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[
1
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[
3
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T
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[
6
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[
7
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As
we
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o
r
e
ab
o
u
t
E
U
ML
,
it'
s
im
p
o
r
tan
t
to
th
in
k
ab
o
u
t
wh
at
u
n
s
u
p
er
v
is
ed
lear
n
in
g
m
ea
n
s
in
C
PS
,
wh
er
e
p
h
y
s
ical
an
d
cy
b
e
r
p
ar
ts
ar
e
lin
k
ed
an
d
r
e
q
u
ir
e
a
h
ig
h
e
r
le
v
el
o
f
o
p
en
n
ess
.
T
h
is
in
tr
o
d
u
ctio
n
lay
s
th
e
g
r
o
u
n
d
wo
r
k
f
o
r
a
m
o
r
e
in
-
d
ep
th
lo
o
k
at
E
UM
L
.
T
h
e
g
o
al
is
to
clo
s
e
th
e
g
ap
b
et
wee
n
th
e
f
ac
t
th
at
u
n
co
n
tr
o
lled
lear
n
in
g
m
o
d
els
ar
en
'
t
alwa
y
s
clea
r
an
d
th
e
n
ee
d
f
o
r
clea
r
,
u
n
d
er
s
tan
d
ab
l
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es
in
C
PS
[
8
]
.
T
h
e
s
u
b
s
eq
u
en
t
s
ec
tio
n
s
o
f
th
is
p
ap
e
r
will
u
n
f
o
ld
t
h
e
lay
e
r
s
o
f
s
u
p
er
v
is
ed
an
d
UM
L
,
ex
am
in
e
th
e
p
r
in
cip
le
s
o
f
ex
p
lain
ab
ilit
y
,
an
d
u
ltima
tely
f
o
cu
s
o
n
th
e
in
ter
s
ec
tio
n
o
f
u
n
s
u
p
er
v
is
ed
lear
n
in
g
an
d
in
ter
p
r
etab
ilit
y
in
C
PS
.
T
h
e
in
v
esti
g
atio
n
will c
u
lm
in
at
e
in
a
d
etailed
ex
p
lo
r
atio
n
o
f
e
x
p
lain
ab
le
SOMs a
s
a
p
r
o
m
is
in
g
a
p
p
r
o
ac
h
to
ad
d
r
ess
th
e
ch
allen
g
es
p
o
s
ed
b
y
t
r
ad
itio
n
al
b
lack
-
b
o
x
m
o
d
els
in
C
PS
ap
p
licatio
n
s
[
9
]
,
[
1
0
]
.
T
h
r
o
u
g
h
th
is
ex
p
lo
r
atio
n
,
we
aim
to
co
n
tr
ib
u
te
to
th
e
ev
o
lv
i
n
g
lan
d
s
ca
p
e
o
f
in
ter
p
r
etab
le
a
n
d
tr
u
s
two
r
th
y
ML
s
o
lu
tio
n
s
f
o
r
t
h
e
in
tr
icate
d
o
m
ain
o
f
C
PS
[
1
1
]
,
[
1
2
]
.
Su
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
(
SML
)
s
tan
d
s
as
a
co
r
n
er
s
to
n
e
in
th
e
f
ield
o
f
ML
,
in
v
o
lv
in
g
th
e
tr
ain
in
g
o
f
m
o
d
els
o
n
lab
eled
d
atasets
to
m
a
k
e
p
r
ed
ictio
n
s
o
r
class
if
icatio
n
s
.
T
h
e
e
f
f
ec
t
iv
en
ess
o
f
SML
in
v
ar
io
u
s
d
o
m
ain
s
h
as
b
ee
n
wel
l
-
estab
lis
h
ed
,
lead
in
g
to
its
wid
esp
r
ea
d
ad
o
p
tio
n
.
H
o
wev
er
,
th
e
in
ter
p
r
etab
ilit
y
o
f
th
ese
m
o
d
els
o
f
ten
d
im
in
i
s
h
es
as
th
ey
g
r
o
w
in
co
m
p
le
x
ity
,
m
ak
in
g
it
ch
allen
g
in
g
to
co
m
p
r
e
h
en
d
t
h
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es.
I
n
s
af
ety
-
cr
itical
a
p
p
licatio
n
s
,
u
n
d
er
s
tan
d
in
g
wh
y
a
m
o
d
el
m
ak
es
a
s
p
ec
if
ic
p
r
ed
ictio
n
is
p
ar
a
m
o
u
n
t,
p
r
o
m
p
tin
g
t
h
e
e
x
p
lo
r
atio
n
o
f
alte
r
n
ativ
e
a
p
p
r
o
ac
h
es
[
1
3
]
,
[
1
4
]
.
Un
lik
e
SML
,
UM
L
d
ea
ls
with
u
n
lab
eled
d
ata,
s
e
ek
in
g
to
u
n
co
v
er
u
n
d
er
ly
i
n
g
p
atter
n
s
an
d
s
tr
u
ctu
r
es
with
o
u
t
ex
p
licit
g
u
id
a
n
ce
.
C
lu
s
ter
in
g
an
d
d
im
e
n
s
io
n
ality
r
ed
u
ctio
n
ar
e
co
m
m
o
n
task
s
in
UM
L
,
wh
er
e
th
e
ab
s
en
ce
o
f
lab
eled
e
x
am
p
les
ch
allen
g
es
th
e
in
ter
p
r
etab
ilit
y
o
f
lear
n
ed
r
ep
r
esen
tatio
n
s
.
As
C
PS
in
v
o
lv
es
in
tr
icate
in
ter
ac
tio
n
s
b
etwe
en
p
h
y
s
ical
an
d
cy
b
er
co
m
p
o
n
e
n
ts
,
th
e
ab
ilit
y
to
d
ec
ip
h
er
th
e
laten
t
r
elatio
n
s
h
ip
s
with
in
d
ata
b
ec
o
m
es
cr
u
cial
f
o
r
ef
f
ec
tiv
e
d
e
cisi
o
n
-
m
a
k
in
g
[
1
5
]
–
[
1
7
]
.
E
x
p
lain
ab
le
m
ac
h
in
e
lear
n
in
g
(
XM
L
)
h
as
em
er
g
ed
as
a
cr
iti
ca
l
f
ield
to
ad
d
r
ess
th
e
b
lack
-
b
o
x
n
at
u
r
e
o
f
m
an
y
ML
m
o
d
els.
W
h
ile
f
ea
tu
r
e
im
p
o
r
tan
ce
an
d
m
o
d
el
-
ag
n
o
s
tic
m
eth
o
d
s
h
av
e
b
ee
n
s
u
cc
ess
f
u
l
in
en
h
an
cin
g
in
ter
p
r
etab
ilit
y
,
th
e
s
e
tec
h
n
iq
u
es
ar
e
p
r
im
ar
ily
d
esig
n
ed
f
o
r
s
u
p
er
v
is
ed
lear
n
i
n
g
s
ce
n
ar
io
s
.
As
th
e
in
teg
r
atio
n
o
f
ML
in
C
PS
in
ten
s
if
ies,
th
e
n
ee
d
f
o
r
ex
p
lain
a
b
ilit
y
in
u
n
s
u
p
er
v
is
ed
lear
n
in
g
b
ec
o
m
es
ap
p
ar
e
n
t,
p
r
o
m
p
tin
g
th
e
ex
p
lo
r
atio
n
o
f
E
UM
L
tech
n
iq
u
es
[
1
8
]
–
[
2
0
]
.
