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f
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T
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m
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ter
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tio
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[
2
]
,
[
3
]
.
Alth
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,
th
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is
s
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if
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t
co
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r
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[
4
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5
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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T
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Vo
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15
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1
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Ma
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20
26
:
39
3
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40
4
394
T
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b
u
tter
f
ly
o
p
tim
izatio
n
(
MSM
B
O)
,
in
co
n
ju
n
ctio
n
with
p
r
iv
ac
y
-
p
r
eser
v
in
g
d
ee
p
lear
n
in
g
m
o
d
els
s
u
ch
as
C
o
n
v
C
ap
s
,
B
i
-
L
STM
,
an
d
Siam
ese
im
itatio
n
n
etwo
r
k
s
,
r
e
m
ain
s
an
u
n
d
er
ex
p
lo
r
ed
d
o
m
ain
in
f
itn
ess
r
ec
o
m
m
en
d
er
s
y
s
tem
s
.
T
h
is
p
r
esen
ts
a
co
m
p
ellin
g
o
p
p
o
r
tu
n
ity
to
ad
v
a
n
ce
th
e
f
i
eld
b
y
d
ev
elo
p
i
n
g
s
ec
u
r
e,
s
ca
lab
le,
an
d
p
er
s
o
n
ali
ze
d
s
o
lu
tio
n
s
th
at
u
p
h
o
ld
u
s
er
tr
u
s
t a
n
d
en
g
ag
em
en
t.
T
h
e
m
ain
p
r
i
n
cip
le
h
er
e
is
t
h
e
s
y
s
tem
’
s
p
r
ec
is
e
s
y
n
th
esis
o
f
m
o
d
e
r
n
tech
n
o
lo
g
y
,
alo
n
g
with
th
e
en
h
an
ch
e
d
p
r
i
v
ac
y
m
ea
s
u
r
es
[
6
]
.
T
h
e
s
y
s
tem
’
s
f
u
n
d
am
e
n
tal
ML
alg
o
r
ith
m
s
an
d
d
ata
an
al
y
tics
p
r
o
ce
s
s
lar
g
e
v
o
lu
m
es
o
f
u
s
er
d
ata,
in
clu
d
i
n
g
ex
e
r
cise
h
is
to
r
y
,
p
r
ef
er
e
n
c
es,
an
d
h
ea
lth
m
ea
s
u
r
es
[
7
]
,
[
8
]
.
T
h
is
lead
s
to
th
e
cr
ea
tio
n
o
f
h
i
g
h
ly
p
er
s
o
n
aliz
ed
ex
er
cise
p
lan
s
th
at
m
a
x
im
ize
f
itn
ess
r
esu
lts
.
Ho
wev
er
,
th
is
s
y
s
tem
’
s
f
ir
m
d
ed
icatio
n
to
u
s
er
p
r
iv
ac
y
,
attain
ed
th
r
o
u
g
h
v
ar
i
o
u
s
ap
p
r
o
a
ch
es,
s
ets
it
ap
ar
t.
Saf
eg
u
ar
d
in
g
u
s
er
in
f
o
r
m
atio
n
is
th
e
k
ey
p
r
in
cip
le
with
in
o
u
r
in
n
o
v
ativ
e
f
r
a
m
ewo
r
k
[
9
]
,
[
1
0
]
.
T
h
e
s
y
s
tem
wo
r
k
s
in
ce
r
tain
way
s
s
u
ch
th
at
it
s
ep
ar
ates
u
s
er
-
s
p
ec
if
ic
d
ata
f
r
o
m
th
e
p
er
s
o
n
ally
i
d
en
tifia
b
le
d
ata
u
s
in
g
a
d
v
an
ce
d
an
o
n
y
m
izatio
n
tech
n
iq
u
es
an
d
en
s
u
r
es
th
at
in
d
iv
id
u
al
id
en
titi
es
r
em
ain
h
id
d
e
n
e
v
en
in
th
e
ca
s
e
o
f
d
ata
b
r
ea
ch
es
[
1
1
]
,
[
1
2
]
.
T
h
is
ef
f
ec
tiv
ely
en
h
an
ce
s
th
e
co
n
f
id
en
ce
o
f
th
e
u
s
er
an
d
also
p
r
o
m
o
tes
a
s
en
s
e
o
f
s
ec
u
r
ity
,
wh
ich
was
cr
u
cial
f
o
r
th
e
s
y
s
tem
’
s
s
u
cc
ess
[
1
3
]
.
T
h
e
s
y
s
tem
wo
r
k
s
ef
f
ec
tiv
e
ly
b
y
u
s
in
g
m
o
d
e
r
n
en
c
r
y
p
ti
o
n
m
ec
h
a
n
is
m
s
to
p
r
o
tect
u
s
er
s
’
s
en
s
itiv
e
d
ata
f
r
o
m
an
y
u
n
au
t
h
o
r
ized
ac
ce
s
s
[
1
4
]
.
E
n
d
-
t
o
-
en
d
e
n
cr
y
p
tio
n
h
elp
s
p
r
o
tectin
g
th
e
co
m
m
u
n
icatio
n
b
etwe
en
u
s
er
s
an
d
th
e
s
y
s
tem
wh
ile
p
r
ev
en
tin
g
a
n
y
p
o
s
s
ib
le
attac
k
er
s
f
r
o
m
in
ter
ce
p
tin
g
s
en
s
itiv
e
in
f
o
r
m
atio
n
[
1
5
]
.
