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e
q
u
ate
f
o
r
5
G’
s
d
y
n
am
ic
tr
af
f
ic
c
o
n
d
itio
n
s
,
wh
er
e
p
r
o
ac
tiv
e
an
d
ad
a
p
tiv
e
s
o
lu
tio
n
s
ar
e
ess
en
tial
[
2
]
.
R
ec
en
t
r
esear
ch
h
as
e
x
p
lo
r
ed
ad
ap
tiv
e
c
o
n
g
esti
o
n
co
n
tr
o
l
m
ec
h
an
is
m
s
d
esig
n
ed
s
p
ec
if
ically
f
o
r
5
G
,
in
clu
d
in
g
r
ate
-
co
n
tr
o
l
alg
o
r
i
th
m
s
an
d
d
y
n
am
ic
b
an
d
wid
th
allo
ca
tio
n
tech
n
iq
u
es
[
3
]
-
[
6
]
.
W
h
ile
th
ese
ap
p
r
o
ac
h
es
o
f
f
e
r
im
p
r
o
v
e
m
en
ts
,
th
ey
o
f
ten
d
ep
en
d
o
n
f
i
x
e
d
r
u
le
s
ets
o
r
th
r
esh
o
ld
-
b
ased
d
ec
is
io
n
s
,
lim
itin
g
s
ca
lab
ilit
y
in
d
iv
er
s
e
tr
af
f
ic
en
v
ir
o
n
m
en
ts
.
T
o
o
v
er
co
m
e
th
ese
co
n
s
tr
ain
ts
,
m
ac
h
in
e
lear
n
in
g
(
ML
)
h
as
em
er
g
ed
as
a
p
r
o
m
is
in
g
s
o
lu
ti
o
n
.
ML
m
o
d
els
ca
n
an
aly
ze
lar
g
e
v
o
lu
m
es
o
f
tr
a
f
f
ic
d
ata,
id
en
tify
p
atter
n
s
,
an
d
p
r
ed
ict
co
n
g
esti
o
n
b
e
f
o
r
e
it o
c
cu
r
s
[
7
]
.
Su
p
er
v
is
ed
lear
n
in
g
m
eth
o
d
s
s
u
ch
as
s
u
p
p
o
r
t v
ec
to
r
m
ac
h
in
es
(
SVM)
,
d
ec
is
io
n
tr
ee
s
,
r
an
d
o
m
f
o
r
ests
,
an
d
g
r
ad
ien
t
b
o
o
s
tin
g
h
av
e
b
ee
n
ap
p
lied
to
p
r
ed
ict
co
n
g
e
s
tio
n
u
s
in
g
lab
eled
d
atasets
[
8
]
.
T
h
ese
m
o
d
els
ac
h
iev
e
h
i
g
h
ac
c
u
r
ac
y
in
k
n
o
wn
tr
af
f
ic
s
ce
n
ar
io
s
b
u
t
d
eg
r
a
d
e
wh
en
f
ac
ed
with
u
n
s
ee
n
o
r
r
a
p
id
ly
ch
an
g
in
g
p
atter
n
s
[
9
]
.
I
n
co
n
tr
ast,
u
n
s
u
p
er
v
is
ed
lear
n
in
g
tech
n
iq
u
es,
in
clu
d
in
g
K
-
m
ea
n
s
clu
s
te
r
in
g
,
p
r
i
n
cip
al
co
m
p
o
n
e
n
t
an
aly
s
is
(
PC
A)
,
an
d
a
u
to
e
n
co
d
er
s
,
e
x
ce
l
at
d
etec
tin
g
an
o
m
alies
an
d
h
i
d
d
en
tr
af
f
ic
p
atter
n
s
with
o
u
t
r
eq
u
i
r
in
g
lab
eled
[
1
0
]
,
[
1
1
]
.
T
h
e
y
a
r
e
ef
f
ec
tiv
e
in
id
en
tify
i
n
g
u
n
f
o
r
eseen
co
n
g
esti
o
n
s
ce
n
ar
io
s
b
u
t
lack
p
r
ed
ictiv
e
p
r
ec
is
io
n
.
T
h
is
lim
itatio
n
u
n
d
er
s
co
r
es
th
e
n
ee
d
f
o
r
h
y
b
r
id
ap
p
r
o
ac
h
es
th
at
co
m
b
in
e
th
e
p
r
ed
ictiv
e
ac
cu
r
a
cy
o
f
s
u
p
e
r
v
is
ed
lear
n
i
n
g
with
th
e
ad
ap
tab
ilit
y
o
f
u
n
s
u
p
er
v
is
ed
lear
n
in
g
[
1
2
]
.
Hy
b
r
id
m
o
d
els
h
a
v
e
r
ec
e
n
tly
g
ain
ed
tr
ac
tio
n
,
lev
er
a
g
in
g
s
u
p
er
v
is
ed
class
if
icatio
n
f
o
r
co
n
g
esti
o
n
p
r
ed
ictio
n
wh
ile
ap
p
ly
in
g
u
n
s
u
p
er
v
is
ed
clu
s
ter
in
g
to
u
n
co
v
er
n
ew
tr
af
f
ic
p
atter
n
s
.
T
h
e
s
e
ap
p
r
o
ac
h
es
h
av
e
d
em
o
n
s
tr
ated
im
p
r
o
v
ed
ac
cu
r
ac
y
an
d
ad
a
p
tab
ilit
y
in
d
y
n
am
ic
5
G
tr
af
f
ic
co
n
d
itio
n
s
[
1
3
]
-
[
1
5
]
.
R
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
f
r
am
ewo
r
k
s
h
av
e
also
b
ee
n
ex
p
lo
r
ed
,
in
teg
r
atin
g
co
n
tin
u
o
u
s
ad
ap
tatio
n
with
p
r
ed
ictiv
e
m
o
d
elin
g
[
1
6
]
.
Nev
er
t
h
eless
,
f
u
r
th
e
r
r
e
s
ea
r
ch
is
r
eq
u
ir
ed
to
o
p
tim
iz
e
th
e
i
n
teg
r
atio
n
o
f
s
u
p
e
r
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
m
o
d
els,
p
ar
ticu
lar
ly
in
h
a
n
d
lin
g
lar
g
e
-
s
ca
le,
r
ea
l
-
tim
e
5
G
d
ata.