W
ith
th
is
b
ac
k
g
r
o
u
n
d
,
y
o
u
ca
n
b
etter
u
n
d
er
s
tan
d
th
e
p
r
o
b
lem
s
th
at
s
tan
d
ar
d
s
u
p
er
v
is
ed
an
d
UM
L
m
o
d
els
ca
u
s
e,
esp
ec
ially
wh
en
it
co
m
es
to
C
PS
.
I
n
th
e
p
ar
ts
th
at
f
o
llo
w,
we'
ll
g
et
in
to
t
h
e
s
p
ec
if
ics
o
f
E
UM
L
b
y
l
o
o
k
i
n
g
at
its
s
h
o
r
tc
o
m
in
g
s
,
m
a
p
p
in
g
ex
is
tin
g
XM
L
ter
m
s
to
u
n
s
u
p
er
v
is
ed
s
itu
atio
n
s
,
r
e
v
iewin
g
r
ec
en
t
r
esear
ch
,
an
d
f
in
ally
f
o
cu
s
in
g
o
n
h
o
w
it
ca
n
b
e
u
s
ed
t
o
s
o
lv
e
th
e
u
n
i
q
u
e
p
r
o
b
lem
s
o
f
C
PS
.
T
h
e
id
ea
o
f
ad
d
in
g
XAI
to
th
e
AI
wo
r
k
f
l
o
w
is
s
h
o
wn
in
i
llu
s
tr
atio
n
1
.
T
h
e
g
o
al
is
to
u
s
e
m
eth
o
d
s
th
a
t c
an
b
e
ex
p
lain
ed
in
d
if
f
e
r
en
t
s
tag
es o
f
th
e
life
cy
cle
o
f
AI
.
2.
E
XP
L
A
I
NA
B
L
E
UN
SUPER
VIS
E
D
M
A
CH
I
N
E
L
E
ARN
I
NG
E
UM
L
en
co
m
p
ass
es
a
s
et
o
f
cr
itical
r
eq
u
ir
em
e
n
ts
th
at
d
is
tin
g
u
is
h
it
f
r
o
m
t
r
ad
itio
n
al
u
n
s
u
p
er
v
is
e
d
lear
n
in
g
ap
p
r
o
ac
h
es.
T
h
e
p
r
i
m
ar
y
d
esid
er
ata
in
clu
d
e
tr
an
s
p
ar
en
cy
,
i
n
ter
p
r
eta
b
ilit
y
,
an
d
t
h
e
ab
ilit
y
to
p
r
o
v
id
e
in
s
ig
h
ts
in
to
th
e
d
ec
is
io
n
-
m
a
k
in
g
p
r
o
ce
s
s
es
o
f
u
n
s
u
p
er
v
is
ed
m
o
d
els.
I
n
th
e
co
n
tex
t
o
f
C
PS
,
wh
er
e
t
h
e
co
n
s
eq
u
en
ce
s
o
f
e
r
r
o
n
eo
u
s
d
ec
is
io
n
s
ca
n
b
e
s
ev
er
e,
th
ese
d
esid
er
ata
b
ec
o
m
e
ess
en
tial
f
o
r
en
s
u
r
in
g
th
e
tr
u
s
two
r
th
in
ess
an
d
r
el
iab
ilit
y
o
f
th
e
d
e
p
lo
y
e
d
ML
m
o
d
els [
2
1
]
.
Dev
elo
p
in
g
ef
f
ec
tiv
e
E
UM
L
alg
o
r
ith
m
s
r
eq
u
ir
es
ad
ap
t
in
g
an
d
ex
ten
d
i
n
g
XM
L
co
n
ce
p
ts
to
u
n
s
u
p
er
v
is
ed
co
n
tex
ts
.
I
n
ter
p
r
etab
ilit
y
,
o
p
e
n
n
ess
,
an
d
ac
c
o
u
n
tab
ilit
y
m
u
s
t
b
e
r
eth
o
u
g
h
t
f
o
r
u
n
s
u
p
er
v
is
ed
lear
n
in
g
.
T
h
e
m
ap
p
i
n
g
o
f
XM
L
ter
m
s
to
E
UM
L
p
r
o
v
id
es
a
f
r
am
ewo
r
k
f
o
r
e
v
alu
atin
g
an
d
im
p
r
o
v
in
g
UM
L
m
o
d
el
in
ter
p
r
eta
b
ilit
y
[
2
2
]
.
A
co
m
p
r
eh
e
n
s
iv
e
r
ev
iew
o
f
th
e
cu
r
r
en
t
s
tate
-
of
-
th
e
-
a
r
t
in
E
UM
L
tech
n
iq
u
es
is
p
r
esen
ted
,
h
ig
h
lig
h
tin
g
a
d
v
an
ce
m
en
ts
,
ch
allen
g
es,
an
d
p
o
te
n
tial
ap
p
licatio
n
s
.
T
h
is
liter
atu
r
e
r
ev
iew
p
r
o
v
id
es
in
s
ig
h
ts
in
to
th
e
p
r
o
g
r
ess
m
ad
e
in
ad
d
r
ess
in
g
th
e
in
ter
p
r
etab
ilit
y
is
s
u
es
o
f
u
n
s
u
p
er
v
is
ed
lear
n
i
n
g
m
o
d
els
,
lay
in
g
th
e
g
r
o
u
n
d
wo
r
k
f
o
r
th
e
s
u
b
s
eq
u
en
t
ex
p
lo
r
atio
n
o
f
E
UM
L
in
th
e
s
p
ec
if
ic
co
n
tex
t
o
f
C
PS
[
2
3
]
,
[
2
4
]
.
T
h
is
s
ec
tio
n
f
o
cu
s
es
o
n
th
e
ap
p
licatio
n
o
f
E
UM
L
with
in
th
e
r
ea
lm
o
f
C
PS
.
I
t
ad
d
r
ess
es
th
e
u
n
iq
u
e
c
h
allen
g
es
p
o
s
ed
b
y
C
PS
,
s
u
ch
as
th
e
d
y
n
am
ic
in
ter
p
lay
b
etwe
en
p
h
y
s
ical
an
d
cy
b
e
r
co
m
p
o
n
en
ts
,
th
e
n
ee
d
f
o
r
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
in
g
,
a
n
d
t
h
e
r
e
q
u
ir
em
en
t
f
o
r
tr
an
s
p
ar
e
n
cy
i
n
co
m
p
lex
,
in
ter
co
n
n
ec
ted
s
y
s
tem
s
.
T
h
e
d
is
cu
s
s
io
n
in
clu
d
es p
o
ten
tial u
s
e
ca
s
es,
b
en
ef
its
,
an
d
co
n
s
id
er
atio
n
s
f
o
r
d
ep
lo
y
i
n
g
E
UM
L
in
C
PS
ap
p
licatio
n
s
[
2
5
]
.
T
h
e
e
x
p
l
o
r
a
t
io
n
o
f
E
U
M
L
in
t
h
i
s
s
e
c
t
i
o
n
s
e
t
s
t
h
e
s
t
a
g
e
f
o
r
a
m
o
r
e
d
e
t
a
i
le
d
ex
a
m
in
a
t
i
o
n
o
f
a
s
p
e
c
i
f
i
c
a
p
p
r
o
ac
h
–
S
O
M
s
i
n
t
h
e
s
u
b
s
e
q
u
en
t
s
e
c
t
i
o
n
s
.
