
ti
c
s,
c
y
b
e
rse
c
u
rit
y
,
sm
a
r
t
c
it
ies
,
a
n
d
th
e
i
n
tern
e
t
o
f
th
in
g
s
(Io
T).
He
h
a
s
e
x
te
n
siv
e
e
x
p
e
rien
c
e
in
AI
-
d
riv
e
n
so
l
u
ti
o
n
s,
a
p
p
l
y
in
g
m
a
c
h
in
e
lea
rn
i
n
g
a
n
d
d
e
e
p
lea
rn
i
n
g
tec
h
n
i
q
u
e
s
to
v
a
rio
u
s
d
o
m
a
in
s,
i
n
c
lu
d
in
g
g
e
n
o
m
ic
d
a
ta
a
n
a
l
y
sis,
in
telli
g
e
n
t
s
y
ste
m
s,
a
n
d
n
e
two
r
k
se
c
u
rit
y
.
His
wo
r
k
i
n
b
io
i
n
fo
r
m
a
ti
c
s
in
v
o
l
v
e
s
lev
e
ra
g
i
n
g
c
o
m
p
u
tati
o
n
a
l
m
o
d
e
ls
to
e
n
h
a
n
c
e
th
e
a
n
a
ly
sis
a
n
d
c
las
sifica
ti
o
n
o
f
b
io
l
o
g
ica
l
d
a
ta.
Ad
d
it
i
o
n
a
ll
y
,
h
e
e
x
p
l
o
re
s
Io
T
-
b
a
se
d
so
l
u
ti
o
n
s
f
o
r
sm
a
rt
e
n
v
ir
o
n
m
e
n
ts
a
n
d
u
r
b
a
n
d
e
v
e
lo
p
m
e
n
t.
Th
r
o
u
g
h
o
u
t
h
is
a
c
a
d
e
m
ic
c
a
re
e
r,
h
e
h
a
s
c
o
n
tri
b
u
ted
t
o
m
u
lt
i
p
le
sc
ien
ti
f
ic
p
u
b
li
c
a
ti
o
n
s,
a
d
d
re
ss
in
g
k
e
y
c
h
a
ll
e
n
g
e
s
in
AI,
b
i
o
i
n
fo
rm
a
ti
c
s,
a
n
d
e
m
e
rg
in
g
tec
h
n
o
lo
g
ies
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
m
m
a
ra
b
d
ra
b
a
@u
o
it
c
.
e
d
u
.
i
q
.
Evaluation Warning : The document was created with Spire.PDF for Python.