I
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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.
39
,
No
.
3
,
Sep
tem
b
er
2
0
2
5
,
p
p
.
1
6
3
3
~
1
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:
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ttp
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//ij
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cs.ia
esco
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Five
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arch
itect
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re wi
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tune
d decisio
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tree
s
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-
co
mm
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pred
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neela
k
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ial
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su
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e
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s
a
n
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v
e
l
F
iv
e
-
Ti
e
r
se
rv
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-
o
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ted
BI
a
rc
h
it
e
c
tu
re
(
F
S
OBIA
)
th
a
t
le
v
e
ra
g
e
s
a
d
v
a
n
c
e
d
t
u
n
e
d
d
e
c
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t
re
e
(ATDT)
tec
h
n
i
q
u
e
s
f
o
r
p
re
d
ict
in
g
o
n
li
n
e
b
u
y
i
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g
b
e
h
a
v
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r.
T
h
e
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se
d
F
S
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ffe
rs
e
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c
o
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latfo
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s
a
sc
a
lab
le
a
n
d
a
d
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ti
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n
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g
a
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n
g
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si
g
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ts
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to
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n
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m
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r
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re
n
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e
s
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n
d
m
a
k
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g
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rm
e
d
b
u
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e
ss
d
e
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n
s.
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e
g
o
a
l
o
f
F
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'
s
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v
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g
u
se
rs
a
n
d
q
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ick
e
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rv
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e
.
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p
e
rime
n
tal
e
v
a
lu
a
t
io
n
s
o
n
re
a
l
-
wo
rld
d
a
tas
e
ts
in
F
S
OBIA
a
c
h
iev
e
d
o
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e
r
9
5
%
p
re
d
ictio
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a
c
c
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ra
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y
,
o
u
tp
e
rfo
rm
in
g
trad
it
io
n
a
l
m
o
d
e
ls:
De
c
isio
n
t
r
e
e
s
(8
2
%
),
a
n
d
XG
Bo
o
st
(9
1
%
),
wh
il
e
o
ffe
rin
g
b
e
tt
e
r
sc
a
lab
il
it
y
a
n
d
c
o
m
p
u
tatio
n
a
l
e
fficie
n
c
y
.
K
ey
w
o
r
d
s
:
C
o
n
s
u
m
er
b
eh
a
v
io
u
r
Fiv
e
-
T
ier
ar
ch
itectu
r
e
Pre
d
ictio
n
Qo
S
-
awa
r
e
Ser
v
ice
d
is
co
v
er
y
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
e
s
p
o
nd
ing
A
uth
o
r
:
T
h
ir
u
n
ee
lak
a
n
d
an
Ar
ju
n
an
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
an
d
E
n
g
in
ee
r
in
g
,
SR
M
I
n
s
ti
tu
te
o
f
Scien
ce
an
d
T
ec
h
n
o
l
o
g
y
R
am
ap
u
r
am
,
C
h
en
n
ai,
I
n
d
ia
E
m
ail:
n
ee
lan
ar
ju
n
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Or
g
an
izatio
n
s
n
o
w
h
av
e
an
e
x
p
an
d
i
n
g
r
an
g
e
o
f
ch
allen
g
es
an
d
q
u
ick
ch
a
n
g
es
d
r
iv
en
b
y
r
is
in
g
u
s
er
ex
p
ec
tatio
n
s
,
u
n
clea
r
b
o
r
d
e
r
s
an
d
f
aster
s
o
f
twar
e
d
e
v
elo
p
m
en
t
cy
cles.
W
e
n
ee
d
a
s
tr
o
n
g
d
ec
is
io
n
-
s
u
p
p
o
r
t
ar
ch
itectu
r
e
[
1
]
to
g
et
u
s
ef
u
l
in
f
o
r
m
atio
n
f
r
o
m
th
e
h
u
g
e
am
o
u
n
ts
o
f
d
ata
th
at
tr
an
s
ac
tio
n
s
g
en
er
ate.
Ag
en
ts
,
web
s
er
v
ices,
an
d
lo
o
s
ely
c
o
u
p
led
s
o
f
twar
e
co
m
p
o
n
e
n
ts
m
ak
e
th
is
ar
ch
itectu
r
e
p
o
s
s
ib
le
s
o
th
at
s
er
v
ice
p
r
o
v
id
e
r
s
m
ay
r
en
d
er
s
o
f
twa
r
e
as
a
s
er
v
ice
(
Saa
S)
ea
s
ily
av
ailab
le
to
co
n
s
u
m
er
s
o
n
d
em
an
d
.
Nu
m
e
r
o
u
s
q
u
ality
cr
iter
ia,
s
u
ch
as
d
e
p
en
d
ab
ilit
y
an
d
c
o
s
t,
co
m
p
en
s
ate
u
s
er
s
f
o
r
s
er
v
ices
[
2
]
.
W
e
d
el
ete
r
elev
an
t
s
ess
io
n
d
ata
at
th
e
en
d
o
f
o
b
lig
atio
n
s
,
th
er
eb
y
p
r
eser
v
in
g
o
n
ly
t
h
e
f
in
al
r
esu
lts
.
Ad
v
a
n
cin
g
d
is
tr
ib
u
ted
a
p
p
licatio
n
s
ac
r
o
s
s
s
ev
er
al
s
ec
to
r
s
,
in
clu
d
in
g
e
-
co
m
m
er
ce
,
in
v
en
to
r
y
m
an
ag
em
en
t,
s
en
s
o
r
n
etwo
r
k
s
,
an
d
b
u
s
in
ess
in
tellig
en
ce
(
B
I
)
,
d
ep
e
n
d
s
o
n
s
er
v
ice
-
o
r
ien
te
d
c
o
m
p
u
tin
g
.
Fo
r
in
-
d
ep
th
r
esear
ch
,
BI
d
ep
en
d
s
o
n
a
b
r
o
ad
r
a
n
g
e
o
f
h
eter
o
g
en
eo
u
s
d
ata
s
o
u
r
ce
s
[
3
]
.
Data
s
o
u
r
cin
g
,
in
te
g
r
atio
n
,
clea
n
in
g
,
f
iltra
tio
n
,
k
n
o
wle
d
g
e
ex
tr
ac
tio
n
,
a
n
d
in
s
ig
h
t b
u
ild
in
g
ar
e
a
f
ew
o
f
t
h
e
v
ar
io
u
s
elem
en
ts
o
f
th
e
B
I
s
y
s
tem
.
Dev
elo
p
in
g
f
r
a
m
ewo
r
k
s
f
o
r
d
ec
is
io
n
s
u
p
p
o
r
t
in
s
id
e
a
s
er
v
ice
-
o
r
ien
ted
BI
en
v
ir
o
n
m
en
t
is
u
n
d
er
m
u
ch
em
p
h
asis
[
4
]
.
T
h
ese
s
y
s
tem
s
u
s
e
a
m
u
lti
-
tier
ed
ap
p
r
o
ac
h
to
p
r
o
v
id
e
co
m
p
lete
b
u
s
in
ess
an
aly
tic
s
ca
p
ab
ilit
y
v
ia
c
o
m
m
u
n
icatio
n
s
b
etwe
en
s
er
v
ices.
T
h
is
ap
p
r
o
ac
h
cr
ea
tes
an
in
teg
r
ate
d
d
ata
r
ep
o
s
ito
r
y
to
o
p
er
ate
a
s
a
m
ed
iato
r
b
etwe
e
n
co
r
p
o
r
ate
in
tellig
en
ce
to
o
ls
an
d
lo
ca
l
d
ata
s
o
u
r
ce
s
.
T
h
e
d
ata
r
ep
o
s
ito
r
ies
ar
e
u
s
u
ally
u
p
d
ated
u
s
in
g
p
u
s
h
o
r
p
u
ll
s
tr
ateg
ies
[
5
]
b
ec
au
s
e
th
ey
d
o
n
'
t
h
av
e
r
ea
l
-
tim
e
d
ata
ca
p
ab
ilit
ies,
wh
ich
m
ea
n
s
th
at
d
r
ill
-
d
o
wn
o
p
er
at
io
n
s
ar
e
lim
ited
t
o
th
e
d
esig
n
o
f
th
e
in
te
g
r
ated
d
ata
s
to
r
e.