B
y
l
a
y
i
n
g
o
u
t
t
h
e
d
e
s
id
e
r
a
t
a,
m
a
p
p
i
n
g
ex
i
s
t
i
n
g
t
e
r
m
s
,
r
e
v
i
e
w
in
g
l
i
te
r
a
tu
r
e,
a
n
d
co
n
t
e
x
tu
a
l
i
z
in
g
E
U
ML
w
i
t
h
i
n
C
P
S
,
th
i
s
p
ap
e
r
a
i
m
s
t
o
p
r
o
v
i
d
e
a
c
o
m
p
r
eh
e
n
s
iv
e
u
n
d
er
s
t
a
n
d
i
n
g
o
f
th
e
p
o
t
en
t
i
a
l
a
n
d
c
h
a
ll
e
n
g
e
s
a
s
s
o
c
ia
t
e
d
w
i
th
i
n
t
er
p
r
e
t
ab
l
e
U
M
L
in
c
o
m
p
l
e
x
,
d
y
n
a
m
i
c
s
y
s
t
e
m
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
41
,
No
.
1
,
J
an
u
ar
y
20
2
6
:
300
-
3
0
8
302
2
.
1
.
E
x
pla
ina
ble
s
elf
-
o
rg
a
ni
zing
m
a
p
s
SOMs h
av
e
em
er
g
ed
as a
p
o
w
er
f
u
l te
ch
n
iq
u
e
in
u
n
s
u
p
er
v
is
e
d
lear
n
in
g
,
p
ar
ticu
la
r
ly
in
clu
s
ter
in
g
an
d
d
im
en
s
io
n
ality
r
ed
u
ctio
n
task
s
.
I
n
tr
o
d
u
ce
d
b
y
Ko
h
o
n
en
,
S
OM
s
m
ap
h
ig
h
-
d
im
en
s
io
n
al
in
p
u
t
d
ata
o
n
to
a
lo
wer
-
d
im
en
s
io
n
al
g
r
i
d
o
f
n
e
u
r
o
n
s
,
p
r
eser
v
i
n
g
th
e
to
p
o
lo
g
i
ca
l
r
elatio
n
s
h
ip
s
o
f
th
e
in
p
u
t
s
p
ac
e.
W
h
ile
SOM
s
ex
h
ib
it
r
em
ar
k
ab
le
ca
p
ab
ilit
ie
s
in
ca
p
tu
r
in
g
co
m
p
lex
s
tr
u
ct
u
r
es
with
in
d
ata,
th
eir
in
ter
p
r
etab
ilit
y
h
as
b
ee
n
lim
it
ed
d
u
e
to
th
e
in
tr
in
s
ic
co
m
p
lex
ity
o
f
th
e
lear
n
e
d
r
e
p
r
esen
tatio
n
s
[
2
6
]
,
[
2
7
].
Fig
u
r
e
1
illu
s
tr
ates
th
e
s
tr
u
ctu
r
e
o
f
a
two
-
d
im
e
n
s
io
n
al
SOM
in
b
o
th
th
e
o
u
tp
u
t
s
p
ac
e
a
n
d
th
e
in
p
u
t
s
p
ac
e.
Fig
u
r
e
1
(
a)
r
ep
r
esen
ts
th
e
SOM
o
u
t
p
u
t
s
p
ac
e,
wh
er
e
n
eu
r
o
n
s
ar
e
a
r
r
an
g
ed
o
n
a
f
ix
ed
two
-
d
im
e
n
s
io
n
al
g
r
id
with
p
r
ed
ef
in
e
d
to
p
o
l
o
g
ic
al
co
n
n
ec
tio
n
s
th
at
p
r
eser
v
e
n
eig
h
b
o
r
h
o
o
d
r
elatio
n
s
h
ip
s
.
Fig
u
r
e
1
(
b
)
s
h
o
ws
th
e
s
am
e
SOM
m
ap
p
ed
in
to
th
e
in
p
u
t
s
p
ac
e
af
ter
tr
ain
in
g
,
w
h
er
e
th
e
n
eu
r
o
n
s
ad
ap
t
th
eir
p
o
s
itio
n
s
to
f
it
th
e
d
is
tr
ib
u
tio
n
an
d
clu
s
ter
s
o
f
th
e
in
p
u
t
d
ata.
T
h
e
lear
n
i
n
g
b
eh
a
v
io
r
o
f
th
e
SOM
is
g
o
v
er
n
ed
b
y
k
e
y
h
y
p
er
p
ar
am
eter
s
,
n
am
ely
th
e
lear
n
in
g
r
ate
an
d
t
h
e
n
ei
g
h
b
o
r
h
o
o
d
r
a
d
iu
s
o
f
th
e
b
est
m
atc
h
in
g
u
n
it
(
B
MU
)
,
b
o
th
o
f
wh
ich
ar
e
g
r
ad
u
ally
r
ed
u
ce
d
at
ea
ch
ep
o
c
h
to
en
s
u
r
e
s
m
o
o
th
co
n
v
e
r
g
e
n
ce
an
d
ac
cu
r
ate
d
at
a
r
ep
r
esen
tatio
n
.
(
a)
(
b
)
Fig
u
r
e
1
.
SOMs
s
h
o
wn
in
(
a
)
t
h
e
o
u
tp
u
t sp
ac
e
an
d
(
b
)
th
e
in
p
u
t sp
ac
e
ch
an
g
ed
to
f
it th
e
2
D
s
p
r
ea
d
o
f
t
h
e
p
o
in
ts
th
at
wer
e
en
ter
e
d
T
o
ad
d
r
ess
th
e
in
ter
p
r
etab
ilit
y
ch
allen
g
es
o
f
tr
a
d
itio
n
al
S
OM
s
,
m
o
d
if
icatio
n
s
an
d
ex
te
n
s
io
n
s
h
av
e
b
ee
n
p
r
o
p
o
s
ed
,
g
iv
in
g
r
is
e
to
e
x
p
lain
ab
le
SOMs
.
T
h
is
s
ec
ti
o
n
d
elv
es
in
to
th
e
e
n
h
an
ce
m
e
n
ts
m
ad
e
to
SOMs,
f
o
cu
s
in
g
o
n
h
o
w
th
ese
m
o
d
if
icatio
n
s
r
en
d
er
th
e
m
o
d
el
m
o
r
e
in
ter
p
r
etab
le.
T
ec
h
n
iq
u
es
s
u
ch
as
n
eu
r
o
n
im
p
o
r
tan
ce
s
co
r
in
g
,
f
ea
tu
r
e
at
tr
ib
u
tio
n
,
an
d
v
is
u
aliza
tio
n
o
f
lear
n
ed
r
ep
r
esen
tatio
n
s
ar
e
e
x
p
lo
r
ed
to
f
ac
ilit
ate
a
d
ee
p
er
u
n
d
er
s
tan
d
in
g
o
f
th
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es
with
in
th
e
S
OM
s
f
r
am
ewo
r
k
[
2
8
].
E
x
p
lain
ab
ilit
y
in
SOMs
is
im
p
o
r
tan
t
in
C
PS
,
w
h
er
e
u
n
d
e
r
s
tan
d
in
g
co
m
p
lex
d
ataset
co
r
r
elatio
n
s
is
cr
itical.
E
x
p
lain
ab
le
SOMs
p
r
o
m
is
e
to
p
r
o
v
id
e
a
r
o
b
u
s
t
s
o
lu
tio
n
f
o
r
u
n
s
u
p
er
v
is
ed
lear
n
in
g
in
C
PS
ap
p
licatio
n
s
b
y
co
m
b
in
in
g
SOMs'
to
p
o
lo
g
ical
s
tr
u
ctu
r
e
ca
p
tu
r
e
with
ex
p
lain
ab
ilit
y
'
s
tr
an
s
p
ar
en
cy
.
