I
nte
rna
t
io
na
l J
o
urna
l o
f
Adv
a
nces in Applie
d Science
s
(
I
J
AAS)
Vo
l.
14
,
No
.
3
,
Sep
tem
b
er
2
0
2
5
,
p
p
.
6
2
7
~
6
3
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I
SS
N:
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DOI
:
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a
a
s
.
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co
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Co
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ly
sis
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chine learn
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ticle
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e
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larly
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rt
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u
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p
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b
l
ish
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b
e
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0
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n
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ly
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d
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h
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d
a
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se
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=
4
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th
a
t
t
h
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fin
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l
p
rima
ry
d
a
t
a
wa
s
a
n
a
ly
z
e
d
.
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e
fi
n
d
i
n
g
s
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re
d
iv
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d
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d
in
to
th
re
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m
e
s:
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sa
fe
ty
a
n
d
risk
m
a
n
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g
e
m
e
n
t,
ii
)
ML
a
n
d
a
rti
ficia
l
in
telli
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e
n
c
e
(
AI
)
a
p
p
li
c
a
ti
o
n
s
i
n
sa
fe
ty
,
a
n
d
i
ii
)
sm
a
rt
tec
h
n
o
lo
g
y
fo
r
sa
fe
ty
.
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e
c
o
n
c
lu
sio
n
h
i
g
h
li
g
h
ts
th
e
n
e
e
d
fo
r
c
o
n
ti
n
u
o
u
s
m
o
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it
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in
g
a
n
d
u
p
d
a
ti
n
g
o
f
t
h
e
sa
fe
ty
p
r
o
t
o
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o
ls
to
k
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e
p
i
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ste
p
with
t
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ro
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g
M
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lan
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s
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a
p
e
.
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is
re
v
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o
n
tri
b
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tes
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o
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h
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u
n
d
e
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n
d
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f
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fe
ty
.
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o
ffe
rs g
lo
b
a
l
les
so
n
s
th
a
t
c
a
n
g
u
i
d
e
fu
tu
re
re
se
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rc
h
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n
d
p
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li
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y
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m
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k
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rts
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n
su
re
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tec
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n
o
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ies
'
sa
fe
a
n
d
e
th
ica
l
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se
.
K
ey
w
o
r
d
s
:
Ma
ch
in
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lear
n
in
g
PR
I
SMA
f
r
am
ewo
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k
R
is
k
m
an
ag
em
en
t
Saf
ety
Sm
ar
t te
ch
n
o
lo
g
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
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SA
li
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se
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C
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p
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A
uth
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:
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T
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izi
Sy
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Sh
az
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Dep
ar
tm
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t o
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Me
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Ma
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Facu
lty
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in
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Un
iv
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s
iti Ma
lay
s
ia
Sar
awa
k
J
alan
Datu
k
Mo
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d
M
u
s
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9
4
3
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K
o
ta
Sam
ar
ah
a
n
,
Sar
awa
k
,
Ma
lay
s
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E
m
ail: star
m
izi@
u
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as.m
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1.
I
NT
RO
D
UCT
I
O
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n
th
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ap
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p
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m
ac
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lear
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ML
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ativ
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f
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ce
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ec
to
r
s
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in
clu
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in
g
h
ea
lt
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ca
r
e,
f
in
a
n
ce
,
tr
a
n
s
p
o
r
tatio
n
,
an
d
e
n
ter
tain
m
en
t
[
1
]
–
[
3
]
.
I
ts
ab
ilit
y
to
an
aly
z
e
v
ast
d
atasets
,
id
en
tify
p
atter
n
s
,
an
d
m
ak
e
d
ata
-
d
r
iv
en
p
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ed
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s
h
a
s
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o
lu
tio
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i
z
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d
d
ec
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-
m
ak
i
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g
p
r
o
ce
s
s
es
an
d
o
p
er
atio
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al
ef
f
icien
cy
.
Ho
wev
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,
in
teg
r
atin
g
ML
in
to
cr
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y
s
tem
s
also
in
tr
o
d
u
ce
s
s
u
b
s
t
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s
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u
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r
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eth
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d
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r
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o
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s
.
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im
p
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s
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ety
in
ML
a
p
p
licatio
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s
ca
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n
o
t
b
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o
v
er
s
tat
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,
as
s
y
s
tem
er
r
o
r
s
,
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iases
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o
r
v
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ln
er
ab
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n
h
av
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s
ev
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co
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s
eq
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en
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s
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r
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er
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s
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p
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ctio
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s
in
h
ea
lth
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r
e
m
ay
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m
is
d
iag
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o
s
es,
im
p
ac
tin
g
p
atien
t
o
u
tco
m
es.
Similar
ly
,
f
lawe
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alg
o
r
ith
m
s
in
f
i
n
an
cial
m
ar
k
ets
c
a
n
r
esu
lt in
s
u
b
s
tan
tial lo
s
s
es,
wh
ile
s
af
ety
f
ailu
r
es in
au
to
n
o
m
o
u
s
v
eh
icles p
o
s
e
life
-
th
r
ea
ten
i
n
g
r
is
k
s
.
Desp
ite
its
ad
v
an
tag
es,
ML
s
y
s
tem
s
f
ac
e
in
h
er
en
t
ch
allen
g
es
d
u
e
to
th
eir
co
m
p
lex
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a
n
d
r
elian
ce
o
n
d
ata
-
d
r
iv
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n
lear
n
in
g
r
ath
er
th
a
n
ex
p
licit
p
r
o
g
r
am
m
in
g
.
T
h
e
q
u
ality
an
d
r
e
p
r
esen
tativ
en
ess
o
f
tr
ain
in
g
d
ata
ar
e
cr
itical;
b
iased
o
r
in
co
m
p
lete
d
atasets
ca
n
lead
to
s
k
ewe
d
p
r
ed
ictio
n
s
,
p
e
r
p
etu
atin
g
s
o
cial
in
eq
u
alities
o
r
p
r
o
d
u
cin
g
u
n
r
eliab
le
o
u
tco
m
e
s
[
4
]
–
[
6
]
.
Fu
r
th
er
m
o
r
e,
th
e
o
p
ac
ity
o
f
m
an
y
ad
v
an
ce
d
ML
m
o
d
els,
co
m
m
o
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ly
ca
lled
th
e
"
b
lack
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x
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p
r
o
b
lem
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lim
its
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s
p
a
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ter
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r
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ilit
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,
u
n
d
e
r
m
in
in
g
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u
s
t
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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ac
co
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ilit
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T
h
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lack
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clar
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o
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d
en
tify
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f
ac
to
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s
in
f
lu
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m
o
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d
ec
is
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s
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o
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m
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eliab
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h
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tu
d
y
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p
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co
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p
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en
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s
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el
t
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d
r
e
s
s
th
e
ch
allen
g
es
o
f
in
teg
r
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g
ML
in
to
cr
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s
y
s
tem
s
.
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h
e
m
o
d
el
e
m
p
h
asi
z
es
k
ey
co
m
p
o
n
e
n
ts
ess
en
tial
f
o
r
m
itig
atin
g
r
is
k
s
an
d
en
h
a
n
cin
g
s
y
s
tem
r
eliab
ilit
y
.
First,
it
ad
v
o
ca
te
s
f
o
r
r
o
b
u
s
t
d
ata
g
o
v
er
n
an
ce
p
r
ac
tices
to
en
s
u
r
e
h
ig
h
-
q
u
ality
,
d
iv
er
s
e,
an
d
r
ep
r
esen
tativ
e
d
atasets
,
with
co
n
tin
u
o
u
s
m
o
n
ito
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n
g
to
d
etec
t
d
ata
d
r
if
t
an
d
d
e
g
r
ad
atio
n
[
7
]
,
[
8
]
.
Seco
n
d
,
th
e
f
r
am
ewo
r
k
p
r
o
m
o
tes
alg
o
r
ith
m
ic
tr
an
s
p
ar
en
c
y
a
n
d
in
te
r
p
r
e
tab
ilit
y
,
en
ab
lin
g
s
tak
eh
o
l
d
er
s
to
u
n
d
er
s
tan
d
an
d
tr
u
s
t
m
o
d
el
o
u
tp
u
ts
.
T
ec
h
n
iq
u
es
s
u
ch
as
f
ea
t
u
r
e
im
p
o
r
tan
ce
an
aly
s
is
an
d
e
x
p
lain
ab
le
AI
(
XAI
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ar
e
cr
u
cial
f
o
r
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h
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g
m
o
d
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l
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m
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en
s
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d
itio
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ally
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r
o
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ed
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el
in
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r
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ates
r
ig
o
r
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u
s
test
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s
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im
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e
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ce
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p
o
ten
tial
f
ailu
r
e
p
o
in
ts
b
ef
o
r
e
d
ep
lo
y
m
en
t.
R
ea
l
-
tim
e
m
o
n
ito
r
in
g
an
d
an
o
m
aly
d
etec
tio
n
s
y
s
tem
s
en
h
an
ce
o
p
er
atio
n
al
s
af
ety
b
y
p
r
o
m
p
tly
id
en
tify
in
g
an
d
ad
d
r
ess
in
g
u
n
ex
p
ec
ted
is
s
u
es.
Fin
ally
,
th
e
m
o
d
el
em
p
h
asi
z
es
a
m
u
lti
-
d
is
cip
lin
ar
y
a
p
p
r
o
ac
h
,
in
teg
r
atin
g
eth
ical,
leg
al,
a
n
d
s
o
cial
co
n
s
id
er
atio
n
s
to
alig
n
ML
s
y
s
tem
s
with
s
o
cieta
l
v
alu
es
[
9
]
,
[
1
0
]
.
C
o
llab
o
r
atio
n
b
etwe
en
p
o
licy
m
a
k
er
s
,
in
d
u
s
t
r
y
s
tak
eh
o
ld
er
s
,
an
d
r
esear
ch
e
r
s
is
es
s
en
tial
to
d
ev
elo
p
r
eg
u
lato
r
y
f
r
am
ewo
r
k
s
an
d
in
d
u
s
tr
y
s
tan
d
ar
d
s
p
r
o
m
o
t
in
g
in
n
o
v
atio
n
an
d
s
af
ety
.
T
h
i
s
ar
ticle
aim
s
to
p
r
o
v
id
e
a
p
r
a
ctica
l
an
d
s
ca
lab
le
f
r
am
ewo
r
k
th
at
o
r
g
an
i
z
atio
n
s
ca
n
ad
o
p
t
t
o
h
ar
n
ess
th
e
b
e
n
ef
its
o
f
ML
wh
ile
m
in
im
i
z
i
n
g
ass
o
ciate
d
r
is
k
s
,
u
ltima
tely
f
o
s
ter
in
g
a
s
af
er
an
d
m
o
r
e
t
r
u
s
two
r
th
y
tec
h
n
o
lo
g
i
ca
l f
u
tu
r
e.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
R
ec
en
t
ad
v
an
ce
m
en
ts
in
ML
h
av
e
s
ig
n
if
ican
tly
co
n
tr
ib
u
t
ed
to
d
ev
elo
p
in
g
s
af
ety
m
o
d
els
ac
r
o
s
s
v
ar
io
u
s
d
o
m
ain
s
.
A
n
o
tab
le
a
p
p
licatio
n
is
in
cy
b
e
r
-
attac
k
d
etec
tio
n
,
wh
e
r
e
ML
m
et
h
o
d
s
h
av
e
b
ee
n
lev
e
r
ag
ed
to
p
r
ed
ict
an
d
p
r
ev
e
n
t
cy
b
er
cr
im
es
.
Sin
g
h
et
a
l
.
[
1
1
]
h
ig
h
lig
h
ted
th
e
im
p
o
r
ta
n
ce
o
f
em
p
l
o
y
in
g
ML
tech
n
iq
u
es
to
an
aly
z
e
an
d
f
o
r
ec
ast
cy
b
e
r
-
attac
k
p
atter
n
s
.
T
h
eir
s
tu
d
y
co
m
p
ar
ed
ei
g
h
t
d
if
f
e
r
en
t
M
L
alg
o
r
ith
m
s
,
with
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
lin
ea
r
d
em
o
n
s
tr
atin
g
s
u
p
er
io
r
ac
cu
r
ac
y
in
d
etec
tin
g
c
y
b
er
-
attac
k
s
,
wh
ile
lo
g
is
tic
r
eg
r
ess
io
n
was
m
o
s
t
e
f
f
ec
tiv
e
in
id
en
tify
i
n
g
m
alicio
u
s
ac
to
r
s
.
T
h
is
wo
r
k
u
n
d
er
s
co
r
es
th
e
cr
itical
r
o
le
o
f
ML
in
e
n
h
an
ci
n
g
cy
b
er
s
ec
u
r
ity
b
y
p
r
o
v
id
in
g
p
r
e
d
ictiv
e
in
s
ig
h
ts
th
at
aid
in
th
e
p
r
o
ac
ti
v
e
m
an
ag
e
m
en
t
o
f
cy
b
er
th
r
ea
ts
.
I
n
t
h
e
r
ea
lm
o
f
f
o
o
d
s
af
ety
,
th
e
i
n
co
r
p
o
r
atio
n
o
f
b
ig
d
ata
an
d
ML
h
as
b
e
en
tr
an
s
f
o
r
m
ativ
e.
Sap
ien
za
an
d
Ved
d
er
[
1
2
]
th
e
s
ec
u
r
ity
,
ac
co
u
n
tab
ilit
y
,
f
air
n
ess
,
ex
p
lain
ab
ilit
y
,
tr
an
s
p
ar
en
cy
,
p
r
iv
ac
y
(
P
-
SAFET
Y
)
m
o
d
el
in
teg
r
at
es
h
ig
h
-
lev
el
p
r
in
cip
les
s
u
c
h
as
SAFET
Y
in
to
f
o
o
d
s
af
e
ty
r
is
k
ass
es
s
m
en
t
f
r
am
ewo
r
k
s
.
