I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
.
13
, N
o.
4
,
D
e
c
e
m
be
r
2024
, pp.
3727
~
37
38
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
13
.i
4
.pp
3727
-
37
38
3727
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
ia
e
s
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.c
om
M
e
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od
ol
ogy ap
p
l
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o c
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h
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O
val
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D
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m
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o de
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nge
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, F
a
c
ul
t
a
d de
I
nge
ni
e
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í
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U
ni
ve
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s
i
da
d T
e
c
nol
ógi
c
a
d
e
l
P
e
r
ú, L
i
m
a
, P
e
r
ú
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
J
a
n 7, 2024
R
e
vi
s
e
d
M
a
r
3, 2024
A
c
c
e
pt
e
d
M
a
r
21, 2024
This
systematic
review
focused
on
evaluating
the
impact
of
the
m
achine
learning
operations
(MLOps)
methodology
on
anomaly
detection
a
nd
the
integration
of
artificia
l
intelligence
(AI)
projects
in
computer
auditin
g.
Data
collecti
on
was
carried
out
by
searching
for
articles
in
databases,
s
uch
as
Scopus
and
PubMed,
covering
the
period
from
2018
to
2024.
The
ri
gorous
applicati
on
of
the
preferred
reporting
items
for
systematic
reviews
and
meta
-
analyses
(
PRISMA
)
methodology
allow
ed
88
significant
records
to
be
selected
from
an
initial
set
of
1
,
389,
highlighting
the
completen
ess
of
the
selection
phase.
Both
quantitative
and
qualitative
analysis
of
th
e
data
obtained
revealed
emerging
trends
in
the
research
and
provided
key
i
nsights
into
the
implementation
of
MLOps
in
AI
projects,
especially
in
resp
onse
to
increasin
g
complexity,
whereby
the
adoption
of
the
MLOps
metho
dology
stands
out
as
a
crucial
component
to
optimize
anomaly
detection
and
improve
integration
in
the
context
of
informat
ion
technology
auditin
g.
This
systematic
approach
not
only
consolidates
current
knowledge
but
a
lso stands
as
an
essential
guide
for
researcher
s
and
practitioners,
and
the
infor
mation
derived
from
this
systematic
review
provides
valuable
guidance
for
future
practices
and
decisions
at
the
intersection
of
AI
and
information
technology
auditin
g.
K
e
y
w
o
r
d
s
:
A
nom
a
li
e
s
de
te
c
ti
on
C
A
E
S
A
R
8
I
nf
or
m
a
ti
on t
e
c
hnol
ogy a
udi
t
M
a
c
hi
ne
l
e
a
r
ni
ng ope
r
a
ti
ons
P
r
e
di
c
ti
ve
m
ode
l
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
C
hr
is
ti
a
n O
va
ll
e
D
e
pa
r
tm
e
nt
o de
I
nge
ni
e
r
ia
,
U
ni
ve
r
s
id
a
d T
e
c
nol
ógi
c
a
de
l
P
e
r
ú
L
im
a
, P
e
r
ú
E
m
a
il
:
dova
ll
e
@
ut
p.e
du.pe
1.
I
N
T
R
O
D
U
C
T
I
O
N
I
n t
h
e
c
ur
r
e
nt
bu
s
in
e
s
s
l
a
nd
s
c
a
p
e
, w
h
e
r
e
di
g
it
i
z
a
ti
o
n i
s
om
ni
pr
e
s
e
nt
, e
n
s
ur
i
ng t
he
in
t
e
gr
it
y
a
n
d
s
e
c
ur
it
y
of
in
f
or
m
a
ti
on
e
m
e
r
ge
s
a
s
a
c
r
it
ic
a
l
pi
l
la
r
to
e
ns
ur
e
t
he
c
o
nt
i
nu
it
y
a
nd e
f
f
i
c
i
e
n
c
y
of
or
g
a
n
iz
a
t
io
n
s
.
A
c
c
or
di
n
g
t
o
G
oe
s
[
1]
,
he
r
e
v
e
a
l
th
a
t
12%
of
c
o
m
p
a
ni
e
s
h
a
v
e
e
xp
e
r
i
e
n
c
e
d
s
ig
ni
f
i
c
a
nt
s
e
c
u
r
it
y i
n
c
id
e
n
t
s
i
n
t
he
l
a
s
t
12
m
ont
h
s
,
ge
n
e
r
a
ti
ng e
c
o
nom
ic
l
o
s
s
e
s
th
a
t
r
e
a
c
h a
n
a
ve
r
a
g
e
of
5
%
of
th
e
ir
a
nn
ua
l
r
e
v
e
n
ue
.
F
ur
th
e
r
m
or
e
,
t
he
y s
u
gg
e
s
t
th
a
t
th
e
r
e
put
a
t
io
n
a
l
im
pa
c
t
of
th
e
s
e
in
c
i
de
nt
s
c
a
n
b
e
e
v
e
n
m
or
e
d
a
m
a
gi
ng,
a
f
f
e
c
ti
ng
th
e
tr
u
s
t
of
c
u
s
t
om
e
r
s
a
nd
bu
s
in
e
s
s
p
a
r
tn
e
r
s
.
I
n
th
e
f
a
c
e
of
t
he
in
c
r
e
a
s
in
g
c
om
pl
e
xi
t
y
of
in
f
or
m
a
ti
on
s
y
s
t
e
m
s
,
in
t
e
r
n
a
l
i
nf
or
m
a
ti
o
n
te
c
hn
ol
o
gy
a
u
di
t
in
g
is
e
m
e
r
gi
n
g
a
s
a
n
i
nd
is
pe
n
s
a
bl
e
to
ol
f
o
r
a
s
s
e
s
s
in
g
th
e
e
f
f
e
c
ti
v
e
n
e
s
s
of
t
he
s
e
c
ur
i
ty
m
a
n
a
g
e
m
e
n
t
in
pl
a
c
e
.
H
a
ll
e
r
[
2]
in
d
ic
a
t
e
t
ha
t
80%
of
c
om
p
a
ni
e
s
c
o
ns
id
e
r
i
nt
e
r
n
a
l
a
ud
it
i
ng
a
s
a
n
e
s
s
e
nt
i
a
l
c
om
po
ne
nt
of
t
he
i
r
r
i
s
k
m
a
n
a
g
e
m
e
nt
s
tr
a
t
e
g
y.
H
ow
e
v
e
r
,
c
h
a
ll
e
n
ge
s
r
e
m
a
in
,
w
it
h
35
%
of
or
g
a
n
iz
a
t
io
n
s
r
e
p
or
ti
ng
di
f
f
ic
ul
ti
e
s
in
a
d
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p
ti
n
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th
e
ir
a
u
di
t
pr
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e
d
ur
e
s
t
o
t
h
e
r
a
pi
d e
vo
lu
t
io
n
of
t
e
c
hno
lo
g
y
[
3]
.
I
n
th
is
c
ont
e
xt
,
e
a
r
ly
a
nom
a
ly
de
te
c
ti
on
a
nd
th
e
im
pl
e
m
e
nt
a
ti
on
of
m
a
c
hi
ne
le
a
r
ni
ng
ope
r
a
ti
ons
(
M
L
O
ps
)
pr
a
c
ti
c
e
s
a
r
e
pr
e
s
e
nt
e
d
a
s
in
nova
ti
ve
s
tr
a
te
gi
e
s
th
a
t
s
tr
e
ngt
he
n
in
f
or
m
a
ti
on
s
e
c
ur
it
y
a
nd
im
pr
ove
th
e
e
f
f
ic
ie
nc
y
of
a
udi
ti
ng
pr
oc
e
s
s
e
s
.
A
ls
a
gh
e
e
r
e
t
al
.
[
4]
in
d
ic
a
te
th
a
t
or
ga
ni
z
a
ti
ons
th
a
t
a
dopt
a
dva
nc
e
d
a
ppr
oa
c
he
s
,
s
u
c
h
a
s
M
L
O
ps
in
te
gr
a
ti
on,
e
xpe
r
ie
nc
e
up
to
45%
f
e
w
e
r
s
e
c
ur
it
y
in
c
id
e
nt
s
,
r
e
duc
in
g
a
s
s
oc
ia
te
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
13
, N
o.
4
,
D
e
c
e
m
be
r
2024
:
3727
-
37
38
3728
c
os
ts
by
30%
.
I
n
a
ddi
ti
on,
N
e
gha
w
i
e
t
al
.
[
5
]
f
ound
th
a
t
65%
of
s
e
c
ur
it
y
pr
of
e
s
s
io
na
ls
be
li
e
ve
th
a
t
a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
(
A
I
)
a
nd ma
c
hi
ne
l
e
a
r
ni
ng (
M
L
)
w
il
l
pl
a
y a
c
r
uc
ia
l
r
ol
e
i
n t
he
f
ut
ur
e
of
i
n
f
or
m
a
ti
on s
e
c
ur
it
y. D
ue
to
th
e
r
is
in
g
nu
m
be
r
of
c
ybe
r
th
r
e
a
ts
,
or
ga
ni
z
a
ti
ons
a
r
e
a
dopt
in
g
a
dva
nc
e
d
a
ppr
oa
c
he
s
to
s
a
f
e
gua
r
d
th
e
ir
di
gi
ta
l
a
s
s
e
ts
.
I
t
is
a
ppr
e
c
ia
te
d
th
a
t
m
os
t
c
om
pa
ni
e
s
ha
v
e
e
xpe
r
ie
nc
e
d
a
n
in
c
r
e
a
s
e
in
a
tt
e
m
pt
e
d
c
ybe
r
-
a
tt
a
c
k
s
c
om
pa
r
e
d t
o t
he
pr
e
vi
ous
ye
a
r
.
T
he
c
om
bi
na
ti
on
of
th
e
s
e
a
ppr
oa
c
he
s
,
a
lo
ng
w
it
h
th
e
a
s
s
e
s
s
m
e
nt
u
s
in
g
th
e
c
ont
in
uous
a
gi
le
e
nt
e
r
pr
is
e
s
e
c
ur
it
y
a
r
c
hi
te
c
tu
r
e
r
e
vi
e
w
in
8
dom
a
in
s
(
C
A
E
S
A
R
8)
m
ode
l,
pr
ovi
de
s
a
c
om
pr
e
he
ns
iv
e
f
r
a
m
e
w
or
k t
ha
t
e
nha
nc
e
s
r
e
s
il
ie
nc
e
a
g
a
in
s
t
pot
e
nt
ia
l
th
r
e
a
ts
a
n
d opti
m
iz
e
s
i
nt
e
r
na
l
a
udi
t
pr
oc
e
dur
e
s
. P
r
e
vi
ous
im
pl
e
m
e
nt
a
ti
ons
ha
ve
s
how
n
a
n
im
pr
ove
m
e
nt
in
th
e
e
f
f
ic
ie
nc
y
of
a
udi
t
pr
oc
e
s
s
e
s
w
he
n
us
in
g
th
e
C
A
E
S
A
R
8
m
ode
l
a
s
a
n a
s
s
e
s
s
m
e
nt
to
ol
[
6]
.
T
he
r
e
f
or
e
,
th
e
que
s
ti
on
a
t
ha
n
d
is
:
H
ow
doe
s
th
e
im
pl
e
m
e
nt
a
ti
on
of
M
L
O
ps
m
e
th
odol
ogy f
or
a
nom
a
ly
i
de
nt
if
ic
a
ti
on a
f
f
e
c
t
th
e
i
nt
e
gr
a
ti
on o
f
AI
pr
oj
e
c
ts
a
ppl
ie
d i
n c
om
put
e
r
a
udi
ti
ng?
T
o
c
om
pr
e
he
ns
iv
e
ly
a
ddr
e
s
s
th
is
qu
e
s
ti
on,
w
e
a
im
to
a
na
ly
z
e
th
e
e
f
f
e
c
ti
ve
ne
s
s
of
pr
e
di
c
ti
ve
a
nom
a
ly
m
ode
ls
in
e
a
r
ly
th
r
e
a
t
de
te
c
ti
on
in
pr
oduc
ti
on
pr
oj
e
c
t
e
nvi
r
onm
e
nt
s
.
R
e
c
e
nt
s
tu
di
e
s
,
A
m
in
i
e
t
al
.
[
7]
ha
ve
s
how
n
th
a
t
th
e
s
e
m
ode
ls
a
c
hi
e
ve
a
n
80%
de
te
c
ti
on
r
a
te
c
om
pa
r
e
d
to
tr
a
di
ti
ona
l
m
e
th
ods
.
A
ls
o,
a
25%
de
c
r
e
a
s
e
in
th
e
im
pa
c
t
of
pr
e
vi
ous
ly
unde
te
c
te
d
in
c
id
e
nt
s
ha
s
be
e
n
ob
s
e
r
ve
d
.
T
he
r
e
f
or
e
,
our
obj
e
c
ti
ve
is
to
e
va
lu
a
te
th
e
e
f
f
e
c
t
of
th
e
in
te
gr
a
ti
on
of
M
L
O
ps
on
th
e
e
f
f
e
c
ti
ve
m
a
na
ge
m
e
nt
of
th
e
s
e
m
ode
ls
,
pa
r
ti
c
ul
a
r
ly
in
th
e
c
ont
e
xt
of
in
te
r
na
l
in
f
or
m
a
ti
on
t
e
c
hn
ol
o
gy
a
udi
ti
ng.