T
h
e
s
u
b
s
eq
u
en
t
s
ec
tio
n
s
will
f
u
r
th
er
ex
p
l
o
r
e
th
e
ex
p
er
im
en
tal
s
etu
p
an
d
r
esu
lts
o
f
e
x
p
lain
ab
le
SOMs,
ev
alu
atin
g
th
eir
m
o
d
el
f
id
elity
,
lo
ca
l
an
d
g
lo
b
al
in
ter
p
r
etab
ilit
y
,
a
n
d
u
s
ab
ilit
y
with
in
C
PS
.
T
h
is
em
p
ir
ical
an
aly
s
is
aim
s
to
v
alid
ate
th
e
ef
f
ec
tiv
en
ess
o
f
e
x
p
lain
ab
le
S
OM
s
in
a
d
d
r
ess
in
g
th
e
s
p
ec
if
ic
ch
allen
g
es
p
o
s
ed
b
y
C
PS
an
d
ass
ess
th
eir
p
o
ten
tial
f
o
r
r
ea
l
-
wo
r
ld
a
p
p
licatio
n
s
in
co
m
p
lex
,
d
y
n
a
m
ic
en
v
ir
o
n
m
en
ts
.
3.
E
XP
E
R
I
M
E
N
T
S
E
T
UP
A
N
D
RE
SU
L
T
S
3
.
1
.
M
o
del
f
idelity
I
n
th
e
ex
p
er
im
en
tal
s
etu
p
,
th
e
f
id
elity
o
f
e
x
p
lain
ab
le
SO
Ms
is
r
ig
o
r
o
u
s
ly
ass
ess
ed
.
C
o
m
p
ar
ativ
e
an
aly
s
es
ar
e
co
n
d
u
cted
ag
ai
n
s
t
tr
ad
itio
n
al
SOMs,
ev
alu
atin
g
th
e
a
b
ilit
y
o
f
e
x
p
lain
ab
le
SOMs
to
ac
cu
r
ately
r
ep
r
esen
t
th
e
in
tr
icate
r
elatio
n
s
h
ip
s
with
in
th
e
g
iv
en
C
PS
d
ataset.
Me
tr
ics
s
u
ch
as
clu
s
ter
in
g
ac
cu
r
ac
y
,
p
r
eser
v
atio
n
o
f
to
p
o
lo
g
ical
s
tr
u
ctu
r
es,
a
n
d
r
ec
o
n
s
tr
u
ctio
n
e
r
r
o
r
s
ar
e
em
p
l
o
y
ed
to
q
u
an
tify
th
e
f
id
elity
o
f
th
e
m
o
d
els.
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:
2
5
0
2
-
4
7
52
C
yb
er p
h
ysica
l sys
tem
s
ma
in
ten
a
n
ce
w
ith
ex
p
la
in
a
b
le
u
n
s
u
p
ervis
ed
…
(
V
.
Du
r
g
a
P
r
a
s
a
d
Ja
s
ti
)
303
3
.
2
.
L
o
ca
l
i
nte
rpre
t
a
bil
it
y
T
h
e
lo
ca
l
i
n
ter
p
r
etab
ilit
y
o
f
e
x
p
lain
ab
le
SOMs
is
ex
am
in
ed
to
u
n
d
e
r
s
tan
d
h
o
w
well
t
h
e
m
o
d
el
p
r
o
v
id
es
in
s
ig
h
t
s
in
to
in
d
iv
id
u
al
d
ata
p
o
in
ts
.
Neu
r
o
n
im
p
o
r
tan
ce
s
co
r
in
g
a
n
d
f
ea
tu
r
e
attr
ib
u
tio
n
tech
n
iq
u
es
ar
e
ap
p
lied
to
id
en
tify
t
h
e
k
e
y
f
ac
to
r
s
in
f
lu
en
cin
g
th
e
d
ec
i
s
io
n
s
m
ad
e
b
y
th
e
m
o
d
el
o
n
a
p
er
-
in
s
tan
ce
b
asis
.
T
h
is
an
aly
s
is
aim
s
to
h
ig
h
li
g
h
t
th
e
g
r
a
n
u
lar
ity
o
f
i
n
te
r
p
r
etab
ilit
y
ac
h
iev
ed
b
y
e
x
p
lain
ab
le
SOMs
in
th
e
co
n
tex
t o
f
C
PS
.
3
.
3
.
G
l
o
ba
l
i
nte
rpre
t
a
bil
it
y
A
b
r
o
a
d
er
p
er
s
p
ec
tiv
e
is
tak
en
to
ev
alu
ate
th
e
g
lo
b
al
in
t
er
p
r
etab
ilit
y
o
f
e
x
p
lain
a
b
le
S
OM
s
.
B
y
ex
am
in
in
g
th
e
lear
n
ed
r
e
p
r
esen
tatio
n
s
at
a
s
y
s
tem
-
wid
e
le
v
el,
th
e
m
o
d
el'
s
ab
ilit
y
to
u
n
co
v
er
o
v
er
ar
ch
i
n
g
p
atter
n
s
,
an
o
m
alies,
an
d
r
elatio
n
s
h
ip
s
with
in
th
e
C
PS
d
atas
et
is
ass
e
s
s
ed
.
Vis
u
aliza
t
io
n
tech
n
iq
u
es,
s
u
ch
a
s
h
ea
tm
ap
s
an
d
clu
s
ter
s
u
m
m
a
r
ies,
ar
e
em
p
lo
y
ed
to
f
ac
ilit
ate
a
co
m
p
r
eh
en
s
iv
e
u
n
d
er
s
ta
n
d
in
g
o
f
th
e
g
lo
b
al
in
te
r
p
r
etab
ilit
y
ac
h
iev
e
d
b
y
e
x
p
lain
ab
le
SOMs.
3
.
4
.
Usa
bil
it
y
wit
hin
cy
ber
-
ph
y
s
ica
l sy
s
t
em
s
R
ea
l
-
wo
r
ld
ex
p
er
im
e
n
ts
ar
e
c
o
n
d
u
cte
d
to
e
v
alu
ate
th
e
u
s
ab
ilit
y
o
f
e
x
p
lain
ab
le
SOMs
with
in
C
PS
.
T
h
e
m
o
d
els
ar
e
d
ep
lo
y
e
d
in
C
PS
en
v
ir
o
n
m
en
ts
,
an
d
th
eir
p
e
r
f
o
r
m
a
n
ce
is
a
s
s
ess
ed
in
s
ce
n
a
r
io
s
th
at
m
im
ic
th
e
d
y
n
am
ic,
in
te
r
co
n
n
ec
ted
n
atu
r
e
o
f
th
ese
s
y
s
tem
s
.
T
h
is
an
aly
s
is
in
clu
d
es c
o
n
s
id
er
atio
n
s
f
o
r
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
in
g
,
a
d
ap
tab
ilit
y
to
c
h
an
g
i
n
g
co
n
d
itio
n
s
,
an
d
th
e
o
v
er
all
im
p
ac
t o
n
s
y
s
tem
r
eliab
ilit
y
a
n
d
s
af
ety
.
T
h
e
v
ar
i
atio
n
in
clu
s
ter
q
u
alit
y
m
atr
ices
u
tili
z
ed
in
th
is
in
v
e
s
tig
atio
n
f
o
r
th
e
b
a
n
k
m
ar
k
eti
n
g
d
ata
s
et
is
illu
s
tr
ated
in
Fig
u
r
e
2
.
I
t
illu
s
tr
ates
h
o
w
th
e
n
u
m
b
er
o
f
clu
s
ter
s
an
d
SOM
d
im
e
n
s
io
n
s
in
f
lu
en
ce
th
e
ev
o
lu
tio
n
o
f
th
ese
m
atr
ices.