T
h
is
m
o
d
el
ad
d
r
ess
es
th
e
r
eg
u
lato
r
y
c
h
allen
g
e
s
p
o
s
ed
b
y
th
e
v
ast
am
o
u
n
t
o
f
d
ata
p
r
o
ce
s
s
ed
an
d
em
p
h
asi
z
es
th
e
n
ee
d
to
b
ala
n
ce
d
ata
co
n
f
id
e
n
tiality
with
p
u
b
lic
d
is
clo
s
u
r
e
r
eq
u
ir
e
m
e
n
ts
.
B
y
p
r
o
p
o
s
in
g
p
r
i
n
cip
le
-
b
ased
r
ec
o
m
m
e
n
d
ati
o
n
s
,
th
eir
r
esear
ch
f
ac
ilit
ates
ef
f
ec
tiv
e
d
ata
g
o
v
er
n
a
n
ce
in
f
o
o
d
s
af
ety
,
en
s
u
r
in
g
th
at
tech
n
o
lo
g
ical
ad
v
an
ce
m
e
n
ts
d
o
n
o
t c
o
m
p
r
o
m
is
e
r
eg
u
lato
r
y
s
tan
d
ar
d
s
.
T
h
e
tr
an
s
p
o
r
tatio
n
s
ec
to
r
h
as
also
s
ee
n
s
ig
n
if
ican
t
b
e
n
ef
its
f
r
o
m
ML
ap
p
lica
tio
n
s
i
n
s
af
et
y
m
o
d
els.
Ma
lik
et
a
l
.
[
1
3
]
d
ev
elo
p
e
d
a
n
in
tellig
en
t
r
ea
l
-
tim
e
lear
n
in
g
f
r
am
ewo
r
k
to
en
h
an
ce
th
e
s
af
ety
o
f
last
-
m
ile
d
eliv
er
y
s
er
v
ices.
T
h
is
f
r
am
e
wo
r
k
u
s
es
s
tatis
t
ical
an
d
ML
tech
n
iq
u
es
to
m
o
d
el
r
id
er
attr
ib
u
tes,
s
u
ch
as
ag
e,
in
f
lu
en
c
e
of
tr
an
s
p
o
r
t
m
o
d
e
,
an
d
r
o
u
te
s
elec
tio
n
.
T
h
eir
s
tu
d
y
r
ev
ea
le
d
th
at
ag
e
-
s
p
ec
i
f
ic
in
f
r
astru
ctu
r
e
u
s
ag
e
s
ig
n
if
ican
tly
im
p
ac
ts
r
id
er
s
af
ety
,
an
d
th
e
ML
m
o
d
el'
s
h
ig
h
p
r
ed
ictiv
e
ac
cu
r
ac
y
d
em
o
n
s
tr
ates
it
s
p
o
ten
tial
in
o
p
tim
i
z
in
g
tr
an
s
p
o
r
tatio
n
p
la
n
n
in
g
a
n
d
in
f
r
astru
ctu
r
e
d
esi
g
n
f
o
r
s
af
er
u
r
b
an
m
o
b
ilit
y
.
I
n
co
n
n
ec
te
d
v
eh
icle
en
v
ir
o
n
m
en
ts
,
th
e
u
s
e
o
f
ML
f
o
r
r
ea
l
-
tim
e
s
af
ety
an
aly
s
is
h
as
s
h
o
wn
p
r
o
m
is
in
g
r
esu
lts
.
Yu
an
et
a
l.
[
1
4
]
ap
p
lied
e
x
p
lain
ab
le
ML
tech
n
iq
u
es
to
ass
ess
tr
af
f
ic
f
lo
w
f
e
atu
r
es
an
d
th
eir
im
p
ac
ts
o
n
s
af
ety
.
T
h
e
r
a
n
d
o
m
f
o
r
est
m
o
d
el
em
er
g
ed
as
th
e
m
o
s
t
ef
f
ec
tiv
e,
ac
h
iev
in
g
a
h
i
g
h
ar
ea
u
n
d
er
th
e
cu
r
v
e
(
AU
C
)
s
co
r
e.
B
y
u
s
in
g
s
h
ap
ley
a
d
d
itiv
e
ex
p
la
n
atio
n
(
SHAP)
v
alu
es,
th
e
s
tu
d
y
p
r
o
v
i
d
ed
m
o
r
e
r
ef
lectiv
e
in
s
ig
h
t
s
in
to
th
e
m
ec
h
an
is
m
s
o
f
tr
a
f
f
ic
c
o
n
f
licts
an
d
p
r
o
m
i
n
en
ce
th
e
s
ig
n
if
ican
ce
o
f
v
ar
iab
les
s
u
ch
as
lan
e
s
p
ee
d
d
if
f
er
en
ce
s
an
d
tr
u
ck
p
r
o
p
o
r
tio
n
s
.
Saf
ety
p
r
e
d
ictio
n
m
o
d
els
in
civ
il
en
g
in
ee
r
in
g
h
av
e
also
b
en
ef
ited
f
r
o
m
ML
.
Ah
m
ed
et
a
l.
[
1
5
]
d
ev
elo
p
e
d
a
m
o
d
el
to
p
r
e
d
ict
th
e
f
ac
to
r
o
f
s
af
ety
(
FS
)
f
o
r
r
ein
f
o
r
ce
d
h
ig
h
way
s
lo
p
es
u
s
i
n
g
r
ec
y
cled
p
last
ic
p
in
s
(
R
PP
)
.
T
h
eir
s
tu
d
y
em
p
l
o
y
ed
s
tatis
tical
an
d
ML
ap
p
r
o
ac
h
es,
p
r
o
v
i
n
g
m
o
r
e
ac
cu
r
ate.
T
h
e
in
teg
r
atio
n
o
f
ML
in
th
is
co
n
tex
t
allo
ws
f
o
r
b
etter
p
r
ed
ictio
n
an
d
v
alid
a
tio
n
o
f
s
lo
p
e
s
tab
ilit
y
,
s
h
o
wc
asin
g
its
u
tili
ty
in
g
eo
tech
n
ical
en
g
in
ee
r
in
g
f
o
r
s
af
er
in
f
r
astru
ct
u
r
e
d
e
v
elo
p
m
e
n
t.
I
n
s
p
o
r
ts
ev
en
t
m
a
n
ag
em
e
n
t,
W
an
g
et
a
l
.
[
1
6
]
p
r
o
p
o
s
ed
a
r
is
k
ea
r
ly
war
n
in
g
s
af
ety
m
o
d
el
u
s
in
g
b
ac
k
p
r
o
p
ag
atio
n
(
B
P)
n
eu
r
al
n
etwo
r
k
s
co
m
b
in
ed
with
fu
zz
y
t
h
eo
r
y
.
T
h
is
m
o
d
el
aim
s
to
m
itig
ate
r
is
k
s
b
y
p
r
o
v
id
in
g
ea
r
ly
war
n
in
g
s
b
ased
o
n
v
ar
i
o
u
s
r
is
k
in
d
icato
r
s
.
T
h
e
em
p
ir
ical
an
aly
s
is
d
em
o
n
s
tr
ated
th
e
m
o
d
el'
s
r
eliab
ilit
y
an
d
ef
f
ec
tiv
en
ess
in
p
r
ed
ict
in
g
an
d
p
r
ev
en
tin
g
p
o
ten
tial
ac
cid
en
ts
d
u
r
in
g
s
p
o
r
ts
ev
en
ts
.
T
h
is
ap
p
licatio
n
illu
s
tr
ates
th
e
v
er
s
atility
o
f
ML
in
en
h
a
n
cin
g
s
af
ety
ac
r
o
s
s
d
iv
er
s
e
s
ce
n
ar
io
s
.
M
o
r
eo
v
er
,
t
h
e
ap
p
licatio
n
o
f
ML
in
ec
o
-
d
r
iv
in
g
s
tr
ateg
ies
f
o
r
a
u
to
m
ated
v
eh
icles
h
as
b
ee
n
ex
p
lo
r
ed
b
y
L
i
e
t
a
l.
[
1
7
]
.
T
h
eir
s
tu
d
y
in
tr
o
d
u
ce
d
a
m
u
lti
-
o
b
jectiv
e
ec
o
-
d
r
iv
i
n
g
s
tr
ateg
y
in
co
r
p
o
r
atin
g
a
s
af
ety
m
o
d
el
t
o
o
p
tim
i
z
e
d
r
iv
in
g
p
e
r
f
o
r
m
an
ce
in
u
r
b
a
n
tr
a
f
f
ic.
B
y
u
s
in
g
d
ee
p
r
ein
f
o
r
ce
m
en
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
C
o
mp
r
eh
en
s
ive
s
tr
u
ctu
r
ed
a
n
a
lysi
s
o
f m
a
ch
in
e
lea
r
n
in
g
in
s
a
fety
mo
d
els
(
Mo
h
d
S
h
u
kri A
b
d
u
l Wa
h
a
b
)
629
lear
n
i
n
g
(
DR
L
)
,
t
h
e
p
r
o
p
o
s
ed
s
tr
ateg
y
ef
f
ec
tiv
ely
b
alan
ce
s
f
u
el
ec
o
n
o
m
y
a
n
d
s
af
ety
,
an
d
p
r
o
v
i
n
g
b
en
ef
icial
in
m
an
ag
in
g
th
e
c
o
m
p
lex
ities
o
f
u
r
b
an
d
r
iv
in
g
en
v
ir
o
n
m
e
n
ts
.
Gh
er
aib
ia
et
a
l.
[
1
8
]
p
r
esen
ted
an
in
n
o
v
ativ
e
ap
p
r
o
ac
h
th
at
co
m
b
i
n
es
f
au
lt
tr
ee
a
n
aly
s
is
(
FTA)
with
ML
to
e
n
h
an
ce
th
e
m
o
d
elin
g
o
f
s
af
ety
-
c
r
itical
s
y
s
tem
s
.
T
h
i
s
m
eth
o
d
u
s
es
r
ea
l
-
tim
e
o
p
er
a
tio
n
al
d
ata
to
d
etec
t
ab
n
o
r
m
alities
an
d
u
p
d
ate
s
af
ety
m
o
d
els
d
y
n
am
ically
.
I
n
te
g
r
atin
g
d
ec
is
io
n
tr
ee
s
ex
p
lain
s
f
au
lts
,
f
ac
ilit
atin
g
co
n
tin
u
o
u
s
im
p
r
o
v
e
m
en
t
in
s
af
ety
m
an
ag
em
e
n
t
p
r
ac
tices.
T
h
is
h
y
b
r
id
ap
p
r
o
ac
h
d
em
o
n
s
tr
ates
h
o
w
ML
ca
n
co
m
p
lem
en
t
tr
ad
itio
n
al
s
af
ety
m
o
d
elin
g
tec
h
n
iq
u
es
to
ac
h
ie
v
e
h
ig
h
er
ac
cu
r
ac
y
an
d
r
eliab
i
lity
.
R
ec
en
t
s
tu
d
ies
h
av
e
s
h
o
wn
th
at
ML
m
o
d
els,
p
ar
ticu
lar
ly
g
r
a
d
ien
t
-
b
o
o
s
tin
g
m
o
d
els
s
u
ch
as
ca
teg
o
r
ical
b
o
o
s
tin
g
(
C
atB
o
o
s
t
)
,
o
u
tp
er
f
o
r
m
tr
a
d
itio
n
al
r
eg
r
ess
i
o
n
m
o
d
els
in
p
r
ed
ictin
g
t
r
af
f
ic
s
af
ety
o
u
tco
m
es.
L
i
et
a
l.
[
1
9
]
u
tili
z
ed
SHAP
to
in
ter
p
r
et
th
e
r
esu
lts
o
f
C
atB
o
o
s
t a
n
d
ex
tr
em
e
g
r
ad
ien
t b
o
o
s
tin
g
(
XGBo
o
s
t
)
m
o
d
els,
id
en
tify
in
g
cr
itica
l f
ac
to
r
s
s
u
ch
as
r
am
p
ty
p
e
an
d
cu
r
v
e
p
r
esen
ce
,
wh
ich
s
ig
n
if
ican
tly
in
f
lu
en
ce
f
r
ee
way
cr
ash
f
r
eq
u
en
cy
.
T
h
ese
m
o
d
els
p
r
o
v
id
e
m
o
r
e
ac
cu
r
ate
p
r
ed
ic
tio
n
s
an
d
v
alu
a
b
le
in
s
ig
h
ts
in
to
th
e
u
n
d
er
ly
in
g
f
ac
to
r
s
af
f
e
ctin
g
tr
af
f
ic
s
af
ety
,
wh
ich
is
ess
en
tial f
o
r
tar
g
ete
d
s
af
ety
m
an
ag
em
e
n
t in
ter
v
e
n
tio
n
s
.
I
n
th
e
h
ea
lth
ca
r
e
s
ec
to
r
,
d
ata
s
ec
u
r
ity
is
p
ar
am
o
u
n
t,
esp
ec
ia
lly
in
telem
ed
icin
e
ap
p
licatio
n
s
f
o
r
r
u
r
al
ar
ea
s
.
B
is
wa
s
et
a
l.
[
2
0
]
p
r
o
p
o
s
ed
a
s
ec
u
r
e
ML
an
d
b
lo
c
k
ch
ain
-
b
ased
telem
ed
icin
e
m
o
d
el
(
SML
B
T
)
to
en
h
an
ce
d
ata
s
ec
u
r
ity
f
o
r
p
ati
en
ts
in
r
e
m
o
te
r
eg
io
n
s
.