Prio
r
wo
r
k
h
as
p
r
im
ar
il
y
f
o
cu
s
ed
o
n
eith
er
tr
ad
itio
n
al
r
u
le
-
b
ased
ap
p
r
o
a
ch
es
o
r
ML
-
b
ased
m
o
d
els,
with
s
u
p
er
v
is
ed
lear
n
in
g
ex
ce
llin
g
in
k
n
o
wn
s
ce
n
ar
io
s
b
u
t
s
tr
u
g
g
lin
g
with
ad
ap
tab
ilit
y
,
an
d
u
n
s
u
p
er
v
is
e
d
lear
n
in
g
d
is
co
v
er
in
g
n
o
v
el
p
atter
n
s
b
u
t
lack
in
g
p
r
ed
ictiv
e
ac
cu
r
ac
y
.
B
u
ild
in
g
o
n
th
ese
d
ev
elo
p
m
e
n
ts
,
th
is
p
ap
er
p
r
o
p
o
s
es
a
h
y
b
r
id
co
n
g
esti
o
n
co
n
tr
o
l
p
r
ed
ict
io
n
m
o
d
el
th
at
in
teg
r
ates
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
ML
tech
n
i
q
u
es.
Su
p
er
v
is
ed
lear
n
i
n
g
(
r
an
d
o
m
f
o
r
est
an
d
SVM)
is
em
p
lo
y
ed
t
o
p
r
e
d
ict
co
n
g
est
io
n
b
ased
o
n
h
is
to
r
ical
tr
af
f
ic
d
ata,
wh
ile
u
n
s
u
p
er
v
is
ed
lear
n
in
g
(
K
-
m
ea
n
s
clu
s
ter
in
g
an
d
a
u
to
e
n
co
d
e
r
s
)
d
etec
ts
an
o
m
alies
an
d
h
id
d
en
tr
af
f
ic
p
atter
n
s
in
r
ea
l
tim
e.
T
h
e
h
y
b
r
i
d
m
o
d
el
is
d
esig
n
ed
to
p
r
o
ac
tiv
ely
m
an
a
g
e
co
n
g
esti
o
n
,
a
d
ap
t
to
n
ew
tr
af
f
ic
b
e
h
av
io
r
s
,
an
d
en
s
u
r
e
h
i
g
h
Qo
S
in
c
o
m
p
lex
5
G
en
v
ir
o
n
m
en
ts
[
1
7
]
.
E
x
p
er
im
en
tal
r
esu
lts
d
em
o
n
s
tr
ate
th
at
th
e
p
r
o
p
o
s
ed
m
o
d
el
ac
h
iev
es
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
,
with
h
ig
h
p
r
ed
i
ctio
n
ac
cu
r
ac
y
,
r
ed
u
ce
d
d
etec
tio
n
laten
cy
,
an
d
m
in
im
ize
d
f
alse
p
o
s
i
tiv
es.
B
y
co
m
b
in
in
g
p
r
e
d
ictiv
e
p
r
ec
is
io
n
with
ad
ap
tab
ilit
y
,
t
h
e
h
y
b
r
id
f
r
am
ewo
r
k
o
f
f
er
s
a
r
o
b
u
s
t,
s
ca
lab
le,
an
d
f
u
tu
r
e
-
r
ea
d
y
s
o
lu
tio
n
f
o
r
co
n
g
esti
o
n
co
n
tr
o
l
in
5
G
n
etwo
r
k
s
,
lay
in
g
th
e
g
r
o
u
n
d
w
o
r
k
f
o
r
in
tellig
en
t
tr
af
f
ic
m
an
ag
em
en
t i
n
n
ex
t
-
g
en
er
atio
n
6
G
an
d
I
o
T
-
d
r
iv
e
n
ec
o
s
y
s
tem
s
.
2.
M
E
T
H
O
D
T
h
e
d
y
n
a
m
ic
n
atu
r
e
an
d
co
m
p
lex
ity
o
f
5
G
n
etwo
r
k
s
,
ch
ar
a
cter
ized
b
y
h
ig
h
d
ata
r
ates,
lo
w
laten
cy
,
an
d
d
iv
er
s
e
tr
af
f
ic
p
atter
n
s
,
d
em
an
d
a
m
o
r
e
in
tellig
en
t
an
d
ad
ap
tiv
e
ap
p
r
o
ac
h
to
co
n
g
esti
o
n
m
an
a
g
em
en
t
.
T
o
ad
d
r
ess
th
is
,
we
p
r
o
p
o
s
e
a
h
y
b
r
id
co
n
g
esti
o
n
c
o
n
tr
o
l
p
r
e
d
ictio
n
m
o
d
el
th
at
lev
er
ag
es
b
o
th
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
m
ac
h
i
n
e
lear
n
in
g
tech
n
i
q
u
es
to
p
r
ed
ict
a
n
d
m
an
ag
e
c
o
n
g
esti
o
n
in
r
ea
l
tim
e.
T
h
e
g
o
al
o
f
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
is
to
p
r
ed
ict
co
n
g
esti
o
n
p
r
o
ac
tiv
ely
,
ad
ap
t
to
n
ew
tr
af
f
ic
p
atter
n
s
,
an
d
e
n
s
u
r
e
h
ig
h
n
etwo
r
k
p
er
f
o
r
m
an
ce
wh
ile
m
in
im
izin
g
d
elay
s
an
d
p
ac
k
et
lo
s
s
[
1
8
]
.
T
h
e
d
etailed
d
is
cu
s
s
io
n
o
f
m
ac
h
in
e
lear
n
in
g
an
d
co
n
g
esti
o
n
co
n
tr
o
l
f
o
c
u
s
es
o
n
h
o
w
ML
tech
n
iq
u
es
ca
n
ad
d
r
ess
th
e
co
m
p
lex
,
d
y
n
am
ic
n
a
tu
r
e
o
f
co
n
g
esti
o
n
co
n
tr
o
l
in
5
G
n
etwo
r
k
s
.
As
5
G
n
etwo
r
k
s
in
tr
o
d
u
ce
h
ig
h
th
r
o
u
g
h
p
u
t,
u
ltra
-
l
o
w
laten
cy
,
an
d
m
ass
iv
e
d
ev
ice
co
n
n
ec
tiv
ity
,
m
an
a
g
in
g
co
n
g
e
s
tio
n
ef
f
ec
tiv
ely
r
eq
u
ir
es
ad
v
an
ce
d
,
d
ata
-
d
r
i
v
en
s
o
lu
tio
n
s
.
T
h
is
is
wh
er
e
ML
b
ec
o
m
es
p
iv
o
tal
[
1
9
]
.
Ma
ch
i
n
e
lear
n
in
g
,
p
a
r
ticu
lar
ly
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
es,
h
as
d
em
o
n
s
tr
ated
s
tr
o
n
g
ca
p
ab
ilit
ies
in
ex
tr
ac
tin
g
m
ea
n
in
g
f
u
l
p
atter
n
s
f
r
o
m
co
m
p
lex
d
ata
an
d
s
u
p
p
o
r
tin
g
au
to
m
ated
p
r
ed
ictio
n
a
n
d
d
etec
tio
n
task
s
[
2
0
]
.