T
h
e
s
y
s
tem
em
p
lo
y
s
d
if
f
er
en
tial
p
r
iv
ac
y
tech
n
iq
u
es
wh
ic
h
ad
d
r
an
d
o
m
v
ar
iatio
n
s
to
ag
g
r
eg
ated
in
f
o
r
m
atio
n
wh
ich
in
tu
r
n
p
r
o
tect
in
d
iv
id
u
al
p
r
iv
ac
y
wh
ile
an
aly
zin
g
u
s
er
d
ata.
T
h
is
ap
p
r
o
ac
h
p
r
eser
v
es
o
v
er
all
d
ata
p
atter
n
s
with
o
u
t
r
ev
ea
lin
g
s
p
ec
if
ic
u
s
er
d
etails.
T
r
an
s
p
ar
en
c
y
an
d
u
s
er
e
n
g
ag
e
m
en
t
ar
e
in
c
r
ea
s
ed
wh
en
p
e
o
p
le
h
av
e
c
o
n
tr
o
l
o
v
er
th
e
d
at
a
th
at
is
g
ath
er
ed
a
n
d
h
o
w
it
is
u
tili
ze
d
u
s
in
g
f
l
ex
ib
le
au
th
o
r
izatio
n
s
tr
u
ctu
r
e.
Sen
s
itiv
e
in
f
o
r
m
atio
n
ca
n
o
n
ly
b
e
ac
ce
s
s
ed
b
y
au
t
h
o
r
ized
p
er
s
o
n
n
el
th
an
k
s
to
r
o
b
u
s
t
au
th
en
ticatio
n
p
r
o
ce
d
u
r
es a
n
d
ac
ce
s
s
co
n
t
r
o
l
s
.
T
h
e
p
r
o
p
o
s
ed
wo
r
k
e
n
h
an
ce
s
p
r
iv
ac
y
o
f
th
e
u
s
er
u
s
in
g
I
E
C
C
an
d
MSM
B
O
f
o
r
s
ec
u
r
in
g
d
ata
th
r
o
u
g
h
en
cr
y
p
tio
n
an
d
d
ec
r
y
p
tio
n
.
I
t
ef
f
ec
tiv
ely
ad
d
r
ess
es
ac
cu
r
ac
y
an
d
d
i
v
er
s
ity
in
f
itn
ess
r
ec
o
m
m
en
d
atio
n
s
,
u
s
in
g
a
th
r
ee
-
tier
d
ee
p
lea
r
n
in
g
m
o
d
el
wh
ich
co
m
b
i
n
es
two
al
g
o
r
ith
m
s
,
n
am
ely
,
C
o
n
v
C
ap
s
an
d
B
i
-
L
STM
to
im
p
r
o
v
e
s
u
g
g
esti
o
n
q
u
ality
.
r
e
c
u
r
s
i
v
e
f
e
a
t
u
r
e
e
l
i
m
i
n
a
t
i
o
n
(
R
F
E
)
r
ed
u
ce
s
f
ea
tu
r
e
s
p
ac
e,
b
o
o
s
ts
ef
f
icien
cy
,
an
d
p
r
ev
en
ts
o
v
er
f
itti
n
g
.
A
n
o
n
y
m
ized
d
ata
f
r
o
m
I
o
T
d
ev
ices
en
s
u
r
es
p
r
iv
ac
y
wh
ile
m
ak
in
g
p
e
r
s
o
n
alize
d
r
ec
o
m
m
en
d
atio
n
s
with
o
u
t
id
e
n
tity
d
ata
[
1
6
]
.
T
h
e
MSM
B
O
alg
o
r
ith
m
in
teg
r
ates
SOA
an
d
MBO
f
o
r
b
etter
o
p
tim
izatio
n
,
y
ield
i
n
g
ac
cu
r
at
e
an
d
d
i
v
er
s
e
f
itn
ess
s
u
g
g
esti
o
n
s
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
A
n
o
v
el
m
eth
o
d
o
f
in
d
ir
ec
t
f
it
n
ess
tr
ac
k
in
g
m
eth
o
d
u
tili
zin
g
m
m
-
wav
e
r
a
d
ar
s
en
s
o
r
s
was
p
r
ev
io
u
s
ly
in
tr
o
d
u
ce
d
in
2
0
2
1
b
y
T
iwar
i
an
d
Gu
p
ta
[
17
]
.
T
h
ee
y
u
s
ed
d
ee
p
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs)
to
d
is
tin
g
u
is
h
d
if
f
er
en
t
ex
e
r
cises
wh
ile
u
s
in
g
r
ea
l
-
tim
e
r
ad
ar
d
ata.
T
h
eir
p
r
o
p
o
s
ed
m
eth
o
d
d
escr
ib
ed
a
r
ea
s
o
n
ab
ly
p
r
ice
r
ed
u
ctio
n
f
o
r
cu
s
to
m
ar
y
b
o
d
y
-
w
o
r
n
f
itn
ess
tr
ac
k
er
s
.
T
h
e
W
FP
V
m
eth
o
d
f
o
r
p
r
ec
is
e
r
ea
l
-
tim
e
h
ea
r
t
r
ate
t
r
ac
k
in
g
was
i
n
tr
o
d
u
ce
d
b
y
T
em
k
o
[
18
]
in
2
0
1
7
.