T
h
is
m
o
d
el
le
v
er
ag
es
s
u
p
er
v
is
ed
a
n
d
u
n
s
u
p
er
v
is
ed
ML
tech
n
iq
u
es
to
an
al
y
z
e
p
atien
t
r
ec
o
r
d
s
,
e
n
s
u
r
in
g
s
ec
u
r
e
,
an
d
s
ca
lab
le
h
ea
lth
ca
r
e
s
er
v
ices.
I
n
te
g
r
atin
g
b
lo
ck
ch
ain
tec
h
n
o
lo
g
y
f
u
r
th
e
r
en
h
an
ce
s
th
e
s
y
s
tem
'
s
s
ec
u
r
ity
,
m
ak
in
g
it
a
v
ia
b
le
s
o
lu
tio
n
f
o
r
d
ev
elo
p
i
n
g
co
u
n
tr
ies.
Ad
a
p
tiv
e
tr
af
f
ic
s
ig
n
al
co
n
tr
o
l
(
AT
SC
)
h
as
s
ee
n
s
ig
n
if
ican
t
ad
v
a
n
ce
m
en
ts
with
th
e
in
te
g
r
atio
n
o
f
ML
,
p
ar
ticu
lar
ly
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
(
R
L
)
.
E
s
s
a
an
d
Say
ed
[
2
1
]
d
e
v
elo
p
ed
a
s
elf
-
lear
n
in
g
AT
SC
alg
o
r
ith
m
th
at
o
p
tim
i
z
es
tr
af
f
ic
s
af
ety
in
r
ea
l
-
tim
e.
B
y
u
til
i
z
in
g
VI
SS
I
M
s
im
u
latio
n
s
an
d
r
ea
l
-
wo
r
ld
d
ata
,
th
eir
R
L
-
b
ased
alg
o
r
ith
m
d
em
o
n
s
tr
ated
a
4
0
%
r
e
d
u
ctio
n
in
tr
af
f
ic
co
n
f
licts
co
m
p
a
r
ed
t
o
t
r
ad
itio
n
al
s
y
s
tem
s
.
T
h
is
ap
p
r
o
ac
h
h
ig
h
lig
h
ts
th
e
p
o
ten
tial o
f
ML
to
s
im
u
ltan
e
o
u
s
ly
en
h
an
ce
tr
af
f
ic
ef
f
icien
c
y
an
d
s
af
ety
.
Z
h
an
g
an
d
Aty
[
2
2
]
ad
d
r
ess
ed
p
ed
estrian
s
af
ety
b
y
d
ev
elo
p
i
n
g
a
r
ea
l
-
tim
e
co
n
f
lict
p
r
ed
icti
o
n
m
o
d
el
u
s
in
g
ML
.
T
h
eir
m
o
d
el
ac
h
ie
v
ed
h
ig
h
p
r
ed
ictiv
e
ac
c
u
r
ac
y
with
th
e
XGBo
o
s
t
alg
o
r
ith
m
b
y
an
aly
z
in
g
c
o
n
f
lict
in
d
icato
r
s
s
u
ch
as
p
o
s
t
en
cr
o
a
ch
m
en
t
tim
e
(
PET
)
an
d
tim
e
to
co
llis
io
n
(
TTC
)
f
r
o
m
clo
s
ed
-
cir
cu
it
telev
is
io
n
(
C
C
T
V
)
f
o
o
tag
e.
T
h
is
m
o
d
el
allo
ws
f
o
r
p
r
o
ac
tiv
e
tr
af
f
ic
m
an
ag
em
en
t
s
tr
ateg
ies
to
ad
ju
s
t
s
ig
n
al
tim
in
g
s
t
o
p
r
ev
en
t
p
e
d
estrian
-
v
e
h
icle
co
n
f
licts
,
th
u
s
en
h
an
cin
g
u
r
b
a
n
tr
af
f
ic
s
af
ety
.
I
n
s
p
o
r
ts
,
ML
h
as
b
ee
n
ap
p
lied
to
en
h
an
ce
s
af
ety
in
p
h
y
s
ical
tr
ain
in
g
.
Yin
an
d
W
an
g
[
2
3
]
u
tili
z
ed
ML
tech
n
i
q
u
es
to
d
ev
elo
p
a
s
af
ety
m
o
d
e
co
n
tr
o
l
m
o
d
el
f
o
r
d
r
a
g
o
n
b
o
at
s
p
o
r
ts
tr
ain
in
g
.
T
h
eir
ap
p
r
o
ac
h
co
m
b
in
ed
b
ig
d
ata
a
n
aly
s
is
with
f
u
zz
y
clu
s
ter
in
g
tech
n
iq
u
es
to
id
en
tify
an
d
m
itig
ate
s
af
ety
r
is
k
s
,
s
ig
n
if
ican
tly
im
p
r
o
v
i
n
g
s
af
ety
m
an
ag
em
en
t
i
n
sp
o
r
ts
tr
ain
in
g
en
v
ir
o
n
m
en
ts
.
E
f
f
ec
tiv
e
m
an
ag
em
e
n
t
o
f
lith
iu
m
-
io
n
(
L
i
-
i
o
n
)
b
atter
ies
is
cr
u
cial
f
o
r
th
eir
s
af
e
u
s
ag
e.
My
ils
am
y
et
a
l.
[
2
4
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
lear
n
i
n
g
m
o
d
el
(
HL
M)
co
m
b
in
in
g
a
u
to
r
e
g
r
ess
iv
e
in
teg
r
ated
m
o
v
in
g
av
e
r
ag
e
(
AR
I
MA
)
,
g
ated
r
ec
u
r
r
en
t
u
n
it
(
GR
U
)
,
an
d
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN
)
to
p
r
e
d
ict
Li
-
io
n
b
atter
ies'
s
tate
o
f
h
ea
lth
(
So
H)
.
T
h
eir
m
o
d
el
d
em
o
n
s
tr
ated
s
u
p
er
io
r
ac
c
u
r
ac
y
a
n
d
r
eliab
ilit
y
,
v
ital
f
o
r
en
s
u
r
in
g
th
e
s
af
ety
an
d
lo
n
g
e
v
ity
o
f
b
atter
ies u
s
ed
in
v
ar
io
u
s
r
ea
l
-
tim
e
ap
p
licatio
n
s
.
I
n
r
o
b
o
tics
,
en
s
u
r
in
g
th
e
s
af
e
tr
an
s
f
er
o
f
p
o
licies
lear
n
ed
in
s
im
u
latio
n
s
to
r
ea
l
-
wo
r
ld
ap
p
l
icatio
n
s
is
ch
allen
g
in
g
d
u
e
to
th
e
r
ea
lity
g
ap
.
Kau
s
h
ik
et
a
l.
[
2
5
]
in
tr
o
d
u
ce
d
Saf
eAPT
,
a
r
o
b
o
t
lear
n
in
g
alg
o
r
ith
m
th
at
s
elec
ts
s
af
e
p
o
licies
th
r
o
u
g
h
B
ay
esian
o
p
tim
i
z
atio
n
.
T
h
is
a
p
p
r
o
ac
h
m
i
n
im
i
z
es
s
af
ety
v
io
latio
n
s
d
u
r
in
g
r
ea
l
-
wo
r
ld
in
ter
ac
tio
n
s
,
m
ak
in
g
it
a
p
r
o
m
is
in
g
s
o
lu
tio
n
f
o
r
s
af
e
r
o
b
o
tic
lear
n
in
g
an
d
d
e
p
lo
y
m
en
t.
I
n
teg
r
atin
g
ML
in
in
d
u
s
tr
ial
s
af
ety
m
o
d
els
h
as
r
ev
o
lu
tio
n
i
z
ed
f
a
u
lt
d
ete
ctio
n
an
d
r
is
k
m
an
a
g
em
en
t.
Ma
r
io
et
a
l.
[
2
6
]
em
p
h
asi
z
ed
ML
f
o
r
p
r
ed
ictin
g
in
d
u
s
tr
ial
f
a
u
lts
an
d
e
n
h
an
c
in
g
s
af
ety
p
r
o
to
c
o
ls
.
T
h
eir
s
t
u
d
y
h
ig
h
lig
h
te
d
th
e
n
ee
d
f
o
r
c
o
n
tin
u
o
u
s
in
n
o
v
atio
n
an
d
th
e
d
ev
elo
p
m
en
t
o
f
r
o
b
u
s
t
s
af
ety
m
o
d
els
to
m
itig
ate
in
d
u
s
tr
ial
r
is
k
s
ef
f
ec
tiv
ely
.
C
o
n
s
tr
u
ctio
n
s
ites
ar
e
p
r
o
n
e
to
ac
cid
en
ts
,
m
ak
i
n
g
s
af
ety
r
is
k
m
o
d
els
cr
u
cial.
Mo
s
to
f
i
et
a
l.
[
2
7
]
d
ev
elo
p
e
d
a
g
r
ap
h
co
n
v
o
lu
ti
o
n
al
n
etwo
r
k
(
GC
N)
to
p
r
e
d
ict
co
n
s
tr
u
ctio
n
ac
cid
en
t
s
ev
er
ity
b
y
lev
er
a
g
in
g
d
ep
en
d
e
n
cy
in
f
o
r
m
at
io
n
b
etwe
en
ac
cid
en
ts
.
T
h
is
ap
p
r
o
ac
h
s
ig
n
if
ican
tly
im
p
r
o
v
ed
r
is
k
ass
ess
m
en
t
ac
cu
r
ac
y
an
d
g
e
n
er
ali
z
atio
n
ab
ilit
y
,
p
r
o
v
id
in
g
a
m
o
r
e
r
eliab
le
s
af
ety
m
o
d
el
f
o
r
co
n
s
tr
u
ctio
n
p
r
o
f
ess
io
n
als.
Hallm
ar
k
an
d
Do
n
g
[
2
8
]
ad
d
r
ess
ed
th
e
s
af
ety
ch
allen
g
es
o
f
win
ter
wea
th
er
o
n
r
o
ad
w
ay
s
.
T
h
eir
s
tu
d
y
id
en
tifie
d
cr
itical
f
ac
t
o
r
s
in
f
lu
en
cin
g
win
ter
cr
ash
r
at
es
b
y
em
p
lo
y
in
g
th
e
B
o
r
u
ta
a
lg
o
r
ith
m
f
o
r
f
ea
tu
r
e
s
elec
tio
n
in
cr
ash
f
r
e
q
u
en
c
y
m
o
d
els.
T
h
is
f
r
am
ewo
r
k
ai
d
s
in
d
e
v
elo
p
in
g
e
f
f
ec
tiv
e
win
ter
m
ain
ten
an
ce
s
tr
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to
en
h
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ce
r
o
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w
ay
s
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ety
u
n
d
e
r
ad
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r
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e
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n
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.
An
aly
z
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h
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r
ical
air
cr
af
t
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y
d
ata
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tr
a
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ic
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t
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r
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d
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Oliv
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d
B
aso
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a
[
2
9
]
d
ev
el
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p
ed
a
f
r
am
ewo
r
k
u
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g
a
u
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t
tr
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ies.
T
h
eir
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r
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id
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l e
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icien
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3.
M
E
T
H
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A
co
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liter
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r
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[
3
0
]
,
[
3
1
]
.
C
o
m
p
r
e
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en
s
iv
e,
s
tr
u
ctu
r
ed
r
ev
iew
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a
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at
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n
with
well
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ed
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ics
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ly
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eth
o
d
ical,
ex
p
licit
tech
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iq
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es
to
ch
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o
s
e
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d
ev
alu
ate
r
elev
a
n
t
r
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ar
e
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e
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o
f
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s
y
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d
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aly
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is
[
3
2
]
.
3
.
1
.
I
dentif
ica
t
io
n
T
h
e
s
elec
tio
n
o
f
a
s
u
b
s
tan
tial
v
o
lu
m
e
o
f
r
elev
an
t
liter
atu
r
e
was
ac
co
m
p
lis
h
ed
b
y
u
tili
z
in
g
s
ev
er
a
l
cr
u
cial
s
tag
es
o
f
t
h
e
s
y
s
tem
atic
r
ev
iew
p
r
o
ce
d
u
r
e
in
th
is
in
v
esti
g
atio
n
.
Fo
llo
win
g
th
e
s
elec
tio
n
o
f
k
ey
wo
r
d
s
,
a
s
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r
ch
f
o
r
s
im
ilar
ter
m
in
o
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g
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n
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cted
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y
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n
s
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ltin
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ictio
n
a
r
ies,
th
esau
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e
n
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an
d
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ast
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o
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t
th
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to
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h
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d
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C
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g
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r
ch
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tr
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s
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o
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th
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Sco
p
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s
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d
W
eb
o
f
Scien
ce
(
W
o
S)
d
atab
ases
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o
r
id
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tif
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in
g
all
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er
tin
en
t
p
h
r
ases
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s
h
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wn
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T
ab
le
1
.
At
th
e
b
eg
in
n
in
g
o
f
th
e
s
y
s
tem
atic
r
ev
iew,
1
,
0
9
1
p
u
b
licatio
n
s
r
elev
an
t to
th
e
s
tu
d
y
'
s
s
u
b
ject
wer
e
s
u
cc
ess
f
u
lly
o
b
tain
ed
f
r
o
m
th
e
two
d
a
tab
ases
.
T
ab
le
1
.