Fo
r
in
n
o
v
ativ
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
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4
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5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
39
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
6
3
3
-
1
6
4
1
1634
co
m
p
an
ies,
d
e
v
elo
p
in
g
ef
f
ec
ti
v
e
m
eth
o
d
s
to
u
s
e
h
ig
h
-
d
im
e
n
s
io
n
al
d
ata
f
o
r
s
ig
n
if
ican
t
o
u
tco
m
es
is
a
m
ajo
r
d
if
f
icu
lty
.
R
ec
en
t
d
ev
elo
p
m
en
ts
in
m
ac
h
in
e
lear
n
in
g
(
ML
)
h
av
e
let
co
m
p
an
ies
ex
ac
tly
p
r
o
ject
a
s
p
ec
tr
u
m
o
f
ev
en
ts
.
T
h
e
co
m
b
in
atio
n
o
f
m
o
d
er
n
d
ata
m
o
d
if
icatio
n
tech
n
o
lo
g
ies [
6
]
with
d
ata
m
in
in
g
a
p
p
r
o
ac
h
es p
r
o
d
u
c
es
ac
tio
n
ab
le
k
n
o
wled
g
e.
B
o
th
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
l
ea
r
n
in
g
ap
p
r
o
ac
h
es
f
in
d
v
alu
e
in
f
o
r
ec
asts
.
Data
m
in
in
g
f
in
d
s
h
id
d
e
n
p
atter
n
s
an
d
in
s
ig
h
ts
in
d
ata
th
at
ca
n
h
elp
p
eo
p
le
m
ak
e
b
etter
d
ec
is
io
n
s
.
T
h
is
is
h
o
w
it
co
n
n
ec
ts
k
n
o
wled
g
e
d
is
co
v
er
y
with
B
I
.
Sti
l
l,
th
e
en
o
r
m
o
u
s
am
o
u
n
ts
o
f
in
f
o
r
m
atio
n
av
aila
b
le
v
ia
o
n
lin
e
r
etail
s
to
r
es
ar
e
s
o
m
etim
es
u
n
d
er
u
s
ed
.
An
aly
zi
n
g
p
ast
d
ata
h
elp
s
c
o
m
p
an
ies
to
f
o
r
ec
ast
cu
s
to
m
er
b
eh
a
v
io
r
an
d
f
in
d
clien
t
g
r
o
u
p
s
th
at
will
o
f
f
e
r
b
en
ef
its
[
7
]
.
T
h
r
o
u
g
h
th
r
ee
f
u
n
d
am
e
n
tal
p
h
ases
—
d
ata
co
l
lectio
n
f
r
o
m
m
a
n
y
s
o
u
r
ce
s
,
d
ata
an
aly
s
is
,
an
d
d
ata
tr
an
s
f
o
r
m
atio
n
—
BI
aim
s
e
s
s
en
tially
to
im
p
r
o
v
e
d
ec
is
io
n
-
m
ak
in
g
.
Seaso
n
al
ad
v
er
tis
in
g
m
ig
h
t
m
o
tiv
ate
c
u
s
to
m
er
s
to
b
u
y
wh
e
n
th
ey
s
h
o
w
u
n
h
ap
p
in
ess
[
8
]
.
Fin
d
i
n
g
m
o
r
e
p
r
o
ac
tiv
e
co
n
s
u
m
er
ca
teg
o
r
ies
h
elp
s
o
n
e
g
r
asp
clien
t
im
p
r
ess
io
n
s
d
u
r
in
g
o
n
lin
e
tr
an
s
ac
tio
n
s
,
t
h
er
eb
y
tu
r
n
in
g
s
ite
v
is
ito
r
s
in
to
p
u
r
ch
aser
s
.
As
m
o
b
ile
u
s
e
f
o
r
o
n
lin
e
s
ea
r
c
h
es
i
s
r
is
in
g
,
co
m
p
an
ies
ar
e
u
s
in
g
t
ar
g
eted
a
p
p
r
o
ac
h
es
to
g
ath
er
ac
c
u
r
ate
k
n
o
wled
g
e
ab
o
u
t th
eir
ta
r
g
et
cu
s
to
m
er
s
[
9
]
.
L
ar
g
e
lan
g
u
ag
e
m
o
d
els
(
L
L
M
s
)
,
am
o
n
g
o
th
er
tech
n
o
lo
g
ical
d
ev
elo
p
m
e
n
ts
,
h
a
v
e
im
p
r
o
v
e
d
co
r
p
o
r
ate
o
p
er
atio
n
s
.
T
o
g
eth
er
with
c
h
an
g
es
in
d
ee
p
lear
n
in
g
tech
n
iq
u
es,
th
e
av
ailab
ilit
y
o
f
s
ig
n
if
ican
t
co
m
p
u
ter
r
eso
u
r
ce
s
an
d
lar
g
e
tr
ai
n
in
g
d
atasets
h
elp
s
ex
p
lain
L
L
Ms.
B
y
u
s
in
g
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
with
b
illi
o
n
s
o
f
p
ar
am
eter
s
[
1
0
]
,
[
1
1
]
,
th
e
m
o
d
els
lear
n
in
tr
icate
p
atter
n
s
an
d
lin
g
u
is
tic
n
u
a
n
ce
s
f
r
o
m
lar
g
e
tex
t
co
r
p
o
r
a.
I
n
co
r
p
o
r
atin
g
A
I
in
to
b
u
s
in
ess
p
r
o
ce
s
s
m
an
ag
em
en
t
s
y
s
tem
s
(
AB
PM
S)
ca
n
h
elp
th
em
m
ak
e
s
m
ar
t,
f
lex
ib
le
ch
o
ices
[
1
2
]
,
[
1
3
]
b
ec
a
u
s
e
th
ey
wo
r
k
with
h
o
w
b
u
s
in
ess
es
d
o
th
in
g
s
.
C
o
n
v
en
tio
n
al
d
ec
is
i
o
n
-
s
u
p
p
o
r
t
s
y
s
tem
s
ca
n
m
ak
e
f
ast,
h
ig
h
-
q
u
ality
d
ec
is
i
o
n
s
an
ch
o
r
ed
in
m
o
r
e
co
m
p
r
eh
en
s
iv
e
k
n
o
wled
g
e
o
f
im
p
o
r
tan
t
to
p
ics
b
y
in
co
r
p
o
r
atin
g
L
L
Ms
[
1
4
]
.
T
h
e
r
o
ck
etin
g
g
r
o
wth
o
f
e
-
c
o
m
m
er
ce
d
em
an
d
s
in
tellig
en
t
B
I
f
r
am
ewo
r
k
s
f
o
r
an
aly
zin
g
co
n
s
u
m
er
b
e
h
av
io
r
in
r
ea
l
tim
e.
T
h
is
s
tu
d
y
in
tr
o
d
u
ce
s
Fiv
e
-
T
ier
s
er
v
ice
-
o
r
ie
n
ted
B
I
ar
ch
itectu
r
e
(
FS
OB
I
A)
co
m
b
in
ed
with
ad
v
an
ce
d
tu
n
e
d
d
ec
is
io
n
tr
ee
(
AT
DT
)
tech
n
iq
u
es
to
im
p
r
o
v
e
p
r
ed
ictiv
e
an
aly
tics
.
Un
lik
e
co
n
v
en
tio
n
al
ap
p
r
o
ac
h
es,
FS
O
B
I
A
lev
er
ag
es
m
ac
h
in
e
lear
n
in
g
,
L
L
M
s
,
an
d
Qo
S
-
awa
r
e
s
er
v
ice
d
is
co
v
er
y
f
o
r
e
n
h
an
ce
d
s
ca
lab
ilit
y
an
d
ac
c
u
r
ac
y
i
n
p
r
ed
ictin
g
th
e
c
o
n
s
u
m
er
d
ec
i
s
io
n
-
m
ak
in
g
,
th
u
s
au
g
m
en
tin
g
th
e
b
u
s
in
ess
es to
r
ea
p
th
e
r
ev
e
n
u
e.
2.
RE
L
AT
E
D
WO
RK
S
I
n
ter
m
s
o
f
I
n
ter
n
et
c
o
m
m
u
n
icatio
n
,
it
h
as
b
ee
n
d
em
o
n
s
tr
ated
th
at
clien
ts
ar
e
d
r
awn
to
an
d
m
o
tiv
ated
to
b
u
y
in
tr
ig
u
in
g
p
r
o
d
u
cts
wh
en
s
ee
b
a
n
n
er
a
d
s
o
r
a
d
v
er
tis
em
en
ts
o
n
th
e
I
n
ter
n
et.