P
e
lt
one
n
a
nd
D
i
a
s
[
8]
s
ugg
e
s
t
th
a
t
or
ga
ni
z
a
ti
ons
th
a
t
a
dopt
M
L
O
ps
e
xpe
r
ie
nc
e
im
pr
ove
d
m
ode
l
a
da
pt
a
bi
li
ty
,
r
e
s
ul
ti
ng
in
in
c
r
e
a
s
e
d
e
f
f
e
c
ti
ve
ne
s
s
dur
in
g
une
xpe
c
te
d
th
r
e
a
t
s
it
ua
ti
ons
.
I
n a
ddi
ti
on,
th
e
y
in
di
c
a
t
e
th
a
t
or
ga
ni
z
a
ti
ons
i
m
pl
e
m
e
nt
in
g
M
L
O
ps
e
xp
e
r
ie
nc
e
a
r
e
duc
ti
on
in
ti
m
e
s
pe
nt
on
m
a
nua
l
a
udi
ti
ng
ta
s
ks
[
9]
.
F
in
a
ll
y,
th
e
y
c
r
it
ic
a
ll
y
e
xa
m
in
e
th
e
va
li
di
ty
a
nd
a
ppl
ic
a
bi
li
ty
of
th
e
C
A
E
S
A
R
8
m
ode
l
a
s
a
n
a
s
s
e
s
s
m
e
nt
to
ol
in
th
is
a
r
e
a
.
A
c
c
or
di
ng
to
L
of
t
e
t
a
l.
[
10]
,
a
pos
it
iv
e
c
or
r
e
la
ti
on
of
85%
w
a
s
f
ound
b
e
twe
e
n
th
e
e
va
lu
a
ti
ons
c
ondu
c
te
d
u
s
in
g
th
e
C
A
E
S
A
R
8
m
ode
l
a
nd
th
e
e
f
f
e
c
ti
ve
n
e
s
s
of
th
e
im
pl
e
m
e
nt
e
d s
e
c
ur
it
y c
ont
r
ol
s
, i
ndi
c
a
ti
ng i
ts
us
e
f
ul
ne
s
s
a
s
a
n e
va
lu
a
ti
on
s
ta
nda
r
d.
T
o
s
y
s
t
e
m
a
t
ic
a
ll
y
a
d
dr
e
s
s
t
he
pr
op
os
e
d
obj
e
c
ti
v
e
s
,
w
e
w
il
l
c
o
ndu
c
t
a
c
om
pr
e
h
e
n
s
i
ve
r
e
vi
e
w
o
f
t
he
a
c
a
d
e
m
ic
l
it
e
r
a
tu
r
e
. T
he
s
e
a
r
c
h
f
or
r
e
l
e
v
a
n
t
s
tu
di
e
s
w
i
ll
b
e
p
e
r
f
o
r
m
e
d i
n da
ta
ba
s
e
s
,
f
o
ll
o
w
i
ng s
p
e
c
if
i
c
i
nc
lu
s
io
n
c
r
it
e
r
i
a
th
a
t
f
oc
u
s
on
w
or
k
s
t
ha
t
im
p
le
m
e
nt
pr
e
d
ic
t
iv
e
m
o
de
ls
of
a
n
om
a
li
e
s
a
nd
M
L
O
p
s
,
e
va
l
u
a
te
d
u
s
i
ng
th
e
C
A
E
S
A
R
8
m
o
de
l
i
n
th
e
c
o
nt
e
xt
of
i
nt
e
r
n
a
l
in
f
or
m
a
ti
on
t
e
c
hn
o
lo
gy
a
u
di
ti
ng
.
S
tu
d
y
s
e
l
e
c
ti
o
n,
d
a
t
a
e
x
tr
a
c
t
io
n
,
a
nd
qu
a
li
t
y
a
s
s
e
s
s
m
e
nt
w
i
ll
b
e
c
ond
uc
te
d
to
e
n
s
ur
e
th
e
in
te
gr
it
y
a
n
d
obj
e
c
ti
vi
t
y
of
th
e
pr
oc
e
s
s
.
T
hi
s
m
e
t
hod
ol
ogy
w
il
l
pr
ovi
de
a
s
tr
o
ng
f
oun
d
a
ti
on
f
or
c
o
m
pr
e
h
e
ns
iv
e
ly
a
d
dr
e
s
s
i
ng
t
he
r
e
s
e
a
r
c
h
qu
e
s
ti
on
a
nd
r
e
a
c
h
in
g
m
e
a
ni
ngf
ul
c
on
c
lu
s
i
on
s
w
i
th
i
n
th
e
pr
op
o
s
e
d
s
c
o
pe
o
f
s
tu
dy
[
11]
–
[
1
5]
.
I
t
i
s
im
p
or
t
a
nt
t
o
not
e
th
a
t
c
om
put
e
r
s
e
c
ur
it
y
a
nd
a
ud
it
e
f
f
i
c
i
e
n
c
y
a
r
e
not
onl
y
r
e
la
te
d
to
t
e
c
hn
ol
o
gy
but
a
l
s
o
to
s
t
a
f
f
tr
a
i
ni
n
g.
M
u
kho
pa
dh
ya
y
a
n
d
J
a
in
[
16
]
s
t
a
t
e
t
ha
t
7
0%
of
s
e
c
ur
it
y
br
e
a
c
h
e
s
a
r
e
c
a
u
s
e
d
b
y
hum
a
n
e
r
r
or
,
e
m
p
ha
s
i
z
i
ng
th
e
s
i
gni
f
ic
a
n
c
e
of
c
ont
i
nu
ou
s
tr
a
i
ni
ng
pr
ogr
a
m
s
.
T
h
e
r
e
f
or
e
,
a
c
o
m
pr
e
he
n
s
iv
e
a
p
pr
o
a
c
h
t
ha
t
i
nc
lu
d
e
s
p
e
r
s
o
nn
e
l
tr
a
i
ni
n
g
a
n
d
a
w
a
r
e
n
e
s
s
c
a
n
a
l
s
o
be
ne
f
it
th
e
im
p
le
m
e
nt
a
ti
o
n
of
pr
e
di
c
ti
v
e
m
o
de
ls
a
nd
M
L
O
p
s
.
I
n
t
he
l
a
s
t
t
w
o
ye
a
r
s
,
th
e
r
e
ha
s
b
e
e
n
a
n
in
c
r
e
a
s
e
in
th
e
a
dop
ti
o
n
of
A
I
-
b
a
s
e
d
a
ppr
o
a
c
h
e
s
i
n
in
t
e
r
n
a
l
a
ud
it
i
ng
[
17]
.
T
hi
s
tr
e
nd
e
m
p
ha
s
iz
e
s
t
he
im
por
ta
nc
e
a
n
d
i
n
c
r
e
a
s
in
g
a
c
c
e
p
ta
nc
e
of
a
d
va
n
c
e
d
t
e
c
hn
ol
ogi
e
s
i
n
a
u
di
t
e
nvi
r
on
m
e
nt
s
.
W
he
n
im
pl
e
m
e
n
ti
n
g
M
L
O
p
s
,
it
i
s
c
r
u
c
i
a
l
to
not
e
t
ha
t
60%
of
or
g
a
ni
z
a
ti
on
s
th
a
t
ha
ve
a
d
opt
e
d
M
L
O
p
s
r
e
por
t
a
3
0%
im
pr
o
v
e
m
e
nt
i
n
c
ol
l
a
b
or
a
ti
o
n
b
e
tw
e
e
n
s
e
c
ur
it
y
a
nd
d
e
v
e
l
opm
e
nt
t
e
a
m
s
[
1
8]
.
T
h
i
s
f
in
di
n
g
un
de
r
s
c
or
e
s
th
e
s
i
gni
f
ic
a
n
c
e
of
c
r
o
s
s
-
f
un
c
ti
on
a
l
c
ol
la
bor
a
ti
on
f
or
t
he
s
uc
c
e
s
s
of
ML
-
b
a
s
e
d
s
e
c
u
r
it
y
in
it
i
a
ti
ve
s
.
R
e
t
ur
n
in
g
to
t
h
e
C
A
E
S
A
R
8
m
od
e
l,
it
ha
s
b
e
e
n
ob
s
e
r
v
e
d
t
ha
t
it
s
a
p
pl
i
c
a
ti
o
n
ha
s
l
e
d
to
a
s
ig
ni
f
i
c
a
nt
im
pr
ov
e
m
e
nt
in
t
h
e
a
li
gnm
e
n
t
of
in
t
e
r
na
l
a
u
di
t
pr
a
c
ti
c
e
s
w
it
h
in
t
e
r
n
a
t
io
n
a
l
ly
r
e
c
ogn
iz
e
d
s
e
c
ur
it
y
s
t
a
n
da
r
ds
.
L
of
t
e
t
al
.
[
10]
s
h
ow
s
th
a
t
80%
of
or
g
a
n
iz
a
t
io
n
s
r
e
p
or
t
im
pr
o
ve
d
a
li
g
nm
e
nt
w
it
h
s
e
c
ur
it
y
f
r
a
m
e
w
or
k
s
s
uc
h
a
s
I
S
O
27
00
1
a
f
t
e
r
im
pl
e
m
e
n
ti
n
g
C
A
E
S
A
R
8.
M
or
e
o
ve
r
,
da
ta
s
ho
w
s
t
ha
t
i
m
pl
e
m
e
n
ti
ng
ML
p
r
o
c
e
s
s
e
s
t
hr
ou
gh
M
L
O
p
s
c
a
n
l
e
a
d
to
a
1
5%
r
e
d
uc
ti
o
n
i
n
o
pe
r
a
t
io
n
a
l
c
o
s
t
s
r
e
l
a
t
e
d
t
o
s
e
c
ur
it
y
m
a
n
a
g
e
m
e
nt
f
or
70%
of
or
g
a
n
iz
a
ti
on
s
[
19]
.
T
h
e
in
t
e
gr
a
ti
on
of
pr
e
d
ic
ti
v
e
a
n
om
a
ly
m
od
e
l
s
s
upp
or
t
e
d
by
M
L
O
p
s
,
e
v
a
l
ua
te
d
u
s
i
ng
th
e
C
A
E
S
A
R
8
m
od
e
l,
r
e
pr
e
s
e
nt
s
a
c
o
m
pr
e
h
e
n
s
i
ve
a
p
pr
o
a
c
h
t
o
s
tr
e
ng
th
e
ni
ng
i
nf
or
m
a
ti
on
t
e
c
hn
ol
o
gy
s
e
c
ur
it
y
a
nd
o
pt
i
m
i
z
in
g
in
te
r
na
l
a
ud
it
pr
o
c
e
s
s
e
s
.
2.
M
E
T
H
O
D
O
L
O
G
Y
I
n
th
e
dyna
m
ic
e
nvi
r
onm
e
nt
o
f
in
te
r
na
l
in
f
or
m
a
t
io
n
te
c
h
no
lo
g
y
a
udi
ti
ng,
w
he
r
e
c
ons
ta
nt
ly
e
vol
vi
ng
in
f
or
m
a
ti
on
s
e
c
ur
it
y
th
r
e
a
ts
de
m
a
nd
th
e
im
pl
e
m
e
nt
a
ti
on
of
a
d
va
nc
e
d
te
c
hnol
ogi
e
s
to
s
a
f
e
gua
r
d
th
e
in
te
gr
it
y
a
nd
s
e
c
ur
it
y
of
s
ys
te
m
s
,
a
r
ig
or
ous
m
e
th
odol
ogy
is
pr
opos
e
d
t
o
pr
ot
e
c
t
th
e
in
te
gr
it
y
a
nd
s
e
c
ur
it
y
of
s
ys
te
m
s
a
ga
in
s
t
c
on
s
ta
nt
ly
e
vol
vi
ng
in
f
or
m
a
ti
on
s
e
c
ur
it
y
th
r
e
a
ts
. T
he
m
e
th
odol
ogy
f
ol
lo
w
s
a
s
tr
uc
tu
r
e
d
a
ppr
oa
c
h
th
a
t
be
gi
ns
w
it
h
th
e
pr
e
c
is
e
f
or
m
ul
a
ti
on
o
f
th
e
r
e
s
e
a
r
c
h
que
s
ti
on.
T
he
in
te
gr
a
ti
on
of
th
e
M
L
O
ps
m
e
th
odol
ogy
f
o
r
a
nom
a
ly
i
de
nt
if
ic
a
ti
on i
n
AI
pr
oj
e
c
ts
a
ppl
ie
d t
o c
om
put
e
r
a
udi
ti
ng, t
oge
th
e
r
w
it
h s
pe
c
ia
li
z
e
d t
ool
s
s
uc
h a
s
t
he
C
A
E
S
A
R
8 m
ode
l,
i
s
e
m
e
r
gi
ng a
s
a
pot
e
nt
ia
ll
y i
nnova
ti
ve
s
tr
a
te
gy.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
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8938
M
e
th
odol
ogy
appli
e
d t
o c
om
put
e
r
audit
w
it
h ar
ti
fi
c
ia
l
in
te
ll
ig
e
nc
e
…
(
She
y
la
R
e
y
m
unde
z
Suar
e
z
)
3729
2.1.
R
e
s
e
ar
c
h
q
u
e
s
t
io
n
T
h
e
r
e
s
e
a
r
c
h qu
e
s
ti
o
n
f
or
t
hi
s
s
y
s
t
e
m
a
ti
c
r
e
vi
e
w
f
o
c
u
s
e
s
on
un
de
r
s
t
a
n
di
n
g t
h
e
i
m
p
a
c
t
of
i
m
p
le
m
e
nt
i
ng
th
e
M
L
O
p
s
m
e
th
od
ol
o
gy
on
a
nom
a
l
y
i
d
e
nt
if
i
c
a
t
io
n
d
ur
i
ng
th
e
in
t
e
gr
a
ti
on
of
A
I
pr
o
je
c
t
s
a
ppl
ie
d
i
n
c
om
pu
te
r
a
ud
it
i
ng
.