T
h
e
s
ea
r
ch
s
tr
in
g
s
D
a
t
a
b
a
s
e
S
e
a
r
c
h
st
r
i
n
g
s
S
c
o
p
u
s
TI
TLE
-
A
B
S
-
K
EY
(
"
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
"
A
N
D
"
safet
y
"
A
N
D
"
t
e
c
h
n
o
l
o
g
y
"
A
N
D
"
r
i
sk
"
)
AND
(
LI
M
I
T
-
TO
(
S
U
B
JA
R
EA
,
"
EN
G
I
"))
AND
(
L
I
M
I
T
-
TO
(
D
O
C
TY
P
E,
"
a
r
"))
AND
(
LI
M
I
T
-
TO
(
P
U
B
Y
EA
R
,
2
0
2
2
)
O
R
LI
M
I
T
-
TO
(
P
U
B
Y
EA
R
,
2
0
2
3
)
O
R
LI
M
I
T
-
TO
(
P
U
B
Y
EA
R
,
2
0
2
4
)
)
AND
(
LI
M
I
T
-
TO
(
LA
N
G
U
A
G
E,
"
En
g
l
i
s
h
"))
AND
(
LI
M
I
T
-
TO
(
S
R
C
T
Y
P
E,
"j
"))
AND
(
LI
M
I
T
-
TO
(
P
U
B
S
TA
G
E,
"
f
i
n
a
l
"))
D
a
t
e
o
f
a
c
c
e
ss
:
D
e
c
e
m
b
e
r
2
0
2
4
W
o
S
"
mac
h
i
n
e
l
e
a
r
n
i
n
g
"
A
N
D
"
safe
t
y
"
A
N
D
"
t
e
c
h
n
o
l
o
g
y
"
A
N
D
"
r
i
s
k
"
(
T
o
p
i
c
)
a
n
d
2
0
2
4
o
r
2
0
2
3
o
r
2
0
2
2
(
P
u
b
l
i
c
a
t
i
o
n
Y
e
a
r
s)
a
n
d
A
r
t
i
c
l
e
(
D
o
c
u
men
t
T
y
p
e
s)
a
n
d
En
g
l
i
s
h
(
La
n
g
u
a
g
e
s)
a
n
d
En
g
i
n
e
e
r
i
n
g
(
R
e
sea
r
c
h
A
r
e
a
s)
D
a
t
e
o
f
a
c
c
e
ss
:
D
e
c
e
m
b
e
r
2
0
2
4
3
.
2
.
Scre
ening
Po
ten
tially
r
elev
an
t
r
esear
ch
i
tem
s
ar
e
co
llected
d
u
r
in
g
th
e
s
cr
ee
n
in
g
to
d
eter
m
in
e
th
ei
r
alig
n
m
en
t
with
th
e
p
r
e
d
ef
in
e
d
r
esear
ch
q
u
esti
o
n
s
.
T
h
is
p
h
ase
c
o
m
m
o
n
l
y
in
v
o
lv
es
u
s
in
g
co
n
ten
t
-
r
elat
ed
cr
iter
ia,
s
u
c
h
as
s
elec
tin
g
r
esear
ch
item
s
r
elate
d
to
a
p
p
ly
in
g
s
af
ety
m
o
d
els
in
ML
as
g
lo
b
al
less
o
n
s
.
All
d
u
p
licate
p
ap
e
r
s
ar
e
r
em
o
v
ed
at
t
h
is
s
tag
e.
I
n
th
e
f
ir
s
t
s
tag
e
o
f
s
cr
ee
n
in
g
,
9
6
8
p
u
b
licatio
n
s
wer
e
ex
cl
u
d
ed
.
I
n
c
o
n
tr
ast,
1
2
3
p
ap
er
s
wer
e
ev
alu
ated
b
ased
o
n
s
p
ec
if
ic
in
clu
s
io
n
an
d
ex
cl
u
s
io
n
cr
i
ter
ia
f
o
r
th
is
s
tu
d
y
in
th
e
s
ec
o
n
d
s
tag
e,
as
s
h
o
wn
in
T
ab
le
2
.
T
h
e
p
r
im
ar
y
c
r
iter
i
o
n
was
liter
atu
r
e
(
r
esear
ch
p
ap
e
r
s
)
,
th
e
p
r
im
a
r
y
s
o
u
r
ce
o
f
p
r
ac
tical
r
ec
o
m
m
en
d
atio
n
s
.
Fu
r
th
er
m
o
r
e,
th
e
r
ev
iew
was
lim
ited
to
E
n
g
lis
h
-
lan
g
u
a
g
e
p
u
b
licatio
n
s
f
r
o
m
2
0
2
2
-
2
0
2
4
.
A
to
tal
o
f
1
0
2
p
u
b
licatio
n
s
wer
e
r
ejec
ted
d
u
e
to
d
u
p
licatio
n
.
T
ab
le
2
.
T
h
e
s
ea
r
ch
s
tr
in
g
s
C
r
i
t
e
r
i
o
n
I
n
c
l
u
s
i
o
n
Ex
c
l
u
si
o
n
La
n
g
u
a
g
e
En
g
l
i
sh
N
o
n
-
E
n
g
l
i
sh
Ti
me
l
i
n
e
2
0
2
2
–
2
0
2
4
<
2
0
2
2
Li
t
e
r
a
t
u
r
e
t
y
p
e
Jo
u
r
n
a
l
(
a
r
t
i
c
l
e
)
C
o
n
f
e
r
e
n
c
e
,
b
o
o
k
,
r
e
v
i
e
w
P
u
b
l
i
c
a
t
i
o
n
s
t
a
g
e
F
i
n
a
l
I
n
p
r
e
ss
S
u
b
j
e
c
t
En
g
i
n
e
e
r
i
n
g
B
e
si
d
e
s
e
n
g
i
n
e
e
r
i
n
g
3
.
3
.
E
lig
ibi
lity
I
n
th
e
th
ir
d
p
h
ase,
th
e
elig
ib
ilit
y
ass
es
s
m
en
t,
1
0
2
ar
ticles
wer
e
co
m
p
iled
.
Du
r
in
g
th
i
s
s
tag
e,
a
th
o
r
o
u
g
h
ex
am
in
atio
n
o
f
all
a
r
ticles
'
titl
es
an
d
co
r
e
co
n
te
n
t
was
co
n
d
u
cted
to
e
n
s
u
r
e
th
e
y
m
et
t
h
e
in
cl
u
s
io
n
cr
iter
ia
an
d
wer
e
r
elev
an
t
t
o
t
h
e
s
tu
d
y
'
s
r
esear
ch
o
b
jectiv
es.
C
o
n
s
eq
u
en
tly
,
6
2
ar
ticles
wer
e
ex
clu
d
e
d
b
ec
au
s
e
th
ey
wer
e
o
u
t
o
f
th
e
f
ield
,
th
eir
titl
es
n
ee
d
ed
t
o
b
e
m
o
r
e
s
ig
n
if
ican
t,
th
ei
r
ab
s
tr
ac
ts
wer
e
n
o
t
r
elate
d
to
th
e
s
tu
d
y
'
s
o
b
jectiv
es,
o
r
th
er
e
n
e
ed
ed
to
b
e
f
u
ll
-
tex
t
ac
ce
s
s
b
as
ed
o
n
em
p
ir
ical
e
v
id
en
ce
.
As
a
r
esu
lt,
4
0
ar
ticles
r
em
ain
ed
f
o
r
th
e
u
p
co
m
in
g
r
e
v
iew.
3
.
4
.
Da
t
a
a
bs
t
ra
ct
io
n a
nd
a
na
ly
s
is
An
in
teg
r
ativ
e
an
aly
s
is
was
em
p
lo
y
ed
in
th
is
s
tu
d
y
to
ex
am
in
e
an
d
s
y
n
t
h
esi
z
e
v
ar
io
u
s
r
esear
ch
d
esig
n
s
(
q
u
a
n
titativ
e
m
eth
o
d
s
)
.
T
h
e
aim
was
to
id
e
n
tify
r
elev
an
t
to
p
ics
an
d
s
u
b
to
p
ics.
T
h
e
in
itial
s
tep
i
n
th
em
e
d
ev
el
o
p
m
en
t
was
th
e
d
ata
co
llectio
n
p
h
ase.
A
s
s
h
o
wn
in
Fig
u
r
e
1
,
th
e
au
th
o
r
s
an
aly
z
ed
4
0
p
u
b
licatio
n
s
f
o
r
ass
er
tio
n
s
o
r
m
ater
ial
p
er
tin
en
t
to
th
e
c
u
r
r
en
t
s
tu
d
y
'
s
to
p
ics.
Su
b
s
eq
u
en
tly
,
th
ey
ev
alu
ate
d
s
ig
n
if
ican
t
s
tu
d
ies
r
elate
d
to
s
af
ety
an
d
ML
,
in
v
esti
g
atin
g
th
e
m
eth
o
d
o
lo
g
ies
a
n
d
r
esear
ch
r
esu
lts
o
f
t
h
ese
s
tu
d
ies.
T
h
e
au
th
o
r
s
co
llab
o
r
a
ted
with
co
-
au
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r
s
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ev
elo
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ased
o
n
th
e
e
v
id
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with
in
th
e
s
tu
d
y
'
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
C
o
mp
r
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en
s
ive
s
tr
u
ctu
r
ed
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lysi
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f m
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fety
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Mo
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kri A
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631
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t.
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was
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ain
tain
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t
h
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o
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g
h
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t
th
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ata
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aly
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is
p
r
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s
s
to
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ec
o
r
d
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y
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aly
s
es,
v
iewp
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ts
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p
u
zz
les,
o
r
o
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e
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th
o
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g
h
ts
r
elev
an
t
to
d
ata
in
ter
p
r
etatio
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.
Fin
ally
,
th
e
au
th
o
r
s
co
m
p
ar
ed
t
h
e
r
esu
lts
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id
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tify
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co
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is
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th
e
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em
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esig
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p
r
o
ce
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s
.
T
h
e
au
th
o
r
s
also
co
m
p
ar
ed
th
e
f
in
d
in
g
s
to
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eso
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d
is
cr
ep
an
cies
in
th
e
th
em
e
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cr
ea
tio
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p
r
o
ce
s
s
.
I
f
in
co
n
s
is
ten
cies a
r
o
s
e,
th
ey
wer
e
ad
d
r
ess
ed
co
llab
o
r
ativ
el
y
.
T
h
e
d
ev
elo
p
ed
th
em
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r
e
th
en
r
ef
in
ed
to
e
n
s
u
r
e
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n
s
is
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cy
.
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o
en
s
u
r
e
t
h
e
v
alid
ity
o
f
th
e
is
s
u
es,
th
e
ex
a
m
in
atio
n
s
wer
e
c
o
n
d
u
cted
b
y
two
ex
p
er
ts
,
o
n
e
s
p
ec
iali
z
in
g
in
en
g
in
ee
r
in
g
an
d
th
e
o
th
er
in
d
ata
s
cien
ce
.
T
h
is
ex
p
er
t
r
ev
iew
p
h
ase
en
s
u
r
ed
ea
ch
s
u
b
-
th
em
e'
s
clar
ity
,
im
p
o
r
ta
n
ce
,
an
d
ad
e
q
u
ac
y
b
y
estab
lis
h
in
g
d
o
m
ain
v
alid
ity
.
Ad
ju
s
tm
en
ts
wer
e
m
ad
e
b
ased
o
n
th
e
au
th
o
r
s
'
d
is
cr
et
io
n
,
in
co
r
p
o
r
at
in
g
ex
p
er
t
f
ee
d
b
ac
k
a
n
d
co
m
m
en
ts
.
T
h
e
q
u
esti
o
n
s
ar
e
as
f
o
llo
w
s
:
i)
H
o
w
ca
n
ML
an
d
ar
tific
ial
in
tellig
en
ce
(
AI
)
en
h
an
ce
v
ar
io
u
s
in
d
u
s
tr
ial
s
ec
to
r
's
r
is
k
as
s
es
s
m
en
t
an
d
s
af
ety
m
ea
s
u
r
es?
,
ii)
H
o
w
ca
n
ML
f
r
am
ewo
r
k
s
b
e
o
p
tim
i
z
ed
f
o
r
r
ea
l
-
tim
e
s
af
ety
m
o
n
ito
r
i
n
g
an
d
in
ci
d
en
t
r
esp
o
n
s
e?
,
an
d
iii)
H
o
w
ca
n
s
m
ar
t te
ch
n
o
lo
g
ies im
p
r
o
v
e
s
af
ety
an
d
s
ec
u
r
ity
in
r
esid
en
tial a
n
d
u
r
b
an
s
ettin
g
s
?
Fig
u
r
e
1
.
Flo
w
d
ia
g
r
am
o
f
th
e
p
r
o
p
o
s
ed
s
ea
r
ch
s
tu
d
y
4.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
As
ML
h
as
em
er
g
ed
as
a
d
is
r
u
p
tiv
e
f
o
r
ce
ac
r
o
s
s
n
u
m
er
o
u
s
in
d
u
s
tr
ies
an
d
co
n
tr
ib
u
ted
to
th
e
r
eg
io
n
'
s
d
ev
elo
p
m
e
n
t,
ML
m
o
d
els
r
a
is
e
s
u
b
s
tan
tial
s
af
ety
is
s
u
es.
B
ased
o
n
th
e
s
ea
r
ch
m
eth
o
d
,
4
0
ar
ticles
wer
e
ex
tr
ac
ted
an
d
ex
am
in
e
d
.