I
n
t
h
e
co
n
tex
t
o
f
5
G
n
etwo
r
k
s
,
ML
alg
o
r
ith
m
s
ca
n
p
r
e
d
ict
a
n
d
m
iti
g
ate
co
n
g
esti
o
n
b
y
an
aly
s
in
g
tr
af
f
ic
p
atter
n
s
,
d
et
ec
tin
g
an
o
m
alies,
an
d
en
ab
li
n
g
p
r
o
ac
tiv
e
m
an
ag
e
m
en
t
o
f
n
etwo
r
k
r
eso
u
r
ce
s
.
T
h
er
e
ar
e
th
r
ee
m
ain
b
r
a
n
c
h
es
o
f
m
ac
h
in
e
lear
n
in
g
u
s
ed
f
o
r
c
o
n
g
esti
o
n
co
n
tr
o
l:
s
u
p
er
v
is
ed
lear
n
in
g
,
u
n
s
u
p
er
v
is
ed
lear
n
in
g
,
an
d
r
ei
n
f
o
r
ce
m
e
n
t
lear
n
in
g
.
Su
p
er
v
is
ed
lear
n
in
g
r
elies
o
n
lab
eled
d
atasets
wh
er
e
in
p
u
t
f
ea
tu
r
es
(
e.
g
.
,
tr
af
f
ic
v
o
lu
m
e
,
laten
cy
,
an
d
p
ac
k
et
lo
s
s
)
ar
e
ass
o
ciate
d
with
k
n
o
wn
co
n
g
esti
o
n
s
tates
(
e.
g
.
,
co
n
g
ested
a
n
d
non
-
co
n
g
ested
)
.
Su
p
er
v
is
ed
alg
o
r
ith
m
s
u
s
e
th
is
d
ata
to
lear
n
p
atter
n
s
an
d
m
ak
e
p
r
e
d
ictio
n
s
ab
o
u
t
f
u
t
u
r
e
co
n
g
esti
o
n
s
ce
n
a
r
io
s
[
2
1
]
.
T
h
ese
m
o
d
els
ex
ce
l
at
p
r
ed
ictin
g
co
n
g
esti
o
n
wh
en
tr
af
f
ic
p
atter
n
s
ar
e
s
im
ilar
to
th
o
s
e
s
ee
n
d
u
r
in
g
tr
ain
in
g
.
Fo
r
ex
am
p
le,
in
a
ty
p
i
ca
l
5
G
n
etwo
r
k
,
th
e
r
an
d
o
m
f
o
r
est
class
i
f
ier
co
u
ld
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.
4
3
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
7
7
2
-
7
7
9
774
an
aly
ze
h
is
to
r
ical
d
ata
o
n
tr
af
f
ic
p
ea
k
s
an
d
u
s
e
it
to
p
r
ed
ict
f
u
tu
r
e
co
n
g
esti
o
n
p
o
in
t
s
.
T
h
is
allo
w
s
f
o
r
p
r
o
ac
tiv
e
r
eso
u
r
ce
m
a
n
ag
em
e
n
t,
s
u
ch
as b
an
d
wid
th
allo
ca
ti
o
n
,
b
e
f
o
r
e
c
o
n
g
esti
o
n
im
p
ac
ts
u
s
er
s
.
Ho
wev
er
,
s
u
p
er
v
is
ed
lea
r
n
in
g
m
o
d
els
a
r
e
c
o
n
s
tr
ain
ed
b
y
t
h
eir
r
elian
ce
o
n
lab
ele
d
d
ata.
W
h
en
th
e
n
etwo
r
k
ex
p
er
ien
ce
s
n
o
v
el
tr
af
f
ic
p
atter
n
s
n
o
t
s
ee
n
i
n
th
e
tr
ain
in
g
p
h
ase
(
wh
ich
is
co
m
m
o
n
in
5
G)
,
th
eir
p
er
f
o
r
m
an
ce
m
ay
d
eg
r
a
d
e.
T
h
is
lim
itatio
n
n
ec
ess
itates
th
e
in
clu
s
io
n
o
f
u
n
s
u
p
er
v
is
ed
le
ar
n
in
g
tech
n
iq
u
es.
Un
s
u
p
er
v
is
ed
lear
n
in
g
d
o
es
n
o
t
r
eq
u
ir
e
lab
ele
d
d
ata,
m
ak
i
n
g
it
id
ea
l
f
o
r
d
etec
tin
g
a
n
o
m
a
lies
o
r
d
is
co
v
er
in
g
h
id
d
en
p
atter
n
s
in
tr
a
f
f
ic
t
h
at
m
ay
s
ig
n
al
im
p
en
d
in
g
co
n
g
esti
o
n
.
I
n
u
n
s
u
p
er
v
is
ed
lear
n
in
g
,
alg
o
r
ith
m
s
g
r
o
u
p
s
im
ilar
tr
af
f
ic
p
atter
n
s
to
g
eth
er
,
m
ak
in
g
it
ea
s
ier
to
id
en
tify
d
ev
iatio
n
s
(
o
r
an
o
m
alies)
th
at
ca
n
lead
to
co
n
g
esti
o
n
.
I
n
t
h
e
co
n
tex
t
o
f
5
G,
u
n
s
u
p
er
v
is
ed
lear
n
in
g
is
u
s
ef
u
l
f
o
r
d
etec
tin
g
s
u
d
d
en
,
u
n
ex
p
ec
ted
tr
a
f
f
ic
s
p
ik
es,
s
u
ch
as
d
u
r
in
g
lar
g
e
e
v
en
ts
o
r
em
er
g
en
cy
s
itu
atio
n
s
.
T
h
e
K
-
m
ea
n
s
clu
s
t
er
in
g
alg
o
r
ith
m
,
f
o
r
ex
am
p
le,
co
u
ld
m
o
n
ito
r
o
n
g
o
in
g
tr
af
f
ic
an
d
s
ig
n
al
co
n
g
esti
o
n
wh
en
cu
r
r
en
t
p
atter
n
s
d
ev
iate
f
r
o
m
p
r
ev
io
u
s
ly
k
n
o
wn
clu
s
ter
s
.
T
h
is
al
lo
ws
th
e
n
et
wo
r
k
to
ad
ju
s
t
b
ef
o
r
e
co
n
g
esti
o
n
wo
r
s
en
s
,
im
p
r
o
v
in
g
o
v
er
all
p
er
f
o
r
m
an
ce
an
d
u
s
er
ex
p
er
ie
n
ce
[
2
2
]
.