T
h
e
a
c
r
on
ym
pop
ul
a
ti
on,
in
te
r
ve
nt
i
on
,
c
om
pa
r
is
on
,
o
ut
c
om
e
(
P
I
C
O
)
pr
ovi
de
s
a
lo
g
ic
a
l
s
tr
u
c
t
ur
e
f
or
br
e
a
ki
ng
do
w
n
th
e
r
e
s
e
a
r
c
h
q
ue
s
t
io
n
.
I
n
th
is
c
ont
e
x
t,
t
h
e
po
pu
la
t
io
n
(
P
)
i
s
de
f
in
e
d
a
s
AI
pr
o
je
c
t
s
a
p
pl
i
e
d
in
c
om
put
e
r
a
u
di
t
in
g
, t
he
i
n
te
r
ve
nt
i
on (
I
)
r
e
f
e
r
s
to
t
he
a
ppl
ic
a
t
io
n
of
t
h
e
M
L
O
p
s
m
e
th
o
do
lo
g
y i
n t
he
s
e
pr
oj
e
c
ts
, t
he
c
om
pa
r
i
s
on
(
C
)
c
o
ul
d
in
v
ol
v
e
p
r
oj
e
c
t
s
th
a
t
do
n
ot
u
s
e
M
L
O
p
s
or
us
e
o
th
e
r
m
e
th
od
s
,
a
nd
t
he
out
c
o
m
e
(
O
)
f
oc
us
e
s
o
n
t
he
id
e
n
ti
f
i
c
a
ti
on
of
a
no
m
a
li
e
s
dur
in
g
th
e
in
t
e
gr
a
t
io
n
of
th
e
s
e
pr
o
je
c
t
s
.
A
ta
bl
e
h
a
s
b
e
e
n
c
r
e
a
t
e
d
to
pr
ov
id
e
a
s
y
nt
h
e
s
i
s
vi
s
ua
ll
y
a
n
d
c
on
c
i
s
e
ly
,
th
i
s
t
a
b
le
i
nc
lu
d
e
s
r
e
l
e
v
a
n
t
c
om
p
on
e
nt
s
s
u
c
h
a
s
th
e
r
e
s
e
a
r
c
h
que
s
ti
o
n,
th
e
P
I
C
O
b
r
e
a
k
do
w
n,
a
nd
th
e
ke
y
s
o
ur
c
e
s
u
s
e
d
i
n
th
e
r
e
vi
e
w
.
T
a
bl
e
1
s
e
r
v
e
s
a
s
a
vi
s
ua
l
r
e
s
our
c
e
t
o
f
a
c
il
i
ta
t
e
a
qu
ic
k
a
n
d
c
l
e
a
r
un
de
r
s
ta
ndi
ng
of
t
h
e
e
s
s
e
nt
i
a
l
e
l
e
m
e
n
t
s
of
t
he
r
e
s
e
a
r
c
h.
T
a
bl
e
1. R
e
s
e
a
r
c
h
s
ynt
he
s
i
s
R
e
s
e
a
r
c
h
que
s
t
i
on
¿
H
ow
doe
s
t
he
i
m
pl
e
m
e
nt
a
t
i
on of
t
he
M
L
O
ps
m
e
t
hodol
ogy a
f
f
e
c
t
t
he
i
de
nt
i
f
i
c
a
t
i
on of
a
nom
a
l
i
e
s
i
n t
he
i
nt
e
gr
a
t
i
on of
AI
pr
oj
e
c
t
s
a
ppl
i
e
d i
n c
om
put
e
r
a
udi
t
i
ng?
P
E
A
K
br
e
a
kdow
n
P
:
T
hi
s
r
e
vi
e
w
f
oc
us
e
s
on
s
pe
c
i
f
i
c
AI
pr
oj
e
c
t
s
a
p
pl
i
e
d
i
n c
om
p
ut
e
r
a
u
di
t
i
ng,
a
d
d
r
e
s
s
i
ng
.
I
:
i
m
p
l
e
m
e
nt
a
t
i
on
o
f
t
he
M
L
O
ps
m
e
t
h
od
ol
o
gy
c
o
m
pa
r
e
d
t
o
p
r
oj
e
c
t
s
t
ha
t
d
o n
ot
us
e
M
L
O
ps
o
r
a
ppl
y
ot
he
r
m
e
t
hod
ol
og
i
e
s
.
C
:
c
om
p
a
r
i
s
o
n w
i
l
l
i
nc
l
u
de
a
s
pe
c
t
s
of
e
f
f
i
c
i
e
nc
y,
pe
r
f
o
r
m
a
nc
e
, a
nd
a
no
m
a
l
y
de
t
e
c
t
i
o
n
du
r
i
ng
i
n
t
e
g
r
a
t
i
on
.
O
:
ob
j
e
c
t
i
v
e
i
s
t
o
e
va
l
ua
t
e
t
he
i
m
pa
c
t
of
M
L
O
ps
o
n t
he
e
f
f
e
c
t
i
ve
i
d
e
n
t
i
f
i
c
a
t
i
o
n o
f
a
no
m
a
l
i
e
s
i
n
t
h
e
s
e
pr
oj
e
c
t
s
K
e
y
s
our
c
e
s
-
A
kki
ne
ni
e
t
al
.
[
3]
f
ound
t
ha
t
t
he
s
uc
c
e
s
s
f
ul
i
m
pl
e
m
e
nt
a
t
i
on
of
M
L
O
ps
s
i
gni
f
i
c
a
nt
l
y
i
m
pr
ove
d
t
he
l
i
f
e
c
yc
l
e
e
f
f
i
c
i
e
nc
y of
AI
m
ode
l
s
, s
ugge
s
t
i
ng a
pos
i
t
i
ve
i
m
pa
c
t
on
i
nf
or
m
a
t
i
on t
e
c
hnol
og
y
a
udi
t
pr
oj
e
c
t
s
.
-
S
pe
t
h
e
t
al
.
[
20]
unde
r
l
i
ne
t
he
ne
e
d t
o a
da
pt
M
L
O
p
s
t
o t
he
s
pe
c
i
f
i
c
c
ha
r
a
c
t
e
r
i
s
t
i
c
s
of
c
om
put
e
r
a
udi
t
i
ng, hi
ghl
i
ght
i
ng
t
he
i
m
por
t
a
nc
e
of
c
ons
i
de
r
i
ng t
he
pa
r
t
i
c
ul
a
r
a
s
pe
c
t
s
of
t
hi
s
di
s
c
i
pl
i
ne
dur
i
ng i
m
pl
e
m
e
nt
a
t
i
on.
-
M
i
ñon
e
t
al
.
[
21]
pr
ovi
de
a
c
om
pr
e
he
ns
i
ve
c
om
pa
r
a
t
i
ve
r
e
vi
e
w
of
va
r
i
ous
m
e
t
hodol
ogi
e
s
u
s
e
d
i
n
AI
pr
oj
e
c
t
s
,
of
f
e
r
i
ng a
ba
s
i
s
f
or
c
ont
r
a
s
t
i
ng t
he
e
f
f
e
c
t
i
ve
ne
s
s
of
M
L
O
ps
w
i
t
h ot
he
r
c
om
m
on
pr
a
c
t
i
c
e
s
.
-
G
ur
s
e
s
a
nd
M
ont
i
[
22]
pr
e
s
e
nt
c
a
s
e
s
t
udi
e
s
t
ha
t
e
xpl
or
e
a
nom
a
l
y
de
t
e
c
t
i
on
a
nd
pe
r
f
or
m
a
nc
e
i
m
pr
ove
m
e
nt
i
n
s
i
m
i
l
a
r
AI
pr
oj
e
c
t
s
, pr
ovi
di
ng va
l
ua
bl
e
i
ns
i
ght
s
f
or
i
m
pa
c
t
e
va
l
ua
t
i
on.
I
n
a
ddi
ti
on,
a
c
om
pa
r
a
ti
ve
ta
bl
e
ha
s
b
e
e
n
c
r
e
a
te
d
to
out
li
ne
th
e
ke
y
a
s
pe
c
ts
c
ove
r
e
d
by
th
e
f
our
s
e
le
c
te
d
s
tu
di
e
s
:
A
kki
ne
ni
e
t
al
.
[
3
]
,
S
pe
th
e
t
al
.
[
20]
,
M
iñ
on
e
t
al
.
[
21
]
,
a
nd
G
ur
s
e
s
a
nd
M
ont
i
[
22
]
.
T
a
bl
e
2
pr
e
s
e
nt
s
c
om
pr
e
he
n
s
iv
e
in
f
or
m
a
ti
on
on
th
e
im
pa
c
t
of
M
L
O
ps
,
th
e
a
da
pt
a
ti
on
of
M
L
O
ps
to
c
om
put
e
r
a
udi
ti
ng,
a
c
om
pa
r
is
on
w
it
h
o
th
e
r
m
e
th
odol
ogi
e
s
,
a
nd
th
e
in
c
lu
s
io
n
of
c
a
s
e
s
tu
di
e
s
.
T
he
ta
bl
e
pr
e
s
e
nt
s
th
e
s
pe
c
if
ic
c
ont
r
ib
ut
io
ns
of
e
a
c
h
s
tu
dy,
s
e
r
vi
ng
a
s
a
qui
c
k
r
e
f
e
r
e
nc
e
to
ol
to
a
s
s
e
s
s
th
e
ir
pe
r
s
pe
c
ti
ve
s
a
nd
li
m
it
a
ti
ons
.
I
n
a
w
a
y,
th
e
c
om
pa
r
a
ti
ve
ta
bl
e
hi
ghl
ig
ht
s
th
e
di
ve
r
s
it
y
of
a
ppr
oa
c
he
s
a
nd
c
onc
lu
s
io
ns
in
th
e
li
te
r
a
tu
r
e
r
e
vi
e
w
e
d,
pr
ovi
di
ng
a
s
ol
id
ba
s
is
f
or
unde
r
s
t
a
ndi
ng
t
he
a
ppl
ic
a
ti
on
of
M
L
O
p
s
in
A
I
pr
oj
e
c
ts
in
th
e
e
xa
c
t
f
ie
ld
of
c
om
put
e
r
a
udi
ti
ng.
T
a
bl
e
2. Q
ui
c
k ove
r
vi
e
w
of
t
he
ke
y
a
s
pe
c
ts
a
ddr
e
s
s
e
d
A
s
pe
c
t
s
A
ut
hor
s
'
a
ppr
oa
c
he
s
a
nd f
i
ndi
ngs
I
m
pa
c
t
of
M
L
O
p
s
T
he
f
i
ndi
ngs
of
A
kki
ne
ni
e
t
al
.
[
3]
r
e
ve
a
l
a
s
i
gni
f
i
c
a
nt
pos
i
t
i
ve
i
m
pa
c
t
of
t
he
i
m
pl
e
m
e
nt
a
t
i
on
of
t
he
M
L
O
ps
m
e
t
hodol
ogy
on t
he
l
i
f
e
c
yc
l
e
e
f
f
i
c
i
e
nc
y
of
A
I
m
ode
l
s
.
I
n
t
he
s
t
udy
by
S
pe
t
h
e
t
al
.
[
20]
,
t
he
i
m
por
t
a
nc
e
of
a
da
pt
i
ng
t
he
M
L
O
p
s
m
e
t
hodol
ogy
t
o
t
he
s
pe
c
i
f
i
c
c
ha
r
a
c
t
e
r
i
s
t
i
c
s
of
c
om
put
e
r
a
udi
t
i
ng
i
s
e
m
pha
s
i
z
e
d.
M
i
ñon
e
t
al
.
[
21]
of
f
e
r
a
de
t
a
i
l
e
d
c
om
pa
r
a
t
i
ve
r
e
vi
e
w
of
va
r
i
ous
m
e
t
hodol
ogi
e
s
us
e
d
i
n
A
I
pr
oj
e
c
t
s
.
T
he
r
e
s
e
a
r
c
h
by
G
ur
s
e
s
e
t
al
.
[
22]
pr
ovi
de
s
s
pe
c
i
f
i
c
c
a
s
e
s
t
udi
e
s
t
ha
t
e
xpl
or
e
a
nom
a
l
y
de
t
e
c
t
i
on
a
nd
pe
r
f
or
m
a
nc
e
i
m
pr
ove
m
e
nt
i
n A
I
pr
oj
e
c
t
s
.
A
da
pt
a
t
i
on t
o
i
nf
or
m
a
t
i
on
t
e
c
hnol
ogy
a
udi
t
A
l
t
hough
A
kki
ne
ni
e
t
al
.
[
3]
do
not
s
pe
c
i
f
y
de
t
a
i
l
s
a
bout
t
he
a
da
pt
a
t
i
on
of
M
L
O
p
s
t
o
c
om
put
e
r
a
udi
t
i
ng,
t
he
pos
i
t
i
ve
r
e
s
ul
t
s
s
ugge
s
t
t
ha
t
t
he
m
e
t
hodol
ogy
c
a
n
be
a
ppl
i
e
d
e
f
f
e
c
t
i
ve
l
y
i
n
t
hi
s
c
ont
e
xt
.
S
pe
t
h
e
t
al
.