Al
l
p
ap
er
s
wer
e
class
if
ied
b
a
s
ed
o
n
th
r
ee
p
r
im
ar
y
th
em
es:
s
af
ety
an
d
r
is
k
m
an
ag
em
en
t
in
v
ar
io
u
s
in
d
u
s
tr
ies
(
1
1
ar
ticles),
ML
an
d
AI
ap
p
licatio
n
s
in
s
af
ety
(
1
5
a
r
ticles),
an
d
s
m
ar
t
tech
n
o
lo
g
ies f
o
r
s
af
ety
an
d
s
e
cu
r
ity
(
1
4
ar
ticles).
4
.
1
.
Sa
f
et
y
a
nd
risk
m
a
na
g
e
m
ent
in v
a
rio
us
ind
us
t
ries
Saf
ety
an
d
r
is
k
m
an
a
g
em
en
t
ar
e
cr
itical
co
m
p
o
n
e
n
ts
ac
r
o
s
s
v
ar
io
u
s
in
d
u
s
tr
ies,
f
r
o
m
m
ar
itime
o
p
er
atio
n
s
to
co
n
s
tr
u
ctio
n
an
d
m
an
u
f
ac
tu
r
in
g
.
I
n
teg
r
atin
g
ad
v
an
ce
d
tech
n
o
lo
g
ies
s
u
ch
as
AI
,
ML
,
an
d
ed
g
e
co
m
p
u
tin
g
h
as
en
h
a
n
ce
d
th
e
ab
ilit
y
to
d
etec
t
an
o
m
alies,
ass
es
s
r
is
k
s
,
an
d
im
p
lem
en
t
p
r
ev
en
tiv
e
m
ea
s
u
r
es.
T
h
is
an
aly
s
is
ex
p
lo
r
es
d
i
f
f
er
en
t
in
d
u
s
tr
y
-
s
p
ec
if
ic
s
af
ety
f
r
am
ewo
r
k
s
,
h
ig
h
lig
h
tin
g
th
e
ef
f
icac
y
o
f
t
h
ese
tech
n
o
lo
g
ical
in
te
r
v
en
tio
n
s
in
m
itig
atin
g
r
is
k
s
an
d
en
s
u
r
in
g
o
p
er
atio
n
al
s
af
ety
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
4
,
No
.
3
,
Sep
tem
b
er
2
0
2
5
:
627
-
6
3
8
632
E
n
s
u
r
in
g
s
af
ety
in
h
i
g
h
-
r
i
s
k
in
d
u
s
tr
ies
r
e
q
u
ir
es
a
d
v
an
ce
d
r
is
k
m
an
a
g
em
en
t
a
p
p
r
o
ac
h
es.
Alg
ar
n
i
et
a
l
.
[
3
3
]
p
r
o
p
o
s
ed
an
ed
g
e
co
m
p
u
tin
g
-
b
ased
f
r
a
m
ewo
r
k
u
s
in
g
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
an
d
is
o
latio
n
f
o
r
ests
(
I
F)
to
en
h
an
ce
cy
b
er
s
ec
u
r
ity
in
m
ar
it
im
e
wir
eless
co
m
m
u
n
icatio
n
s
(
MWC
)
,
r
ed
u
cin
g
laten
cy
an
d
s
tr
en
g
th
en
in
g
an
o
m
aly
d
etec
tio
n
.
R
u
en
g
d
ec
h
et
a
l.
[
3
4
]
in
tr
o
d
u
ce
d
t
h
e
r
is
k
ass
ess
m
en
t sy
s
tem
f
o
r
m
u
s
cle
in
ju
r
ies
(
R
ASMI
)
,
an
AI
-
d
r
iv
en
s
y
s
tem
th
at
a
p
p
lies
r
ap
id
en
tire
b
o
d
y
ass
ess
m
en
t
(
R
E
B
A)
s
tan
d
ar
d
s
to
d
etec
t
u
n
s
af
e
p
o
s
tu
r
es
in
m
an
u
f
ac
tu
r
in
g
,
o
f
f
e
r
in
g
r
ea
l
-
t
im
e
war
n
in
g
s
a
n
d
co
s
t
-
ef
f
ec
ti
v
e
r
is
k
ass
ess
m
en
t.
W
h
ile
th
ese
m
eth
o
d
s
im
p
r
o
v
e
s
af
ety
,
th
ey
lack
a
co
m
p
r
e
h
en
s
iv
e
ap
p
r
o
ac
h
to
b
r
o
a
d
er
o
p
er
atio
n
al
r
is
k
s
.
I
n
co
n
tr
ast,
o
u
r
p
r
o
p
o
s
ed
s
af
ety
f
r
am
ewo
r
k
ac
h
iev
es
s
af
ety
p
er
f
o
r
m
a
n
ce
,
o
u
tp
er
f
o
r
m
i
n
g
e
x
is
tin
g
m
eth
o
d
s
b
y
p
r
o
v
id
i
n
g
a
m
o
r
e
co
m
p
r
eh
e
n
s
iv
e
s
o
lu
tio
n
f
o
r
th
e
o
il
an
d
g
as
i
n
d
u
s
tr
y
,
en
s
u
r
in
g
im
p
r
o
v
ed
s
af
ety
an
d
r
is
k
m
itig
atio
n
.
T
h
e
in
teg
r
ati
o
n
o
f
o
p
er
atio
n
al
tec
h
n
o
lo
g
y
(
OT
)
a
n
d
i
n
f
o
r
m
atio
n
tech
n
o
lo
g
y
(
I
T
)
h
as
en
h
an
ce
d
s
af
ety
r
is
k
p
r
ed
ictio
n
in
p
o
wer
m
o
n
ito
r
in
g
s
y
s
tem
s
.
W
ei
an
d
W
ei
[
3
5
]
im
p
r
o
v
ed
th
e
XG
B
o
o
s
t
alg
o
r
ith
m
b
y
in
co
r
p
o
r
atin
g
th
e
w
h
ale
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
OA
)
-
XGBo
o
s
t m
o
d
el,
wh
ich
r
e
d
u
ce
s
p
r
ed
ictio
n
er
r
o
r
s
an
d
in
cr
ea
s
es
s
en
s
itiv
ity
,
lead
in
g
to
m
o
r
e
ac
cu
r
ate
an
d
tim
el
y
r
is
k
ass
ess
m
en
ts
.
Si
m
ilar
ly
,
Xu
[
3
6
]
u
tili
z
ed
co
m
p
u
ter
v
is
io
n
to
d
etec
t
u
n
s
af
e
b
eh
av
i
o
r
s
in
co
n
s
tr
u
ctio
n
h
o
is
tin
g
o
p
er
atio
n
s
,
en
ab
lin
g
r
ea
l
-
tim
e
war
n
in
g
s
,
an
d
p
r
e
v
en
tiv
e
ac
tio
n
s
.
W
h
ile
th
ese
ap
p
r
o
ac
h
es
en
h
an
ce
r
is
k
m
itig
ati
o
n
in
th
eir
r
esp
ec
tiv
e
d
o
m
ain
s
,
th
e
y
f
o
cu
s
o
n
s
p
ec
if
ic
h
az
ar
d
s
.
I
n
co
n
tr
ast,
o
u
r
p
r
o
p
o
s
al
to
r
ed
u
ce
th
e
r
is
k
p
r
o
v
id
es
a
m
o
r
e
p
r
ec
is
e
s
o
lu
tio
n
,
ac
h
iev
in
g
s
af
ety
p
r
o
t
o
co
ls
an
d
o
u
tp
e
r
f
o
r
m
in
g
e
x
is
tin
g
m
o
d
els
in
th
e
f
u
el
s
tatio
n
in
d
u
s
tr
y
,
en
s
u
r
in
g
m
o
r
e
r
o
b
u
s
t s
af
ety
m
a
n
ag
em
en
t.
Hig
h
way
co
n
s
tr
u
ctio
n
e
n
tails
s
ig
n
if
ican
t
s
af
ety
r
is
k
s
,
r
eq
u
ir
in
g
ad
v
an
ce
d
ac
cid
en
t
an
aly
s
is
tech
n
iq
u
es.
Sm
etan
a
et
a
l.
[
3
7
]
u
s
ed
a
lar
g
e
lan
g
u
ag
e
m
o
d
el
(
L
L
M)
to
an
aly
ze
d
ata
f
r
o
m
th
e
o
cc
u
p
atio
n
al
s
af
ety
an
d
h
ea
lth
ad
m
in
is
tr
atio
n
(
OSHA
)
s
ev
er
e
in
ju
r
y
r
ep
o
r
ts
(
SIR)
d
atab
ase.
T
h
eir
s
tu
d
y
em
p
lo
y
s
n
atu
r
al
lan
g
u
ag
e
p
r
o
ce
s
s
in
g
(
NL
P)
to
id
en
tify
m
ajo
r
ac
cid
en
t
ca
u
s
es,
s
u
ch
as
h
ea
t
-
r
elate
d
in
ju
r
ies
an
d
s
tr
u
ck
-
b
y
in
cid
en
ts
,
lead
in
g
t
o
im
p
r
o
v
ed
p
r
ev
en
tiv
e
m
ea
s
u
r
es.
I
n
lean
m
an
u
f
ac
tu
r
in
g
,
t
h
e
5
S+1
m
eth
o
d
o
lo
g
y
h
i
g
h
lig
h
ts
s
af
ety
.
Sh
ah
in
et
a
l.
[
3
8
]
d
e
m
o
n
s
tr
ate
h
o
w
co
m
p
u
ter
v
is
io
n
an
d
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o
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ith
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s
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p
ar
ticu
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ly
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ly
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ce
(
YOL
O
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v
7
ar
c
h
itectu
r
e,
ca
n
e
n
s
u
r
e
co
m
p
lian
ce
with
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al
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ip
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tan
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ar
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s
,
s
ig
n
if
ican
tly
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ed
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cin
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az
ar
d
s
.
T
h
e
v
is
u
a
l
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etr
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o
u
p
-
16
(
VGG
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-
1
6
alg
o
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ith
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also
p
r
o
v
id
es h
i
g
h
ac
c
u
r
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n
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e
al
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tim
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s
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f
o
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en
h
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ci
n
g
wo
r
k
p
lace
s
af
ety
.
W
ater
s
p
o
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ts
,
esp
ec
ially
d
i
v
in
g
,
r
eq
u
ir
e
s
tr
ict
s
af
ety
m
ea
s
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ev
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ac
ci
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en
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d
ec
o
m
p
r
ess
io
n
s
ick
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ess
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L
in
g
et
a
l.
[
3
9
]
c
r
ea
ted
a
wea
r
ab
l
e
d
ev
ice
with
a
s
af
ety
alar
m
t
h
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co
s
t
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ec
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e
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o
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GPS,
an
d
B
lu
eto
o
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m
o
n
ito
r
d
iv
er
s
an
d
aler
t
th
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an
d
th
eir
co
a
ch
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ab
o
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t
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o
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tial
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is
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s
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h
is
s
y
s
tem
ev
alu
ates
a
d
iv
er
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lth
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d
p
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es
ti
m
ely
war
n
in
g
s
to
e
n
h
an
ce
s
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ety
.
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h
e
ad
v
en
t
o
f
au
to
n
o
m
o
u
s
s
h
ip
s
b
r
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g
s
n
e
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s
af
ety
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alle
n
g
es
lin
k
e
d
t
o
AI
a
n
d
ML
.
Kh
an
et
a
l.
[
4
0
]
p
er
f
o
r
m
ed
a
r
is
k
ass
es
s
m
en
t
u
s
in
g
an
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h
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en
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m
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f
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to
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d
o
p
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is
s
u
es
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k
ey
ac
cid
en
t
ca
u
s
es.
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h
is
as
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es
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m
en
t
h
elp
s
s
tak
eh
o
ld
er
s
d
e
v
elo
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s
tr
o
n
g
e
r
s
af
ety
s
y
s
te
m
s
f
o
r
au
to
n
o
m
o
u
s
m
ar
itime
o
p
er
atio
n
s
.
E
f
f
icien
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an
d
s
a
f
e
h
an
d
lin
g
o
f
h
az
ar
d
o
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s
waste
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cr
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cial
in
civ
il
en
g
in
ee
r
i
n
g
.
Siv
ak
u
m
ar
et
a
l.
[
4
1
]
ex
p
lo
r
e
AI
-
en
h
a
n
ce
d
d
ec
is
io
n
s
u
p
p
o
r
t
s
y
s
tem
s
(
DSS)
th
at
u
tili
ze
ML
an
d
p
r
ed
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e
m
o
d
elin
g
to
o
p
tim
ize
waste
co
llectio
n
,
tr
an
s
p
o
r
tatio
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,
an
d
d
is
p
o
s
al.
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h
ese
s
y
s
tem
s
im
p
r
o
v
e
r
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k
ass
ess
m
en
t,
en
s
u
r
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ir
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d
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ak
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m
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u
s
tain
ab
le
p
r
ac
tice
s
.
Alek
p
er
o
v
a
[
4
2
]
h
ig
h
lig
h
ts
th
e
r
o
le
o
f
AI
an
d
ML
in
en
h
an
cin
g
th
e
s
af
ety
o
f
o
il
an
d
g
as
p
r
o
d
u
ctio
n
.
B
y
co
n
s
id
er
in
g
th
e
en
tir
e
life
cy
cle
o
f
f
ac
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th
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tech
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im
p
r
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v
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em
e
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g
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cy
m
a
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t
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ce
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t
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cr
o
wd
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ar
ea
s
,
Z
h
a
n
g
et
a
l.
[
4
3
]
p
r
o
p
o
s
e
a
m
o
d
el
u
s
in
g
v
id
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r
ec
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ML
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z
e
b
eh
a
v
io
r
,
id
en
tify
co
n
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esti
o
n
,
an
d
i
s
s
u
e
ea
r
ly
war
n
in
g
s
to
p
r
ev
en
t
ac
cid
en
ts
.