W
h
ile
u
n
s
u
p
er
v
is
ed
lear
n
in
g
m
o
d
els
ar
e
p
o
wer
f
u
l
in
d
etec
tin
g
u
n
k
n
o
wn
p
atter
n
s
,
th
e
y
d
o
n
o
t
h
av
e
p
r
ed
ictiv
e
p
o
wer
in
th
e
s
am
e
way
as
s
u
p
er
v
is
ed
lear
n
in
g
m
o
d
els,
s
in
ce
th
ey
d
o
n
o
t
d
ir
ec
tly
lear
n
co
n
g
esti
o
n
o
u
tco
m
es.
T
h
is
m
a
k
es
th
em
c
o
m
p
lem
e
n
tar
y
to
,
r
ath
er
th
a
n
a
r
ep
lace
m
e
n
t
f
o
r
,
s
u
p
er
v
is
ed
lear
n
in
g
.
T
h
e
co
n
g
esti
o
n
p
r
ed
ictio
n
m
o
d
el
is
s
h
o
wn
in
Fig
u
r
e
1
.
Net
wo
r
k
p
r
o
to
co
ls
lik
e
tr
an
s
m
is
s
io
n
co
n
tr
o
l
p
r
o
to
c
o
l
(
T
C
P
)
an
d
s
tr
ea
m
co
n
tr
o
l
tr
an
s
m
is
s
io
n
p
r
o
to
co
l
(
SC
T
P
)
we
r
e
test
ed
in
5
G
u
s
in
g
th
e
m
m
W
av
e
n
s
-
3
m
o
d
u
le.
T
h
is
m
m
W
av
e
5
G
ce
llu
lar
n
etwo
r
k
s
im
u
latio
n
m
o
d
u
le
in
clu
d
es
v
ar
io
u
s
f
ea
tu
r
es,
in
clu
d
in
g
cwn
d
r
esear
ch
.
T
h
e
p
h
y
s
ical
lay
er
(
PHY
)
an
d
m
ed
iu
m
ac
ce
s
s
co
n
tr
o
l
(
MA
C
)
class
e
s
s
u
p
p
o
r
t
3
GPP
NR
f
r
am
e
s
tr
u
ctu
r
e,
an
d
th
e
s
ch
ed
u
ler
s
s
u
p
p
o
r
t
d
y
n
am
ic
tim
e
d
iv
is
io
n
d
u
p
lex
in
g
(
T
DD
)
f
o
r
m
ats.
Pack
et
r
eseg
m
e
n
tatio
n
ca
n
im
p
r
o
v
e
th
e
R
L
C
lay
er
,
o
n
e
o
f
its
k
ey
b
en
ef
its
.
Mo
d
el
p
r
o
v
id
es
r
a
p
id
s
ec
o
n
d
ar
y
ce
ll
ch
a
n
g
eo
v
er
,
ch
an
n
el
t
r
ac
k
in
g
,
an
d
d
u
al
L
T
E
b
ase
s
tatio
n
co
m
m
u
n
icatio
n
.
Fig
u
r
e
1
.
Mo
d
el
f
o
r
p
r
e
d
ictin
g
r
ed
u
ctio
n
s
in
co
n
g
esti
o
n
T
r
ad
itio
n
al
c
o
n
g
esti
o
n
co
n
tr
o
l
m
ec
h
an
is
m
s
,
s
u
ch
as
th
o
s
e
in
tr
an
s
m
is
s
io
n
co
n
tr
o
l
p
r
o
t
o
co
l
(
T
C
P),
wer
e
d
esig
n
ed
f
o
r
wir
e
d
n
et
wo
r
k
s
with
f
ewe
r
d
ev
ices
an
d
s
im
p
ler
tr
af
f
ic
p
atter
n
s
.
T
h
ese
m
eth
o
d
s
r
ely
o
n
p
ac
k
et
lo
s
s
o
r
n
etwo
r
k
d
elay
a
s
in
d
icato
r
s
o
f
co
n
g
esti
o
n
,
r
ea
ctin
g
o
n
ly
a
f
ter
co
n
g
esti
o
n
h
a
s
alr
ea
d
y
o
cc
u
r
r
ed
.
I
n
5
G,
t
h
is
r
ea
ctiv
e
ap
p
r
o
ac
h
is
in
ad
eq
u
ate
d
u
e
to
th
e
h
i
g
h
s
p
ee
d
an
d
d
i
v
er
s
e
tr
af
f
ic
d
e
m
an
d
s
.
T
o
m
an
ag
e
co
n
g
esti
o
n
in
5
G,
p
r
o
ac
tiv
e
an
d
ad
ap
tiv
e
c
o
n
g
esti
o
n
co
n
tr
o
l
tech
n
iq
u
es
ar
e
n
ee
d
ed
,
an
d
th
is
is
wh
er
e
m
ac
h
in
e
lear
n
in
g
-
b
ased
ap
p
r
o
ac
h
es
co
m
e
in
to
p
lay
.
I
n
c
o
n
tr
ast
to
tr
a
d
itio
n
al
r
ea
ctiv
e
m
eth
o
d
s
,
m
ac
h
in
e
lear
n
in
g
en
a
b
les p
r
o
ac
tiv
e
co
n
g
e
s
tio
n
co
n
tr
o
l b
y
p
r
e
d
ictin
g
c
o
n
g
esti
o
n
b
ef
o
r
e
it h
ap
p
en
s
.
Su
p
er
v
is
ed
lear
n
i
n
g
m
o
d
els
ca
n
a
n
aly
ze
h
is
to
r
ical
d
ata
an
d
p
r
e
d
ict
wh
en
an
d
wh
er
e
co
n
g
esti
o
n
is
lik
ely
t
o
o
cc
u
r
.
T
h
is
allo
ws
th
e
n
etwo
r
k
to
tak
e
p
r
ev
e
n
tiv
e
m
ea
s
u
r
es,
s
u
ch
as
allo
ca
tin
g
m
o
r
e
b
an
d
wid
t
h
to
c
r
itical
s
er
v
ices
o
r
r
er
o
u
tin
g
tr
af
f
ic
to
less
co
n
g
ested
ar
ea
s
[
2
3
]
.