T
o
d
o
th
e
p
r
o
ce
s
s
,
p
eo
p
le
n
ee
d
ad
d
itio
n
al
d
etails
b
ef
o
r
e
d
ec
id
in
g
t
o
b
u
y
.
C
u
s
to
m
er
s
wh
o
f
ee
l
th
ey
ar
e
n
o
t
g
iv
en
s
u
f
f
icien
t
d
ata
will
lo
o
k
f
o
r
it
o
n
lin
e
th
r
o
u
g
h
l
o
ca
tio
n
s
,
o
n
li
n
e
in
d
ex
es,
an
d
web
en
g
in
es
,
am
o
n
g
o
t
h
er
m
ea
n
s
[
1
5
]
-
[
1
7
]
.
On
c
e
th
e
y
h
av
e
s
u
f
f
icien
t
k
n
o
wled
g
e,
clien
ts
ch
o
o
s
e
to
ev
al
u
ate
th
o
s
e
o
p
t
io
n
s
f
o
r
g
o
o
d
s
o
r
s
er
v
ices.
T
h
ey
m
ay
s
ea
r
c
h
f
o
r
co
n
s
u
m
er
co
m
m
en
ts
o
r
e
v
alu
a
tio
n
s
o
f
p
r
o
d
u
cts at
th
is
p
o
in
t.
T
h
ey
ev
alu
ate
a
n
d
id
en
tify
th
e
b
r
an
d
o
r
co
m
p
an
y
th
at
b
est
s
u
its
th
eir
n
ee
d
s
.
An
ef
f
icien
t
s
ite
ad
m
in
is
tr
atio
n
an
d
th
e
s
tr
u
ctu
r
al
p
lan
o
f
b
u
s
in
ess
ar
e
ess
en
tial
th
in
g
s
th
at
ce
r
tain
ly
in
f
lu
en
ce
th
e
m
in
d
s
et
o
f
co
n
s
u
m
er
s
.
T
h
er
ef
o
r
e
,
p
eo
p
le
ar
e
o
cc
u
p
ied
with
p
u
r
c
h
asin
g
ite
m
s
an
d
ad
m
in
is
tr
atio
n
o
n
e
-
s
h
o
p
p
in
g
.
Als
o
,
th
e
ten
d
en
cy
o
f
in
f
o
r
m
atio
n
s
o
u
r
ce
s
m
ak
es a
n
im
p
ac
t o
n
p
u
r
ch
asin
g
b
eh
a
v
io
u
r
[
1
8
].
T
h
e
m
o
s
t
ad
v
a
n
tag
eo
u
s
f
ea
tu
r
e
o
f
th
e
web
is
th
at
it
f
ac
ilit
ates
th
e
p
r
e
-
p
u
r
ch
ase
p
h
ase
b
y
allo
win
g
co
n
s
u
m
er
s
to
co
n
s
id
er
m
an
y
o
p
tio
n
s
.
C
u
s
to
m
er
s
o
cc
asio
n
ally
ex
p
er
ien
c
e
p
r
o
b
lem
s
with
th
e
p
r
o
d
u
ct,
wo
r
r
y
ab
o
u
t
it,
o
r
n
ee
d
to
r
et
u
r
n
o
r
alter
th
e
th
in
g
th
ey
b
o
u
g
h
t
[
1
9
]
.
R
ef
u
n
d
an
d
tr
ad
e
p
r
o
ce
d
u
r
es
th
er
ef
o
r
e
p
r
o
v
e
to
b
e
m
o
r
e
cr
u
cial
at
th
is
p
o
in
t
.
On
e
im
p
o
r
tan
t
asp
ec
t
o
f
c
u
s
to
m
er
s
'
o
n
lin
e
p
u
r
c
h
ase
h
a
b
its
is
th
e
in
q
u
ir
y
p
r
o
ce
s
s
.
T
h
e
s
o
u
r
ce
r
is
k
a
r
is
es
th
r
o
u
g
h
o
u
t
t
h
e
k
n
o
wled
g
e
-
g
a
th
er
in
g
an
d
ev
alu
atio
n
s
tag
es
as
th
er
e
m
i
g
h
t
b
e
a
f
ew
er
r
o
r
s
in
t
h
e
d
ata
o
n
th
e
web
s
ites
.
B
ef
o
r
e
ac
ce
s
s
in
g
th
eir
web
s
ite,
v
is
ito
r
s
to
ce
r
ta
in
web
s
ites
h
av
e
to
r
eg
is
ter
[
2
0
]
.
As
a
r
esu
lt,
cu
s
t
o
m
er
s
r
u
n
t
h
e
r
is
k
o
f
th
e
s
ec
u
r
ity
o
f
in
f
o
r
m
atio
n
as
an
ad
d
itio
n
al
to
th
e
item
's
h
az
ar
d
.
T
h
is
m
eth
o
d
s
h
o
ws
h
o
w
to
ex
t
r
ac
t
s
u
r
p
r
is
in
g
an
d
f
ascin
atin
g
p
atter
n
s
f
r
o
m
lar
g
e
am
o
u
n
ts
o
f
in
f
o
r
m
atio
n
.
T
h
is
m
eth
o
d
r
es
tr
icts
th
e
lead
g
r
ad
e
m
et
r
ic
t
o
b
asic
attr
ib
u
tes
an
d
m
a
k
es
s
o
u
n
d
ass
u
m
p
tio
n
s
ab
o
u
t
t
h
e
ty
p
e
o
f
r
u
le.
T
h
e
m
ar
k
et
-
b
ased
an
aly
s
is
is
o
n
e
o
f
th
e
m
o
s
t
co
m
m
o
n
an
d
o
n
g
o
in
g
ex
a
m
p
les
o
f
co
n
n
ec
tio
n
r
e
g
u
latio
n
.
B
y
id
e
n
tify
in
g
r
elatio
n
s
h
ip
s
b
etwe
en
th
e
d
if
f
er
en
t
item
s
th
at
co
n
s
u
m
er
s
p
lace
in
th
eir
s
h
o
p
p
in
g
b
o
x
es,
th
is
tech
n
iq
u
e
lo
o
k
s
at
th
e
p
u
r
c
h
ase
p
atter
n
s
o
f
co
n
s
u
m
er
s
.
T
h
e
r
ev
elatio
n
en
ab
les
th
e
r
etailer
to
cr
ea
te
m
ar
k
etin
g
t
ec
h
n
iq
u
es
b
y
p
ick
in
g
u
p
k
n
o
wled
g
e
in
to
w
h
ich
th
i
n
g
s
ar
e
as
o
f
ten
o
b
tain
e
d
to
g
eth
er
b
y
clien
ts
an
d
w
h
ich
th
in
g
s
b
r
in
g
th
em
b
etter
b
e
n
e
f
its
wh
en
s
et
in
p
r
o
x
im
ity
[
2
1
].
Data
m
in
in
g
e
n
ab
les
to
id
e
n
tific
atio
n
o
f
d
esig
n
s
,
an
ticip
atin
g
th
e
f
u
tu
r
e,
an
d
s
ettlem
en
t
o
f
in
f
o
r
m
ed
d
ec
is
io
n
s
in
th
e
v
iew
o
f
h
ig
h
d
im
en
s
io
n
al
d
ata
co
n
f
ir
m
a
tio
n
.
Fo
r
i
n
s
tan
ce
,
d
ata
m
i
n
in
g
p
r
o
ce
s
s
es
an
d
n
u
m
er
ical
s
h
o
p
p
er
s
'
d
ata
en
a
b
le
e
-
r
eta
iler
s
to
co
m
p
r
e
h
en
d
wh
ich
th
in
g
s
a
r
e
b
o
u
g
h
t
b
y
s
im
ilar
clien
ts
.
T
h
ey
s
h
all
an
ticip
ate
o
f
f
er
s
o
f
r
eg
u
lar
th
in
g
s
an
d
m
o
r
e
p
r
o
f
ici
en
tly
d
ea
l
with
its
s
to
ck
.
B
a
s
ically
,
d
ata
m
in
in
g
r
eq
u
ir
es
a
s
tan
d
ar
d
p
r
o
ce
d
u
r
e
,
d
ata
s
to
r
e
o
r
d
is
tr
ib
u
tio
n
ce
n
t
r
e,
in
n
o
v
atio
n
s
,
an
d
m
aster
y
[
2
2
]
.