T
h
is
ap
p
r
o
ac
h
im
p
r
o
v
es r
is
k
m
an
a
g
em
en
t in
p
ed
estrian
en
v
ir
o
n
m
en
ts
,
p
r
o
m
o
tin
g
s
af
er
p
u
b
lic
s
p
ac
es
.
Ad
v
a
n
ce
d
tech
n
o
lo
g
ies
s
u
ch
as
AI
,
ML
,
ed
g
e
co
m
p
u
tin
g
,
an
d
co
m
p
u
ter
v
is
io
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r
ev
o
lu
tio
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i
z
e
s
af
ety
an
d
r
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m
a
n
ag
em
en
t
ac
r
o
s
s
v
ar
io
u
s
in
d
u
s
tr
ies.
B
y
lev
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a
g
in
g
th
ese
tech
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o
lo
g
ies,
in
d
u
s
tr
ies
ca
n
en
h
an
ce
th
eir
ab
ilit
y
to
d
etec
t
an
o
m
ali
es,
ass
e
s
s
r
is
k
s
,
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d
im
p
lem
en
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p
r
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en
tiv
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m
ea
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r
es,
en
s
u
r
in
g
s
af
er
an
d
m
o
r
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ef
f
icien
t o
p
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ati
o
n
s
.
4
.
2
.
M
a
chine
l
ea
rning
a
nd
a
rt
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icia
l int
ellig
ence
a
pp
lica
t
io
ns
in s
a
f
et
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AI
ap
p
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h
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v
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n
in
c
r
ea
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in
g
ly
u
tili
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ed
to
en
h
an
ce
s
af
ety
ac
r
o
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s
v
ar
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u
s
d
o
m
ain
s
.
I
n
teg
r
atin
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v
a
n
ce
d
AI
tec
h
n
o
lo
g
ies
in
th
ese
f
ield
s
h
as
s
h
o
wn
s
ig
n
if
ican
t
p
r
o
m
is
e
in
m
itig
atin
g
r
is
k
s
an
d
im
p
r
o
v
in
g
p
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ed
ictiv
e
ca
p
a
b
ilit
ies.
I
n
teg
r
atin
g
ML
an
d
AI
in
to
s
af
ety
ap
p
licatio
n
s
h
as
g
r
ea
tly
en
h
an
ce
d
v
ar
i
o
u
s
s
ec
to
r
s
b
y
im
p
r
o
v
in
g
r
is
k
p
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ed
ictio
n
an
d
m
an
a
g
e
m
en
t.
I
n
m
ar
itime
s
af
ety
,
No
u
r
m
o
h
am
m
ad
i
et
a
l.
[
4
4
]
d
ev
elo
p
ed
a
d
ee
p
s
p
atio
tem
p
o
r
al
o
ce
a
n
ac
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t
p
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n
(
DSTO
AP)
m
o
d
el
th
at
f
o
r
ec
asts
ac
cid
en
ts
in
So
u
th
Ko
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s
ter
r
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wate
r
s
with
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v
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7
8
%
ac
cu
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m
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a
n
8
4
%
f
o
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co
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c
id
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T
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p
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r
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2252
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8
8
1
4
C
o
mp
r
eh
en
s
ive
s
tr
u
ctu
r
ed
a
n
a
lysi
s
o
f m
a
ch
in
e
lea
r
n
in
g
in
s
a
fety
mo
d
els
(
Mo
h
d
S
h
u
kri A
b
d
u
l Wa
h
a
b
)
633
s
af
ety
.
I
n
th
e
ae
r
o
s
p
ac
e
in
d
u
s
t
r
y
,
Her
n
án
d
ez
an
d
Pra
ts
[
4
5
]
cr
ea
ted
AI
-
b
ased
m
eth
o
d
o
lo
g
i
es
to
en
h
an
ce
er
r
o
r
p
r
ed
ictio
n
an
d
r
is
k
m
itig
atio
n
d
u
r
in
g
air
cr
a
f
t
ass
em
b
ly
.
T
h
eir
s
tu
d
y
em
p
lo
y
ed
SVM
s
,
r
an
d
o
m
f
o
r
ests
,
an
d
lo
g
is
tic
r
eg
r
ess
io
n
to
s
ig
n
if
ica
n
tly
r
ed
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ce
er
r
o
r
r
ates a
n
d
p
r
o
ce
s
s
in
g
tim
es.
T
h
is
h
ig
h
lig
h
ts
th
e
p
o
ten
tial f
o
r
AI
to
o
p
tim
ize
co
m
p
lex
m
a
n
u
f
ac
t
u
r
in
g
s
y
s
tem
s
an
d
im
p
r
o
v
e
s
af
ety
o
u
tco
m
es.
Ma
r
itime
tr
an
s
p
o
r
t
is
en
c
o
u
n
t
er
in
g
n
ew
s
af
ety
ch
allen
g
es
d
u
e
to
in
tel
lig
en
t
an
d
a
u
to
n
o
m
o
u
s
s
h
ip
s
.
L
i
et
a
l.
[
4
6
]
an
aly
ze
d
v
a
r
io
u
s
s
h
ip
tr
ajec
to
r
y
p
r
e
d
ictio
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m
et
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o
d
s
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s
in
g
au
to
m
atic
id
en
tific
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s
y
s
tem
(
AI
S
)
d
ata,
co
m
p
ar
in
g
f
iv
e
ML
an
d
s
ev
en
d
ee
p
lear
n
in
g
a
p
p
r
o
ac
h
es.
T
h
eir
f
in
d
in
g
s
h
ig
h
lig
h
t
t
h
e
ef
f
ec
tiv
en
ess
o
f
th
ese
m
eth
o
d
s
in
id
en
tif
y
in
g
a
b
n
o
r
m
al
s
h
ip
b
eh
av
io
r
s
an
d
e
n
h
an
cin
g
m
ar
itime
s
af
ety
.
I
n
t
h
e
m
in
in
g
in
d
u
s
tr
y
,
Yin
et
a
l.
[
4
7
]
p
r
o
p
o
s
ed
a
d
ata
-
d
r
iv
en
m
eth
o
d
f
o
r
p
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ed
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tin
g
wate
r
in
r
u
s
h
in
ci
d
en
ts
u
s
in
g
m
icr
o
s
eismic
m
o
n
ito
r
in
g
d
ata.
B
y
co
m
b
i
n
in
g
ML
an
d
d
ee
p
lear
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i
n
g
m
o
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els
to
an
aly
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s
p
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te
m
p
o
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ata,
th
e
y
s
ig
n
if
ican
tly
im
p
r
o
v
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d
p
r
ed
ic
tio
n
ac
cu
r
ac
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s
h
o
wca
s
in
g
th
e
v
alu
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o
f
ad
v
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ce
d
d
ata
an
aly
tics
f
o
r
m
in
in
g
s
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ety
an
d
o
p
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n
al
e
f
f
icien
cy
.
T
h
e
co
al
m
in
i
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g
s
ec
to
r
is
b
e
n
ef
itin
g
f
r
o
m
AI
-
d
r
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e
n
s
af
ety
ap
p
licatio
n
s
.
W
an
g
et
a
l.
[
4
8
]
u
s
e
d
ad
ap
tiv
e
b
o
o
s
tin
g
(
Ad
aBo
o
s
t
)
-
d
r
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n
r
ea
l
-
tim
e
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tem
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k
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t
r
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k
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y
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aly
zi
n
g
ex
ten
s
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s
p
ati
o
tem
p
o
r
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d
ata.
T
h
is
m
eth
o
d
p
r
o
v
id
es
tim
ely
war
n
in
g
s
,
s
h
o
wca
s
in
g
AI
'
s
p
o
ten
tial
to
en
h
an
ce
s
af
ety
in
h
az
ar
d
o
u
s
e
n
v
ir
o
n
m
en
ts
.
I
n
in
d
u
s
tr
ial
s
ettin
g
s
,
Xu
et
a
l.
[
4
9
]
d
ev
elo
p
ed
a
n
L
ST
M
-
b
ased
s
eq
u
en
ce
-
to
-
s
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f
w
o
r
k
e
r
s
in
c
o
n
f
in
e
d
s
p
ac
es,
u
s
in
g
d
ata
f
r
o
m
wea
r
ab
le
d
ev
ices.
T
h
is
h
y
b
r
id
m
o
d
el
ef
f
ec
tiv
ely
r
ec
o
g
n
izes
h
ea
lth
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n
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itio
n
s
,
illu
s
tr
atin
g
h
o
w
AI
c
an
im
p
r
o
v
e
wo
r
k
er
s
af
ety
an
d
p
r
o
d
u
ctiv
ity
in
co
m
p
lex
en
v
ir
o
n
m
e
n
ts
.
Sli
p
an
d
f
all
ac
cid
e
n
ts
,
a
m
aj
o
r
ca
u
s
e
o
f
in
ju
r
ies,
ca
n
b
e
r
e
d
u
ce
d
u
s
in
g
in
tellig
en
t
i
n
s
o
les
with
ML
alg
o
r
ith
m
s
.
Xu
et
a
l.
[
5
0
]
d
e
v
elo
p
ed
a
m
et
h
o
d
e
m
p
lo
y
i
n
g
s
en
s
o
r
f
u
s
io
n
tech
n
o
lo
g
y
to
p
r
ed
ict
s
lip
r
is
k
s
b
y
tr
ain
in
g
ML
m
o
d
els
o
n
d
ata
f
r
o
m
in
s
tr
u
m
en
ted
s
h
o
e
in
s
o
les
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d
a
s
lip
s
im
u
lato
r
.
T
h
is
ap
p
r
o
ac
h
s
h
o
ws
p
r
o
m
is
e
f
o
r
r
ea
l
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tim
e
s
lip
r
is
k
p
r
ed
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n
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d
en
h
a
n
ce
d
s
af
e
ty
.
Desh
p
an
d
e
[
5
1
]
f
o
cu
s
ed
o
n
p
r
ed
ictin
g
m
ar
in
e
icin
g
f
r
o
m
f
r
ee
zi
n
g
s
ea
s
p
r
a
y
u
s
in
g
a
n
ML
m
o
d
el
ca
lled
"
Sp
ice
,
"
d
ev
elo
p
e
d
f
r
o
m
e
x
p
er
im
en
tal
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ata.
T
h
is
r
esear
ch
u
n
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er
s
co
r
es
th
e
s
ig
n
if
ican
ce
o
f
d
ata
-
d
r
i
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en
m
o
d
els
in
m
an
ag
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ar
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e
icin
g
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n
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ib
u
tin
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th
e
s
af
ety
o
f
m
ar
i
n
e
v
ess
els.
L
u
o
et
a
l.
[
5
2
]
an
aly
ze
d
r
is
k
s
ass
o
ciate
d
with
cu
t
-
in
s
-
lan
e
-
c
h
an
g
in
g
b
eh
a
v
io
r
o
n
u
r
b
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e
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ess
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s
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u
lti
-
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v
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im
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latio
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ata
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m
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ar
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ec
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r
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r
ad
ien
t
b
o
o
s
tin
g
d
ec
is
io
n
tr
ee
s
(
GB
DT
)
,
an
d
L
STM
m
o
d
els.
T
h
e
L
STM
m
o
d
el
p
r
o
v
e
d
th
e
m
o
s
t
ac
cu
r
ate,
d
em
o
n
s
tr
atin
g
th
e
ef
f
ec
tiv
e
n
e
s
s
o
f
ad
v
a
n
ce
d
ML
in
im
p
r
o
v
i
n
g
tr
af
f
ic
s
af
ety
.
I
n
av
iatio
n
,
Haselein
et
a
l.
[
5
3
]
u
s
ed
B
ay
esian
n
etwo
r
k
s
(
B
Ns)
to
m
o
d
el
n
ea
r
-
m
id
-
air
co
llis
io
n
s
(
NM
AC
)
b
ased
o
n
NASA's
av
iatio
n
s
af
ety
r
ep
o
r
t
in
g
s
y
s
tem
d
ata.
T
h
e
ir
m
o
d
els
p
r
o
v
id
e
d
in
s
ig
h
ts
in
to
r
is
k
f
ac
to
r
s
an
d
h
ig
h
lig
h
ted
th
e
b
e
n
ef
its
o
f
co
m
b
in
in
g
B
Ns
wi
th
ML
f
o
r
en
h
an
ce
d
av
iatio
n
s
af
ety
.
Fo
r
en
v
ir
o
n
m
en
tal
s
af
ety
,
L
i
et
a
l.
[
5
4
]
d
ev
el
o
p
e
d
a
n
ML
f
r
am
ewo
r
k
f
o
r
d
et
ec
tin
g
wastewate
r
p
o
llu
tio
n
with
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o
T
-
b
ased
s
p
ec
tr
al
tech
n
o
l
o
g
y
.
T
h
eir
s
tu
d
y
i
m
p
r
o
v
e
d
n
ea
r
-
in
f
r
ar
ed
(
NI
R
)
ca
lib
r
at
io
n
m
o
d
els
f
o
r
r
a
p
id
p
o
llu
tan
t d
etec
tio
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,
illu
s
tr
atin
g
h
o
w
AI
ca
n
ad
d
r
ess
in
d
u
s
tr
ial
p
o
llu
tio
n
an
d
e
n
h
an
ce
wate
r
s
af
ety
.
W
an
g
et
a
l.
[
5
5
]
p
r
o
p
o
s
ed
a
s
p
atio
-
tem
p
o
r
al
d
ee
p
lear
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in
g
m
eth
o
d
f
o
r
s
im
u
latin
g
co
n
f
lict
r
is
k
o
n
f
r
ee
way
s
.