C
lu
s
ter
q
u
ality
m
etr
ics
ar
e
co
m
p
u
ted
f
o
r
b
o
th
th
e
SOM
n
e
u
r
o
n
weig
h
ts
(
b
lu
e
)
an
d
th
e
tr
ain
i
n
g
d
ataset
(
o
r
an
g
e)
f
o
r
a
g
i
v
en
SOM
s
ize.
T
h
e
o
b
jectiv
e
o
f
t
h
is
an
aly
s
is
is
to
d
eter
m
in
e
t
h
e
id
ea
l
SOM
d
im
en
s
io
n
an
d
clu
s
ter
c
o
u
n
t.
W
h
en
th
e
tr
ain
e
d
SOM
n
eu
r
o
n
s
ac
cu
r
ately
r
ep
r
esen
t
t
h
e
en
tire
d
ataset,
it
ca
n
b
e
ex
p
ec
ted
th
at
cl
u
s
ter
an
aly
s
is
o
f
th
e
SOM
weig
h
ts
wo
u
ld
r
ev
ea
l
c
o
m
p
a
r
ab
le
p
a
tter
n
s
to
th
at
o
f
t
h
e
en
tire
d
ataset.
Fig
u
r
e
2
illu
s
tr
ates
th
at
a
s
th
e
n
u
m
b
er
o
f
clu
s
ter
s
in
cr
ea
s
es,
th
ey
ad
h
er
e
to
th
e
s
am
e
p
atter
n
s
.
T
h
e
Dav
ies
-
b
o
u
ld
in
g
in
d
ex
v
a
lu
e
an
d
th
e
Sil
h
o
u
ette
c
o
ef
f
icien
t
in
d
icate
th
at
th
r
ee
to
f
iv
e
clu
s
ter
s
ar
e
o
p
tim
al
f
o
r
th
e
e
x
am
in
ed
SOM
d
im
en
s
io
n
s
(
8
,
1
6
,
2
,
4
0
)
.
Fig
u
r
e
2
.
A
way
to
ju
d
g
e
th
e
q
u
ality
o
f
K
clu
s
ter
s
u
s
in
g
th
e
Sil
h
o
u
ette
c
o
ef
f
icien
t a
n
d
th
e
Dav
ies
-
b
o
u
ld
in
i
n
d
ex
f
o
r
v
a
r
io
u
s
SOM
m
ap
s
izes
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
41
,
No
.
1
,
J
an
u
ar
y
20
2
6
:
300
-
3
0
8
304
W
e
s
o
r
ted
f
ea
tu
r
es
b
y
s
tan
d
ar
d
d
ev
iatio
n
an
d
ad
j
u
s
ted
r
an
d
o
m
o
r
in
co
n
s
eq
u
en
tial
f
ea
t
u
r
e
s
to
test
o
u
r
h
y
p
o
th
esis
.
T
h
at
is
,
we
m
o
d
if
i
ed
p
% f
o
r
t
h
e
m
o
s
t im
p
o
r
tan
t
f
ea
tu
r
es (
th
o
s
e
with
th
e
lo
west
s
tan
d
ar
d
d
e
v
iatio
n
v
alu
es),
r
an
d
o
m
ly
s
elec
ted
f
e
atu
r
es,
an
d
least
im
p
o
r
ta
n
t
f
ea
tu
r
es,
all
g
iv
en
p
%
ca
r
d
in
ality
.
W
e
ch
ec
k
ed
ea
ch
d
ata
r
ec
o
r
d
i
n
th
e
test
s
et
to
s
ee
if
m
o
d
if
y
i
n
g
t
h
e
f
ea
t
u
r
e
v
a
lu
e
ch
an
g
ed
its
clu
s
ter
lab
el
i
n
ea
ch
o
f
th
e
t
h
r
ee
s
itu
atio
n
s
ab
o
v
e.
T
wo
p
o
s
s
ib
ilit
ies we
r
e
s
tu
d
ied
f
o
r
ea
ch
s
ce
n
ar
io
:
i
)
th
e
p
r
o
p
o
r
tio
n
o
f
test
d
ata
r
ec
o
r
d
s
wh
er
e
an
o
th
er
clu
s
ter
m
ay
r
ep
lace
th
e
clu
s
ter
lab
el,
an
d
ii
)
th
e
p
r
o
p
o
r
tio
n
wh
er
e
all
o
th
er
clu
s
ter
s
ca
n
b
e
s
u
b
s
titu
ted
.
Fo
r
s
o
m
e
d
ata
p
o
in
ts
,
p
er
t
u
r
b
i
n
g
f
ea
t
u
r
e
v
alu
es
with
clo
s
e
clu
s
ter
cr
iter
ia
m
a
y
n
o
t
b
e
en
o
u
g
h
to
r
em
o
v
e
t
h
em
f
r
o
m
th
e
in
itial
clu
s
ter
.
W
e
m
ad
e
s
u
r
e
f
ea
tu
r
e
v
al
u
es
f
r
o
m
a
t
least
o
n
e
ex
tr
a
clu
s
ter
m
ig
h
t
af
f
ec
t
a
d
ata
p
o
in
t'
s
clu
s
ter
ca
teg
o
r
izatio
n
.
C
o
n
s
id
er
a
f
o
u
r
-
cl
u
s
ter
s
ce
n
ar
io
with
d
ata
p
o
in
t
j
in
clu
s
ter
2
.
W
e
a
ttem
p
t
ch
an
g
i
n
g
its
clu
s
ter
d
esig
n
atio
n
f
r
o
m
2
to
a
n
o
th
er
a
n
d
r
ep
lacin
g
its
f
ea
tu
r
e
v
alu
es
with
th
e
av
er
a
g
es
o
f
clu
s
ter
s
1
,
2
,
an
d
4
.
R
ed
u
cin
g
ca
r
d
in
ality
n
%
an
d
g
r
o
wi
n
g
s
wap
p
e
d
p
e
r
ce
n
tag
es
ar
e
ex
p
ec
te
d
.
T
h
e
s
wap
%
f
o
r
all
d
atasets
is
s
h
o
wn
in
Fig
u
r
e
3
.
E
x
ce
p
t
f
o
r
th
e
s
ec
o
n
d
KDD
d
ataset
s
ce
n
ar
io
(
wh
ich
ask
s
,
"Wh
at
is
th
e
p
er
ce
n
tag
e
o
f
test
d
ata
r
ec
o
r
d
s
wh
er
e
all
o
th
e
r
c
lu
s
ter
s
ca
n
s
wap
th
e
clu
s
ter
l
ab
el?"
)
,
b
lu
e
b
a
r
s
r
e
p
r
esen
t
r
e
lev
an
t
f
ea
tu
r
es
an
d
b
r
o
wn
b
a
r
s
r
an
d
o
m
f
ea
tu
r
es.
T
h
is
ap
p
lies
to
all
d
atasets
.
T
h
e
KDD
d
ataset
'
s
ex
tr
em
ely
u
n
b
alan
ce
d
class
es
an
d
s
ig
n
if
ican
t
tr
ain
in
g
-
test
in
g
g
ap
m
ay
ex
p
lain
th
is
p
o
o
r
p
er
f
o
r
m
a
n
ce
.
Ho
we
v
er
,
KDD
wo
r
k
s
as
ex
p
ec
te
d
i
n
th
e
f
ir
s
t
s
ce
n
a
r
io
(
h
o
w
m
an
y
test
d
ata
r
ec
o
r
d
s
co
n
tain
a
clu
s
ter
lab
el
th
at
m
ig
h
t
b
e
ch
an
g
e
d
?)
.
T
h
ese
p
r
ac
tical
r
esu
lts
v
alid
ated
o
u
r
p
r
ed
ictio
n
b
y
s
h
o
win
g
th
at
th
e
s
u
g
g
ested
SOM
tech
n
iq
u
e
s
elec
ted
d
ata
r
ec
o
r
d
clu
s
ter
lab
els b
ased
o
n
cr
itical
cr
iter
ia
.