T
h
e
p
r
o
ce
d
u
r
e
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
F
ive
-
Tier
B
I
a
r
ch
itectu
r
e
w
it
h
tu
n
ed
d
ec
is
io
n
tr
ee
s
fo
r
e
-
co
mme
r
ce
p
r
ed
ictio
n
(
Th
ir
u
n
ee
la
ka
n
d
a
n
A
r
ju
n
a
n
)
1635
m
u
s
t
b
e
s
o
lid
an
d
r
ep
ea
tab
le
b
y
in
d
i
v
id
u
als
with
f
ew
d
ata
m
in
in
g
ab
ilit
ies.
Ho
wev
e
r
,
th
e
s
tan
d
ar
d
d
ata
ex
tr
ac
tio
n
p
r
o
ce
s
s
o
u
g
h
t
to
in
clu
d
e
wo
r
k
u
n
d
er
s
tan
d
in
g
wh
ich
d
ec
id
es
th
e
ac
tiv
it
y
tar
g
ets,
ev
alu
atio
n
o
f
th
e
wo
r
k
f
o
u
n
d
at
io
n
cir
cu
m
s
tan
c
es
s
o
o
n
an
d
s
o
f
o
r
th
.
T
r
ailed
b
y
th
e
d
ata
u
n
d
er
s
tan
d
in
g
t
ask
wh
ich
g
ath
er
s
,
d
ep
icts
,
in
v
esti
g
ates
d
ata,
an
d
ch
ec
k
s
d
ata
q
u
ality
[
2
3
]
.
T
h
e
r
ea
d
in
ess
in
clu
d
es
th
e
d
ata
s
e
t
p
o
r
tr
ay
al,
ch
o
ice,
ap
p
r
aisal,
s
o
lid
if
icatio
n
,
d
ata
f
o
r
m
attin
g
,
p
r
o
ce
s
s
p
r
o
t
o
ty
p
in
g
,
p
r
o
ce
s
s
ass
es
s
m
en
t,
s
en
d
in
g
,
an
d
s
o
f
o
r
t
h
[
2
4
].
Dev
elo
p
ed
a
f
r
am
ewo
r
k
th
at
co
m
p
ar
es
u
n
c
o
n
n
ec
te
d
d
ec
is
io
n
-
m
ak
in
g
to
o
n
lin
e
co
n
s
u
m
er
ch
o
ice
-
m
ak
in
g
.
T
h
e
r
esear
c
h
s
u
g
g
ests
a
b
r
o
ad
f
r
am
ewo
r
k
f
o
r
co
n
s
u
m
er
b
eh
a
v
io
u
r
th
at
h
as
to
b
e
im
p
r
o
v
ed
to
tak
e
i
n
to
ac
co
u
n
t
f
r
esh
in
f
o
r
m
atio
n
.
W
h
en
it
co
m
es
tim
e
f
o
r
cu
s
to
m
er
s
to
m
ak
e
p
u
r
ch
ases
,
t
h
ey
will
lo
o
k
at
t
h
e
m
an
y
b
r
a
n
d
s
an
d
th
e
f
ea
tu
r
es
lik
e
p
r
o
d
u
cts,
q
u
ality
,
p
r
ice,
an
d
s
o
lu
tio
n
s
.
C
er
tain
th
in
g
s
ca
n
b
e
ef
f
icien
tly
p
u
r
ch
ased
a
n
d
s
h
ip
p
e
d
o
n
lin
e,
in
clu
d
in
g
s
o
f
twar
e,
p
u
b
lic
atio
n
s
,
s
m
ar
tp
h
o
n
es,
co
m
p
u
t
er
s
,
an
d
tex
tb
o
o
k
s
.
T
h
en
,
s
elec
tin
g
s
o
m
e
p
r
o
d
u
cts
v
ia
an
in
ter
n
et
ch
a
n
n
el
m
ig
h
t b
e
ch
allen
g
in
g
[
2
5
]
.
Ad
d
itio
n
ally
cr
u
cial
ar
e
s
ite
ch
ar
ac
ter
is
tics
,
co
m
p
an
y
co
m
p
eten
cies,
m
ar
k
etin
g
co
m
m
u
n
icatio
n
s
,
an
d
clien
t
attitu
d
e
s
.
State
th
at
o
n
lin
e
m
er
ch
an
ts
u
s
e
cu
ttin
g
-
e
d
g
e
te
ch
n
o
lo
g
ies to
im
p
r
o
v
e
t
h
eir
web
s
ites
to
f
av
o
u
r
ab
ly
ca
p
tu
r
e
c
u
s
to
m
er
s
'
atten
tio
n
.
B
u
y
er
p
r
ep
ar
ed
n
ess
to
tr
y
o
r
b
u
y
th
in
g
s
f
r
o
m
th
e
s
ite
m
ay
b
e
ad
v
er
s
ely
a
f
f
ec
ted
if
th
e
s
ite
is
to
o
m
ild
,
n
o
t
s
af
e,
o
r
n
o
t a
d
e
q
u
ately
s
af
eg
u
ar
d
ed
.
C
u
s
t
o
m
e
r
p
a
r
t
i
ci
p
a
t
i
o
n
i
n
o
n
li
n
e
p
u
r
c
h
a
s
i
n
g
o
r
s
h
o
p
p
e
r
t
al
e
n
t
s
,
w
h
i
c
h
h
i
n
t
t
h
a
t
c
o
n
s
u
m
e
r
s
h
a
v
e
o
p
i
n
i
o
n
s
o
n
t
h
e
p
r
o
d
u
c
t
,
i
n
a
d
d
i
t
i
o
n
t
o
t
h
e
w
a
y
w
e
b
-
b
a
s
e
d
p
u
r
c
h
a
s
i
n
g
f
u
n
c
t
i
o
n
s
,
c
a
n
i
n
f
l
u
e
n
c
e
o
n
l
i
n
e
b
u
y
i
n
g
h
a
b
i
t
s
.
C
l
i
c
k
-
s
t
r
e
a
m
a
ct
i
v
it
y
i
s
y
e
t
a
n
o
t
h
e
r
c
r
u
c
i
a
l
el
e
m
e
n
t
o
f
t
h
e
o
n
l
i
n
e
e
n
v
i
r
o
n
m
e
n
t
[
2
6
]
.
I
t
d
e
s
c
r
i
b
e
s
h
o
w
u
s
e
r
s
b
e
h
a
v
e
w
h
e
n
t
h
e
y
u
s
e
w
e
b
s
i
t
e
s
t
o
l
o
o
k
u
p
i
n
f
o
r
m
a
t
i
o
n
.
E
a
c
h
o
f
t
h
e
s
e
f
a
c
t
o
r
s
h
a
s
a
r
o
l
e
a
s
a
s
t
i
m
u
l
a
n
t
f
o
r
c
e
r
t
a
i
n
m
i
n
d
s
e
t
s
a
n
d
b
e
h
a
v
i
o
u
r
s
r
e
l
a
t
e
d
t
o
o
n
l
i
n
e
t
r
a
d
i
n
g
.
T
h
r
o
u
g
h
t
h
e
i
n
t
e
r
n
e
t
,
i
n
d
i
v
i
d
u
a
l
s
g
e
t
t
h
e
i
m
p
r
e
s
s
i
o
n
t
h
a
t
t
h
e
i
r
p
u
r
c
h
a
s
i
n
g
c
i
r
c
u
m
s
t
a
n
c
e
s
w
i
l
l
b
e
s
o
m
e
w
h
a
t
s
a
t
i
s
f
y
i
n
g
.
I
t
g
o
e
s
f
o
r
t
h
e
d
i
s
t
i
n
g
u
i
s
h
i
n
g
p
r
o
o
f
o
f
i
n
t
e
r
r
e
l
a
t
i
o
n
s
b
e
t
w
e
e
n
d
e
c
i
s
i
o
n
s
o
f
v
a
r
i
o
u
s
i
t
e
m
s
b
o
u
g
h
t
i
n
a
p
a
r
t
i
c
u
l
a
r
r
e
t
a
i
l
l
o
c
a
t
i
o
n
,
f
o
r
e
x
a
m
p
l
e
,
a
g
r
o
c
e
r
y
s
t
o
r
e
[
2
7
].