[
20]
hi
ghl
i
ght
t
he
ne
e
d
t
o
a
da
pt
M
L
O
p
s
t
o
t
he
pa
r
t
i
c
ul
a
r
i
t
i
e
s
of
c
om
put
e
r
a
udi
t
i
ng,
unde
r
l
i
ni
ng
t
he
i
m
por
t
a
nc
e
of
c
ons
i
de
r
i
ng
t
he
s
pe
c
i
f
i
c
a
s
pe
c
t
s
of
t
hi
s
di
s
c
i
pl
i
ne
w
he
n
i
m
pl
e
m
e
nt
i
ng
t
he
m
e
t
hodol
ogy.
T
he
a
da
pt
a
t
i
on
of
M
L
O
p
s
t
o
c
om
put
e
r
a
udi
t
i
ng
i
s
not
de
t
a
i
l
e
d
i
n
t
he
c
om
pa
r
a
t
i
ve
r
e
vi
e
w
by
M
i
ñon
e
t
al
.
[
21]
.
N
o
s
pe
c
i
f
i
c
i
nf
or
m
a
t
i
on
on
t
he
a
da
pt
a
t
i
on
of
M
L
O
ps
t
o
i
nf
or
m
a
t
i
on
t
e
c
hnol
ogy
a
udi
t
i
ng
i
s
pr
ovi
de
d
i
n
t
he
s
t
udy by G
ur
s
e
s
e
t
al
.
[
22
]
.
C
om
pa
r
i
s
on
w
i
t
h ot
he
r
m
e
t
hodol
ogi
e
s
A
kki
ne
ni
e
t
al
.
[
3
]
do
not
de
t
a
i
l
t
he
c
om
pa
r
i
s
on
of
M
L
O
p
s
w
i
t
h
ot
he
r
m
e
t
hodol
ogi
e
s
i
n
t
he
i
r
s
t
udy.
T
he
a
bs
e
n
c
e
of
t
hi
s
i
nf
or
m
a
t
i
on
c
oul
d
be
c
ons
i
de
r
e
d
a
l
i
m
i
t
a
t
i
on
of
t
he
s
t
udy.
S
pe
t
h
e
t
al
.
[
20]
do
not
s
pe
c
i
f
i
c
a
l
l
y
a
ddr
e
s
s
a
c
om
pa
r
i
s
on
w
i
t
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[
21]
of
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1072
179
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.
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ddr
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th
e
im
pl
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m
e
nt
a
ti
on
of
th
e
M
L
O
p
s
m
e
th
odol
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in
AI
pr
oj
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t
s
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ppl
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to
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put
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li
m
it
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to
s
tu
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s
publ
is
h
e
d
be
tw
e
e
n
2018
a
nd
2024
in
E
ngl
is
h
a
nd
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pa
ni
s
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to
m
a
in
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r
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e
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s
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bl
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s
5
a
nd
6
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s
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us
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th
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in
c
lu
s
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a
nd
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xc
lu
s
io
n
c
r
it
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r
ia
a
nd
pr
ovi
de
a
de
ta
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l
s
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s
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s
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a
ti
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r
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vi
e
w
.
F
ig
ur
e
1
s
how
s
th
e
a
ppl
ic
a
ti
on
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th
e
P
R
I
S
M
A
m
e
th
odol
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e
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ul
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tt
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ta
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d
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lu
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nd
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xc
lu
s
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r
it
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r
ia
,
w
hi
c
h
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ll
ow
e
d
th
e
i
de
nt
if
ic
a
ti
on of
r
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va
nt
s
our
c
e
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t
ha
t
w
il
l
s
uppor
t
th
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obj
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c
t
iv
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of
t
hi
s
s
ys
te
m
a
ti
c
r
e
vi
e
w
.
T
a
bl
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5. I
nc
lu
s
io
n
c
r
it
e
r
ia
a
nd i
t
i
s
j
us
ti
f
ic
a
ti
on
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nc
l
us
i
on
c
r
i
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e
r
i
a
J
us
t
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f
i
c
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t
i
on
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nc
l
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pr
i
m
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r
y s
t
udi
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s
, s
y
s
t
e
m
a
t
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c
r
e
vi
e
w
s
, a
nd m
e
t
a
-
a
na
l
ys
e
s
.
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r
i
m
a
r
y
s
t
udi
e
s
pr
ovi
de
de
t
a
i
l
e
d
i
nf
or
m
a
t
i
on,
w
hi
l
e
s
ys
t
e
m
a
t
i
c
r
e
vi
e
w
s
pr
ovi
de
a
n ove
r
vi
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w
of
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he
e
xi
s
t
i
ng l
a
nds
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a
pe
.
-
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nc
l
ude
s
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udi
e
s
publ
i
s
he
d be
t
w
e
e
n 2018 a
nd 2024.
T
hi
s
t
i
m
e
r
a
nge
gua
r
a
nt
e
e
s
t
he
i
nc
l
u
s
i
on of
r
e
c
e
nt
s
t
udi
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s
r
e
l
e
va
nt
t
o t
he
c
ur
r
e
nt
M
L
O
ps
m
e
t
hodol
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-
I
nc
l
ude
s
t
udi
e
s
i
n E
ngl
i
s
h a
nd S
pa
ni
s
h.
T
he
i
nc
l
us
i
on
of
m
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pl
e
l
a
ngua
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s
e
xpa
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t
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a
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a
l
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i
ngui
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D
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m
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m
e
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L
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p
s
i
n
AI
pr
oj
e
c
t
s
i
n c
om
put
e
r
a
udi
t
i
ng.
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he
a
i
m
i
s
t
o
e
ns
ur
e
t
he
r
e
l
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va
n
c
e
a
nd
f
oc
us
of
t
he
i
nc
l
ud
e
d
s
t
udi
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s
i
n
r
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l
a
t
i
on t
o t
he
r
e
s
e
a
r
c
h que
s
t
i
on.
T
a
bl
e
6. E
xc
lu
s
io
n
c
r
it
e
r
ia
a
nd i
t
i
s
j
us
ti
f
ic
a
ti
on
E
xc
l
us
i
on
c
r
i
t
e
r
i
a
J
us
t
i
f
i
c
a
t
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on
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E
xc
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or
i
a
l
s
,
c
om
m
e
nt
s
a
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s
t
udi
e
s
not
r
e
l
a
t
e
d
t
o
t
he
i
m
pl
e
m
e
nt
a
t
i
on of
M
L
O
ps
i
n c
om
put
e
r
a
udi
t
i
ng.
S
our
c
e
s
t
ha
t
do
not
di
r
e
c
t
l
y
c
ont
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but
e
t
o
t
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e
va
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ua
t
i
on
of
t
he
i
m
pa
c
t
of
M
L
O
ps
a
r
e
e
xc
l
ude
d.
F
ig
ur
e
1. I
de
nt
if
ic
a
ti
on of
s
tu
di
e
s
t
hr
ough P
R
I
S
M
A
da
ta
ba
s
e
a
n
d r
e
gi
s
tr
ie
s
T
he
s
e
a
r
c
h
s
tr
a
te
gy,
a
li
gne
d
w
it
h
th
e
P
R
I
S
M
A
m
e
th
odol
ogy,
w
a
s
de
ve
lo
pe
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in
th
r
e
e
f
unda
m
e
nt
a
l
pha
s
e
s
to
e
n
s
ur
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th
e
c
om
pl
e
te
ne
s
s
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qu
a
li
ty
of
th
e
s
ys
t
e
m
a
ti
c
r
e
vi
e
w
[
23]
.
I
n
th
e
f
ir
s
t
id
e
nt
if
ic
a
ti
on
pha
s
e
,
ke
y
da
ta
ba
s
e
s
w
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r
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s
e
le
c
te
d,
s
u
c
h
a
s
P
ubM
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d
a
nd
S
c
opus
,
r
e
c
ogni
z
e
d
f
or
th
e
ir
br
e
a
dt
h
a
nd
r
e
le
va
nc
e
in
th
e
f
ie
ld
of
AI
a
nd
c
om
put
e
r
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udi
ti
ng.
T
hi
s
s
tr
a
te
gi
c
c
hoi
c
e
e
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s
ur
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e
in
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of
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s
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pe
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tr
um
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r
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s
s
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nt
ia
l
to
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s
s
th
e
c
om
pl
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xi
ty
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im
pl
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m
e
nt
in
g
M
L
O
ps
in
c
om
put
e
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udi
t
pr
oj
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c
ts
.
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n
a
ddi
ti
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dupl
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te
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m
in
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te
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opt
im
iz
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o
ur
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ti
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e
n
s
ur
e
th
e
in
te
gr
it
y
of
th
e
pr
oc
e
s
s
.
T
he
s
e
c
ond
pha
s
e
,
s
c
r
e
e
ni
ng,
f
oc
us
e
d
on
th
e
a
ppl
ic
a
ti
on
of
pr
e
vi
ous
ly
de
f
in
e
d
in
c
lu
s
io
n
a
nd
e
xc
lu
s
io
n
c
r
it
e
r
ia
[
23]
,
f
or
th
is
,
a
n
in
it
ia
l
s
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ph
a
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s
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id
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t
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it
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nd
r
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le
va
nc
e
of
th
e
e
vi
de
nc
e
c
ol
le
c
te
d.
T
hi
s
m
e
th
odol
ogi
c
a
ll
y
r
ig
or
ous
a
ppr
oa
c
h
pr
ovi
de
s
a
s
ound
a
nd
r
e
li
a
bl
e
ba
s
is
f
or
s
ys
te
m
a
ti
c
r
e
vi
e
w
,
f
ol
lo
w
in
g
P
R
I
S
M
A
gui
de
li
ne
s
[
23]
.
T
hr
ough
th
e
a
c
c
e
pt
a
nc
e
of
a
tr
a
ns
pa
r
e
nt
a
nd
s
tr
uc
tu
r
e
d
s
tr
a
te
gy,
th
e
r
e
pr
oduc
ib
il
it
y
a
nd
va
li
di
ty
of
th
e
pr
oc
e
s
s
a
r
e
pr
om
ot
e
d,
c
ont
r
ib
ut
in
g
to
th
e
ge
ne
r
a
ti
on
of
knowle
dge
in
th
e
f
ie
ld
of
M
L
O
ps
im
pl
e
m
e
nt
a
ti
on
in
in
f
or
m
a
ti
on t
e
c
hnol
ogy
a
udi
t
pr
oj
e
c
ts
.
3.
R
E
S
U
L
T
S
A
N
D
D
I
S
C
U
S
S
I
O
N
I
n
th
is
s
ys
te
m
a
ti
c
r
e
vi
e
w
,
a
c
om
pr
e
he
ns
iv
e
a
na
ly
s
i
s
of
va
r
io
u
s
r
e
s
e
a
r
c
h
s
our
c
e
s
w
a
s
c
a
r
r
ie
d
out
to
e
va
lu
a
te
a
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s
ynt
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s
i
z
e
th
e
a
va
il
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bl
e
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vi
de
nc
e
in
th
e
f
ie
ld
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M
L
O
ps
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ie
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nom
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ly
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te
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ti
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de
ve
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e
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oj
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c
ts
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n t
he
s
pe
c
if
ic
c
ont
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xt
of
in
f
or
m
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ti
on t
e
c
hnol
ogy
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udi
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he
r
e
s
ul
ts
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a
in
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ve
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l
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ve
r
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e
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oa
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he
s
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nd
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a
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ti
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e
s
in
th
e
im
pl
e
m
e
nt
a
ti
on
of
M
L
O
ps
m
e
th
odol
ogi
e
s
,
hi
ghl
ig
ht
in
g
th
e
ne
e
d t
o a
da
pt
t
he
s
e
s
tr
a
te
gi
e
s
t
o a
ddr
e
s
s
t
he
c
om
pl
e
xi
ti
e
s
i
nhe
r
e
nt
i
n t
he
i
nt
e
gr
a
ti
on
of
AI
pr
oj
e
c
ts
i
n
t
he
f
ie
ld
of
a
udi
ti
ng.
I
n
a
ddi
ti
on,
br
e
a
c
he
s
a
nd
a
r
e
a
s
of
oppor
tu
ni
ty
w
e
r
e
id
e
nt
if
ie
d
th
a
t
c
oul
d
gui
de
f
ut
ur
e
r
e
s
e
a
r
c
h,
in
c
lu
di
ng pr
oc
e
s
s
opt
im
iz
a
ti
on, s
ta
nda
r
di
z
a
ti
on of
pr
a
c
ti
c
e
s
, a
nd i
nc
or
por
a
ti
on o
f
a
dva
nc
e
d
ML
te
c
hni
que
s
t
o
im
pr
ove
a
nom
a
ly
de
te
c
ti
on
a
nd
c
om
put
e
r
a
udi
ti
ng.
T
he
s
e
f
in
di
ngs
pr
ovi
de
a
c
om
pr
e
he
n
s
iv
e
ove
r
vi
e
w
of
th
e
c
ur
r
e
nt
s
ta
te
of
th
e
a
r
t
in
th
e
im
pl
e
m
e
nt
a
ti
on
of
M
L
O
ps
m
e
th
odol
ogi
e
s
,
s
e
r
vi
ng
a
s
a
ba
s
i
s
f
or
th
e
de
ve
lo
pm
e
nt
a
nd
r
e
f
in
e
m
e
nt
of
e
f
f
e
c
ti
ve
s
tr
a
te
gi
e
s
in
th
e
f
ie
ld
of
c
om
put
e
r
a
udi
ti
ng
ba
s
e
d
on
th
e
im
pl
e
m
e
nt
a
ti
on of
AI
.