An
ad
d
itio
n
al
ex
p
e
r
im
e
n
t w
as c
o
n
d
u
cted
to
v
er
i
f
y
th
e
p
r
o
p
o
r
tio
n
o
f
th
e
ch
o
s
en
K
ch
ar
ac
te
r
is
tics
th
at
wer
e
in
clu
d
ed
in
th
e
m
o
s
t c
r
u
cial
f
ea
tu
r
e
lis
t o
f
a
B
M
U
(
Al
g
o
r
ith
m
I
V)
.
T
h
e
f
ea
tu
r
e
-
wis
e
l
1
d
is
tan
ce
b
etwe
en
ea
ch
d
ata
r
ec
o
r
d
an
d
its
B
M
U
was
co
m
p
u
ted
f
o
r
ev
er
y
d
a
ta
r
ec
o
r
d
in
th
e
test
s
et.
T
h
is
was
f
o
llo
wed
b
y
an
ar
r
an
g
em
e
n
t
o
f
th
e
f
ea
tu
r
es
ac
co
r
d
in
g
to
t
h
e
in
cr
ea
s
in
g
l1
d
is
tan
ce
s
.
W
e
p
o
s
tu
lated
th
at
th
e
m
o
s
t
p
r
o
m
in
en
t
f
ea
tu
r
es
o
f
a
d
ata
p
o
in
t
wo
u
ld
b
e
th
o
s
e
th
at
ar
e
g
eo
g
r
ap
h
ica
lly
n
ea
r
est
to
it,
an
d
th
at
th
ese
f
ea
tu
r
es
wo
u
l
d
b
e
p
ar
t
o
f
th
e
B
MU
'
s
p
r
io
r
itis
ed
f
ea
tu
r
e
lis
ts
.
Af
ter
th
e
f
ea
tu
r
e
d
is
tan
ce
s
ar
e
s
o
r
ted
in
in
cr
ea
s
in
g
o
r
d
er
,
o
n
e
o
f
th
r
ee
s
tr
ateg
ies
—
i
)
clo
s
est,
ii
)
r
an
d
o
m
,
o
r
iii
)
f
u
r
th
est
—
is
u
s
ed
to
ch
o
o
s
e
K
f
ea
tu
r
es.
Fu
r
th
er
m
o
r
e,
we
d
eter
m
in
ed
wh
at
p
r
o
p
o
r
tio
n
o
f
K
ch
a
r
ac
ter
is
tics
m
ak
e
it
o
n
to
th
e
B
MU
'
s
k
ey
f
ea
tu
r
e
lis
t.
Fig
u
r
e
4
s
h
o
ws
th
e
r
esu
lts
,
with
th
e
X
-
a
x
is
s
h
o
wi
n
g
th
e
to
tal
n
u
m
b
er
o
f
f
ea
tu
r
es
(
K)
an
d
t
h
e
Y
-
a
x
is
s
h
o
win
g
th
e
p
r
o
p
o
r
tio
n
o
f
th
o
s
e
ch
ar
ac
ter
is
tics
(
%)
th
at
wer
e
co
n
s
id
er
ed
r
elev
an
t
(
f
ea
t
u
r
e
lis
t)
.
T
h
e
co
lo
u
r
b
lu
e
d
en
o
tes
ch
ar
ac
ter
is
tics
th
at
ar
e
cl
o
s
e
b
y
,
wh
e
r
ea
s
y
e
llo
w
d
en
o
tes
f
ea
tu
r
es
th
at
ar
e
r
an
d
o
m
an
d
g
r
ee
n
d
en
o
tes
f
ea
tu
r
es
th
at
ar
e
f
ar
awa
y
.
W
h
ile
th
e
y
ello
w
b
a
r
d
is
p
lay
s
th
e
s
ec
o
n
d
-
h
i
g
h
est
p
er
ce
n
tag
e,
th
e
b
lu
e
b
ar
s
h
o
ws
th
e
h
ig
h
est
p
er
ce
n
tag
e
f
o
r
all
K
ch
a
r
ac
ter
is
tics
.
T
h
is
s
u
g
g
ests
th
at
ea
ch
B
MU
'
s
d
eter
m
in
ed
ess
en
tial
f
ea
tu
r
e
lis
ts
co
n
tain
th
e
n
ea
r
b
y
f
ea
tu
r
es.
Fig
u
r
e
5
s
h
o
ws th
e
g
lo
b
al
in
te
r
p
r
etab
ilit
y
o
f
t
h
e
'
f
lag
'
f
ea
tu
r
e
in
th
e
KDD
d
ataset
as it v
ar
ies b
etwe
en
clu
s
ter
s
.
T
h
e
SOM
n
eu
r
o
n
s
wer
e
g
r
o
u
p
ed
in
t
o
th
r
ee
g
r
o
u
p
s
,
an
d
th
e
U
-
m
atr
ix
s
h
o
we
d
th
e
d
is
tan
ce
s
an
d
s
ep
ar
atio
n
b
etwe
en
th
e
clu
s
ter
s
.
C
r
itical
f
ea
tu
r
e
v
alu
e
r
an
g
es
wer
e
s
h
o
wn
ag
ain
s
t
cl
u
s
ter
ass
ig
n
m
en
ts
.
Ad
d
itio
n
ally
,
u
-
m
ap
s
wer
e
em
p
lo
y
ed
to
v
er
if
y
th
e
d
is
p
er
s
io
n
o
f
clu
s
ter
s
.
T
h
e
'
f
lag
'
ch
a
r
ac
ter
is
tic
o
f
th
e
KDD
d
ataset
is
i
llu
s
tr
ated
in
Fig
u
r
e
5
.
T
h
e
f
ir
s
t
p
ictu
r
e
is
th
e
r
aw
d
ata,
an
d
it
d
ep
icts
th
e
SO
M
n
eu
r
o
n
s
'
clu
s
ter
s
ep
ar
atio
n
.
Acr
o
s
s
th
r
ee
clu
s
ter
s
(
co
m
p
o
n
e
n
t
p
lan
s
)
,
th
e
'
f
lag
'
f
ea
tu
r
e's
v
alu
e
is
d
ep
icted
i
n
th
e
s
ec
o
n
d
im
ag
e
o
f
th
e
f
ir
s
t
r
o
w.
T
h
e
f
ea
tu
r
e
v
alu
e
o
f
"f
lag
"
v
ar
ies
b
etwe
e
n
th
e
th
r
ee
g
r
o
u
p
s
.
T
h
e
th
r
ee
clu
s
ter
s
ar
e
clea
r
ly
d
elin
ea
ted
in
th
e
th
ir
d
im
a
g
e
o
f
th
e
in
itial
r
aw
d
ata
s
et,
wh
er
e
a
r
eg
io
n
o
f
lig
h
ter
co
l
o
u
r
s
ig
n
if
ies
th
e
d
is
tan
ce
b
etwe
en
n
eu
r
o
n
s
.
T
h
e
g
r
ea
te
r
th
e
ar
ea
,
th
e
m
o
r
e
d
is
tin
ct
th
e
clu
s
ter
s
ar
e.
A
f
in
e
-
g
r
ain
ed
r
e
p
r
esen
tatio
n
o
f
th
e
s
ca
le
o
f
f
ea
tu
r
e
v
alu
es
ac
r
o
s
s
clu
s
ter
s
is
s
h
o
wn
in
th
e
s
ec
o
n
d
r
o
w
o
f
Fig
u
r
e
5
.