W
FP
V
d
r
asti
ca
lly
lo
we
r
s
er
r
o
r
r
ates
u
s
in
g
W
ien
er
f
ilter
in
g
,
p
h
ase
v
o
co
d
er
,
an
d
u
s
er
-
ad
a
p
tiv
e
p
o
s
t
-
p
r
o
ce
s
s
in
g
,
m
ak
in
g
it
p
o
ten
tial
f
o
r
wea
r
ab
le
h
ea
lth
m
o
n
ito
r
in
g
.
Ye
an
d
Z
h
en
g
[
19
]
cr
ea
ted
an
ac
cu
r
ate
Hu
m
an
Gestu
r
e
R
ec
o
g
n
itio
n
s
y
s
tem
in
2
0
2
2
u
s
in
g
cu
ttin
g
-
ed
g
e
alg
o
r
ith
m
s
.
T
h
i
s
s
tr
ateg
y
im
p
r
o
v
es
co
m
p
r
e
s
s
io
n
an
d
r
ec
o
g
n
itio
n
,
s
o
lv
i
n
g
d
if
f
icu
lties
in
ex
er
cisi
n
g
wh
ile
r
ec
o
g
n
izin
g
a
p
er
s
o
n
’
s
p
o
s
itio
n
.
Ad
v
an
ce
d
n
o
n
-
c
o
n
tact
h
ea
r
t
r
ate
m
ea
s
u
r
in
g
m
eth
o
d
s
th
at
co
m
b
in
e
ad
a
p
tiv
e
s
k
in
co
lo
r
r
ec
o
g
n
itio
n
with
f
r
eq
u
en
c
y
-
d
o
m
ain
p
u
ls
e
r
ate
ap
p
r
o
ac
h
es
wer
e
in
tr
o
d
u
ce
d
in
2
0
2
2
b
y
C
h
o
u
et
a
l.
[2
0
]
.
B
y
im
p
r
o
v
in
g
ac
cu
r
ac
y
,
th
ese
ad
v
a
n
ce
s
s
tr
en
g
th
en
th
e
C
ADN
+
DSS
s
tr
ateg
y
.
T
h
ey
o
u
tp
er
f
o
r
m
e
d
p
r
e
v
io
u
s
C
ADN
+
D
S
S
ap
p
r
o
ac
h
es
in
r
ea
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tim
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ex
p
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r
im
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,
d
em
o
n
s
tr
atin
g
s
u
p
e
r
io
r
p
u
ls
e
r
ate
m
ea
s
u
r
em
e
n
t
w
ith
m
ea
n
a
b
s
o
lu
te
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mme
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tio
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ified
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395
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r
o
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MA
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f
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n
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ill
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k
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r
esp
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tiv
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h
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m
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a
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[
2
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d
ev
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m
eth
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ates H
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with
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r
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T
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2
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p
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ld
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[
2
3
]
p
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a
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to
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tech
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iq
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ch
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k
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4
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in
v
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tiv
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p
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io
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g
ical
f
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ns
[
2
5
]
.
I
n
an
o
th
er
wo
r
k
,
L
ee
et
a
l.
[2
6
]
p
r
o
p
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ed
a
u
n
iq
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u
ltich
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el
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is
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[
2
7
]
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San
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[
28
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g
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r
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af
f
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ea
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tim
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ter
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s
[
2
9
]
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p
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3.
M
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396
3.1.
Da
t
a
-
prepro
ce
s
s
ing
a
n
d det
a
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o
f
a
lg
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hm
s
3
.
1
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1
.
P
re
-
pr
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d
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c
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Input: s
eagull
popu
lation
Output:
optima
l sea
rch ag
e
nt
Procedur
e:
M
S
MB
O
-
b
as
e
d
p
ri
v
a
t
e
k
e
y
s
e
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e
ct
i
o
n
Initiali
ze par
amete
rs
,
,
Calculat
e fitn
ess
=
m
i
n
(
T
i
m
e
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Sort the
seagu
ll po
pulati
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n based
on
f
i
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e
s
s
v
a
l
u
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s
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Proposed
Migra
tion
–
E
x
p
lo
r
a
t
i
o
n
Enhances
explo
rator
y move
m
ent
For each
seagu
ll ag
ent
a.
Avoid co
llisio
ns us
ing Eq
.
(
2
)
a
n
d
E
q
.
(
3
).
b.
A
f
t
e
r
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l
i
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a
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ce
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be
s
t
n
e
ig
h
b
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r
t
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co
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v
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r
ge
a
s
per Eq.
(
4
).
c.
T
h
e
r
e
o
r
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e
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a
t
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f
t
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g
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s
r
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n
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a
s
per Eq.
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e
a
r
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h
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g
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a
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n
g
c
o
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e
r
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e
nc
e,
as per E
q. (
6
).
ii.
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Attac
king
–
E
x
p
lo
i
t
a
t
i
o
n
Enhance
iterat
ive a
ttacks
with upd
ated m
emory
pool
o
ptimi
z
a
ti
o
n
F
o
r
e
a
c
h
se
a
g
u
ll
a
g
e
nt
a.
Simulate
attac
king
behavi
o
r using
Eq. (
7
)
t
o
E
q
.
(
9
)
b.
Calculat
e the
curre
nt pos
i
tions of
searc
h age
nts as
per Eq.
(
10
)
c.