T
a
bl
e
7
pr
e
s
e
nt
s
a
de
ta
il
e
d
s
ynt
he
s
is
of
th
e
im
pl
e
m
e
nt
a
ti
ons
of
M
L
O
ps
in
th
e
in
c
or
por
a
ti
on
of
AI
pr
oj
e
c
ts
.
T
hi
s
or
ga
ni
z
a
ti
ona
l
s
tr
uc
tu
r
e
a
ll
ow
s
f
or
a
c
le
a
r
a
nd
hi
e
r
a
r
c
hi
c
a
l
vi
e
w
of
how
M
L
O
ps
ha
s
be
e
n
e
m
pl
oye
d
to
a
ddr
e
s
s
s
pe
c
if
ic
c
ha
ll
e
nge
s
in
va
r
io
us
dom
a
in
s
,
f
r
om
c
lo
ud
c
om
put
in
g
s
e
c
ur
it
y
to
in
f
or
m
a
ti
on
s
e
c
ur
it
y
a
nd
a
udi
ti
ng.
T
he
a
na
ly
s
is
id
e
nt
if
ie
d
s
e
ve
n
th
e
m
a
ti
c
c
lu
s
te
r
s
,
e
a
c
h
f
oc
us
e
d
on
s
pe
c
if
ic
M
L
O
ps
im
pl
e
m
e
nt
a
ti
ons
.
I
n
th
e
gr
oup
f
oc
us
e
d
on
s
e
c
ur
it
y
in
C
lo
ud
a
n
d
L
a
bor
a
to
r
y
C
om
put
in
g,
va
lu
a
bl
e
in
s
ig
ht
s
on
how
M
L
O
ps
is
a
ppl
ie
d
to
e
ns
ur
e
te
c
hni
c
a
l
a
nd
or
ga
ni
z
a
ti
ona
l
s
e
c
ur
it
y
in
c
lo
ud
-
ba
s
e
d
la
bor
a
to
r
y
e
nvi
r
onm
e
nt
s
a
r
e
pr
ovi
de
d
[
24]
.
I
n
th
e
e
nt
e
r
pr
is
e
I
T
gove
r
na
n
c
e
gr
oup,
th
e
P
ubM
e
d
s
tu
dy
[
25]
hi
ghl
ig
ht
e
d
th
e
i
m
por
ta
nc
e
of
M
L
O
ps
i
n t
he
e
f
f
ic
ie
nt
m
a
na
ge
m
e
nt
of
AI
m
ode
ls
i
n t
he
I
T
gove
r
na
nc
e
f
r
a
m
e
w
or
k. F
or
t
he
ML
m
ode
l
de
ve
lo
pm
e
nt
a
nd
de
pl
oym
e
nt
gr
oup,
m
ul
ti
pl
e
r
e
gi
s
tr
ie
s
s
u
c
h
a
s
[
26]
–
[
55]
c
ont
r
ib
ut
e
d
to
a
c
om
pr
e
he
ns
iv
e
vi
e
w
of
how
M
L
O
ps
opt
im
iz
e
s
th
e
e
nt
ir
e
li
f
e
c
yc
le
of
m
ode
ls
,
f
r
om
in
it
ia
l
de
v
e
lo
p
pm
e
nt
to
de
pl
oym
e
nt
in
pr
oduc
ti
on
e
nvi
r
onm
e
nt
s
.
I
n
th
e
c
ont
e
xt
of
dr
ug
di
s
c
ove
r
y,
Y
a
da
v
a
nd
T
ha
kka
r
[
56]
hi
ghl
ig
ht
e
d
th
e
s
pe
c
if
ic
a
ppl
ic
a
ti
on
of
M
L
O
ps
in
th
e
th
e
ne
ur
a
l
os
c
il
la
ti
on
a
tt
e
nt
io
n
lo
ng
s
hor
t
-
te
r
m
m
e
m
or
y
(
N
O
A
-
L
S
T
M
)
a
r
c
hi
te
c
tu
r
e
f
or
ti
m
e
s
e
r
ie
s
f
or
e
c
a
s
ti
ng.
H
ow
e
ve
r
,
m
ul
ti
pl
e
r
e
c
or
ds
[
4]
,
[
17]
,
[
56]
–
[
67
]
hi
ghl
ig
ht
th
a
t
th
e
im
pl
e
m
e
nt
a
ti
on
f
oc
us
e
s
on
e
f
f
e
c
ti
ve
ti
m
e
s
e
r
ie
s
da
ta
m
a
n
a
ge
m
e
nt
,
opt
im
iz
a
ti
on,
a
nd
im
pl
e
m
e
nt
a
ti
on
of
de
pl
oym
e
nt
pi
pe
li
ne
s
th
a
t
e
na
bl
e
s
e
a
m
le
s
s
i
nt
e
gr
a
ti
on
in
th
e
di
s
c
ov
e
r
y
dom
a
in
.
T
h
e
gr
oup
f
oc
us
e
d
on
a
ppl
ic
a
ti
on
s
in
I
nf
or
m
a
ti
on
s
e
c
ur
it
y
a
nd
pol
ic
y
c
om
pl
ia
nc
e
,
in
[
60]
de
ta
il
e
d
th
e
c
r
e
a
ti
on
of
de
e
pc
he
c
k
s
,
a
li
br
a
r
y
th
a
t
u
s
e
s
M
L
O
ps
to
te
s
t
a
nd
va
li
da
te
ML
m
ode
ls
a
nd
da
ta
li
ke
w
is
e
,
th
e
ot
he
r
r
e
c
or
ds
a
s
[
67]
–
[
82]
s
how
th
a
t
th
is
ty
pe
of
im
pl
e
m
e
nt
a
ti
on
a
ddr
e
s
s
e
s
c
r
it
ic
a
l
s
e
c
ur
it
y
a
nd
c
om
pl
ia
nc
e
c
onc
e
r
ns
by
pr
ovi
di
ng
a
ut
om
a
te
d
to
ol
s
to
a
s
s
e
s
s
th
e
in
te
gr
it
y
a
nd
e
th
ic
s
of
AI
m
ode
ls
us
e
d
in
di
f
f
e
r
e
nt
a
ppl
ic
a
ti
ons
.
T
he
D
e
vO
ps
-
r
e
la
te
d
gr
oup
on
s
of
twa
r
e
e
ngi
ne
e
r
in
g
a
nd
a
nom
a
ly
d
e
te
c
ti
on
s
how
e
d
how
th
e
pa
pe
r
[
83]
out
li
ne
d
a
c
om
pl
e
te
AI
li
f
e
c
yc
le
,
in
te
gr
a
ti
ng
M
L
O
ps
f
or
e
f
f
ic
ie
nt
a
nom
a
ly
de
te
c
ti
on
th
r
oughout
th
e
de
ve
lo
pm
e
nt
a
nd
pr
oduc
ti
on
pr
oc
e
s
s
.
F
in
a
ll
y,
th
e
gr
oup
li
nke
d
to
c
yb
e
r
s
e
c
ur
it
y
a
nd
a
udi
ti
ng
r
e
ve
a
le
d
th
e
s
pe
c
if
ic
a
ppl
ic
a
ti
on
of
M
L
O
ps
in
th
e
c
ybe
r
s
e
c
ur
it
y
de
c
i
s
io
n
s
uppor
t
m
ode
l
[
84
]
,
pr
ovi
di
ng
to
ol
s
f
or
r
is
k
id
e
nt
if
ic
a
ti
on
a
nd
m
a
na
ge
m
e
nt
in
c
ybe
r
e
nvi
r
onm
e
nt
s
.
T
hi
s
s
ys
te
m
a
ti
c
a
na
ly
s
i
s
pr
ovi
de
s
a
c
om
pr
e
he
ns
iv
e
ove
r
vi
e
w
of
th
e
va
r
io
us
im
pl
e
m
e
nt
a
ti
ons
of
M
L
O
ps
in
s
p
e
c
if
ic
c
ont
e
xt
s
,
hi
gh
li
ght
in
g
th
e
ir
c
r
uc
ia
l
r
ol
e
in
th
e
opt
im
iz
a
ti
on
a
nd s
e
c
ur
it
y of
AI
pr
oj
e
c
ts
a
ppl
ie
d i
n c
om
put
e
r
a
udi
ti
ng.
T
a
bl
e
7. S
pe
c
if
ic
im
pl
e
m
e
nt
a
ti
ons
of
M
L
O
ps
i
n t
h
e
in
te
gr
a
ti
on of
A
I
pr
oj
e
c
ts
#
I
m
pl
e
m
e
nt
a
t
i
ons
R
e
f
e
r
e
nc
e
1
C
l
oud C
om
put
i
ng a
nd L
a
bor
a
t
or
y S
e
c
ur
i
t
y
[
24]
2
G
ove
r
na
nc
e
of
I
nf
or
m
a
t
i
on T
e
c
hnol
ogi
e
s
i
n C
ol
om
bi
a
n C
om
pa
ni
e
s
[
25]
3
D
e
ve
l
opm
e
nt
a
nd D
e
pl
oym
e
nt
of
ML
M
ode
l
s
[
26]
–
[
55]
4
M
L
O
p
s
M
e
t
hodol
ogy i
n D
r
ug D
i
s
c
ov
e
r
y
[
4]
, [
17]
, [
56]
–
[
66]
5
A
ppl
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a
t
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I
nf
or
m
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on S
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y a
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ol
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y C
om
pl
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nc
e
[
67]
–
[
82]
6
D
e
vO
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i
n S
of
t
w
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r
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E
ngi
ne
e
r
i
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nd A
nom
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e
t
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c
t
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on
[
83]
, [
85]
–
[
89]
7
C
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r
s
e
c
ur
i
t
y a
nd A
udi
t
[
10]
, [
84]
, [
90]
–
[
104]
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[
26
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,
[
6
0]
.
T
h
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hi
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30
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[
56
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T
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[
34]
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4
3]
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60
]
.
T
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38
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44
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[
63]
.
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3
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39
]
,
[
5
9]
.
I
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s
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g
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t
[
4
]
,
[
3
3]
,
[
6
6]
.
F
i
n
a
l
ly
,
t
h
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s
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h
gr
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up
,
[
24]
,
[
6
7]
,
[
81
]
,
pr
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s
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t
s
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hn
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a
bo
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. T
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d
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f
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y.
T
a
bl
e
8. M
a
tr
ix
of
a
lt
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r
na
ti
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m
e
th
odol
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e
s
a
nd dif
f
e
r
e
nc
e
i
n e
f
f
ic
ie
nc
y i
n M
L
O
ps
pr
oj
e
c
ts
#
A
l
t
e
r
na
t
i
ve
m
e
t
hodol
ogy
D
i
f
f
e
r
e
nc
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i
n e
f
f
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c
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nc
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R
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f
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nc
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1
I
m
pl
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m
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nt
a
s
pe
c
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f
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c
M
L
O
ps
a
r
c
hi
t
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c
t
ur
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t
o
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f
f
i
c
i
e
nt
l
y
c
oor
di
na
t
e
t
he
de
ve
l
opm
e
nt
a
nd
d
e
pl
oym
e
nt
of
ML
m
ode
l
s
i
n
i
nf
or
m
a
t
i
on t
e
c
hnol
ogy
a
udi
t
pr
oj
e
c
t
s
.
O
pt
i
m
i
z
e
l
a
bor
a
t
or
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l
oud
s
e
c
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t
y
by
a
ppl
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M
L
O
p
s
,
t
he
r
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by
i
m
pr
ovi
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ope
r
a
t
i
ona
l
e
f
f
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c
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nc
y
i
n pr
oj
e
c
t
m
a
na
ge
m
e
nt
.
[
26]
, [
60]
2
U
s
e
a
n
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nt
e
r
pr
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t
a
bl
e
ML
a
ppr
oa
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ul
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s
f
r
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S
P
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C
T
M
P
I
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C
T
A
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m
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s
,
pr
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a
c
l
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a
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unde
r
s
t
a
ndi
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m
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l
de
c
i
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ons
.
I
m
pr
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f
f
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c
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i
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t
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de
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f
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c
a
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on
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dve
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c
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put
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r
a
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s
.
[
30]
,
[
36]
, [
56]
3
A
ppl
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dva
nc
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d
ML
t
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c
hni
que
s
t
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t
hol
ogi
c
a
l
i
m
a
ge
s
t
o
i
m
pr
ove
pa
t
hol
ogy
i
de
nt
i
f
i
c
a
t
i
on
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t
he
c
ont
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xt
of
pa
t
hol
ogy.
O
pt
i
m
i
z
e
t
he
de
t
e
c
t
i
on
of
a
nom
a
l
i
e
s
i
n
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om
put
e
r
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udi
t
i
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t
hr
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or
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c
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ur
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r
pr
e
t
a
t
i
on
of
m
e
di
c
a
l
i
m
a
ge
s
.
[
34]
,
[
43]
, [
60]
4
I
m
pl
e
m
e
nt
i
t
e
r
a
t
i
ve
a
nd s
e
m
i
-
s
upe
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s
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s
i
n
ML
t
o
opt
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m
i
z
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f
f
i
c
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nc
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n
r
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ha
bi
l
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t
a
t
i
on
s
c
i
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nc
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,
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s
pe
c
i
a
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m
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t
a
s
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t
s
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I
m
pr
ove
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f
f
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c
i
e
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i
n
c
om
put
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pr
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t
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he
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ough
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nd
s
e
m
i
-
s
upe
r
vi
s
e
d pr
oc
e
s
s
e
s
.