On
e
th
in
g
to
k
ee
p
in
m
i
n
d
is
th
at
ev
en
with
in
t
h
e
s
am
e
clu
s
ter
,
th
er
e
ca
n
b
e
n
eu
r
o
n
s
with
v
ar
y
in
g
f
ea
tu
r
e
v
alu
es
f
o
r
t
h
e
s
am
e
f
ea
tu
r
e.
I
f
a
d
o
m
ain
ex
p
e
r
t
wan
ts
to
k
n
o
w
h
o
w
a
s
p
ec
if
ic
f
ea
t
u
r
e
ac
ts
i
n
s
id
e
a
cl
u
s
ter
,
th
ey
n
ee
d
th
is
d
a
ta.
C
lu
s
ter
0
h
as
a
lar
g
er
f
ea
tu
r
e
v
alu
e
f
o
r
th
e
'
f
lag
'
f
ea
tu
r
e
(
0
.
7
-
1
.
0
)
,
clu
s
ter
1
d
is
p
lay
s
an
in
ter
m
ed
iate
r
an
g
e
(
0
.
4
5
-
0
.
5
2
)
,
a
n
d
clu
s
ter
3
d
is
p
lay
s
a
v
er
y
lo
w
r
an
g
e
(
0
.
1
5
)
.
I
n
ad
d
itio
n
,
it
d
i
s
p
lay
s
th
e
lik
elih
o
o
d
o
f
a
p
a
r
t
icu
lar
f
ea
tu
r
e
v
alu
e
b
ein
g
p
r
esen
t w
ith
in
a
clu
s
ter
.
T
ak
e
clu
s
ter
0
as a
n
e
x
am
p
le;
th
e
9
0
atu
r
e
v
alu
e
v
ar
ies f
r
o
m
clu
s
ter
to
clu
s
ter
.
I
n
itial
ex
p
er
im
e
n
t
in
teg
r
it
y
te
s
t
(
Fig
u
r
e
4
)
.
We
tr
ied
s
ev
er
a
l
v
alu
es
f
o
r
p
,
ac
tiv
e
f
ea
tu
r
es,
r
an
d
o
m
ly
p
ick
ed
f
ea
tu
r
es,
an
d
least
im
p
o
r
tan
t
f
ea
tu
r
es.
We
ca
lcu
lated
th
e
p
er
ce
n
ta
g
e
o
f
d
ata
p
o
i
n
ts
wh
er
e
th
e
clu
s
ter
lab
el
ch
an
g
e
d
af
te
r
ch
a
n
g
in
g
p
o
u
t
o
f
all
f
ea
tu
r
es.
W
e
ex
a
m
in
ed
two
s
ce
n
a
r
io
s
:
th
e
p
r
o
p
o
r
tio
n
o
f
test
d
ata
r
ec
o
r
d
s
wh
e
r
e
an
o
th
er
clu
s
ter
'
s
lab
el
co
u
ld
r
e
p
lace
th
e
cl
u
s
ter
lab
el
(
lef
t)
an
d
th
e
p
r
o
p
o
r
tio
n
wh
er
e
n
o
clu
s
ter
's lab
el
co
u
ld
r
ep
lace
it
(
r
ig
h
t)
.
T
h
e
r
esu
lts
o
b
tain
ed
f
r
o
m
th
e
ex
p
er
im
en
tal
s
etu
p
ar
e
cr
itically
d
is
cu
s
s
ed
,
em
p
h
asizin
g
th
e
s
tr
en
g
th
s
an
d
p
o
ten
tial
lim
itatio
n
s
o
f
e
x
p
lain
a
b
le
SOMs
in
t
h
e
co
n
tex
t
o
f
C
PS
.
C
o
n
s
id
er
atio
n
s
f
o
r
s
ca
lab
ilit
y
,
co
m
p
u
tatio
n
al
ef
f
icien
cy
,
an
d
g
en
er
aliza
b
ilit
y
ar
e
ad
d
r
e
s
s
ed
.
Ad
d
itio
n
ally
,
in
s
ig
h
ts
in
to
th
e
p
r
ac
tical
im
p
licatio
n
s
o
f
d
e
p
lo
y
in
g
e
x
p
lain
ab
le
SOMs
in
r
ea
l
-
wo
r
l
d
C
PS
ap
p
licatio
n
s
a
r
e
d
is
cu
s
s
ed
,
p
r
o
v
id
in
g
a
co
m
p
r
eh
e
n
s
iv
e
u
n
d
e
r
s
tan
d
in
g
o
f
th
eir
ef
f
ec
tiv
e
n
ess
in
en
h
an
cin
g
m
o
d
el
tr
an
s
p
ar
en
c
y
a
n
d
in
ter
p
r
eta
b
ilit
y
.
T
h
is
s
ec
tio
n
s
h
o
ws
h
o
w
e
x
p
l
ain
ab
le
SOMs
ca
n
s
o
l
v
e
UM
L
p
r
o
b
lem
s
in
C
PS
.
T
h
e
n
ex
t
s
ec
tio
n
will
d
r
aw
in
f
er
en
ce
s
f
r
o
m
th
e
d
ata
an
d
s
u
g
g
est f
u
r
t
h
er
s
tu
d
y
an
d
d
ev
el
o
p
m
en
t in
th
is
f
ast ex
p
an
d
i
n
g
s
ec
to
r
.
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:
2
5
0
2
-
4
7
52
C
yb
er p
h
ysica
l sys
tem
s
ma
in
ten
a
n
ce
w
ith
ex
p
la
in
a
b
le
u
n
s
u
p
ervis
ed
…
(
V
.
Du
r
g
a
P
r
a
s
a
d
Ja
s
ti
)
305
Fig
u
r
e
3
.
C
lu
s
ter
in
g
p
er
f
o
r
m
a
n
ce
co
m
p
a
r
is
o
n
u
s
in
g
SOM
a
n
d
in
p
u
t d
ataset
Fig
u
r
e
4
.
T
h
e
p
er
ce
n
tag
e
o
f
n
e
ar
est K
f
ea
tu
r
es in
th
e
B
MU
'
s
m
o
s
t sig
n
if
ican
t f
ea
tu
r
e
lis
t
Fig
u
r
e
5
.
Featu
r
e
b
eh
a
v
io
r
f
o
r
th
e
'
f
lag
'
f
ea
tu
r
e
o
f
th
e
KDD
d
ata
s
et
ac
r
o
s
s
clu
s
ter
s
(
SOM
n
eu
r
o
n
s
wer
e
clu
s
ter
ed
in
to
th
r
ee
c
ateg
o
r
ies;
th
e
d
is
tan
ce
s
b
etwe
en
clu
s
ter
s
an
d
th
e
d
eg
r
ee
o
f
s
ep
ar
atio
n
b
etwe
en
clu
s
ter
s
wer
e
r
ep
r
esen
ted
b
y
a
U
-
m
atr
i
x
)
; th
e
'
f
lag
'
f
ea
tu
r
e
v
alu
e
v
a
r
ies ac
r
o
s
s
clu
s
ter
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
41
,
No
.
1
,
J
an
u
ar
y
20
2
6
:
300
-
3
0
8
306
4.
CO
NCLU
SI
O
N
T
h
e
ex
p
lo
r
atio
n
o
f
E
UM
L
,
d
elv
ed
in
to
its
d
esid
er
ata,
th
e
ad
ap
tatio
n
o
f
ex
is
tin
g
XM
L
ter
m
s
to
u
n
s
u
p
er
v
is
ed
s
ce
n
a
r
io
s
,
a
r
e
v
iew
o
f
cu
r
r
e
n
t
liter
atu
r
e,
a
n
d
th
e
ap
p
licatio
n
o
f
E
UM
L
p
r
in
cip
les
with
in
th
e
co
m
p
lex
lan
d
s
ca
p
e
o
f
C
PS
.