S
O
A
e
n
h
a
nc
e
d
w
i
t
h
M
B
O
:
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l
a
n
c
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g
E
x
p
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ra
t
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d
E
x
p
l
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t
at
i
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n
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or
E
f
f
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ci
e
nt
Problem
Solvin
g
Return
th
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o
p
ti
m
a
l
s
e
a
r
c
h
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g
e
n
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r
k
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End proc
edure
−
A
u
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t
d
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n
d
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n
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p
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t
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s
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h
e
m
o
d
e
l
u
s
e
s
R
F
E
t
o
r
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n
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t
r
a
ct
e
d
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e
a
tu
r
e
s
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n
h
a
n
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h
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ov
e
r
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l
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d
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f
f
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ci
e
n
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f
t
h
e
s
y
s
te
m
.
3
.
3
.
F
e
a
t
ure
s
elec
t
io
n
R
F
E
i
s
e
m
p
l
o
y
e
d
t
o
m
i
t
i
g
at
e
th
e
f
e
a
t
u
r
e
s
p
a
c
e
’
s
c
o
m
p
l
e
x
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y
.
T
h
e
e
x
t
r
a
c
t
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d
f
e
at
u
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f
r
o
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e
l
e
ct
e
d
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h
is
t
e
c
h
n
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m
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n
d
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at
e
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o
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y
.
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e
at
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r
e
s
d
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m
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d
l
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c
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g
t
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d
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l
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f
f
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c
ie
n
c
y
a
n
d
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t
e
r
p
r
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t
a
b
i
l
it
y
w
h
i
l
e
p
r
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s
e
r
v
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n
g
i
ts
p
r
e
d
i
c
ti
v
e
c
a
p
a
b
i
li
ty
.
T
h
e
p
s
e
u
d
o
c
o
d
e
f
o
r
R
F
E
is
g
i
v
e
n
i
n
Al
g
o
r
i
t
h
m
2
.
A
l
g
o
r
i
t
h
m
2
.
R
F
E
Use ever
y feat
ure t
o trai
n
the mod
el.
Analyze
the ac
curac
y of t
h
e model
Determin
e the
featu
re
’
s
im
p
o
r
t
a
n
c
e
t
o
t
he
m
o
de
l
f
o
r
e
a
c
h
f
e
at
u
r
e
for Each
subse
t siz
e
,
=
1
.
.
.
do
Keep the
most impo
rtant
f
eatures
Train th
e mode
l usi
ng
f
e
at
u
r
e
s
Determin
e the
model
’
s
a
c
cu
r
a
c
y
end for
Calculat
e the
accur
acy pr
o
file ove
r the
Determin
e the
appro
priate
number o
f feat
ures
Use the
model
corre
spondi
n
g to the
optim
al
R
F
E
i
m
p
r
o
v
es
c
o
m
p
u
t
a
t
i
o
n
a
l
e
f
f
i
c
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y
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d
m
o
d
e
l
i
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t
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r
p
r
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t
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b
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l
it
y
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y
r
e
d
u
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n
g
t
h
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f
e
a
t
u
r
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s
p
a
c
e
’
s
c
o
m
p
l
e
x
i
t
y
.
R
F
E
m
i
n
i
m
i
z
es
th
e
p
o
s
s
i
b
il
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y
o
f
o
v
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r
f
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t
ti
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a
n
d
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v
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m
o
d
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l
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e
r
a
l
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b
y
c
h
o
o
s
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g
t
h
e
m
o
s
t
i
n
f
o
r
m
a
t
i
v
e
f
ea
t
u
r
e
s
.
3
.
4
.
C
l
a
s
s
i
f
i
c
a
t
i
o
n
I
n
th
is
p
h
ase,
th
e
O
-
R
NN
m
o
d
el
’
s
p
er
f
o
r
m
a
n
ce
is
s
u
b
s
ta
n
tially
im
p
r
o
v
ed
th
r
o
u
g
h
th
e
MSM
B
O
alg
o
r
ith
m
,
o
p
tim
izin
g
t
h
e
n
etwo
r
k
f
o
r
ef
f
ec
tiv
e
an
al
y
s
is
o
f
s
eq
u
en
tial
d
ata.
Usi
n
g
MSM
B
O,
SOA
f
o
c
u
s
es
o
n
o
p
tim
izin
g
th
e
h
y
p
er
p
a
r
am
ete
r
s
lik
e
lear
n
in
g
r
ate
a
n
d
m
o
m
en
tu
m
,
wh
ile
MBO
f
in
e
-
t
u
n
es
th
e
weig
h
ts
o
f
th
e
n
etwo
r
k
,
th
u
s
e
n
ab
lin
g
p
r
ec
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s
e
ad
ju
s
tm
en
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,
r
esu
ltin
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ett
er
ac
cu
r
ac
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a
n
d
ef
f
ec
tiv
ely
h
an
d
lin
g
co
m
p
lex
,
s
eq
u
en
tial d
ata.
T
h
e
R
NN
m
o
d
e
l
i
s
t
o
b
e
i
m
p
r
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v
e
d
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t
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r
m
s
o
f
a
c
c
u
r
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c
y
a
n
d
e
f
f
e
c
t
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v
e
n
e
s
s
.
T
h
e
t
e
c
h
n
i
q
u
e
o
f
o
p
t
i
m
i
z
i
n
g
a
m
a
c
h
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n
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l
e
a
r
n
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n
g
m
o
d
e
l
’
s
p
a
r
a
m
e
t
e
r
t
o
b
o
o
s
t
p
e
r
f
o
r
m
a
n
ce
i
s
k
n
o
w
n
a
s
h
y
p
e
r
p
a
r
a
m
e
t
er
t
u
n
i
n
g
.