[
38]
,
[
44]
, [
63]
5
C
a
r
r
y
out
t
he
c
om
pa
r
i
s
on
of
di
f
f
e
r
e
nt
s
upe
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vi
s
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d
ML
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l
gor
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t
hm
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f
or
di
s
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pr
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di
c
t
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e
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c
t
i
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he
m
os
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f
f
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t
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ve
one
.
O
pt
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m
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z
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e
f
f
i
c
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nc
y
i
n
c
om
put
e
r
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pr
oj
e
c
t
s
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l
a
t
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d
t
o
m
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di
c
a
l
e
va
l
ua
t
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on
t
hr
ough
c
a
r
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u
l
s
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l
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c
t
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on of
di
s
e
a
s
e
pr
e
di
c
t
i
on a
l
gor
i
t
hm
s
.
[
31]
,
[
39]
, [
59]
6
I
m
pl
e
m
e
nt
de
c
e
nt
r
a
l
i
z
e
d
gove
r
na
nc
e
of
ML
,
pr
ovi
di
ng
a
n
ove
r
vi
e
w
a
nd
ove
r
c
om
i
ng
c
ha
l
l
e
nge
s
t
o
i
m
pr
ove
e
f
f
i
c
i
e
nc
y
i
n m
a
na
gi
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i
nf
or
m
a
t
i
on t
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c
hnol
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a
udi
t
pr
oj
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c
t
s
.
O
pt
i
m
i
z
e
i
nf
or
m
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t
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on
t
e
c
hnol
ogy
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pr
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t
m
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na
ge
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nt
by
de
c
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L
O
ps
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ddr
e
s
s
i
ng s
p
e
c
i
f
i
c
c
ha
l
l
e
nge
s
.
[
4]
, [
33]
,
[
66]
7
A
ddr
e
s
s
t
e
c
hni
c
a
l
a
nd or
ga
ni
z
a
t
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ona
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s
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c
ur
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t
y c
on
s
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de
r
a
t
i
ons
f
or
l
a
bor
a
t
or
y c
l
oud c
om
put
i
ng i
n M
L
O
ps
pr
oj
e
c
t
s
.
I
m
pr
ove
t
he
s
e
c
ur
i
t
y
e
f
f
i
c
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nc
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of
i
nf
or
m
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t
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hnol
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pr
oj
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c
t
s
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a
bor
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de
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us
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hni
c
a
l
a
nd or
ga
ni
z
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t
i
ona
l
s
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c
ur
i
t
y.
[
24]
,
[
67]
, [
81]
U
pon
e
xa
m
in
in
g
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bl
e
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it
be
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om
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c
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pe
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ts
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pha
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li
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L
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ps
. T
he
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f
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s
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m
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ont
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26]
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32]
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34]
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43]
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51]
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60]
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63]
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[
66]
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72]
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7
7]
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[
83]
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[
86]
,
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f
f
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50]
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52]
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55]
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61]
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71]
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88]
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1
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106]
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25]
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31]
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36]
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38]
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44]
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46]
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54]
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57]
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70]
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76]
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84]
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91]
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AI
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e
nt
R
e
f
e
r
e
nc
e
1
E
f
f
i
c
i
e
nc
y
i
n
p
r
oj
e
c
t
s
w
i
t
h M
L
O
ps
[
4]
, [
26]
, [
29]
, [
32]
, [
34
]
, [
43]
, [
51]
, [
60
]
, [
63]
, [
66]
, [
72]
, [
77
]
, [
83]
, [
86]
2
P
e
r
f
or
m
a
nc
e
i
n
p
r
oj
e
c
t
s
w
i
t
h
M
L
O
ps
[
17]
, [
27]
, [
28]
, [
30]
, [
33
]
, [
35]
, [
37]
, [
39
]
, [
41]
, [
42]
, [
45]
, [
48
]
–
[
50]
, [
52]
, [
53
]
, [
55]
, [
56]
,
[
58]
-
[
61]
, [
68]
, [
69]
, [
71]
, [
73
]
–
[
75]
, [
78]
–
[
82]
, [
85
]
, [
87]
, [
88]
, [
90
]
, [
92]
–
[
94]
, [
105]
,
[
106]
3
D
e
t
e
c
t
i
on
of
a
nom
a
l
i
e
s
i
n
pr
oj
e
c
t
s
w
i
t
h M
L
O
p
s
[
10]
, [
24]
, [
25]
, [
31]
, [
36
]
, [
38]
, [
40]
, [
44
]
, [
46]
, [
47]
, [
54]
, [
57
]
, [
62]
, [
64]
, [
65
]
, [
67]
, [
70]
, [
76
]
,
[
84]
, [
91]
, [
95]
–
[
104]
4
E
f
f
i
c
i
e
nc
y
i
n
p
r
oj
e
c
t
s
w
i
t
hout
M
L
O
ps
[
10]
, [
25]
, [
28]
, [
29]
,
[
33
]
, [
34]
, [
37]
, [
43
]
, [
44]
, [
51]
, [
54]
, [
56
]
, [
59]
, [
60]
, [
61
]
, [
63]
, [
65]
, [
67
]
,
[
72]
, [
75]
, [
78]
, [
80]
, [
83
]
, [
85]
, [
88]
, [
89
]
, [
91]
–
[
93]
, [
95]
–
[
105
]
5
P
e
r
f
or
m
a
nc
e
i
n
p
r
oj
e
c
t
s
w
i
t
hout
M
L
O
ps
[
4]
, [
17]
, [
24]
, [
26]
, [
27
]
, [
30]
–
[
32]
, [
35
]
, [
36]
, [
38]
–
[
42]
, [
45
]
–
[
50]
, [
52]
, [
53
]
,
[
55]
, [
57]
, [
58]
,
[
60]
, [
64]
, [
66]
, [
68]
–
[
71
]
, [
73]
, [
74]
, [
76
]
, [
77]
, [
79]
, [
81]
, [
82
]
, [
86]
, [
87]
, [
90
]
, [
106]
6
A
nom
a
l
y
de
t
e
c
t
i
on
i
n
pr
oj
e
c
t
s
w
i
t
hout
M
L
O
ps
[
10]
, [
87]
, [
92]
, [
94]
–
[
104
]
4.
C
O
N
C
L
U
S
I
O
N
S
T
hi
s
s
y
s
te
m
a
ti
c
r
e
vi
e
w
f
ol
lo
w
s
a
s
ound me
th
odol
ogy tha
t
in
c
lu
de
s
f
or
m
ul
a
ti
ng t
he
r
e
s
e
a
r
c
h que
s
ti
on
us
in
g
th
e
P
I
C
O
a
ppr
oa
c
h
a
nd
a
ppl
yi
ng
th
e
P
R
I
S
M
A
m
e
th
od
ol
ogy.
T
he
r
e
s
ul
ts
s
ig
ni
f
ic
a
nt
ly
c
ont
r
ib
ut
e
to
unde
r
s
ta
ndi
ng
th
e
im
pl
e
m
e
nt
a
ti
on
of
th
e
M
L
O
ps
m
e
th
odol
og
y
in
id
e
nt
i
f
yi
ng
a
nom
a
li
e
s
in
in
te
gr
a
ti
ng
AI
pr
oj
e
c
ts
in
c
om
put
e
r
a
udi
ti
ng.
T
he
in
it
ia
l
s
e
a
r
c
h
s
tr
a
te
gy,
w
hi
c
h
c
ove
r
e
d
1098
P
ubM
e
d
a
nd
291
S
c
opus
r
e
c
or
ds
,
w
a
s
r
e
f
in
e
d
by
in
c
lu
s
io
n
a
nd
e
xc
lu
s
io
n
c
r
it
e
r
ia
,
r
e
s
ul
ti
ng
in
a
f
in
a
l
s
a
m
pl
e
of
88
r
e
le
va
nt
r
e
c
or
ds
.
T
hi
s
s
e
l
e
c
ti
on pr
oc
e
s
s
r
e
s
ul
te
d i
n a
r
e
te
nt
io
n r
a
te
of
8%
, hi
ghl
ig
ht
in
g t
he
r
ig
or
a
ppl
ie
d t
o e
ns
ur
e
t
he
r
e
le
va
nc
e
of
th
e
da
ta
a
na
ly
z
e
d.
T
h
e
bi
bl
io
m
e
tr
ic
a
na
ly
s
i
s
r
e
ve
a
le
d
ke
y
tr
e
nds
in
th
e
li
te
r
a
tu
r
e
r
e
vi
e
w
e
d.
I
n
r
e
c
e
nt
ye
a
r
s
,
th
e
r
e
ha
s
be
e
n a
s
te
a
dy i
nc
r
e
a
s
e
i
n t
he
publi
c
a
ti
on of
r
e
s
e
a
r
c
h o
n M
L
O
ps
a
nd
AI
pr
oj
e
c
ts
a
ppl
ie
d i
n c
om
put
e
r
a
udi
ti
ng.
I
n
a
ddi
ti
on,
w
e
id
e
nt
if
ie
d
s
pe
c
if
ic
to
pi
c
a
r
e
a
s
th
a
t
h
a
ve
r
e
c
e
iv
e
d
in
c
r
e
a
s
e
d
a
tt
e
nt
io
n,
pr
ovi
di
ng
a
qua
nt
it
a
ti
ve
vi
e
w
of
th
e
m
os
t
pr
om
in
e
nt
a
r
e
a
s
of
f
oc
us
.
A
t
th
e
le
ve
l
of
ha
ndw
r
it
te
n
r
e
s
ul
ts
,
e
a
c
h
of
th
e
88
s
e
le
c
te
d
lo
gs
c
ont
r
ib
ut
e
d
e
s
s
e
nt
ia
l
qua
li
ta
ti
ve
da
ta
.
I
n
th
is
c
ont
e
xt
,
th
e
qua
nt
it
a
ti
ve
f
ig
ur
e
s
s
uppor
t
th
e
r
obus
tn
e
s
s
of
th
e
f
in
di
ngs
a
nd
pr
ovi
de
a
s
ol
id
ba
s
i
s
f
or
c
onc
lu
s
io
ns
.
A
ls
o,
th
e
di
ve
r
s
it
y
a
nd
de
pt
h
of
th
e
qua
li
ta
ti
ve
da
ta
e
xt
r
a
c
te
d f
r
om
t
he
l
ogs
c
ont
r
ib
ut
e
t
o a
m
o
r
e
c
o
m
pl
e
te
unde
r
s
ta
ndi
ng of
t
he
im
pa
c
t
of
M
L
O
ps
on
a
nom
a
ly
id
e
nt
if
ic
a
ti
on
in
th
e
in
te
gr
a
ti
on
of
AI
pr
oj
e
c
t
s
in
c
om
put
e
r
a
udi
ti
ng.
T
he
s
e
r
e
s
ul
ts
s
uppor
t
th
e
im
por
ta
nc
e
of
c
ons
id
e
r
in
g
M
L
O
ps
m
e
th
odol
ogy
a
s
a
c
r
uc
ia
l
c
om
pone
nt
in
A
I
pr
oj
e
c
ts
in
c
om
put
e
r
a
udi
ti
ng,
pr
ovi
di
ng
ta
ngi
bl
e
da
ta
a
nd
qua
li
ta
ti
ve
in
s
ig
ht
s
to
in
f
or
m
f
u
tu
r
e
r
e
s
e
a
r
c
h
a
nd
pr
a
c
ti
c
a
l
de
c
i
s
io
ns
in
th
is
dyna
m
ic
f
ie
ld
.
A
C
K
N
O
WL
E
D
G
M
E
N
T
S
O
ur
de
e
pe
s
t
gr
a
ti
tu
de
to
th
e
T
e
c
hnol
ogi
c
a
l
U
ni
ve
r
s
it
y
of
P
e
r
u
f
or
be
in
g
a
f
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m
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nt
a
l
pi
ll
a
r
in
th
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ve
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s
e
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c
h
s
in
c
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th
e
ir
a
s
s
is
ta
n
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,
bot
h
in
te
r
m
s
of
li
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r
a
tu
r
e
a
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la
bor
a
to
r
y
f
a
c
il
it
ie
s
,
ha
s
be
e
n
c
r
uc
ia
l
to
a
c
hi
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vi
ng
th
e
pr
opos
e
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obj
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c
ti
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,
a
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th
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ir
m
a
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m
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uppor
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ha
s
m
a
de
pos
s
ib
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th
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ni
f
ic
a
nt
pr
ogr
e
s
s
of
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hi
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r
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a
r
c
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3735
R
E
F
E
R
E
N
C
E
S
[
1]
M
.
V.
D.
G
oe
s
,
“
S
c
a
l
i
ng
e
nt
e
r
pr
i
s
e
r
e
c
om
m
e
nde
r
s
y
s
t
e
m
s
f
or
de
c
e
nt
r
a
l
i
z
a
t
i
on
,”
i
n
F
i
f
t
e
e
nt
h
A
C
M
C
onf
e
r
e
nc
e
on
R
e
c
o
m
m
e
nde
r
Sy
s
t
e
m
s
, N
e
w
Y
or
k, U
S
A
:
A
C
M
, S
e
p. 2021, pp. 592
–
594
, doi
:
10.1145/
34602
31.3474616.