Fo
r
in
s
tan
ce
,
in
a
lar
g
e
-
s
ca
le
5
G
n
etwo
r
k
,
a
r
an
d
o
m
f
o
r
est
m
o
d
el
co
u
ld
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
Hyb
r
id
s
u
p
ervis
ed
–
u
n
s
u
p
ervis
ed
ma
ch
in
e
lea
r
n
in
g
mo
d
el
f
o
r
p
r
o
a
ctive
co
n
g
esti
o
n
…
(
A
r
u
n
K
o
d
ir
ek
ka
)
775
an
aly
ze
p
ast
tr
af
f
ic
d
ata
an
d
p
r
ed
ict
co
n
g
esti
o
n
d
u
r
in
g
p
ea
k
u
s
ag
e
tim
es,
en
ab
lin
g
th
e
n
etwo
r
k
to
allo
ca
te
re
s
o
u
r
ce
s
in
ad
v
an
ce
.
T
h
is
r
e
d
u
ce
s
d
elay
s
an
d
p
ac
k
et
lo
s
s
,
im
p
r
o
v
in
g
th
e
o
v
er
all
Qo
S
f
o
r
en
d
u
s
er
s
.
T
h
e
h
ig
h
ly
d
y
n
am
ic
n
atu
r
e
o
f
5
G
tr
af
f
ic
m
ea
n
s
th
at
n
ew
tr
af
f
ic
p
atter
n
s
will
co
n
tin
u
o
u
s
ly
em
er
g
e,
p
o
ten
tially
lead
in
g
to
co
n
g
esti
o
n
.
Un
s
u
p
er
v
is
ed
lear
n
i
n
g
p
r
o
v
id
es
th
e
ab
ilit
y
to
a
d
ap
t
t
o
th
ese
n
ew
p
atter
n
s
with
o
u
t
th
e
n
ee
d
f
o
r
lab
ele
d
tr
ain
in
g
d
ata.
B
y
co
n
tin
u
o
u
s
ly
m
o
n
ito
r
in
g
tr
af
f
ic
in
r
ea
l
tim
e,
u
n
s
u
p
e
r
v
is
ed
m
o
d
els
ca
n
id
en
tify
u
n
u
s
u
al
p
atter
n
s
th
at
co
u
ld
s
ig
n
al
c
o
n
g
esti
o
n
b
ef
o
r
e
it
o
cc
u
r
s
.
Fo
r
e
x
am
p
le,
a
K
-
m
ea
n
s
clu
s
ter
in
g
m
o
d
el
co
u
ld
b
e
u
s
ed
to
g
r
o
u
p
tr
af
f
ic
p
atter
n
s
in
to
clu
s
ter
s
.
I
f
a
n
ew
tr
af
f
ic
p
atter
n
d
ev
iates
s
ig
n
if
ican
tly
f
r
o
m
ex
is
tin
g
clu
s
ter
s
,
th
e
m
o
d
el
co
u
ld
aler
t
th
e
n
etwo
r
k
to
p
o
te
n
tial
co
n
g
esti
o
n
.
T
h
is
ad
ap
tiv
e
ap
p
r
o
ac
h
e
n
s
u
r
es
th
at
th
e
n
etwo
r
k
r
em
ain
s
r
esil
ien
t e
v
en
in
th
e
f
ac
e
o
f
u
n
ex
p
e
cted
tr
af
f
ic
s
u
r
g
es.
T
o
f
u
lly
lev
er
ag
e
th
e
s
tr
en
g
t
h
s
o
f
b
o
t
h
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
lear
n
i
n
g
,
h
y
b
r
id
m
o
d
els
ca
n
b
e
u
s
ed
.
T
h
ese
m
o
d
els
co
m
b
in
e
th
e
p
r
ed
ictiv
e
ac
c
u
r
ac
y
o
f
s
u
p
er
v
is
ed
lear
n
in
g
with
t
h
e
ad
ap
ta
b
ilit
y
o
f
u
n
s
u
p
er
v
is
ed
lear
n
in
g
.
Fo
r
ex
am
p
le,
th
e
s
u
p
er
v
is
ed
m
o
d
el
ca
n
p
r
ed
ict
co
n
g
esti
o
n
b
ased
o
n
k
n
o
wn
p
atter
n
s
,
wh
ile
th
e
u
n
s
u
p
er
v
is
ed
m
o
d
el
co
n
tin
u
o
u
s
ly
m
o
n
ito
r
s
f
o
r
n
e
w
p
atter
n
s
,
u
p
d
atin
g
t
h
e
s
u
p
er
v
is
ed
m
o
d
el
as
n
ew
tr
af
f
ic
b
eh
a
v
io
r
s
em
er
g
e
[
2
4
]
.
A
h
y
b
r
i
d
m
o
d
el
c
o
u
ld
b
e
d
esig
n
ed
to
p
r
e
d
ict
co
n
g
esti
o
n
in
f
am
iliar
tr
af
f
ic
co
n
d
itio
n
s
u
s
in
g
a
r
an
d
o
m
f
o
r
est
class
if
ier
,
wh
ile
s
im
u
ltan
eo
u
s
ly
em
p
lo
y
in
g
K
-
m
ea
n
s
c
lu
s
ter
in
g
to
d
etec
t
an
o
m
alies
o
r
n
ew
t
r
af
f
ic
p
atte
r
n
s
th
at
co
u
ld
lead
to
c
o
n
g
esti
o
n
.
T
h
i
s
h
y
b
r
id
ap
p
r
o
ac
h
en
s
u
r
es
th
at
th
e
m
o
d
el
r
em
ain
s
ac
cu
r
ate
in
k
n
o
wn
s
ce
n
ar
io
s
wh
ile
b
ein
g
ad
ap
tiv
e
to
n
ew
o
n
es,
p
r
o
v
id
in
g
a
m
o
r
e
co
m
p
r
eh
en
s
iv
e
co
n
g
esti
o
n
m
an
a
g
em
en
t
s
o
lu
t
io
n
[
2
5
]
.
Ma
ch
in
e
lear
n
in
g
o
f
f
er
s
p
o
wer
f
u
l
to
o
ls
f
o
r
co
n
g
e
s
tio
n
co
n
tr
o
l
in
5
G
n
etwo
r
k
s
b
y
e
n
ab
lin
g
p
r
o
ac
tiv
e,
ad
ap
tiv
e
,
an
d
r
ea
l
-
tim
e
tr
af
f
ic
m
an
ag
e
m
en
t.
Su
p
e
r
v
is
ed
lear
n
in
g
p
r
o
v
id
es
ac
cu
r
ate
co
n
g
esti
o
n
p
r
ed
ictio
n
s
b
ased
o
n
h
is
to
r
ical
d
ata,
u
n
s
u
p
er
v
is
ed
lear
n
in
g
h
elp
s
d
etec
t
n
ew
tr
af
f
ic
p
atter
n
s
an
d
a
n
o
m
alies,
an
d
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
a
l
lo
ws
f
o
r
co
n
tin
u
o
u
s
a
d
ap
t
atio
n
in
d
y
n
am
ic
en
v
ir
o
n
m
en
ts
.