M
S
M
B
O
o
p
t
i
m
i
z
es
t
h
e
n
e
u
r
a
l
n
e
t
w
o
r
k
w
e
i
g
h
t
s
a
n
d
t
h
e
h
y
p
e
r
p
a
r
a
m
e
te
r
s
(
l
e
a
r
n
i
n
g
r
a
t
e
,
e
p
o
c
h
,
a
n
d
m
o
m
e
n
t
u
m
)
i
n
t
h
e
c
o
n
t
e
x
t
o
f
t
h
e
O
-
R
N
N
m
o
d
e
l
.
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h
i
s
t
h
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g
h
o
p
t
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m
i
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a
t
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n
e
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r
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s
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h
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m
o
d
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l
is
a
p
p
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a
t
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t
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k
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m
p
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t
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e
f
f
i
ci
e
n
c
y
a
n
d
a
c
c
u
r
a
c
y
.
4.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
I
n
o
u
r
m
eth
o
d
,
Py
th
o
n
is
u
s
ed
to
im
p
lem
en
t
th
e
s
u
g
g
ested
m
o
d
el.
T
h
e
e
f
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
is
ass
ess
ed
,
an
d
th
e
r
esu
lts
ar
e
o
b
tain
ed
with
th
o
s
e
o
f
o
th
er
alg
o
r
ith
m
s
,
wh
ich
in
clu
d
es,
d
ee
p
b
elief
n
etwo
r
k
(
DB
N)
,
MBO
,
S
OA
,
d
ee
p
co
n
v
o
lu
tio
n
al
n
eu
r
a
l
n
etwo
r
k
(
DC
NN)
,
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
.
T
o
ca
lcu
late
th
e
ef
f
icien
cy
,
we
u
s
e
s
ev
er
al
er
r
o
r
ca
lcu
latio
n
alg
o
r
ith
m
s
,
e.
g
.
,
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
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,
n
o
r
m
alize
d
m
ea
n
s
q
u
ar
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r
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r
(
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SE)
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m
ea
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u
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m
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.
W
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s
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wed
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p
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p
r
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itn
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ata
g
ath
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r
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it
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it
h
f
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n
d
i
n
g
s
b
y
Z
h
a
n
g
e
t
a
l
.
[
4
]
w
h
e
r
e
f
e
a
t
u
r
e
p
r
u
n
i
n
g
i
m
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B
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ep
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t
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b
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M
a
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o
n
i
et
a
l
.
[
5
]
i
n
t
h
e
i
r
w
e
a
r
a
b
l
e
r
e
s
p
i
r
a
t
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o
n
i
t
o
r
i
n
g
s
y
s
t
e
m
.
U
n
l
i
k
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c
o
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v
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n
t
i
o
n
a
l
f
i
t
n
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s
r
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co
m
m
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n
d
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r
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y
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at
p
r
i
o
r
i
t
iz
e
e
i
t
h
e
r
p
r
i
v
a
c
y
[
1
2
]
o
r
p
e
r
s
o
n
a
l
i
z
a
ti
o
n
[
9
]
,
o
u
r
s
t
u
d
y
s
h
o
w
s
t
h
a
t
b
o
t
h
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a
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c
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n
c
u
r
r
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n
t
l
y
.
T
h
e
c
o
m
b
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n
a
t
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o
n
o
f
p
r
i
v
a
c
y
-
p
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s
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r
v
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h
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i
q
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t
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al
p
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y
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to
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n
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p
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d
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o
r
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o
m
m
e
n
d
a
t
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o
n
a
cc
u
r
a
c
y
(
9
8
.
9
%
)
,
s
u
r
p
as
s
i
n
g
b
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n
c
h
m
a
r
k
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t
a
b
li
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h
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d
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p
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v
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o
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k
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s
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S
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g
s
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w
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[
8
]
,
w
h
e
r
e
C
NN
-
b
a
s
e
d
m
o
d
els
la
c
k
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d
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n
te
g
r
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d
p
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v
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t
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.
A
d
d
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t
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o
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a
l
l
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,
o
u
r
f
i
n
d
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o
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s
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r
a
t
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h
a
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b
i
d
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ct
i
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m
p
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m
o
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B
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-
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M
c
a
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l L
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m
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l
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n
T
e
m
k
o
[
1
8
]
,
w
h
i
l
e
o
u
r
a
d
d
e
d
s
p
a
t
i
a
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d
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C
o
n
v
C
a
p
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o
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f
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s
a
s
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n
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f
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ca
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t
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d
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c
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m
p
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t
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c
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n
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t
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o
n
a
n
d
h
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a
r
t
r
a
t
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p
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d
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c
t
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o
n
t
a
s
k
s
.
I
n
s
u
m
m
a
r
y
,
t
h
e
p
r
o
p
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s
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d
s
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m
n
o
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d
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m
a
n
d
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f
h
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g
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z
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d
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d
a
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f
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n
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r
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c
o
m
m
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n
d
a
t
io
n
s
b
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d
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s
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w
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m
a
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n
ta
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o
b
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tiv
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x
is
t
i
n
g
li
t
e
r
at
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r
e.