[
2]
K
. H
a
l
l
e
r
,
M
anagi
ng A
I
i
n t
he
E
nt
e
r
pr
i
s
e
. B
e
r
ke
l
e
y,
C
A
:
A
pr
e
s
s
, 2022
, doi
:
10.1007/
978
-
1
-
4842
-
7824
-
6.
[
3]
A
.
A
kki
ne
ni
,
S
.
K
oohbor
f
a
r
dha
ghi
ghi
,
a
nd
S
.
S
i
ngh,
“
C
e
nt
r
a
l
i
t
y
of
A
I
qua
l
i
t
y
i
n
M
L
O
P
s
l
i
f
e
c
yc
l
e
a
nd
i
t
s
i
m
pa
c
t
on
t
he
a
dopt
i
o
n
of
A
I
/
M
L
s
ol
ut
i
ons
,”
i
n
I
nt
e
l
l
i
ge
nt
Sy
s
t
e
m
s
D
e
s
i
gn and A
ppl
i
c
at
i
ons
, 2023, pp. 436
–
448
, doi
:
10.1007/
978
-
3
-
031
-
35510
-
3_42.
[
4]
D
.
A
l
s
a
ghe
e
r
,
L
.
X
u,
a
nd
W
.
S
hi
,
“
D
e
c
e
nt
r
a
l
i
z
e
d
m
a
c
hi
ne
l
e
a
r
ni
ng
gove
r
na
nc
e
:
ove
r
vi
e
w
,
oppor
t
uni
t
i
e
s
,
a
nd
c
ha
l
l
e
nge
s
,”
I
E
E
E
A
c
c
e
s
s
, vol
. 11, pp. 96718
–
96732, 2023, doi
:
10.1109/
A
C
C
E
S
S
.2023.3311713.
[
5]
E
.
N
e
gha
w
i
,
Z
.
W
a
ng,
J
.
H
ua
ng,
a
nd
Y
.
L
i
u,
“
L
i
nki
ng
t
e
a
m
-
l
e
ve
l
a
nd
or
ga
ni
z
a
t
i
on
-
l
e
ve
l
gove
r
na
nc
e
i
n
m
a
c
hi
ne
l
e
a
r
ni
ng
ope
r
a
t
i
ons
t
hr
ough
e
xpl
a
i
na
bl
e
A
I
a
nd
r
e
s
pons
i
bl
e
A
I
c
onn
e
c
t
or
,”
i
n
2023
I
E
E
E
47
th
A
nnual
C
om
put
e
r
s
,
Sof
t
w
ar
e
,
and
A
ppl
i
c
at
i
ons
C
onf
e
r
e
nc
e
(
C
O
M
P
SA
C
)
, I
E
E
E
, J
un. 2023, pp. 1223
–
1230
, doi
:
1
0.1109/
C
O
M
P
S
A
C
57700.2023.00185.
[
6]
S
.
S
.
S
o
unc
hi
o
,
L
.
G
e
ne
s
t
e
,
B
.
K
.
-
F
og
ue
m
,
C
.
B
é
l
e
r
,
S
.
N
.
A
r
a
gh
i
,
a
nd
M
.
R
.
N
a
qv
i
,
“
A
n
e
nt
e
r
pr
i
s
e
a
r
c
h
i
t
e
c
t
u
r
e
f
o
r
i
n
t
e
r
pe
r
s
ona
l
a
c
t
i
vi
t
y
kn
ow
l
e
d
ge
m
a
n
a
ge
m
e
n
t
,”
i
n
K
now
l
e
dg
e
G
r
ap
hs
a
nd
Se
m
an
t
i
c
W
e
b
, 2
02
3
, p
p.
66
–
81
,
do
i
:
10
.10
07
/
9
78
-
3
-
0
31
-
4
774
5
-
4_
6.
[
7]
L
.
A
m
i
ni
,
E
.
H
.
E
.
K
a
r
n
i
,
M
.
O
ube
na
l
i
,
H
.
E
.
O
ua
f
y
,
M
.
M
ba
r
k
i
,
a
nd
B
.
E
.
O
ua
di
,
“
P
r
e
d
i
c
t
i
v
e
s
t
u
dy
,
us
i
ng
d
e
ns
i
t
y
f
u
nc
t
i
o
na
l
t
h
e
o
r
y
a
nd
t
i
m
e
de
pe
n
de
n
t
f
un
c
t
i
on
a
l
t
he
or
y,
o
n
t
he
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t
r
uc
t
ur
e
-
pr
ope
r
t
y
q
ua
n
t
i
f
i
c
a
t
i
o
n
o
f
m
e
t
h
yl
e
ne
b
l
ue
a
nd
m
e
t
hy
l
r
e
d
dye
s
f
o
r
t
he
a
pp
l
i
c
a
t
i
on
i
n o
r
g
a
n
i
c
s
ol
a
r
c
e
l
l
s
,”
C
ur
r
e
nt
C
he
m
i
s
t
r
y
L
e
t
t
e
r
s
,
v
ol
.
1
3,
no
. 1
,
pp.
1
87
–
198
,
202
4,
do
i
:
1
0.5
26
7/
j
.c
c
l
.
20
23.
7.
002
.
[
8]
E
.
P
e
l
t
o
ne
n
a
nd
S
.
D
i
a
s
,
“
L
i
nk
E
d
ge
:
ope
n
-
s
ou
r
c
e
d
M
L
O
ps
i
nt
e
gr
a
t
i
on
w
i
t
h
I
o
T
e
d
ge
,
”
i
n
T
he
3
r
d
E
c
l
i
ps
e
Se
c
ur
i
t
y
,
A
I
,
A
r
c
h
i
t
e
c
t
ur
e
and
M
ode
l
l
i
n
g C
onf
e
r
e
nc
e
o
n C
l
o
ud
t
o
E
dg
e
C
o
nt
i
n
uum
, N
e
w
Y
o
r
k,
U
S
A
:
A
C
M
, 2
023
,
pp.
6
7
–
76
,
do
i
:
1
0.1
14
5/
362
44
86.
36
244
96
.
[
9]
E
.
D
.
C
a
ne
do
e
t
al
.
,
“
I
nf
or
m
a
t
i
on
a
nd
c
om
m
uni
c
a
t
i
on
t
e
c
hnol
ogy
(
I
C
T
)
gove
r
na
nc
e
pr
oc
e
s
s
e
s
:
a
c
a
s
e
s
t
udy
,”
I
nf
or
m
at
i
on
,
vol
.
11, no. 10, S
e
p. 2020, doi
:
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i
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P
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L
of
t
,
Y
.
H
e
,
I
.
Y
e
vs
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ye
va
,
a
nd
I
.
W
a
gne
r
,
“
C
A
E
S
A
R
8:
A
n
a
gi
l
e
e
nt
e
r
pr
i
s
e
a
r
c
hi
t
e
c
t
ur
e
a
ppr
oa
c
h
t
o
m
a
na
gi
ng
i
nf
or
m
a
t
i
on
s
e
c
ur
i
t
y r
i
s
ks
,”
C
om
put
e
r
s
and
S
e
c
ur
i
t
y
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R
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C
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n,
“
D
i
gi
t
a
l
s
t
r
a
t
e
gy,
m
a
c
hi
ne
l
e
a
r
ni
ng,
a
nd
i
ndu
s
t
r
y
s
ur
ve
y
of
M
L
O
ps
,”
i
n
D
i
gi
t
al
St
r
at
e
gi
e
s
and
O
r
gani
z
at
i
onal
T
r
ans
f
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S
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K
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t
i
l
,
“
A
nove
l
s
y
s
t
e
m
f
or
pot
e
nt
i
a
l
m
os
qui
t
o
br
e
e
di
ng
hot
s
pot
i
nt
i
m
a
t
i
on
a
nd
m
oni
t
or
i
ng
us
i
ng
M
L
O
p
s
a
n
d
i
m
pr
ove
d Y
ol
oV
3,”
I
ns
t
r
um
e
nt
at
i
on M
e
s
ur
e
M
é
t
r
ol
ogi
e
, vol
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J
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P
ool
,
S
.
A
khl
a
ghpour
,
F
.
F
a
t
e
hi
,
a
nd
A
.
B
.
-
J
one
s
,
“
A
s
y
s
t
e
m
a
t
i
c
a
na
l
y
s
i
s
of
f
a
i
l
ur
e
s
i
n
pr
ot
e
c
t
i
ng
pe
r
s
ona
l
h
e
a
l
t
h
da
t
a
:
A
s
c
opi
ng r
e
vi
e
w
,”
I
nt
e
r
nat
i
onal
J
our
nal
of
I
nf
or
m
at
i
on M
anage
m
e
nt
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M
.
S
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f
da
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t
al
.
,
“
F
unda
m
e
nt
a
l
r
e
qui
r
e
m
e
nt
s
of
a
m
a
c
hi
ne
l
e
a
r
ni
ng
o
pe
r
a
t
i
ons
pl
a
t
f
or
m
f
o
r
i
ndus
t
r
i
a
l
m
e
t
a
l
a
ddi
t
i
ve
m
a
nuf
a
c
t
ur
i
ng,”
C
om
put
e
r
s
i
n I
ndus
t
r
y
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D
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A
r
c
a
y
a
,
A
.
I
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T
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B
a
s
t
i
d
a
,
R
.
M
i
ñ
ó
n,
a
nd
A
.
A
l
m
e
i
d
a
,
“
O
r
f
e
on
:
a
n
A
I
O
ps
f
r
a
m
e
w
o
r
k
f
o
r
t
he
g
oa
l
-
d
r
i
ve
n
o
pe
r
a
t
i
on
a
l
i
z
a
t
i
o
n
of
d
i
s
t
r
i
b
u
t
e
d
a
n
a
l
y
t
i
c
a
l
p
i
p
e
l
i
ne
s
,”
F
ut
ur
e
G
e
ne
r
at
i
on
C
om
pu
t
e
r
Sy
s
t
e
m
s
,
v
ol
.
1
40
,
pp
.
1
8
–
3
5,
20
23
,
d
o
i
:
1
0
.1
01
6
/
j
.
f
ut
u
r
e
.2
0
22
.1
0
.0
08
.
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A
.
M
ukhopa
dhya
y
a
nd
S
.
J
a
i
n,
“
A
f
r
a
m
e
w
or
k
f
or
c
ybe
r
-
r
i
s
k
i
ns
ur
a
nc
e
a
ga
i
ns
t
r
a
ns
om
w
a
r
e
:
A
m
i
xe
d
-
m
e
t
hod
a
ppr
oa
c
h,”
I
nt
e
r
nat
i
onal
J
our
nal
of
I
nf
or
m
at
i
on M
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m
e
nt
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L
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S
undbe
r
g
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nd
J
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r
öm
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e
m
oc
r
a
t
i
z
i
ng
a
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
ge
nc
e
:
H
ow
no
-
c
ode
A
I
c
a
n
l
e
ve
r
a
ge
m
a
c
hi
ne
l
e
a
r
ni
ng
ope
r
a
t
i
ons
,”
B
us
i
ne
s
s
H
or
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z
on
s
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M
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hi
ni
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ä
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t
ba
c
ka
,
a
nd
D
.
T
a
i
bi
,
“
M
L
O
ps
pi
pe
l
i
ne
de
ve
l
opm
e
nt
,
”
i
n
P
r
oc
e
e
di
ngs
of
t
he
I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on
R
e
s
e
ar
c
h i
n A
dapt
i
v
e
and C
onv
e
r
ge
nt
Sy
s
t
e
m
s
, N
e
w
Y
or
k, U
S
A
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K
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t
hol
e
,
S
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hi
nde
,
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nd
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dhw
a
,
“
I
nt
e
gr
a
t
i
ng
M
L
O
ps
a
nd
E
E
G
t
e
c
hni
que
s
f
or
e
nha
n
c
e
d
c
r
i
m
e
d
e
t
e
c
t
i
on
a
nd
pr
e
ve
nt
i
on,
”
M
ul
t
i
di
s
c
i
pl
i
nar
y
Sc
i
e
nc
e
J
ou
r
nal
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F
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pe
t
h, C
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a
r
t
m
a
nn, U
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e
bs
c
hul
l
, D
. S
a
ba
t
h,
a
nd F
. S
e
l
l
m
a
i
e
r
, “
T
ow
a
r
ds
t
r
a
ns
pa
r
e
nt
A
I
-
s
ys
t
e
m
s
:
b
e
ne
f
i
t
s
of
M
L
O
p
s
pi
pe
l
i
ne
s
f
or
s
pa
c
e
s
ys
t
e
m
de
v
e
l
opm
e
nt
,”
i
n
P
r
oc
e
e
di
ng
s
of
t
he
I
nt
e
r
nat
i
onal
A
s
t
r
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M
i
ñon
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ya
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I
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B
a
s
t
i
da
,
a
nd P
. H
a
r
t
l
i
e
b, “
P
a
nge
a
:
A
n
m
l
ops
t
ool
f
or
a
ut
om
a
t
i
c
a
l
l
y ge
n
e
r
a
t
i
ng i
nf
r
a
s
t
r
uc
t
ur
e
a
nd de
pl
oyi
ng a
na
l
yt
i
c
pi
pe
l
i
ne
s
i
n e
dge
, f
og a
nd c
l
oud l
a
y
e
r
s
,”
Se
n
s
or
s
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G
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T
r
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A
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nc
e
s
i
n
t
i
m
e
s
e
r
i
e
s
f
or
e
c
a
s
t
i
ng
de
v
e
l
opm
e
nt
f
or
pow
e
r
s
ys
t
e
m
s
’
ope
r
a
t
i
on
w
i
t
h
M
L
O
p
s
,
”
F
or
e
c
as
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R
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G
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G
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C
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r
e
r
a
s
,
“
T
ow
a
r
ds
t
he
unde
r
s
t
a
ndi
ng
of
c
ons
um
e
r
be
ha
vi
or
i
n
t
he
m
e
t
a
ve
r
s
e
,”
i
n
I
G
I
G
l
obal
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O
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ga
ni
z
a
t
i
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l
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t
e
c
hni
c
a
l
s
e
c
ur
i
t
y
c
ons
i
de
r
a
t
i
ons
f
or
l
a
bor
a
t
or
y
c
l
oud
c
om
put
i
ng
,”
T
he
J
our
nal
of
A
ppl
i
e
d
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abor
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e
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G
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t
a
s
e
t
a
bout
i
nf
or
m
a
t
i
on
t
e
c
hnol
ogy
gove
r
n
a
nc
e
:
A
s
ur
ve
y
i
n
C
ol
om
bi
a
n
e
nt
e
r
pr
i
s
e
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,”
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at
a
i
n
B
r
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T
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ve
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opm
e
nt
a
nd
de
pl
oym
e
nt
of
m
a
c
hi
ne
l
e
a
r
ni
ng
m
ode
l
s
,”
K
ne
e
Sur
ge
r
y
,
Spor
t
s
T
r
aum
at
ol
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W
.