T
h
e
m
ain
is
s
u
e
is
h
o
w
m
u
c
h
L
L
Ms
ca
n
s
h
o
w
th
at
th
ey
ar
e
ca
p
a
b
le
o
f
th
in
k
in
g
.
B
y
o
f
f
er
i
n
g
a
th
o
r
o
u
g
h
a
n
d
cu
r
r
e
n
t
an
al
y
s
is
o
f
th
e
s
u
b
ject,
th
is
p
a
p
er
h
o
p
es
to
s
tim
u
late
s
tim
u
latin
g
co
n
v
e
r
s
atio
n
s
an
d
d
ir
ec
t
f
u
tu
r
e
s
tu
d
ies
in
L
L
Ms
-
b
ased
r
ea
s
o
n
in
g
[
2
8
]
.
An
o
th
er
wo
r
k
th
at
p
r
o
v
id
es
a
th
o
r
o
u
g
h
an
al
y
s
is
o
f
th
e
d
ev
elo
p
m
e
n
t
an
d
s
ig
n
if
ican
ce
o
f
L
L
Ms
in
th
e
f
ield
s
o
f
M
L
an
d
th
e
p
r
o
ce
s
s
in
g
o
f
n
atu
r
al
lan
g
u
ag
es
is
th
e
s
u
r
v
ey
o
n
L
L
Ms.
Fro
m
th
e
f
i
r
s
t
lan
g
u
ag
e
m
o
d
els
to
th
e
m
o
s
t
cu
r
r
en
t
d
ev
elo
p
m
en
t
o
f
p
r
e
-
tr
ain
ed
lan
g
u
ag
e
m
o
d
els
(
PLM
s
)
with
b
illi
o
n
s
o
f
v
a
r
iab
les,
it
ch
ar
t
s
th
eir
h
i
s
to
r
ical
ev
o
lu
tio
n
[
2
9
]
.
T
h
e
s
tu
d
y
h
ig
h
lig
h
ts
th
e
s
p
ec
ial
ca
p
ac
ities
o
f
L
L
Ms a
s
th
ey
g
r
o
w
in
s
ize,
in
clu
d
i
n
g
in
-
co
n
tex
t le
ar
n
in
g
.
T
h
e
f
o
u
r
m
ai
n
f
ac
ets
o
f
L
L
M
s
th
at
co
m
p
r
is
e
th
e
s
u
r
v
e
y
'
s
a
r
ch
itectu
r
e
ar
e
in
itial
tr
ain
in
g
,
ad
ap
tio
n
tu
n
in
g
,
u
tili
za
tio
n
,
an
d
ab
ilit
y
ass
ess
m
en
t.
T
h
e
r
e
p
o
r
t
also
r
ec
o
m
m
en
d
s
to
p
ics
f
o
r
f
u
r
th
e
r
in
v
esti
g
atio
n
an
d
g
r
o
wth
an
d
o
f
f
e
r
s
in
s
ig
h
ts
in
to
th
e
ass
ets
th
at
ar
e
ac
ce
s
s
ib
le
t
o
s
u
p
p
o
r
t
th
e
g
r
o
wth
o
f
L
L
Ms
[
3
0
]
.
Alo
n
g
with
tr
ac
k
in
g
d
ev
elo
p
m
en
ts
in
r
e
s
ea
r
ch
th
r
o
u
g
h
o
u
t
t
h
e
d
esig
n
ated
p
e
r
i
o
d
,
th
e
i
n
v
esti
g
atio
n
also
e
x
am
in
es
s
ig
n
if
ican
t
NL
P
task
s
,
ad
v
an
ce
s
in
b
asic
m
eth
o
d
s
,
an
d
th
eir
ap
p
licatio
n
s
in
f
ield
s
in
cl
u
d
in
g
tech
n
o
l
o
g
y
,
h
ea
lth
,
s
o
cial
s
cien
c
e,
an
d
t
h
e
ar
ts
an
d
s
cien
ce
s
[
3
1
]
.
T
h
e
s
tu
d
y
h
ig
h
lig
h
ts
th
e
s
ig
n
if
ican
ce
o
f
ass
ess
in
g
L
L
Ms
a
s
a
co
r
e
d
is
cip
lin
e
to
ass
i
s
t
th
e
cr
ea
tio
n
o
f
m
o
r
e
c
o
m
p
eten
t
L
L
Ms
an
d
it
also
h
ig
h
lig
h
ts
f
u
tu
r
e
is
s
u
es
in
L
L
M
ass
ess
m
en
t.
T
h
e
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws:
Sectio
n
3
d
em
o
n
s
tr
ate
th
e
ex
p
er
im
en
tal
s
etu
p
an
d
th
e
al
g
o
r
ith
m
ic
im
p
lem
e
n
tatio
n
an
d
Sectio
n
4
d
is
cu
s
s
es th
e
r
esu
lts
ar
r
iv
ed
b
y
th
e
p
r
o
p
o
s
ed
s
y
s
tem
.
Fin
ally
,
th
e
co
n
clu
d
in
g
s
ec
tio
n
s
u
m
m
ar
izes th
e
ac
h
iev
ed
o
u
tp
u
t a
n
d
o
u
tlin
es th
e
p
o
s
s
ib
le
f
u
tu
r
e
e
x
ten
s
io
n
s
o
f
t
h
e
wo
r
k
.
3.
M
E
T
H
O
D
I
n
o
r
d
e
r
t
o
e
x
e
r
c
i
s
e
t
h
e
p
r
o
p
o
s
e
d
s
y
s
t
e
m
,
t
h
e
f
o
l
l
o
w
i
n
g
e
x
p
e
r
i
m
e
n
t
a
l
s
e
t
u
p
i
s
d
e
v
i
s
e
d
.
T
h
a
t
i
s
a
h
i
g
h
-
e
n
d
c
o
m
p
u
t
e
r
s
y
s
t
e
m
w
i
t
h
i
n
t
e
l
i
7
p
r
o
c
e
s
s
o
r
w
i
t
h
3
2
G
B
R
A
M
w
i
t
h
N
V
I
D
I
A
G
e
F
o
r
c
e
R
T
X
3
0
6
0
i
s
u
t
i
l
i
z
e
d
f
o
r
t
h
e
i
m
p
l
e
m
e
n
t
a
t
i
o
n
o
f
t
h
e
p
r
o
p
o
s
e
d
s
y
s
t
e
m
.
T
h
e
p
r
o
p
o
s
e
d
a
l
g
o
r
i
t
h
m
i
s
c
o
d
e
d
i
n
p
y
t
h
o
n
3
.
9
,
e
x
e
c
u
t
e
d
i
n
P
y
c
h
a
r
m
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a
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D
e
p
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d
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h
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w
a
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r
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k
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a
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.
3
.
1
.
Da
t
a
c
o
ns
o
lid
a
t
io
n
Data
co
n
s
o
lid
atio
n
is
th
e
p
r
o
c
ess
o
f
co
n
s
tr
u
ctin
g
a
p
e
r
m
an
e
n
t in
teg
r
ated
d
ata
s
to
r
e
e
x
tr
ac
t
in
g
all
d
ata
f
r
o
m
da
ta
s
o
u
r
ce
s
u
s
in
g
g
l
o
b
a
l
s
ch
em
a
as
s
h
o
wn
in
Fig
u
r
e
1
.
T
h
e
d
ata
co
n
s
o
lid
atio
n
is
p
er
f
o
r
m
e
d
u
s
in
g
two
m
eth
o
d
o
l
o
g
ies
(
i)
cr
ea
tin
g
a
n
ew
web
s
er
v
ice
f
o
r
p
o
p
u
latin
g
th
e
in
teg
r
ated
d
ata
s
to
r
e
an
d
(
ii)
m
o
d
if
y
in
g
th
e
ex
is
tin
g
o
n
lin
e
tr
a
n
s
ac
tio
n
p
r
o
ce
s
s
in
g
(
OL
T
P)
m
o
d
u
le
to
p
u
s
h
th
e
c
o
n
ten
ts
to
th
e
in
te
g
r
ated
d
ata
s
to
r
e
.