C
o
m
b
in
in
g
th
e
s
e
ap
p
r
o
ac
h
es
in
to
a
h
y
b
r
id
m
o
d
el
o
f
f
e
r
s
a
r
o
b
u
s
t
an
d
s
ca
lab
le
s
o
lu
tio
n
f
o
r
m
an
ag
in
g
co
n
g
esti
o
n
in
th
e
c
o
m
p
lex
,
h
i
g
h
-
d
e
m
an
d
e
n
v
ir
o
n
m
en
t o
f
5
G
n
etwo
r
k
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
e
ctio
n
,
we
p
r
esen
t
th
e
p
er
f
o
r
m
an
ce
e
v
alu
atio
n
o
f
th
e
p
r
o
p
o
s
ed
co
n
g
esti
o
n
c
o
n
tr
o
l
p
r
ed
ictio
n
m
o
d
el
f
o
r
5
G
n
etwo
r
k
s
u
s
in
g
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es.
T
h
e
r
esu
lts
ar
e
an
aly
ze
d
b
ased
o
n
s
ev
er
al
k
e
y
p
er
f
o
r
m
an
ce
i
n
d
icato
r
s
(
KPI
s
)
,
in
c
lu
d
in
g
p
r
ed
ictio
n
ac
cu
r
ac
y
,
d
etec
tio
n
tim
e
,
f
alse
p
o
s
itiv
es,
an
d
n
etwo
r
k
t
h
r
o
u
g
h
p
u
t.
W
e
also
c
o
m
p
ar
e
th
e
ef
f
ec
tiv
en
ess
o
f
d
if
f
er
en
t
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
an
d
d
is
cu
s
s
th
e
tr
ad
e
-
o
f
f
s
b
etwe
en
th
eir
p
er
f
o
r
m
an
ce
in
h
a
n
d
lin
g
c
o
n
g
es
tio
n
co
n
tr
o
l
in
5
G
en
v
ir
o
n
m
en
ts
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
was
tr
ain
e
d
a
n
d
e
v
alu
a
ted
u
s
in
g
an
8
0
/2
0
s
p
lit
o
f
th
e
d
ataset
f
o
r
tr
ain
in
g
an
d
test
in
g
,
r
esp
ec
tiv
ely
.
Var
i
o
u
s
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
wer
e
im
p
lem
e
n
ted
,
in
clu
d
in
g
r
a
n
d
o
m
f
o
r
est
,
SVM,
K
-
m
ea
n
s
clu
s
ter
in
g
,
an
d
au
to
e
n
co
d
er
s
.
Per
f
o
r
m
a
n
c
e
was
ev
alu
ated
o
n
th
e
test
s
et
to
d
eter
m
in
e
th
e
m
o
d
el'
s
ef
f
ec
tiv
en
ess
in
p
r
ed
ictin
g
co
n
g
esti
o
n
a
n
d
d
etec
tin
g
a
n
o
m
alies.
T
h
e
s
u
p
er
v
is
ed
lear
n
in
g
m
o
d
els
(
r
an
d
o
m
f
o
r
est
an
d
SVM)
wer
e
ev
alu
ated
b
ased
o
n
th
eir
ac
cu
r
ac
y
in
p
r
e
d
ictin
g
co
n
g
e
s
tio
n
.
As
s
h
o
wn
i
n
T
ab
le
1
,
b
o
t
h
m
o
d
els
ac
h
iev
e
d
h
ig
h
ac
cu
r
ac
y
,
with
r
a
n
d
o
m
f
o
r
est
s
lig
h
tly
o
u
tp
er
f
o
r
m
i
n
g
SVM
in
ter
m
s
o
f
p
r
ed
ictio
n
ac
c
u
r
ac
y
.
T
h
e
r
an
d
o
m
f
o
r
est
m
o
d
el,
d
u
e
to
its
en
s
em
b
le
a
p
p
r
o
ac
h
,
d
em
o
n
s
tr
ated
b
etter
h
an
d
lin
g
o
f
co
m
p
le
x
tr
af
f
ic
p
atter
n
s
an
d
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
in
th
e
d
ata,
lea
d
i
n
g
to
a
h
ig
h
er
ac
cu
r
ac
y
in
p
r
e
d
ictin
g
co
n
g
esti
o
n
p
o
in
ts
.
Dete
ctio
n
tim
e
r
ef
e
r
s
t
o
th
e
laten
cy
b
etwe
en
tr
a
f
f
ic
c
h
an
g
es
an
d
th
e
m
o
d
el’
s
ab
ilit
y
to
d
etec
t
p
o
ten
tial
co
n
g
esti
o
n
.
T
h
e
r
a
n
d
o
m
f
o
r
est
m
o
d
el
ex
h
ib
ited
f
aster
d
etec
tio
n
tim
es th
an
SVM,
as
it i
s
b
e
tter
s
u
ited
f
o
r
r
ea
l
-
tim
e
ap
p
licatio
n
s
t
h
at
r
e
q
u
ir
e
im
m
ed
iate
r
esp
o
n
s
es
s
h
o
wn
in
T
ab
le
2
.
T
h
is
r
ed
u
ctio
n
i
n
d
etec
tio
n
laten
c
y
is
cr
u
cial
f
o
r
5
G
n
etwo
r
k
s
wh
er
e
q
u
ick
a
d
ju
s
tm
en
ts
ar
e
n
ec
ess
ar
y
to
m
ain
tain
Qo
S.
T
ab
le
1
.
Acc
u
r
ac
y
o
f
s
u
p
er
v
is
ed
lear
n
in
g
m
o
d
els
M
o
d
e
l
A
c
c
u
r
a
c
y
(
%)
R
a
n
d
o
m
f
o
r
e
s
t
9
4
.
2
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%
S
V
M
9
1
.
5
0
%
T
ab
le
2
.
Av
e
r
ag
e
d
etec
tio
n
tim
e
M
o
d
e
l
D
e
t
e
c
t
i
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(
ms)
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h
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f
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d
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f
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h
ig
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itab
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f
o
r
r
ea
l
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c
o
n
g
esti
o
n
p
r
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n
in
5
G
en
v
ir
o
n
m
en
t
s
wh
er
e
laten
cy
r
eq
u
ir
em
en
ts
ar
e
s
tr
in
g
en
t.