T
h
eir
s
p
atio
tem
p
o
r
a
l
tr
an
s
f
o
r
m
e
r
n
etwo
r
k
(
STT
N)
ef
f
ec
tiv
ely
p
r
e
d
icts
r
is
k
p
atter
n
s
u
s
in
g
a
co
n
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lict
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ex
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n
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s
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r
r
o
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ate
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ety
m
ea
s
u
r
es,
h
ig
h
lig
h
tin
g
its
p
o
ten
tial
f
o
r
tr
af
f
ic
m
a
n
ag
em
en
t
an
d
s
af
ety
s
y
s
tem
s
.
S
im
ilar
ly
,
Alawa
d
a
n
d
Kae
wu
n
r
u
e
n
[
5
6
]
u
tili
ze
d
u
n
s
u
p
er
v
is
ed
ML
in
r
ailway
s
tatio
n
s
to
en
h
an
ce
s
af
ety
m
an
ag
em
e
n
t.
T
h
eir
s
t
u
d
y
o
p
tim
i
z
ed
laten
t
Dir
ich
l
et
allo
ca
tio
n
(
L
DA)
f
o
r
an
al
y
z
in
g
tex
tu
al
d
ata,
o
f
f
er
in
g
v
alu
a
b
le
in
s
ig
h
ts
f
r
o
m
h
is
to
r
ical
ac
cid
en
t d
ata
to
im
p
r
o
v
e
s
af
ety
in
r
ailway
o
p
er
atio
n
s
.
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h
e
s
h
ip
p
in
g
in
d
u
s
tr
y
ca
n
s
ig
n
if
ican
tly
b
e
n
ef
it
f
r
o
m
f
ed
er
ated
lear
n
in
g
(
FL)
i
n
p
r
ed
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e
m
ain
ten
an
ce
(
Pd
M)
.
An
g
elo
p
o
u
lo
s
et
a
l.
[
5
7
]
d
em
o
n
s
tr
ated
th
at
FL
im
p
r
o
v
es
m
ain
ten
an
c
e
d
ec
is
io
n
-
m
ak
i
n
g
an
d
r
ed
u
c
es
d
o
wn
tim
e
in
S
h
ip
p
in
g
4
.
0
ap
p
licatio
n
s
,
h
ig
h
lig
h
tin
g
th
e
p
o
te
n
tial
o
f
d
e
ce
n
tr
ali
z
ed
ML
to
en
h
an
ce
o
p
e
r
atio
n
al
ef
f
icien
c
y
an
d
s
af
ety
.
I
n
ch
em
ical
p
r
o
ce
s
s
in
g
,
W
an
g
et
a
l.
[
5
8
]
p
r
o
p
o
s
ed
a
v
ir
tu
al
m
ac
h
in
e
(
VM
)
b
ased
p
r
ed
ict
iv
e
m
ain
ten
a
n
ce
m
o
d
el
to
r
e
d
u
ce
e
q
u
ip
m
e
n
t
f
ailu
r
es
an
d
en
h
a
n
ce
s
af
ety
b
y
in
teg
r
atin
g
I
o
T
a
n
d
ML
.
T
h
ei
r
r
esear
ch
u
n
d
er
s
co
r
es
th
e
cr
itical
r
o
le
o
f
AI
in
p
r
o
m
o
tin
g
o
p
er
atio
n
al
s
af
ety
in
h
ig
h
-
r
is
k
in
d
u
s
tr
ial
s
ettin
g
s
.
ML
an
d
AI
e
n
h
an
ce
s
af
ety
a
cr
o
s
s
in
d
u
s
tr
ies
b
y
im
p
r
o
v
in
g
r
is
k
p
r
ed
ictio
n
an
d
m
a
n
ag
e
m
en
t.
AI
-
d
r
iv
en
m
o
d
els
o
p
tim
i
z
e
ac
cid
en
t
f
o
r
ec
asti
n
g
,
h
az
ar
d
d
etec
tio
n
,
an
d
o
p
er
atio
n
al
ef
f
icien
cy
in
m
ar
itime
s
af
ety
,
ae
r
o
s
p
ac
e,
m
in
in
g
,
an
d
in
d
u
s
t
r
ial
s
ett
in
g
s
.
Ap
p
licatio
n
s
in
clu
d
e
s
h
ip
tr
ajec
to
r
y
an
aly
s
is
,
p
r
ed
ictiv
e
m
ain
ten
an
ce
,
e
n
v
ir
o
n
m
en
tal
s
af
ety
,
an
d
wo
r
k
e
r
h
ea
lth
m
o
n
i
to
r
in
g
,
d
em
o
n
s
tr
atin
g
AI
’
s
tr
a
n
s
f
o
r
m
ativ
e
r
o
le
in
r
is
k
m
itig
atio
n
.
4
.
3
.
S
m
a
rt
t
ec
hn
o
lo
g
ies f
o
r
s
a
f
et
y
Sm
ar
t
tech
n
o
lo
g
ies
ar
e
r
ev
o
l
u
tio
n
i
z
in
g
s
af
ety
b
y
in
teg
r
ati
n
g
AI
,
ML
,
an
d
th
e
I
o
T
t
o
p
r
ed
ict
r
is
k
s
,
en
h
an
ce
m
o
n
ito
r
in
g
,
an
d
p
r
e
v
en
t
ac
cid
en
ts
.
T
h
ese
in
n
o
v
a
tio
n
s
im
p
r
o
v
e
s
af
ety
ac
r
o
s
s
in
d
u
s
tr
ies,
en
ab
lin
g
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
i
n
g
an
d
p
r
o
ac
tiv
e
h
az
ar
d
m
an
ag
em
e
n
t
f
o
r
s
af
er
e
n
v
ir
o
n
m
en
ts
.
I
m
p
lem
en
tin
g
s
m
ar
t
tech
n
o
lo
g
ies
is
cr
u
cial
f
o
r
en
h
an
cin
g
s
af
ety
an
d
s
ec
u
r
ity
,
p
ar
ticu
lar
ly
in
th
e
co
n
s
tr
u
ctio
n
in
d
u
s
tr
y
t
h
r
o
u
g
h
h
u
m
an
-
r
o
b
o
t
team
in
g
.
Sh
ay
e
s
teh
et
a
l.
[
5
9
]
p
r
o
p
o
s
e
a
tr
ai
n
in
g
p
latf
o
r
m
u
s
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
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Ap
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1
4
,
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3
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Sep
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2
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2
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634
im
m
er
s
iv
e
tech
n
o
lo
g
ies
an
d
wea
r
ab
le
s
en
s
o
r
s
to
im
p
r
o
v
e
s
af
ety
tr
ain
in
g
in
h
u
m
an
-
r
o
b
o
t
co
llab
o
r
atio
n
(
HR
C
)
.
T
h
is
p
latf
o
r
m
ass
ess
es
co
g
n
itiv
e
lo
a
d
,
en
s
u
r
in
g
e
f
f
ec
tiv
e
tr
ain
in
g
an
d
p
r
o
m
p
ti
n
g
s
af
er
b
eh
av
i
o
r
s
am
o
n
g
co
n
s
tr
u
ctio
n
wo
r
k
e
r
s
.
Ad
d
itio
n
ally
,
elec
tr
o
n
ic
s
k
in
s
(
e
-
s
k
in
s
)
d
ev
elo
p
ed
b
y
Ge
et
a
l.
[
6
0
]
en
h
a
n
ce
s
af
ety
in
h
u
m
an
-
r
o
b
o
t
in
ter
ac
t
io
n
s
b
y
s
en
s
in
g
en
v
ir
o
n
m
en
ta
l
p
ar
am
eter
s
.
T
h
is
tech
n
o
lo
g
y
d
em
o
n
s
tr
ates
h
o
w
in
teg
r
atin
g
s
en
s
o
r
y
d
ata
ca
n
s
ig
n
if
ican
tly
im
p
r
o
v
e
co
llab
o
r
atio
n
s
b
etwe
en
h
u
m
a
n
s
an
d
r
o
b
o
ts
in
co
m
p
lex
s
ettin
g
s
.
Dee
p
lear
n
in
g
m
eth
o
d
s
h
a
v
e
s
h
o
wn
co
n
s
id
er
ab
le
p
o
ten
tial
in
h
o
m
e
s
ec
u
r
ity
.
Var
d
ak
is
et
a
l.
[
6
1
]
d
is
cu
s
s
u
s
in
g
ML
tech
n
iq
u
es
to
r
ec
o
g
n
ize
f
ac
es
an
d
h
u
m
a
n
ac
tiv
ities
,
aim
in
g
to
cr
ea
te
s
af
er
u
r
b
a
n
h
o
m
es.
T
h
is
tech
n
o
lo
g
y
also
h
as
a
p
p
l
icatio
n
s
in
f
ield
s
lik
e
m
ed
icin
e
f
o
r
d
iag
n
o
s
tics
.
Mo
r
eo
v
er
,
th
e
d
e
v
elo
p
m
e
n
t
o
f
in
tellig
en
t
d
o
o
r
lo
ck
s
y
s
tem
s
h
ig
h
lig
h
ts
th
e
f
o
cu
s
o
n
d
ee
p
lear
n
in
g
f
o
r
s
ec
u
r
ity
.
Mr
ab
et
et
a
l.
[
6
2
]
p
r
esen
ted
a
T
in
y
ML
(
T
in
y
ML
)
-
b
ased
s
y
s
tem
f
o
r
r
ea
l
-
tim
e
f
ac
e
m
ask
d
etec
tio
n
,
v
ital
in
h
ig
h
-
r
is
k
ar
ea
s
lik
e
h
ea
lth
ca
r
e.
T
h
ese
ad
v
an
ce
m
e
n
ts
em
p
h
asize
th
e
s
ig
n
if
ican
t
r
o
le
o
f
ML
i
n
ad
d
r
ess
in
g
m
o
d
er
n
s
ec
u
r
ity
ch
allen
g
es
in
s
m
ar
t
h
o
m
e
s
y
s
tem
s
.
I
n
h
ea
lth
ca
r
e,
in
teg
r
atin
g
s
m
ar
t
tech
n
o
lo
g
ies
h
as
p
r
o
v
en
ess
en
tial
f
o
r
en
s
u
r
in
g
s
af
ety
,
p
ar
t
icu
lar
ly
in
u
s
in
g
PP
E
.
C
h
ap
m
an
et
a
l.
[
6
3
]
in
v
esti
g
ated
th
e
u
s
e
o
f
in
f
r
a
-
r
ed
im
ag
in
g
co
m
b
i
n
ed
with
ML
to
d
etec
t
leak
s
in
r
esp
ir
ato
r
s
,
a
cr
itical
f
ac
to
r
in
p
r
o
tectin
g
h
ea
lth
ca
r
e
wo
r
k
er
s
.
T
h
is
m
eth
o
d
s
u
r
p
ass
es
tr
ad
itio
n
al
f
it
-
ch
ec
k
s
,
o
f
f
er
in
g
a
m
o
r
e
r
eliab
le
ap
p
r
o
ac
h
to
en
s
u
r
in
g
th
e
p
r
o
p
er
f
it
o
f
r
esp
ir
ato
r
s
,
th
er
eb
y
e
n
h
a
n
cin
g
o
cc
u
p
atio
n
al
s
af
ety
in
h
ea
lth
ca
r
e
s
ettin
g
s
.
Mo
r
eo
v
er
,
th
e
in
teg
r
atio
n
o
f
wea
r
ab
le
s
en
s
o
r
s
an
d
ML
f
o
r
m
o
n
ito
r
in
g
d
r
iv
er
s
'
h
ea
lth
co
n
d
itio
n
s
,
as
ex
p
lo
r
ed
b
y
So
h
ail
et
a
l.
[
6
4
]
,
s
h
o
wca
s
es
th
e
p
o
ten
tial
o
f
th
ese
tech
n
o
lo
g
ies
in
r
e
d
u
cin
g
ac
cid
en
ts
ca
u
s
ed
b
y
h
ea
lth
-
r
e
lated
is
s
u
es
s
u
ch
as
d
iab
etes.
T
h
e
c
o
m
b
in
atio
n
o
f
v
e
h
icu
l
ar
ad
-
h
o
c
n
etwo
r
k
s
(
VANE
T
)
tech
n
o
lo
g
y
an
d
we
ar
ab
le
s
en
s
o
r
s
p
r
o
v
id
es
r
ea
l
-
ti
m
e
h
ea
lth
m
o
n
ito
r
in
g
,
s
ig
n
if
i
ca
n
tly
co
n
tr
ib
u
tin
g
to
r
o
ad
s
af
ety
.
I
n
teg
r
atin
g
s
m
ar
t
tech
n
o
lo
g
ies
in
h
ea
lth
ca
r
e
is
cr
u
cial
f
o
r
s
af
ety
,
esp
ec
ially
r
eg
ar
d
in
g
PP
E
.
C
h
ap
m
an
et
a
l.
[
6
3
]
ex
am
i
n
ed
h
o
w
in
f
r
ar
e
d
im
ag
in
g
an
d
M
L
ca
n
d
etec
t r
esp
ir
ato
r
leak
s
,
a
n
d
p
r
o
v
id
ed
a
m
o
r
e
r
eliab
le
m
eth
o
d
t
h
a
n
tr
ad
itio
n
al
f
it
-
ch
ec
k
s
to
en
s
u
r
e
p
r
o
p
er
f
it
an
d
en
h
an
ce
o
cc
u
p
atio
n
al
s
af
ety
.
Similar
ly
,
So
h
ail
et
a
l.