B
es
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d
e
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al
l
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a
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d
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d
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y
b
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d
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d
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n
f
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m
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ts
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n
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r
a
l
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z
a
b
i
li
t
y
a
c
r
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s
s
d
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s
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d
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m
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g
r
a
p
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g
r
o
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p
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d
f
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t
n
e
s
s
le
v
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l
s
,
es
p
e
c
ia
l
l
y
r
e
g
a
r
d
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n
g
r
e
a
l
-
w
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l
d
d
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p
l
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y
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e
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t
a
n
d
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n
g
-
t
e
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m
a
d
a
p
t
a
b
i
l
it
y
.
M
o
r
e
o
v
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r
,
t
h
e
c
o
m
p
u
t
a
t
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o
n
a
l
d
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m
a
n
d
s
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f
t
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C
o
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v
C
a
p
s
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B
i
L
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M
a
r
c
h
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t
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c
t
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r
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n
d
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o
m
p
l
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x
i
t
y
o
f
M
S
MB
O
o
p
t
i
m
iz
a
t
i
o
n
c
o
u
l
d
l
i
m
i
t
t
h
e
m
o
d
e
l
’
s
s
c
al
ab
i
l
i
t
y
o
n
l
o
w
-
p
o
w
e
r
e
d
g
e
d
e
v
i
c
e
s
,
w
h
i
c
h
a
r
e
c
o
m
m
o
n
l
y
u
s
e
d
i
n
w
ea
r
a
b
l
e
f
it
n
e
s
s
t
e
c
h
n
o
l
o
g
y
.
T
h
e
r
e
f
o
r
e
,
we
a
r
g
u
e
t
h
a
t
f
u
t
u
r
e
r
e
s
e
a
r
c
h
o
n
l
i
g
h
t
w
ei
g
h
t
m
o
d
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c
o
m
p
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s
s
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o
n
a
n
d
f
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d
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r
a
t
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d
l
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a
r
n
i
n
g
e
x
t
e
n
s
i
o
n
s
is
e
s
s
e
n
t
i
a
l
t
o
e
n
h
a
n
c
e
t
h
e
a
p
p
l
i
c
a
b
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t
y
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n
d
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f
f
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ie
n
c
y
o
f
t
h
e
p
r
o
p
o
s
e
d
s
y
s
t
e
m
.
4
.
1
.
Da
t
a
s
et
des
cr
iptio
n
Data
co
llectio
n
:
th
e
Fit
R
ec
p
r
o
ject
d
atasets
[
3
0
]
en
co
m
p
ass
u
s
er
s
p
o
r
t
r
ec
o
r
d
s
o
b
ta
in
ed
f
r
o
m
E
n
d
o
m
o
n
d
o
,
o
f
f
e
r
in
g
a
co
m
p
r
eh
en
s
iv
e
co
llectio
n
o
f
s
eq
u
e
n
tial
s
en
s
o
r
d
ata
s
u
ch
as
h
ea
r
t
r
ate,
s
p
ee
d
,
GPS
co
o
r
d
in
ates,
an
d
ad
d
itio
n
al
p
ar
am
eter
s
lik
e
s
p
o
r
t
ty
p
e,
g
e
n
d
er
,
an
d
wea
th
er
co
n
d
itio
n
s
.
T
h
e
d
atasets
ar
e
ex
clu
s
iv
ely
m
ad
e
av
ailab
le
f
o
r
ac
ad
em
ic
p
u
r
p
o
s
es,
em
p
h
asi
zin
g
n
o
n
-
r
ed
is
tr
ib
u
tio
n
an
d
n
o
n
-
co
m
m
er
cial
u
s
e.
T
h
r
ee
d
is
tin
ct
d
ataset
v
er
s
io
n
s
ar
e
p
r
o
v
id
ed
:
r
aw,
f
ilter
ed
a
n
d
r
esam
p
led
.
Data
p
r
ep
r
o
ce
s
s
in
g
:
h
o
wev
er
,
u
n
p
r
o
ce
s
s
ed
d
ata,
s
u
c
h
as
w
ea
th
er
an
d
m
etad
ata,
ar
e
in
clu
d
ed
in
o
u
r
r
aw
d
ataset.
Heu
r
is
tics
ar
e
u
s
ed
to
clea
n
th
e
d
ata
in
th
e
f
ilter
ed
v
e
r
s
io
n
,
w
h
er
e
we
el
im
in
ate
an
o
m
alo
u
s
wo
r
k
o
u
t
s
am
p
les
an
d
d
eter
m
i
n
in
g
ch
ar
ac
te
r
is
tics
lik
e
d
is
ta
n
ce
an
d
s
p
ee
d
.
O
u
r
d
ataset
is
in
ter
p
o
lated
in
th
e
r
esam
p
led
v
er
s
io
n
with
th
e
ai
m
to
p
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th
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p
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eg
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2
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9
4
,
h
ig
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lig
h
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n
ess
at
Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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d
s
at
0
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2
1
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5
8
2
,
an
d
(
MA
PE)
is
0
.
2
7
7
6
7
7
.
T
h
ese
v
alu
es
co
llectiv
ely
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p
h
asize
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e
co
n
s
is
ten
t
o
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ak
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r
o
b
u
s
t
ch
o
ice
f
o
r
p
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d
ictiv
e
task
s
.
T
ab
le
2
p
r
o
v
id
es
in
s
ig
h
ts
o
f
p
r
ed
icted
h
ea
r
t
r
at
es
d
u
r
in
g
r
o
b
u
s
t
s
p
o
r
ts
ac
tiv
ities
.