On
e
o
f
th
e
cr
itical
ch
allen
g
es
in
co
n
g
esti
o
n
co
n
t
r
o
l
is
m
in
im
izin
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f
alse
p
o
s
itiv
e
r
ates,
wh
er
e
th
e
m
o
d
el
in
co
r
r
ec
tly
p
r
ed
icts
co
n
g
esti
o
n
.
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.
4
3
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
7
7
2
-
7
7
9
776
As
s
h
o
wn
in
T
ab
le
3
,
b
o
t
h
m
o
d
els
h
ad
r
elativ
ely
lo
w
f
alse
p
o
s
itiv
e
r
ates
,
b
u
t
r
an
d
o
m
f
o
r
est
ag
ain
p
er
f
o
r
m
ed
s
lig
h
tly
b
etter
,
r
esu
ltin
g
in
f
e
wer
in
co
r
r
ec
t
p
r
ed
ictio
n
s
.
T
h
e
lo
wer
f
alse
p
o
s
itiv
e
r
ate
o
f
r
an
d
o
m
f
o
r
est
in
d
icate
s
its
s
u
p
er
io
r
a
b
ilit
y
to
d
is
tin
g
u
is
h
b
etwe
en
co
n
g
ested
an
d
n
o
n
-
c
o
n
g
ested
s
tates,
r
ed
u
cin
g
th
e
u
n
n
ec
ess
ar
y
tr
ig
g
er
in
g
o
f
co
n
g
esti
o
n
co
n
tr
o
l
m
ec
h
an
is
m
s
.
T
h
e
u
n
s
u
p
er
v
is
ed
lear
n
in
g
m
o
d
els
(
K
-
m
ea
n
s
an
d
a
u
to
e
n
c
o
d
er
s
)
wer
e
e
v
alu
ated
b
ased
o
n
th
eir
ab
ilit
y
to
d
etec
t
an
o
m
alies
in
tr
af
f
ic
p
atter
n
s
th
at
co
u
ld
s
ig
n
al
p
o
ten
tia
l
co
n
g
esti
o
n
.
B
o
th
m
o
d
els
s
u
c
ce
s
s
f
u
lly
id
en
tifie
d
u
n
u
s
u
al
tr
af
f
ic
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o
r
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h
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u
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er
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o
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u
e
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o
n
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u
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tr
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ic
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atter
n
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d
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b
tle
d
ev
i
atio
n
s
.
Au
to
en
co
d
e
r
s
p
r
o
v
ed
m
o
r
e
ef
f
ec
tiv
e
at
d
etec
tin
g
an
o
m
a
lies
in
th
e
tr
af
f
ic
d
ata,
m
a
k
in
g
th
em
p
ar
ticu
lar
ly
u
s
ef
u
l
f
o
r
d
etec
tin
g
ea
r
ly
s
i
g
n
s
o
f
co
n
g
esti
o
n
b
ef
o
r
e
it
b
ec
o
m
es
p
r
o
b
l
em
atic.
K
-
m
ea
n
s
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wh
ile
ef
f
ec
tiv
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s
tr
u
g
g
led
wi
th
s
u
b
tler
d
e
v
iatio
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s
in
tr
a
f
f
i
c,
lead
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g
t
o
a
s
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h
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ate
in
T
ab
le
4
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T
ab
le
3
.
Fals
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o
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itiv
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ates
M
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F
a
l
se
p
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si
t
i
v
e
r
a
t
e
(
%)
R
a
n
d
o
m
f
o
r
e
s
t
5
.
8
0
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V
M
8
.
1
0
%
T
ab
le
4
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An
o
m
aly
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tes
M
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D
e
t
e
c
t
i
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n
r
a
t
e
(
%)
K
-
m
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s
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7
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o
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9
1
.
0
0
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On
e
o
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th
e
k
ey
ad
v
an
tag
es
o
f
u
n
s
u
p
er
v
is
ed
lear
n
in
g
is
its
ad
ap
tab
ilit
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to
n
ew
an
d
u
n
s
ee
n
tr
af
f
ic
p
atter
n
s
.
I
n
th
e
co
n
tex
t
o
f
5
G,
wh
er
e
tr
af
f
ic
ca
n
v
ar
y
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ig
n
if
ican
tly
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h
e
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t
o
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et
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t
n
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atter
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f
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r
m
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tain
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k
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er
f
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r
m
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ce
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th
m
o
d
els
ad
ap
ted
well
to
p
r
ev
io
u
s
ly
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n
s
ee
n
tr
af
f
ic,
b
u
t
Au
to
en
co
d
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r
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em
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ated
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ig
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ap
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e
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lex
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atter
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e
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d
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n
s
u
p
e
r
v
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g
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a
h
y
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r
i
d
m
o
d
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was
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ev
elo
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e
d
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co
m
b
in
in
g
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d
o
m
f
o
r
est
f
o
r
co
n
g
esti
o
n
p
r
ed
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with
a
u
to
en
co
d
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s
f
o
r
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n
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m
aly
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h
e
h
y
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r
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m
o
d
el
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ig
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if
ican
tly
im
p
r
o
v
e
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o
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er
all
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er
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m
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ce
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r
ate
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a
n
d
r
ea
l
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tim
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o
m
aly
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ted
in
T
ab
le
5
.
T
ab
le
5
.
Hy
b
r
id
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o
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el
p
e
r
f
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m
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ce
M
o
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l
D
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t
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t
i
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A
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r
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5
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5
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n
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l
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2
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5
0
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T
h
e
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y
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o
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el
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u
p
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er
f
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ac
r
o
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s
all
m
etr
ics
co
m
p
ar
ed
to
in
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iv
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m
o
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els,
p
ar
ticu
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ly
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n
r
e
d
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cin
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al
s
e
p
o
s
itiv
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an
d
d
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tim
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ile
m
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ig
h
ac
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u
r
ac
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d
ad
ap
tab
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.
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h
is
m
ak
es th
e
h
y
b
r
id
ap
p
r
o
ac
h
id
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l f
o
r
r
ea
l
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wo
r
ld
5
G
en
v
ir
o
n
m
en
ts
wh
er
e
n
etwo
r
k
co
n
d
itio
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s
ar
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co
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tly
ev
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h
e
ex
p
er
im
en
tal
r
esu
lts
d
em
o
n
s
tr
ate
th
at
m
ac
h
in
e
lear
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-
b
ased
ap
p
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ac
h
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s
ig
n
if
ican
tly
en
h
a
n
ce
c
o
n
g
esti
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n
co
n
t
r
o
l
in
5
G
n
etwo
r
k
s
.