I
n
eith
er
ca
s
e,
th
e
g
l
o
b
al
s
ch
em
a
is
to
b
e
d
esig
n
ed
an
d
m
ap
p
ed
to
th
e
l
o
ca
l
s
ch
em
a,
r
eso
l
v
in
g
h
eter
o
g
en
eities
lik
e
n
am
in
g
h
eter
o
g
e
n
eity
,
s
ch
em
atic
h
eter
o
g
e
n
eity
,
s
tr
u
ctu
r
al
h
eter
o
g
en
eity
,
an
d
s
em
an
tic
h
eter
o
g
e
n
eity
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
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4
7
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2
I
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d
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J
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lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
39
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
6
3
3
-
1
6
4
1
1636
Fig
u
r
e
1
.
I
n
teg
r
ated
XM
L
d
at
a
s
to
r
e
u
s
in
g
co
n
s
o
lid
atio
n
o
r
f
ed
er
atio
n
T
h
e
s
tep
s
in
v
o
lv
ed
in
d
ata
c
o
n
s
o
lid
atio
n
ar
e
lis
ted
b
elo
w:
Step
1
: Cre
atio
n
o
f
a
g
lo
b
al
s
c
h
em
a
th
at
s
atis
f
ies all
d
ec
is
io
n
s
u
p
p
o
r
t a
n
d
an
al
y
tics
r
eq
u
ir
e
m
en
ts
.
Step
2
: Cre
atio
n
o
f
a
n
XM
L
D
atab
ase
u
s
in
g
th
e
g
lo
b
al
s
ch
e
m
a.
Step
3
: M
ap
p
in
g
lo
ca
l d
atab
as
e
attr
ib
u
tes with
XM
L
d
atab
ase
attr
ib
u
tes.
Ste
p
4
:
Po
p
u
latin
g
t
h
e
XM
L
d
atab
ase
with
d
ata
s
o
u
r
ce
co
n
ten
ts
u
s
in
g
th
e
r
eq
u
ir
ed
t
r
an
s
f
o
r
m
atio
n
an
d
lo
ad
in
g
T
h
e
d
ata
c
o
n
s
o
lid
atio
n
p
r
o
ce
s
s
s
to
r
es
th
e
in
teg
r
ated
d
ata
s
to
r
ed
in
t
h
e
d
ata
tier
an
d
p
er
m
it
s
th
e
u
s
er
to
an
aly
s
e
an
d
e
x
tr
ac
t th
e
k
n
o
wled
g
e
f
o
r
t
h
e
q
u
er
ies.
3
.
2
.
LL
M
wit
h F
SO
B
I
A
T
o
co
m
p
letely
u
n
d
er
s
tan
d
s
ig
n
if
ican
t
s
u
b
jects
s
u
ch
as
to
k
e
n
izatio
n
,
atten
tio
n
p
r
o
ce
s
s
es,
ac
tiv
atio
n
f
u
n
ctio
n
s
,
an
d
lay
e
r
n
o
r
m
aliza
tio
n
,
y
o
u
m
u
s
t
s
tu
d
y
LLMs
.
On
e
f
ir
s
t
s
tep
b
ef
o
r
e
p
r
o
ce
s
s
in
g
is
to
k
en
izin
g
.
I
t
b
r
ea
k
s
tex
t u
p
in
to
to
k
en
s
—
th
at
is
,
d
is
t
in
ct
wo
r
d
s
,
s
u
b
wo
r
d
s
,
o
r
s
en
ten
ce
s
.
Fo
r
th
is
we
ap
p
ly
m
eth
o
d
s
s
u
ch
as
W
o
r
d
Piece
,
b
y
te
p
air
en
co
d
i
n
g
(
B
PE)
an
d
Un
ig
r
am
L
an
g
u
ag
e
Mo
d
el.
C
r
o
s
s
-
atten
tio
n
an
d
s
elf
-
atten
tio
n
am
o
n
g
o
t
h
er
atten
tio
n
m
ec
h
a
n
is
m
s
,
h
elp
o
n
e
to
ar
r
an
g
e
s
en
s
ib
le
p
atter
n
s
.
T
h
is
is
th
e
way
m
o
d
els co
u
ld
cr
ea
te
s
ig
n
if
ican
t
lin
k
s
b
etwe
en
elem
en
ts
.
Sev
er
al
d
is
tr
ib
u
ted
p
r
o
ce
d
u
r
es
ar
e
u
s
ed
i
n
L
L
M
lear
n
in
g
,
s
u
ch
as
p
ip
elin
e
an
alo
g
y
,
ten
s
o
r
p
ar
allelis
m
,
m
o
d
els
p
ar
allelis
m
,
o
p
tim
izatio
n
p
ar
allelis
m
an
d
in
f
o
r
m
atio
n
p
ar
allelis
m
.
T
h
ese
m
eth
o
d
s
a
id
in
co
m
p
r
e
h
en
d
i
n
g
th
e
o
r
etica
l
an
d
p
r
ac
tical
l
ea
r
n
in
g
s
h
o
wn
in
Fig
u
r
e
2
.
Fo
r
th
e
lear
n
in
g
an
d
s
u
b
s
eq
u
en
t
e
x
ec
u
tio
n
,
o
t
h
er
p
r
o
g
r
am
s
an
d
s
tr
u
ctu
r
es
ar
e
als
o
o
f
ten
u
tili
ze
d
,
s
u
c
h
as
t
h
e
T
r
an
s
f
o
r
m
er
s
,
Dee
p
Sp
ee
d
,
Py
T
o
r
c
h
,
T
en
s
o
r
Flo
w,
MX
Net,
an
d
Min
d
Sp
o
r
e.
W
h
en
p
r
e
-
p
r
o
ce
s
s
in
g
in
f
o
r
m
atio
n
,
th
e
im
p
o
r
ta
n
ce
o
f
q
u
al
ity
f
ilter
in
g
,
i
n
f
o
r
m
atio
n
d
e
-
d
u
p
licatio
n
an
d
p
r
iv
ac
y
m
in
im
izatio
n
is
e
m
p
h
asized
to
p
r
e
p
ar
e
in
f
o
r
m
a
tio
n
f
o
r
tr
ain
i
n
g
f
o
r
L
L
Ms.
T
h
e
f
ilter
in
g
m
eth
o
d
aid
s
in
th
e
r
ed
u
ctio
n
o
f
u
n
n
e
ce
s
s
ar
y
an
d
p
o
o
r
-
q
u
ality
in
f
o
r
m
atio
n
.
Ad
d
itio
n
ally
,
it
lo
wer
s
th
e
co
m
p
u
tatio
n
co
m
p
lex
ity
b
y
d
is
r
eg
a
r
d
in
g
t
h
e
in
p
u
t'
s
p
o
in
tles
s
p
atter
n
.
T
h
e
d
e
-
d
u
p
licatio
n
a
p
p
r
o
ac
h
el
im
in
ates
d
u
p
licated
s
am
p
les
an
d
p
r
ev
e
n
ts
th
e
m
o
d
el'
s
in
clin
atio
n
to
war
d
o
v
e
r
f
itti
n
g
.
L
astl
y
,
p
r
iv
ac
y
m
in
im
i
za
tio
n
s
u
p
p
o
r
ts
th
e
s
af
eg
u
ar
d
in
g
o
f
p
r
iv
ate
in
f
o
r
m
atio
n
wh
ile
g
u
ar
a
n
teein
g
i
n
f
o
r
m
atio
n
s
af
ety
an
d
co
m
p
lian
ce
.
3
.
3
.
Da
t
a
e
x
t
ra
ct
i
o
n serv
ice
a
nd
da
t
a
f
eder
a
t
io
n
Data
E
x
tr
ac
tio
n
s
er
v
ice
ac
ce
p
ts
th
e
s
u
b
-
q
u
er
y
an
d
u
s
es
an
XPa
th
q
u
er
y
to
n
av
ig
ate
th
e
r
esp
ec
tiv
e
lo
ca
l
XM
L
d
ata
s
o
u
r
ce
s
an
d
ex
tr
ac
t
th
e
co
n
ten
ts
.
T
h
ese
co
n
ten
ts
ar
e
s
to
r
ed
as
a
s
ep
ar
ate
d
ata
s
et
an
d
in
teg
r
ated
.
T
h
e
s
tep
s
in
v
o
lv
ed
in
Data
E
x
tr
ac
tio
n
Ser
v
ice
ar
e
lis
ted
b
elo
w,
Step
1
:
E
x
ec
u
tio
n
o
f
ea
ch
X
Path
s
u
b
-
q
u
er
y
o
v
er
r
esp
ec
tiv
e
XM
L
d
ata
s
o
u
r
ce
s
f
o
r
e
x
tr
ac
tio
n
o
f
r
e
q
u
ir
e
d
r
ec
o
r
d
s
.