[
6
4
]
ex
p
lo
r
ed
wea
r
ab
le
s
en
s
o
r
s
c
o
m
b
in
e
d
with
ML
to
m
o
n
ito
r
d
r
iv
e
r
s
'
h
ea
lth
,
aim
in
g
to
r
e
d
u
ce
ac
cid
en
ts
lin
k
ed
to
h
ea
lth
is
s
u
es
lik
e
d
iab
etes.
T
h
is
in
t
eg
r
ati
o
n
o
f
VANE
T
tech
n
o
lo
g
y
with
wea
r
ab
le
s
en
s
o
r
s
en
ab
les r
ea
l
-
tim
e
h
ea
lth
m
o
n
it
o
r
in
g
,
im
p
r
o
v
in
g
r
o
ad
s
af
ety
.
T
h
e
ap
p
licatio
n
o
f
s
m
ar
t
tec
h
n
o
lo
g
ies
to
en
h
a
n
ce
in
f
r
astr
u
ctu
r
e
s
af
ety
is
cr
u
cial
.
L
u
et
a
l.
[
6
5
]
d
ev
elo
p
e
d
an
ea
r
ly
war
n
in
g
s
y
s
tem
f
o
r
d
r
in
k
in
g
wate
r
s
u
p
p
ly
in
s
m
ar
t
cities,
lev
er
ag
in
g
o
n
lin
e
s
en
s
o
r
n
etwo
r
k
s
an
d
ML
t
o
im
p
r
o
v
e
r
is
k
m
an
ag
em
en
t.
Sh
ah
r
iar
et
a
l.
[
6
6
]
p
r
o
p
o
s
ed
a
v
eh
icle
-
to
-
in
f
r
astru
ctu
r
e
(
V
2
I
)
f
r
am
ewo
r
k
u
s
in
g
ML
to
e
n
h
a
n
ce
in
ter
s
ec
tio
n
s
af
ety
an
d
r
e
d
u
ce
co
llis
io
n
s
.
Ho
u
et
a
l.
[
6
7
]
in
tr
o
d
u
ce
d
a
n
ML
m
eth
o
d
f
o
r
d
etec
tin
g
v
o
r
tex
-
i
n
d
u
ce
d
v
i
b
r
atio
n
s
in
b
r
id
g
es,
en
s
u
r
in
g
s
tr
u
ctu
r
al
s
tab
ilit
y
.
T
h
ese
s
tu
d
ies
f
o
cu
s
o
n
s
p
ec
if
ic
ap
p
licatio
n
s
;
th
e
s
af
ety
m
o
d
el
o
f
f
e
r
s
a
m
o
r
e
ad
ap
tiv
e
s
o
lu
tio
n
,
d
em
o
n
s
tr
atin
g
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
an
d
en
h
an
cin
g
o
v
er
all
in
f
r
astru
ctu
r
e
s
af
ety
.
T
h
e
o
p
tim
i
z
atio
n
o
f
elec
tr
ic
v
eh
icle
s
u
p
p
ly
e
q
u
ip
m
e
n
t
(
E
VSE)
in
m
u
lti
-
u
n
it
r
es
id
en
tia
l
b
u
ild
in
g
s
(
MRB
s
)
h
as
al
s
o
b
en
ef
ited
f
r
o
m
s
m
ar
t
tech
n
o
lo
g
ies.
Sam
ad
i
an
d
Fattah
i
[
6
8
]
d
is
cu
s
s
ed
th
e
ef
f
ec
tiv
en
ess
o
f
an
en
er
g
y
m
an
a
g
em
en
t
s
y
s
tem
(
E
MS)
th
at
u
s
es
ML
to
o
ls
to
o
p
tim
i
z
e
E
VSE
o
p
er
atio
n
s
,
en
s
u
r
in
g
ef
f
icien
t
en
er
g
y
u
s
e
an
d
r
e
d
u
cin
g
co
s
ts
.
T
h
is
ap
p
r
o
ac
h
s
u
p
p
o
r
ts
th
e
s
af
e
an
d
s
u
s
tain
ab
le
in
teg
r
atio
n
o
f
elec
tr
ic
v
eh
icles
in
r
esid
en
tial a
r
ea
s
.
T
h
e
ad
v
an
ce
m
e
n
t
o
f
s
m
ar
t
tech
n
o
lo
g
ies
ex
ten
d
s
to
c
y
b
er
s
ec
u
r
ity
,
p
ar
ticu
lar
ly
in
p
r
o
tectin
g
AI
-
b
ased
s
y
s
tem
s
f
r
o
m
p
o
ten
tial
th
r
ea
ts
.
T
ar
eq
et
a
l.
[
6
9
]
h
ig
h
lig
h
t
t
h
e
v
u
l
n
er
ab
ilit
ies
o
f
AI
s
y
s
te
m
s
to
cy
b
er
-
attac
k
s
an
d
th
e
cr
itical
r
o
le
o
f
d
ee
p
lear
n
i
n
g
an
d
f
ed
e
r
ated
le
ar
n
in
g
in
en
h
a
n
cin
g
cy
b
e
r
s
ec
u
r
ity
.
Me
a
n
wh
ile,
C
ar
lo
et
a
l.
[
7
0
]
d
is
cu
s
s
th
e
n
ee
d
f
o
r
r
e
g
u
lato
r
y
f
r
a
m
ewo
r
k
s
to
ad
d
r
ess
AI
'
s
eth
ical
an
d
t
ec
h
n
ical
ch
allen
g
es
in
s
p
ac
e
ap
p
licatio
n
s
,
u
n
d
e
r
s
co
r
in
g
th
e
i
n
ter
d
is
cip
lin
ar
y
n
atu
r
e
o
f
th
ese
t
h
r
ea
ts
an
d
th
e
im
p
o
r
tan
ce
o
f
co
m
p
r
eh
e
n
s
iv
e
s
af
ety
m
ea
s
u
r
es.
B
o
th
em
p
h
asi
z
e
th
e
im
p
o
r
tan
ce
o
f
r
o
b
u
s
t
cy
b
er
s
ec
u
r
ity
f
r
am
ewo
r
k
s
to
s
af
eg
u
ar
d
AI
tec
h
n
o
l
o
g
ies.
T
h
e
u
s
e
o
f
s
m
ar
t te
ch
n
o
lo
g
ies s
ig
n
if
ican
tly
im
p
r
o
v
es saf
ety
an
d
s
ec
u
r
ity
ac
r
o
s
s
v
ar
io
u
s
f
ield
s
.
T
h
ese
in
n
o
v
atio
n
s
ad
d
r
ess
m
o
d
er
n
s
af
ety
ch
allen
g
es,
en
h
an
ce
c
o
n
s
tr
u
ctio
n
tr
ain
in
g
,
an
d
p
r
o
tect
in
f
r
astru
ctu
r
e
.
Stro
n
g
cy
b
er
s
ec
u
r
ity
f
r
am
ewo
r
k
s
ar
e
also
cr
u
cial
f
o
r
s
af
eg
u
ar
d
in
g
AI
-
b
ased
s
y
s
tem
s
,
em
p
h
asi
z
in
g
th
e
n
ee
d
f
o
r
in
ter
d
is
cip
lin
ar
y
ap
p
r
o
ac
h
es in
to
d
ay
’
s
s
af
ety
lan
d
s
ca
p
e.
5.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
h
as
h
ig
h
lig
h
ted
th
e
tr
an
s
f
o
r
m
ativ
e
p
o
ten
tial
o
f
ML
in
e
n
h
an
ci
n
g
s
af
ety
an
d
r
is
k
m
an
ag
em
en
t
ac
r
o
s
s
d
iv
er
s
e
in
d
u
s
tr
ies.
T
h
r
o
u
g
h
a
s
y
s
tem
atic
r
ev
iew
o
f
s
tu
d
ies
p
u
b
lis
h
ed
b
etwe
en
2
0
2
2
an
d
2
0
2
4
,
g
u
id
ed
b
y
th
e
PR
I
SMA
f
r
am
ewo
r
k
,
th
r
ee
co
r
e
th
em
es
wer
e
id
en
tifie
d
:
s
af
ety
an
d
r
is
k
m
an
a
g
em
en
t,
th
e
ap
p
licatio
n
s
o
f
ML
an
d
A
I
,
an
d
th
e
u
tili
z
atio
n
o
f
s
m
ar
t
tech
n
o
lo
g
y
f
o
r
s
af
ety
e
n
h
an
ce
m
en
t.
T
h
e
f
in
d
in
g
s
d
em
o
n
s
tr
ate
n
o
tab
le
ad
v
an
ce
m
en
ts
,
in
clu
d
in
g
im
p
r
o
v
e
d
p
r
ed
ictiv
e
ca
p
ab
ilit
ies,
ef
f
icien
t
an
o
m
aly
d
etec
tio
n
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
C
o
mp
r
eh
en
s
ive
s
tr
u
ctu
r
ed
a
n
a
lysi
s
o
f m
a
ch
in
e
lea
r
n
in
g
in
s
a
fety
mo
d
els
(
Mo
h
d
S
h
u
kri A
b
d
u
l Wa
h
a
b
)
635
an
d
p
r
o
ac
tiv
e
p
r
e
v
en
tiv
e
m
ea
s
u
r
es,
all
co
n
t
r
ib
u
tin
g
to
s
af
er
o
p
e
r
atio
n
al
e
n
v
ir
o
n
m
en
ts
.
N
o
n
eth
eless
,
th
e
r
a
p
id
ad
o
p
tio
n
o
f
ML
tech
n
o
l
o
g
ies
p
r
esen
ts
ce
r
tain
ch
allen
g
es
an
d
lim
itatio
n
s
.
T
h
ese
in
clu
d
e
th
e
n
ec
ess
ity
f
o
r
h
ig
h
-
q
u
ality
d
ata,
eth
ical
co
n
ce
r
n
s
r
elate
d
to
alg
o
r
ith
m
ic
d
ec
is
io
n
-
m
ak
in
g
,
a
n
d
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d
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DATA AV
AI
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AB
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L
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Y
Data
av
ailab
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y
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n
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t
ap
p
li
ca
b
le
to
th
is
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ap
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w
d
ata
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cr
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an
aly
ze
d
in
th
is
s
tu
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
W
.
W
i
h
a
r
t
o
a
n
d
F
.
N
.
M
u
f
i
d
a
h
,
“
Ea
r
l
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t
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t
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se
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s
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si
n
g
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t
e
r
p
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t
a
b
l
e
ma
c
h
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n
e
l
e
a
r
n
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n
g
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
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l
o
f
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d
v
a
n
c
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s
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n
Ap
p
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s
,
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.
1
3
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.
4
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9
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–
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e
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a
s.
v
1
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.
i
4
.
p
p
9
4
4
-
9
5
6
.
[
2
]
K
.
B
e
r
g
g
r
e
n
e
t
a
l
.
,
“
R
o
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d
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p
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merg
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d
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y
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,
”
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y
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.
3
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3
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2
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[
3
]
A
.
La
v
i
n
e
t
a
l
.
,
“
Te
c
h
n
o
l
o
g
y
r
e
a
d
i
n
e
ss
l
e
v
e
l
s
f
o
r
ma
c
h
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n
e
l
e
a
r
n
i
n
g
s
y
st
e
ms,”
N
a
t
u
re
C
o
m
m
u
n
i
c
a
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o
n
s
,
v
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1
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s
4
1
4
6
7
-
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3
1
2
8
-
9.
[
4
]
J.
Zh
a
o
e
t
a
l
.
,
“
B
a
t
t
e
r
y
safe
t
y
:
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
-
b
a
se
d
p
r
o
g
n
o
st
i
c
s,
”
Pr
o
g
r
e
ss
i
n
E
n
e
r
g
y
a
n
d
C
o
m
b
u
s
t
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,
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.
[
5
]
M
.
A
l
k
a
i
ssy
e
t
a
l
.
,
“
E
n
h
a
n
c
i
n
g
c
o
n
st
r
u
c
t
i
o
n
safe
t
y
:
ma
c
h
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n
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-
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se
d
c
l
a
ssi
f
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f
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j
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,
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a
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t
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e
,
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.
1
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p
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,
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ssc
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.
2
0
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3
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1
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.
[
6
]
Y
.
Ji
a
,
J.
M
c
D
e
r
mi
d
,
T.
La
w
t
o
n
,
a
n
d
I
.
H
a
b
l
i
,
“
Th
e
r
o
l
e
o
f
e
x
p
l
a
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n
a
b
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l
i
t
y
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ss
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r
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g
s
a
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y
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m
a
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n
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g
i
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h
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a
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t
h
c
a
r
e
,
”
I
EEE
T
r
a
n
sa
c
t
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o
n
s
o
n
Em
e
r
g
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n
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o
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[
7
]
M
.
S
.
Esser
a
n
d
T
.
S
.
J
o
h
n
s
o
n
,
“
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a
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ma
t
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a
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U
,
”
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l
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l
.
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.
1
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–
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:
1
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1
8
9
1
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1
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-
T
-
7
4
7
.
[
8
]
L.
B
o
r
t
e
y
,
D
.
J.
Ed
w
a
r
d
s,
C
.
R
o
b
e
r
t
s,
a
n
d
I
.
R
i
l
l
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e
,
“
A
r
e
v
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e
w
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f
saf
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y
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o
p
me
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t
o
f
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d
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g
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t
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l
h
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g
h
w
a
y
c
o
n
st
r
u
c
t
i
o
n
safe
t
y
r
i
s
k
m
o
d
e
l
,
”
D
i
g
i
t
a
l
,
v
o
l
.
2
,
n
o
.
2
,
p
p
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2
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