Su
p
er
v
is
ed
m
o
d
els
lik
e
r
an
d
o
m
f
o
r
est
p
r
o
v
id
e
h
ig
h
ac
c
u
r
ac
y
an
d
f
ast
d
etec
ti
o
n
,
m
a
k
in
g
th
em
s
u
itab
le
f
o
r
p
r
ed
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g
co
n
g
esti
o
n
in
k
n
o
wn
tr
af
f
ic
co
n
d
itio
n
s
.
On
th
e
o
t
h
er
h
an
d
,
u
n
s
u
p
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v
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s
ed
m
o
d
els,
p
a
r
ticu
lar
ly
a
u
to
en
co
d
er
s
,
e
x
ce
l
at
d
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tin
g
a
n
o
m
alies
in
tr
af
f
ic
p
atter
n
s
,
p
r
o
v
id
in
g
th
e
ad
a
p
tab
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y
n
ec
es
s
ar
y
f
o
r
5
G’
s
d
y
n
a
m
ic
en
v
ir
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n
m
en
t.
T
h
e
h
y
b
r
id
m
o
d
el,
co
m
b
i
n
in
g
th
e
s
tr
en
g
t
h
s
o
f
b
o
th
a
p
p
r
o
ac
h
es,
r
e
p
r
esen
ts
th
e
m
o
s
t
ef
f
ec
tiv
e
s
o
lu
tio
n
f
o
r
p
r
o
ac
tiv
e
c
o
n
g
e
s
tio
n
co
n
tr
o
l.
B
y
in
teg
r
atin
g
p
r
ed
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ca
p
a
b
ilit
ies
with
r
ea
l
-
tim
e
an
o
m
aly
d
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tio
n
,
th
e
h
y
b
r
id
m
o
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n
ab
les
5
G
n
etwo
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k
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to
h
an
d
l
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co
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g
esti
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n
m
o
r
e
ef
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icien
tly
an
d
m
ain
tain
h
i
g
h
Qo
S lev
els,
ev
en
in
th
e
f
ac
e
o
f
f
lu
ctu
atin
g
tr
af
f
ic
d
em
an
d
s
.
I
n
co
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clu
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n
,
t
h
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p
r
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p
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m
o
d
el
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a
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t,
s
ca
lab
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an
d
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p
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s
o
lu
tio
n
to
co
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g
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co
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l
in
5
G
n
etwo
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,
ad
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in
g
th
e
ch
all
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g
es
p
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b
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th
e
d
iv
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s
e
an
d
d
y
n
a
m
ic
n
atu
r
e
o
f
5
G
tr
af
f
ic.
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
Hyb
r
id
s
u
p
ervis
ed
–
u
n
s
u
p
ervis
ed
ma
ch
in
e
lea
r
n
in
g
mo
d
el
f
o
r
p
r
o
a
ctive
co
n
g
esti
o
n
…
(
A
r
u
n
K
o
d
ir
ek
ka
)
777
4.
CO
NCLU
SI
O
N
T
h
is
r
esear
ch
in
tr
o
d
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d
a
h
y
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m
o
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r
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f
o
r
th
e
d
y
n
am
ic
r
e
q
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ir
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ts
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f
5
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n
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k
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in
teg
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b
o
t
h
s
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p
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v
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u
p
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r
v
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tech
n
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h
e
r
ap
id
g
r
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wth
o
f
5
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ch
a
r
ac
ter
ized
b
y
u
ltra
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lo
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laten
cy
,
h
i
g
h
t
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h
p
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d
m
ass
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d
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n
ec
tiv
ity
,
p
r
esen
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n
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h
allen
g
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co
n
g
e
s
tio
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m
an
ag
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m
en
t
d
u
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to
u
n
p
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ed
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le
tr
af
f
ic
v
ar
iab
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an
d
th
e
d
em
an
d
f
o
r
r
ea
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-
tim
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s
er
v
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B
y
co
m
b
in
in
g
r
an
d
o
m
f
o
r
est
an
d
SVM
s
f
o
r
ac
cu
r
at
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co
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g
esti
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p
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e
d
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with
K
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m
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s
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s
ter
in
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d
a
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r
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ated
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le
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a
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with
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tr
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f
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g
f
ast
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d
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d
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alies
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tr
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f
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e
n
s
u
r
in
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ap
tab
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ty
to
u
n
f
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eseen
tr
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f
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s
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r
g
es.
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h
e
h
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b
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p
r
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ac
h
s
u
cc
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f
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lly
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ac
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n
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lin
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co
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g
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o
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d
etec
tio
n
an
d
r
ea
l
-
tim
e
tr
af
f
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m
an
ag
em
en
t.
T
h
is
estab
lis
h
es
th
e
m
o
d
el
as
a
s
ca
l
ab
le
an
d
r
esil
ien
t
s
o
lu
tio
n
f
o
r
5
G
c
o
n
g
esti
o
n
co
n
tr
o
l,
e
n
h
an
ci
n
g
Qo
S,
r
eli
ab
ilit
y
,
an
d
o
v
e
r
all
n
etwo
r
k
p
er
f
o
r
m
an
ce
.
T
h
e
f
in
d
in
g
s
u
n
d
e
r
s
co
r
e
th
e
p
o
te
n
t
ial
o
f
h
y
b
r
i
d
ML
f
r
am
ewo
r
k
s
to
s
er
v
e
as
f
o
u
n
d
atio
n
al
s
tr
ateg
ies
f
o
r
co
n
g
esti
o
n
m
an
ag
em
en
t
in
f
u
tu
r
e
6
G
an
d
I
o
T
-
d
r
iv
en
n
etwo
r
k
s
,
wh
er
e
tr
af
f
ic
d
iv
er
s
ity
an
d
s
ca
le
will
b
e
ev
en
m
o
r
e
d
em
an
d
in
g
.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
A
t
t
a
r
a
n
,
“
T
h
e
i
m
p
a
c
t
o
f
5
G
o
n
t
h
e
e
v
o
l
u
t
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t
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d
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n
d
u
s
t
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y
d
i
g
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t
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z
a
t
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o
n
,
”
J
o
u
rn
a
l
o
f
Am
b
i
e
n
t
I
n
t
e
l
l
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g
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n
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a
n
d
H
u
m
a
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d
C
o
m
p
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g
,
v
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l
.
1
4
,
n
o
.
5
,
p
p
.
5
9
7
7
–
5
9
9
3
,
M
a
y
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
2
6
5
2
-
020
-
0
2
5
2
1
-
x.
[
2
]
P
.
A
n
i
t
h
a
,
H
.
S
.
V
i
ma
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Un
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NLP
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c
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two
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sa
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k
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rth
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Evaluation Warning : The document was created with Spire.PDF for Python.
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5
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n
ra
m
e
sh
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m
a
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.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.