Step
2
:
Sto
r
in
g
t
h
e
ex
tr
ac
ted
co
n
ten
ts
in
r
esp
ec
tiv
e
g
lo
b
al
s
ch
em
a
attr
ib
u
tes
u
s
in
g
th
e
m
a
p
p
in
g
ta
b
le
cr
ea
ted
in
s
ch
em
a
m
ap
p
i
n
g
.
Step
3
: Rep
ea
t Step
2
u
n
til th
e
ex
tr
ac
ted
co
n
ten
ts
o
f
al
l d
ata
s
o
u
r
ce
s
ar
e
p
o
p
u
lated
.
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
F
ive
-
Tier
B
I
a
r
ch
itectu
r
e
w
it
h
tu
n
ed
d
ec
is
io
n
tr
ee
s
fo
r
e
-
co
mme
r
ce
p
r
ed
ictio
n
(
Th
ir
u
n
ee
la
ka
n
d
a
n
A
r
ju
n
a
n
)
1637
3
.
4
.
ML
f
ra
m
ew
o
rk
a
nd
F
SO
B
I
A
f
o
r
predict
ing
o
nli
ne
bu
y
ing
beha
v
io
ur
o
f
co
ns
um
er
s
T
h
e
p
u
r
p
o
s
e
o
f
th
e
r
esear
ch
is
to
b
r
ea
k
d
o
wn
th
e
av
ailab
le
d
ata
an
d
an
aly
s
e
it
d
ee
p
ly
.
T
h
e
d
ata
is
v
iab
ly
u
tili
ze
d
f
o
r
u
n
d
e
r
s
tan
d
i
n
g
th
e
p
r
esen
t u
s
er
b
e
h
av
io
u
r
.
T
h
e
o
u
tco
m
e
o
f
th
e
p
r
o
p
o
s
ed
wo
r
k
d
em
o
n
s
tr
ates
th
at
u
s
in
g
s
u
c
h
in
v
esti
g
atio
n
s
wis
ely
an
y
o
r
g
an
izatio
n
ca
n
f
o
r
esee
th
e
f
u
tu
r
e
p
u
r
ch
aser
b
eh
av
io
u
r
a
n
d
ta
k
e
th
eir
co
m
p
an
y
o
n
e
s
tep
a
h
ea
d
.
Pre
d
ictiv
e
an
aly
s
is
s
o
lu
tio
n
s
ar
e
co
n
v
ey
ed
b
y
u
ti
lizin
g
d
ata
m
in
in
g
tech
n
o
lo
g
ies
th
at
u
tili
ze
ex
p
la
n
ato
r
y
m
o
d
els
t
o
f
i
n
d
e
x
em
p
l
ar
y
d
esig
n
s
an
d
ap
p
l
y
th
e
m
t
o
an
ticip
ate
f
u
tu
r
e
p
atter
n
s
an
d
p
r
ac
tices.
Fig
u
r
e
2
.
B
ac
k
g
r
o
u
n
d
o
f
L
L
M
s
Alg
o
r
ith
m
: FSOB
I
A
with
AT
DT
Step 1: Data p
re
-
processing
{
Step 1.1: D
clean
= RemoveMissingValues(D
raw
)
Step 1.2: D
encoded
= RemoveMissingValues(D
clean
)
Step 1.3: D
scaled
= RemoveMissingValues(D
encoded
)
Step 1.4: I
selected
= FeatureSelection(D
scaled
)
}
Step 2: Model Training and Tuning
{
Step 2.1:
DT = TrainDecisionTree(I
selected
, j)
Step 2.2: DT_tuned = TuneHyperparameters(DT, I
selected
, j)
}
St
ep
3:
In
te
gr
at
io
n
wi
th
FS
OB
IA
:
Th
e
de
ci
si
on
t
re
e
mo
de
l
(D
T_
tu
ne
d)
is
in
te
gr
at
ed
in
to
FSOBIA for prediction
St
ep
4:
In
te
gr
at
io
n
wi
th
LL
Ms
:
LL
Ms
ca
n
be
in
te
gr
at
ed
in
to
th
e
FS
OB
IA
ar
ch
it
ec
tu
re
to
enhance predictive capabilities and contextual understanding
Step 5: QoS Considerations
{
St
ep
5.
1:
De
fi
ne
Qo
S
me
tr
ic
s:
Qo
S_
me
tr
ic
s
=
{A
cc
ur
ac
y,
Re
sp
on
se
_t
im
e,
Re
li
ab
il
it
y,
an
d
Scalability}
Step 5.2: Monitor and optimiz
e QoS: QoS = Monitor And Optimize QoS(QoS_metrics)
}
Step 6: Performance Evaluation:
Performance (DT_tuned, Xselected, y)
St
ep
7:
Op
ti
mi
za
ti
on
an
d
Re
fi
ne
me
nt
:
Re
fi
ne
mo
de
l
an
d
ar
ch
it
ec
tu
re
ba
se
d
on
pe
rf
or
ma
nc
e
evaluation results.
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.
39
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
6
3
3
-
1
6
4
1
1638
Step 8:
Deployment and Monitoring
{
Step 8.1: Deploy model with FSOBIA: DeployModel (DT_tuned, FSOBIA)
Step 8.2: Monitor performance: Monitor Performance (DT_tuned, FSOBIA)
}
T
h
is
alg
o
r
ith
m
o
u
tlin
es
th
e
s
tep
s
in
v
o
lv
ed
in
d
ev
elo
p
in
g
an
d
d
ep
l
o
y
in
g
an
ad
v
an
ce
d
p
r
ed
i
ctiv
e
an
aly
tics
s
o
lu
tio
n
in
teg
r
ated
with
FS
OB
I
A,
lev
er
ag
in
g
L
L
M
an
d
co
n
s
id
er
in
g
Q
o
S
r
e
q
u
ir
em
en
ts
.
Var
io
u
s
co
n
s
u
m
er
b
eh
av
io
r
d
ata
ar
e
ass
ig
n
ed
v
ar
y
i
n
g
s
ig
n
if
ica
n
t d
eg
r
ee
s
b
y
th
e
af
o
r
em
e
n
tio
n
ed
m
eth
o
d
o
lo
g
y
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
NS
T
h
e
f
r
am
ewo
r
k
o
f
f
er
ed
b
y
t
h
e
ar
ch
itectu
r
e
s
u
g
g
ested
en
ab
les
p
ar
ticip
an
t
co
m
p
o
n
en
t
v
ar
iatio
n
,
in
teg
r
atio
n
,
a
n
d
v
er
s
atility
in
an
ad
ap
ta
b
le
s
ettin
g
.
Data
f
e
d
er
atio
n
is
im
p
lem
en
te
d
in
t
h
e
p
r
o
p
o
s
ed
FS
OB
I
A.
T
h
e
ex
p
er
im
en
t
was
co
n
d
u
ct
ed
in
a
lo
ca
l
ar
ea
n
etwo
r
k
(
L
AN)
with
o
n
e
s
er
v
er
c
o
n
ta
in
in
g
r
eq
u
ir
ed
B
I
s
er
v
ices.
T
h
e
XM
L
d
ata
s
o
u
r
ce
s
ar
e
u
p
d
ated
alo
n
g
with
r
elatio
n
al
d
ata
s
o
u
r
ce
s
u
s
in
g
th
e
u
p
d
ate
s
er
v
ice
wh
ich
will
d
elete
,
m
o
d
if
y
,
an
d
ap
p
en
d
r
ec
o
r
d
s
to
en
s
u
r
e
co
n
s
is
ten
cy
.
T
h
e
m
is
s
in
g
v
alu
es
in
XM
L
d
ata
s
o
u
r
ce
s
a
r
e
f
illed
with
av
er
ag
e
v
alu
es
an
d
f
r
eq
u
en
tly
u
s
ed
v
alu
es.
T
h
e
p
r
o
ce
s
s
is
r
ep
ea
ted
b
y
ap
p
e
n
d
in
g
2
5
r
ec
o
r
d
s
till
th
e
n
u
m
b
er
o
f
r
ec
o
r
d
s
r
ea
ch
es 2
5
0
in
ea
ch
d
ata
s
o
u
r
ce
.