Fo
r
a
h
ea
r
t
r
ate
p
r
ed
ictio
n
o
f
1
4
0
,
th
e
r
ec
o
m
m
en
d
atio
n
is
“
Yes,
”
s
tatin
g
t
h
at
th
e
u
s
er
is
d
o
in
g
well
in
th
eir
p
er
f
o
r
m
an
ce
.
W
h
en
th
e
p
r
e
d
icted
h
ea
r
t
r
at
e
is
1
7
0
,
a
n
d
th
e
co
n
te
x
t
is
“
Yes
”
,
th
e
ad
v
ice
to
th
e
u
s
e
r
is
“
Slo
w
d
o
wn
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”
in
d
icatin
g
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n
ee
d
to
r
ed
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ce
e
x
er
tio
n
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Similar
ly
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th
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p
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7
0
with
a
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tex
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o
f
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Yes
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f
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th
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id
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n
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a
m
o
d
i
f
icatio
n
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th
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ac
t
iv
ity
to
m
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tain
a
s
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e
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d
ef
f
ec
tiv
e
lev
el
o
f
e
x
e
r
cise
in
ten
s
ity
.
T
h
is
tab
le
o
f
f
er
s
v
alu
a
b
le
r
ea
l
-
tim
e
in
s
ig
h
ts
f
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r
in
d
iv
id
u
als
en
g
ag
ed
in
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o
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u
s
t sp
o
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ts
to
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p
tim
ize
th
eir
p
er
f
o
r
m
an
ce
a
n
d
well
-
b
ein
g
.
T
ab
le
1.
E
x
is
tin
g
v
s
.
Pro
p
o
s
ed
p
er
f
o
r
m
an
ce
a
n
aly
s
is
(
lear
n
in
g
r
ate:
8
0
%)
M
e
t
r
i
c
s
S
O
A
M
B
O
D
C
N
N
LSTM
D
B
N
P
r
o
p
o
se
d
M
S
E
0
.
2
9
5
6
2
3
0
.
2
6
7
5
8
6
0
.
2
8
2
3
2
5
0
.
2
7
9
9
9
1
0
.
2
6
8
9
8
6
0
.
2
5
6
9
4
4
M
S
R
E
0
.
2
6
9
5
2
6
0
.
2
8
7
0
4
5
0
.
2
7
2
5
6
6
0
.
2
4
1
8
8
4
0
.
2
4
9
3
2
2
0
.
2
3
8
9
4
8
N
M
S
E
0
.
4
2
5
3
6
5
0
.
4
1
7
6
2
0
.
4
2
1
7
5
6
0
.
3
9
4
5
3
8
0
.
3
9
1
7
5
0
.
3
7
4
8
9
4
R
M
S
E
0
.
2
8
5
2
1
0
.
2
6
1
1
7
5
0
.
2
4
9
7
6
2
0
.
2
1
9
2
4
4
0
.
2
2
6
0
1
1
0
.
2
1
6
5
8
2
M
A
P
E
0
.
2
8
9
5
6
2
0
.
3
3
3
5
9
2
0
.
2
8
1
0
9
0
.
3
2
0
2
2
2
0
.
3
0
1
5
5
9
0
.
2
7
7
6
7
7
T
ab
le
2.
Pre
d
icted
h
ea
r
t r
ate
r
ec
o
m
m
en
d
atio
n
s
an
aly
s
is
P
r
e
d
i
c
t
e
d
h
e
a
r
t
r
a
t
e
R
o
b
u
st
s
p
o
r
t
R
e
s
u
l
t
1
4
0
Y
e
s
D
o
i
n
g
w
e
l
l
1
7
0
Y
e
s
S
l
o
w
d
o
w
n
1
7
0
Y
e
s
C
h
a
n
g
e
p
a
t
h
T
h
e
p
e
r
f
o
r
m
a
n
c
e
o
f
t
h
e
s
u
g
g
es
t
e
d
s
t
r
at
e
g
y
wi
t
h
a
n
d
w
i
t
h
o
u
t
f
e
a
t
u
r
e
s
e
l
e
ct
i
o
n
is
c
o
m
p
a
r
e
d
in
T
a
b
l
e
3
.
T
h
e
m
etr
ics
ev
alu
ate
d
en
c
o
m
p
ass
v
ital
asp
ec
ts
o
f
p
r
e
d
ictiv
e
ac
cu
r
ac
y
.
W
ith
o
u
t
f
ea
tu
r
e
s
elec
tio
n
,
th
e
m
et
h
o
d
y
ield
s
an
(
MSE
)
o
f
0
.
3
6
3
8
8
8
,
in
d
icatin
g
th
e
in
itial
p
r
ed
icti
o
n
v
ar
ian
ce
.
(
MSR
E
)
is
0
.
3
2
4
9
0
7
,
r
ef
lectin
g
th
e
r
elativ
e
d
is
p
ar
ities
in
p
r
ed
icti
o
n
s
.
(
NM
SE)
is
0
.
4
8
2
1
5
7
,
s
ig
n
if
y
in
g
t
h
e
m
o
d
el
’
s
ad
ju
s
tm
en
t
to
d
ata
v
ar
iatio
n
s
.
(
R
MSE
)
is
0
.
3
9
0
2
4
7
,
an
d
(
MA
PE)
is
0
.