T
h
e
in
tr
o
d
u
ctio
n
o
f
e
x
p
lain
ab
le
SOMs
as
a
p
r
o
m
is
in
g
E
UM
L
tech
n
iq
u
e
ad
d
r
ess
ed
th
e
n
ee
d
f
o
r
in
ter
p
r
etab
ilit
y
in
u
n
s
u
p
er
v
is
ed
lear
n
in
g
m
o
d
els.
T
h
e
s
u
b
s
eq
u
en
t
s
ec
tio
n
d
etailed
th
e
ex
p
er
im
en
t
s
etu
p
an
d
r
esu
lts
,
cr
itically
ex
am
in
in
g
th
e
m
o
d
el
f
id
elity
,
lo
ca
l
a
n
d
g
lo
b
al
in
te
r
p
r
etab
ilit
y
,
an
d
t
h
e
u
s
ab
ilit
y
o
f
e
x
p
lain
ab
le
SOMs
with
in
C
PS
en
v
ir
o
n
m
en
ts
.
B
y
co
n
d
u
ctin
g
r
ea
l
-
wo
r
l
d
ex
p
er
im
en
ts
an
d
an
aly
zin
g
p
er
f
o
r
m
an
ce
m
etr
i
cs,
th
is
s
ec
tio
n
p
r
o
v
id
ed
em
p
ir
ical
ev
id
en
ce
s
u
p
p
o
r
tin
g
t
h
e
ef
f
ec
tiv
e
n
ess
o
f
e
x
p
lain
ab
le
SOMs
in
en
h
an
cin
g
tr
an
s
p
ar
e
n
cy
a
n
d
i
n
ter
p
r
etab
ilit
y
in
C
PS
ap
p
licatio
n
s
.
T
h
e
d
is
cu
s
s
io
n
b
r
o
u
g
h
t
to
g
et
h
er
th
eo
r
etica
l
in
s
ig
h
ts
an
d
em
p
ir
ical
f
in
d
in
g
s
,
h
ig
h
lig
h
tin
g
th
e
s
tr
en
g
t
h
s
,
lim
itatio
n
s
,
an
d
p
r
ac
tical
im
p
licatio
n
s
o
f
e
x
p
la
in
ab
le
SOMs
in
C
PS
.
C
o
n
s
id
er
atio
n
s
f
o
r
s
ca
lab
ili
ty
,
co
m
p
u
t
atio
n
al
ef
f
icien
cy
,
an
d
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
in
g
wer
e
ad
d
r
ess
ed
,
p
r
o
v
i
d
in
g
a
h
o
lis
tic
v
iew
o
f
th
e
p
o
te
n
tial
im
p
ac
t
o
f
E
UM
L
o
n
th
e
f
ield
.
As
we
lo
o
k
to
th
e
f
u
tu
r
e,
c
o
n
tin
u
e
d
r
esear
c
h
in
E
UM
L
,
esp
ec
ially
with
in
t
h
e
c
o
n
tex
t
o
f
C
PS
,
h
o
ld
s
g
r
ea
t
p
r
o
m
is
e.
Ad
v
a
n
ce
m
en
ts
in
in
ter
p
r
etab
le
u
n
s
u
p
e
r
v
is
ed
lear
n
in
g
tech
n
i
q
u
es
ca
n
co
n
t
r
ib
u
te
s
ig
n
if
ican
tly
to
th
e
o
n
g
o
in
g
d
ev
el
o
p
m
en
t
o
f
s
af
e,
r
eliab
le,
a
n
d
tr
a
n
s
p
ar
en
t
ML
s
o
lu
tio
n
s
f
o
r
co
m
p
lex
,
d
y
n
am
ic
s
y
s
tem
s
.
T
h
is
p
ap
er
s
er
v
es
as
a
s
tep
p
i
n
g
s
to
n
e,
en
co
u
r
ag
in
g
f
u
r
th
er
ex
p
lo
r
atio
n
an
d
in
n
o
v
atio
n
i
n
th
e
in
ter
s
ec
tio
n
o
f
E
UM
L
an
d
C
PS
.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
e
au
th
o
r
s
wo
u
ld
lik
e
to
ex
p
r
ess
th
eir
s
in
ce
r
e
g
r
atitu
d
e
to
t
h
eir
r
esp
ec
tiv
e
in
s
titu
tio
n
s
f
o
r
p
r
o
v
id
in
g
th
e
n
ec
ess
ar
y
f
ac
ilit
ies,
in
f
r
ast
r
u
ctu
r
e,
an
d
ac
ad
em
ic
s
u
p
p
o
r
t
to
ca
r
r
y
o
u
t
th
is
r
esear
ch
wo
r
k
.
T
h
e
au
th
o
r
s
also
th
an
k
c
o
lleag
u
es
a
n
d
r
ev
iew
er
s
f
o
r
th
eir
v
alu
a
b
le
s
u
g
g
esti
o
n
s
an
d
co
n
s
tr
u
ctiv
e
f
ee
d
b
ac
k
,
wh
ich
h
el
p
ed
im
p
r
o
v
e
t
h
e
q
u
ality
o
f
th
is
p
a
p
er
.
F
UNDING
I
NF
O
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B
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c
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n
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c
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tac
ted
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m
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:
p
ra
sa
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jas
ti
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0
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@
g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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5
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52
In
d
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J
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&
C
o
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p
Sci
,
Vo
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41
,
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ra
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h
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s
2
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ro
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fr
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Un
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,
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.
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se
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rc
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c
a
n
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iate
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a
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h
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k
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r
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k
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Un
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a
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Un
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m
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d
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ra
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d
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is
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ro
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ss
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telli
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ra
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n
d
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m
a
in
re
se
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r
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h
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lu
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ftwa
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g
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,
m
a
c
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rn
in
g
.
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c
a
n
b
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c
o
n
tac
ted
a
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m
a
il
:
p
ra
b
h
a
k
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rc
s@
g
m
a
il
.
c
o
m
.
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li
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ted
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ru
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sh
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ti
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ti
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d
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ra
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h
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s
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rs
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c
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ti
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re
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d
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h
in
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fro
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ry
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Na
g
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n
a
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rsit
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.
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in
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o
m
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ich
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(P
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.
D.)
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m
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o
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d
ich
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rr
y
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n
tral
Un
iv
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rsity
.
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Re
se
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ts
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m
a
c
h
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g
,
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e
e
p
lea
rn
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g
,
n
e
two
rk
s,
a
rt
ifi
c
ial
in
tell
ig
e
n
c
e
,
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n
d
c
l
o
u
d
se
c
u
rit
y
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
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m
a
il
:
b
e
n
a
rji
@k
l
u
n
i
v
e
rsity
.
in
.
Anu
sha
B.
is
c
u
rre
n
t
ly
i
n
t
h
e
De
p
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rtme
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t
o
f
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c
tro
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n
d
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m
m
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g
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,
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d
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ra
U
n
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rsit
y
,
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d
h
ra
P
ra
d
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sh
,
In
d
ia.
Re
se
a
rc
h
in
tere
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a
re
a
rti
ficia
l
in
telli
g
e
n
c
e
in
ECE
,
e
m
b
e
d
d
e
d
sy
ste
m
s,
n
a
n
o
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lec
tro
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,
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n
tern
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f
th
in
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s
(Io
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se
c
u
rit
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,
ro
b
o
ti
c
s a
n
d
a
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o
m
a
ti
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n
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
n
u
tan
h
a
r
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.