T
h
e
r
esp
o
n
s
e
tim
e
o
f
d
ata
f
ed
er
atio
n
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
with
XM
L
d
ata
s
o
u
r
ce
o
v
er
FS
OB
I
A
is
co
m
p
ar
ed
with
ex
is
tin
g
d
ata
f
ed
er
atio
n
m
eth
o
d
o
l
o
g
y
th
at
u
s
es
th
e
o
r
i
g
in
al
d
ata
s
o
u
r
ce
s
an
d
d
ata
b
ase
co
n
tr
o
ller
s
o
v
er
a
f
iv
e
-
lay
er
ed
ar
ch
itectu
r
e.
T
h
e
s
er
v
ice
d
is
co
v
er
y
p
r
o
ce
s
s
,
ex
tr
ac
ts
all
s
ati
s
f
y
in
g
s
er
v
ices.
T
o
aid
u
s
er
s
in
s
er
v
ice
s
elec
tio
n
,
th
e
s
er
v
ices
ar
e
r
an
k
ed
u
s
in
g
th
e
c
o
ef
f
icien
t
o
f
v
ar
ian
ce
m
eth
o
d
.
T
h
e
co
e
f
f
ici
en
t o
f
v
a
r
ian
ce
(
C
V)
is
ca
lcu
la
ted
u
s
in
g
th
e
(
1
)
.
=
(
1
)
W
h
er
e
is
s
tan
d
ar
d
d
ev
iatio
n
a
n
d
is
m
ea
n
T
h
e
Qo
S
attr
ib
u
tes
ar
e
d
iv
id
ed
in
to
two
ca
teg
o
r
ies
b
y
th
e
r
an
k
in
g
p
r
o
ce
s
s
:
m
ax
im
izatio
n
ch
ar
ac
ter
is
tics
an
d
r
ed
u
ctio
n
ch
ar
ac
ter
is
tics
.
R
esp
o
n
s
iv
e
ti
m
e
an
d
laten
cy
a
r
e
in
clu
d
e
d
i
n
th
e
r
ed
u
ctio
n
s
et,
wh
er
ea
s
th
r
o
u
g
h
p
u
t
a
n
d
d
ep
en
d
ab
ilit
y
ar
e
in
cl
u
d
ed
in
th
e
m
ax
im
izatio
n
s
et.
T
h
e
r
e
d
u
ctio
n
attr
ib
u
te
is
tr
an
s
f
o
r
m
ed
i
n
to
a
m
ax
im
izati
o
n
attr
ib
u
te
u
s
in
g
t
h
e
s
u
g
g
est
ed
r
an
k
in
g
p
r
o
ce
d
u
r
e.
Fo
r
ex
a
m
p
le,
th
e
r
esp
o
n
s
e
tim
e
ch
ar
ac
ter
is
tic
in
T
ab
le
1
(
Min
im
izatio
n
attr
ib
u
te)
is
co
n
v
er
ted
to
its
r
esp
ec
tiv
e
r
an
k
v
a
lu
e
(
Ma
x
im
izatio
n
attr
ib
u
te)
.
T
h
is
tab
le
co
n
tain
s
f
iv
e
s
er
v
ices
wh
o
s
e
r
esp
o
n
s
e
tim
es
ar
e
r
an
k
ed
s
u
ch
th
at
th
e
h
ig
h
est
r
esp
o
n
s
e
tim
e
is
r
an
k
ed
an
d
1
a
n
d
th
e
o
th
er
s
ar
e
s
u
b
s
eq
u
en
tly
r
an
k
ed
at
5
.
T
ab
le
1
.
T
r
a
n
s
f
o
r
m
atio
n
o
f
m
i
n
im
ize
attr
ib
u
tes to
m
ax
im
ize
attr
ib
u
te
u
s
in
g
p
r
o
p
o
s
ed
m
eth
o
d
S
e
r
v
i
c
e
r
e
g
i
st
r
y
(
se
t
o
f
ser
v
i
c
e
s)
R
e
s
p
o
n
se
t
i
me
R
a
n
k
o
f
r
e
sp
o
n
se
t
i
m
e
G
o
a
l
-
B
a
s
e
d
N
o
n
-
I
n
t
r
u
s
i
v
e
R
e
c
o
mm
e
n
d
a
t
i
o
n
(
G
B
N
I
R
)
serv
i
c
e
1
6
2
3
G
o
a
l
-
B
a
s
e
d
E
v
a
l
u
a
t
i
o
n
a
n
d
A
d
a
p
t
i
v
e
W
e
i
g
h
t
i
n
g
(
G
B
EA
W
)
s
e
r
v
i
c
e
1
2
9
.
3
2
2
B
u
s
i
n
e
ss
Ef
f
i
c
i
e
n
c
y
a
n
d
A
n
a
l
y
t
i
c
a
l
W
o
r
k
f
l
o
w
(
B
EA
W
)
ser
v
i
c
e
1
2
7
.
1
8
1
E
x
p
er
im
en
tatio
n
is
c
o
n
d
u
cted
o
n
th
e
B
en
ch
m
ar
k
q
u
ality
o
f
web
s
er
v
ices
(
QW
S)
Data
s
et
with
2
5
0
7
web
s
er
v
ices.
T
h
is
QW
S
d
at
a
s
et
co
n
tain
s
th
e
Ser
v
ice
n
am
e,
B
in
d
in
g
ad
d
r
ess
o
f
th
e
s
er
v
ice,
an
d
Qo
S
attr
ib
u
tes
with
v
alu
es
f
o
r
av
ai
lab
ilit
y
,
r
esp
o
n
s
e
tim
e,
d
o
c
u
m
en
tatio
n
,
r
eliab
ilit
y
,
b
est
p
r
ac
tice,
s
u
cc
ess
ab
ilit
y
,
co
m
p
lian
ce
,
laten
cy
a
n
d
th
r
o
u
g
h
p
u
t
.
A
s
er
v
ice
r
eq
u
est
with
a
k
ey
wo
r
d
a
n
d
d
o
m
ai
n
ty
p
e
is
s
u
b
m
itted
a
n
d
th
e
av
er
ag
e
r
esp
o
n
s
e
tim
e
f
o
r
th
e
s
am
e
r
eq
u
est is
ca
lcu
lated
.
T
h
e
d
ev
elo
p
ed
m
o
d
el
was
d
ep
lo
y
ed
o
n
AW
S
L
am
b
d
a,
f
o
r
r
ea
l
tim
e
p
r
ed
ictio
n
s
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
co
m
p
r
is
in
g
FS
OB
I
A
an
d
AT
DT
’
s
p
er
f
o
r
m
a
n
ce
was
co
m
p
ar
ed
with
tr
ad
itio
n
al
m
o
d
el
s
u
ch
as
s
tan
d
ar
d
d
ec
is
io
n
tr
ee
s
,
XGBo
o
s
t
an
d
R
an
d
o
m
Fo
r
est.
T
h
e
p
r
ed
ic
tio
n
ac
cu
r
ac
y
,
s
er
v
ice
d
is
co
v
er
y
ef
f
icien
cy
an
d
r
esp
o
n
s
e
tim
e
ar
e
s
h
o
wn
in
T
a
b
le
2
.
FS
OB
I
A
b
len
d
ed
with
AT
DT
im
p
r
o
v
es
ac
c
u
r
ac
y
b
y
4
-
7
%
o
v
er
XGBo
o
s
t
an
d
1
0
-
1
3
%
o
v
e
r
d
ec
is
io
n
tr
ee
s
d
u
e
to
h
y
p
e
r
p
ar
am
eter
t
u
n
in
g
,
f
ea
tu
r
e
s
elec
tio
n
,
an
d
d
a
ta
in
teg
r
atio
n
.
Pre
cisi
o
n
an
d
R
ec
all
ar
e
o
p
tim
ized
th
r
o
u
g
h
Qo
S
-
awa
r
e
s
er
v
ice
d
i
s
co
v
er
y
,
r
e
d
u
cin
g
m
is
class
if
ic
atio
n
s
in
cu
s
to
m
er
b
e
h
av
io
r
p
r
ed
ictio
n
.
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
F
ive
-
Tier
B
I
a
r
ch
itectu
r
e
w
it
h
tu
n
ed
d
ec
is
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[
2
9
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A
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d
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in
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Evaluation Warning : The document was created with Spire.PDF for Python.