3
5
6
6
1
1
.
Ho
wev
er
,
with
f
ea
tu
r
e
s
elec
tio
n
,
p
e
r
f
o
r
m
a
n
ce
im
p
r
o
v
es
ac
r
o
s
s
th
e
b
o
ar
d
.
MSE
d
r
o
p
s
to
0
.
3
1
7
6
7
2
,
MSR
E
im
p
r
o
v
es
to
0
.
2
9
5
4
2
2
,
NM
SE
en
h
an
ce
s
to
0
.
4
2
9
1
6
6
,
R
MSE
d
ec
r
ea
s
es
to
0
.
3
7
8
7
3
3
,
an
d
MA
PE
im
p
r
o
v
es
s
ig
n
if
ican
tly
to
0
.
3
1
1
3
1
8
,
h
ig
h
lig
h
tin
g
th
e
im
p
ac
t
o
f
f
ea
tu
r
e
s
elec
tio
n
in
e
n
h
an
cin
g
p
r
ed
ictiv
e
ac
cu
r
ac
y
.
T
ab
le
3
.
I
m
p
ac
t
o
f
f
ea
tu
r
e
s
elec
tio
n
o
n
ac
c
u
r
ac
y
M
e
t
r
i
c
s
W
i
t
h
o
u
t
f
e
a
t
u
r
e
s
e
l
e
c
t
i
o
n
W
i
t
h
f
e
a
t
u
r
e
s
e
l
e
c
t
i
o
n
M
S
E
0
.
3
6
3
8
8
8
0
.
3
1
7
6
7
2
M
S
R
E
0
.
3
2
4
9
0
7
0
.
2
9
5
4
2
2
N
M
S
E
0
.
4
8
2
1
5
7
0
.
4
2
9
1
6
6
R
M
S
E
0
.
3
9
0
2
4
7
0
.
3
7
8
7
3
3
M
A
P
E
0
.
3
5
6
6
1
1
0
.
3
1
1
3
1
8
Fig
u
r
e
2
d
is
p
lay
s
th
e
c
o
m
p
ar
is
o
n
o
f
m
etr
ics
with
an
d
with
o
u
t
f
ea
tu
r
e
s
elec
tio
n
.
I
t
h
as
b
ee
n
n
o
te
d
th
at
th
e
NM
SE
m
atr
ix
,
b
o
th
with
an
d
with
o
u
t
f
ea
tu
r
e
s
elec
tio
n
,
s
h
o
ws
th
e
h
i
g
h
est
v
alu
es
co
m
p
ar
e
d
to
th
e
o
t
h
e
r
s
.
F
i
g
u
r
e
3
s
h
o
w
s
t
h
e
c
o
m
p
a
r
i
s
o
n
b
e
t
we
e
n
t
h
e
p
r
o
p
o
s
ed
a
n
d
e
x
i
s
t
i
n
g
a
l
g
o
r
it
h
m
w
h
e
r
e
F
i
g
u
r
es
3
(
a
)
a
n
d
(
b
)
s
h
o
ws
th
e
en
cr
y
p
tio
n
an
d
d
ec
r
y
p
tio
n
tim
e
r
esp
ec
tiv
ely
.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
p
er
f
o
r
m
s
n
o
ticea
b
ly
f
aster
b
y
0
.
0
4
0
m
s
th
an
SOA
an
d
MBO.
T
h
is
s
u
g
g
ests
th
at
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
p
er
f
o
r
m
s
well
wh
en
q
u
ick
ly
en
cr
y
p
tin
g
d
ata,
m
ak
in
g
it
a
v
iab
le
o
p
tio
n
f
o
r
ap
p
licatio
n
s
wh
er
e
en
cr
y
p
tio
n
s
p
ee
d
is
o
f
th
e
ess
en
ce
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
o
n
ce
p
r
o
v
es
its
s
u
p
er
io
r
ity
with
a
d
ec
r
y
p
t
io
n
tim
e
o
f
o
n
ly
0
.
8
3
2
m
s
.
T
h
is
r
esu
lt
s
u
g
g
ests
th
at
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
p
er
f
o
r
m
s
ad
m
i
r
ab
l
y
in
ter
m
s
o
f
en
cr
y
p
tio
n
s
p
ee
d
an
d
d
ec
r
y
p
tio
n
ef
f
icien
cy
.
Fig
u
r
e
4
s
h
o
ws
th
e
c
o
m
p
ar
is
o
n
b
etwe
en
th
e
p
r
o
p
o
s
ed
a
n
d
ex
is
tin
g
m
eth
o
d
s
at
d
if
f
er
e
n
t
lear
n
in
g
r
ates
at
8
0
%
an
d
7
0
%
lear
n
in
g
r
ate
,
wh
er
e
Fig
u
r
es
4
(
a)
an
d
(
b
)
s
h
o
ws
th
e
ac
cu
r
ac
y
an
d
p
r
ec
is
io
n
co
m
p
ar
is
o
n
r
esp
ec
tiv
ely
.
T
h
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Fig
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u
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5
s
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‘
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5
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3
D
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ed
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d
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e
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er
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h
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ce
m
e
n
ts
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llectiv
ely
led
to
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r
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co
m
m
en
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tr
ad
itio
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al
s
y
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tem
s
th
at
f
o
cu
s
o
n
eith
er
Evaluation Warning : The document was created with Spire.PDF for Python.