S
a
e
ys
,
P
.
V
a
n
de
W
a
l
l
e
,
S
.
T
r
ui
j
e
n,
a
nd
P
.
M
e
yns
,
“
G
a
i
t
s
t
r
i
de
l
e
ngt
h
e
s
t
i
m
a
t
i
on
us
i
n
g
e
m
be
dde
d m
a
c
hi
ne
l
e
a
r
ni
n
g,”
Se
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nj
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F
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z
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r
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F
a
r
a
s
ha
h,
V
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M
e
hr
nous
h,
F
.
D
a
r
s
a
r
e
h,
a
nd
N
.
R
ooz
be
h,
“
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a
c
hi
ne
l
e
a
r
ni
ng
-
ba
s
e
d
a
ppr
oa
c
h
f
or
pr
e
di
c
t
i
ng l
ow
bi
r
t
h w
e
i
ght
,”
B
M
C
P
r
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gnanc
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vi
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e
d
pr
e
-
t
r
a
i
ni
ng
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i
t
h
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ont
r
a
s
t
i
ve
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nd
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a
s
k
e
d
a
ut
o
e
nc
ode
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m
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or
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l
i
ng
w
i
t
h
s
m
a
l
l
da
t
a
s
e
t
s
i
n
de
e
p l
e
a
r
ni
ng f
or
m
e
di
c
a
l
i
m
a
gi
ng,”
Sc
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e
nt
i
f
i
c
R
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Y
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H
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n,
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M
.
A
l
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M
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l
a
h,
“
P
a
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i
e
nt
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l
e
ve
l
e
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i
na
bl
e
m
a
c
hi
ne
l
e
a
r
ni
ng
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o
pr
e
di
c
t
m
a
j
or
a
dve
r
s
e
c
a
r
di
ova
s
c
ul
a
r
e
v
e
nt
s
f
r
om
S
P
E
C
T
M
P
I
a
nd
C
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A
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r
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vi
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d
m
a
c
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l
e
a
r
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a
l
gor
i
t
hm
s
f
or
di
s
e
a
s
e
pr
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c
t
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on,”
B
M
C
M
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T
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c
hi
ne
l
e
a
r
ni
ng
l
i
f
e
c
yc
l
e
a
nd
t
h
e
c
l
oud:
i
m
pl
i
c
a
t
i
ons
f
or
dr
ug
di
s
c
ove
r
y,”
E
x
pe
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t
O
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on on D
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que
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f
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T
H
z
i
m
a
gi
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a
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t
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dom
a
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c
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T
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nds
,
p
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s
pe
c
t
i
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,”
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a
na
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c
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a
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s
:
a
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i
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a
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gui
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f
i
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l
l
i
ge
nc
e
t
o
de
ve
l
op
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m
ul
t
i
va
r
i
a
t
e
m
ode
l
w
i
t
h
a
m
a
c
hi
ne
l
e
a
r
ni
ng
m
ode
l
t
o
pr
e
di
c
t
c
om
pl
i
c
a
t
i
ons
i
n
M
e
xi
c
a
n
di
a
be
t
i
c
pa
t
i
e
nt
s
w
i
t
hout
a
r
t
e
r
i
a
l
hype
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t
e
ns
i
on
(
na
t
i
ona
l
ne
s
t
e
d
c
a
s
e
-
c
ont
r
ol
s
t
udy)
:
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e
t
f
or
m
i
n
a
nd
e
l
e
va
t
e
d nor
m
a
l
bl
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s
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,”
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ou
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a
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de
nt
i
f
i
e
s
s
m
a
r
t
w
a
t
c
h
-
ba
s
e
d
phys
i
ol
ogi
c
a
l
bi
om
a
r
ke
r
f
or
p
r
e
di
c
t
i
ng
di
s
r
upt
i
ve
be
ha
vi
or
i
n
c
hi
l
dr
e
n:
a
f
e
a
s
i
bi
l
i
t
y
s
t
udy
,”
J
ou
r
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of
C
hi
l
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T
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a
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i
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s
s
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:
a
r
e
vi
e
w
of
s
e
m
i
-
s
upe
r
vi
s
e
d m
a
c
hi
ne
l
e
a
r
ni
ng i
n
r
e
ha
bi
l
i
t
a
t
i
on
s
c
i
e
nc
e
,”
D
i
s
abi
l
i
t
y
and
R
e
habi
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of
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gl
uc
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e
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m
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l
e
a
r
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ng
a
ppl
i
c
a
t
i
ons
i
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t
ype
1 di
a
be
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,”
A
r
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f
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a
di
om
i
c
a
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ge
nom
i
c
m
a
c
hi
ne
l
e
a
r
ni
ng
m
e
t
hod
pe
r
f
or
m
a
nc
e
f
or
pr
os
t
a
t
e
c
a
n
c
e
r
di
a
gnos
i
s
:
s
ys
t
e
m
a
t
i
c
l
i
t
e
r
a
t
ur
e
r
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vi
e
w
,”
J
our
nal
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on
m
a
c
hi
ne
l
e
a
r
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ng
m
ode
l
s
f
or
onl
i
ne
l
e
a
r
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ng
a
n
d
e
xa
m
i
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t
i
on s
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he
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f
i
c
a
t
i
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o
f
c
l
i
ni
c
a
l
l
y
s
i
gn
i
f
i
c
a
nt
pr
os
t
a
t
e
c
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nc
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r
on
M
R
I
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a
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e
l
l
i
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nc
e
a
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m
a
c
hi
ne
l
e
a
r
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,”
A
r
c
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vi
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d
m
a
c
hi
ne
l
e
a
r
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ng
a
ppl
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e
d
t
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di
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i
ve
i
nt
e
r
s
t
i
t
i
a
l
gl
uc
os
e
l
e
v
e
l
c
l
a
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f
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ur
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hi
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l
e
a
r
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a
s
s
i
s
t
e
d
de
t
e
c
t
i
on
of
ke
r
a
t
oc
onus
:
a
s
ys
t
e
m
a
t
i
c
r
e
vi
e
w
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t
a
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ys
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,”
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G
I
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c
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a
c
t
i
c
a
l
gui
da
n
c
e
i
n
how
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t
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I
D
c
a
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r
dr
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ve
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anc
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e
di
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t
i
ng
m
a
c
hi
ne
l
e
a
r
ni
ng
pi
pe
l
i
ne
r
unt
i
m
e
s
i
n
t
he
c
ont
e
xt
of
a
ut
om
a
t
e
d
m
a
c
hi
ne
l
e
a
r
ni
n
g,”
I
E
E
E
T
r
ans
ac
t
i
ons
on
P
at
t
e
r
n
A
nal
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I
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W
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i
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w
,
“
M
a
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hi
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l
e
a
r
ni
ng
a
ppr
oa
c
he
s
a
ppl
i
e
d
i
n
s
pi
na
l
pa
i
n
r
e
s
e
a
r
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J
our
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R
i
c
ha
r
ds
,
“
A
dva
nc
e
m
e
nt
s
w
i
t
hi
n
m
ode
r
n
m
a
c
hi
ne
l
e
a
r
ni
ng
m
e
t
hodol
ogy:
i
m
pa
c
t
s
a
nd
p
r
os
pe
c
t
s
i
n
bi
om
a
r
ke
r
di
s
c
ove
r
y,”
C
u
r
r
e
nt
M
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“
A
ppl
i
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a
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i
on
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a
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
ge
nc
e
a
nd
m
a
c
hi
ne
l
e
a
r
ni
ng
i
n
dr
ug
di
s
c
ove
r
y
,”
i
n
A
r
t
i
f
i
c
i
al
I
nt
e
l
l
i
ge
nc
e
i
n
D
r
ug
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J
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S
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h,
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B
e
l
l
om
o,
“
M
a
c
hi
ne
l
e
a
r
ni
ng
i
n
r
i
s
k
pr
e
di
c
t
i
on
f
or
c
a
r
di
a
c
s
ur
ge
r
y
-
a
n
e
m
e
r
gi
ng
t
r
e
nd?
”
H
e
ar
t
,
L
ung and C
i
r
c
ul
at
i
on
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nc
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z
yk,
M
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B
e
r
na
ś
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nd
T
.
B
oc
z
a
r
,
“
C
l
a
s
s
i
f
i
c
a
t
i
on
of
l
ow
f
r
e
que
nc
y
s
i
gna
l
s
e
m
i
t
t
e
d
by
pow
e
r
t
r
a
ns
f
or
m
e
r
s
us
i
ng
s
e
n
s
or
s
a
nd m
a
c
hi
ne
l
e
a
r
ni
ng m
e
t
hod
s
,”
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g,
“
T
he
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om
pa
t
i
bi
l
i
t
y
of
t
he
or
e
t
i
c
a
l
f
r
a
m
e
w
or
ks
w
i
t
h
m
a
c
hi
ne
l
e
a
r
ni
ng
a
na
l
ys
e
s
i
n
ps
yc
hol
ogi
c
a
l
r
e
s
e
a
r
c
h,”
C
u
r
r
e
nt
O
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on i
n P
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c
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H
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Y
a
da
v
a
nd
A
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T
ha
kka
r
,
“
N
O
A
-
L
S
T
M
:
A
n
e
f
f
i
c
i
e
nt
L
S
T
M
c
e
l
l
a
r
c
hi
t
e
c
t
ur
e
f
or
t
i
m
e
s
e
r
i
e
s
f
or
e
c
a
s
t
i
ng,”
E
x
pe
r
t
Sy
s
t
e
m
s
w
i
t
h
A
ppl
i
c
at
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M
.
M
.
R
i
va
s
,
a
nd
I
.
A
.
D
e
l
ga
do,
“
M
e
t
hod
t
o
m
a
na
ge
t
he
s
e
c
ur
i
t
y
of
i
n
f
or
m
a
t
i
on
a
s
s
e
t
s
(
i
n
S
pa
ni
s
h:
m
é
t
odo
pa
r
a
ge
s
t
i
ona
r
l
a
s
e
gur
i
da
d
de
a
c
t
i
vos
de
I
nf
or
m
a
c
i
ón)
,”
R
e
v
i
s
t
a
I
be
r
i
c
a
de
Si
s
t
e
m
as
e
T
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c
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ng
a
ppr
oa
c
h
on
s
hor
t
-
t
e
r
m
s
pa
t
i
ot
e
m
por
a
l
di
s
t
r
i
bu
t
i
on
f
or
e
c
a
s
t
i
ng
of
doc
kl
e
s
s
bi
ke
-
s
ha
r
i
ng
s
ys
t
e
m
,”
N
e
ur
al
C
om
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i
r
s
c
hl
,
“
M
a
c
hi
ne
l
e
a
r
ni
ng
ope
r
a
t
i
ons
(
M
L
O
ps
)
:
ove
r
vi
e
w
,
de
f
i
ni
t
i
on,
a
nd
a
r
c
hi
t
e
c
t
ur
e
,”
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E
E
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A
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a
l
i
br
a
r
y
f
or
t
e
s
t
i
ng
a
nd
va
l
i
da
t
i
ng
m
a
c
hi
ne
l
e
a
r
ni
ng
m
ode
l
s
a
nd
da
t
a
,”
J
our
nal
of
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D
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O
.
-
A
bde
s
l
a
m
,
“
D
e
m
ys
t
i
f
yi
ng
M
L
O
ps
a
nd
pr
e
s
e
nt
i
ng
a
r
e
c
i
pe
f
or
t
he
s
e
l
e
c
t
i
on
of
ope
n
-
s
our
c
e
t
ool
s
,”
A
ppl
i
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d Sc
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a
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z
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M
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a
pa
e
l
i
a
s
, a
nd
F
. P
. G
.
M
á
r
que
z
, “
P
a
t
t
e
r
n r
e
c
o
gni
t
i
on ba
s
e
d on
s
t
a
t
i
s
t
i
c
a
l
m
e
t
hods
c
om
bi
ne
d w
i
t
h
m
a
c
hi
ne
l
e
a
r
ni
ng i
n r
a
i
l
w
a
y s
w
i
t
c
he
s
,”
E
x
pe
r
t
Sy
s
t
e
m
s
w
i
t
h A
ppl
i
c
at
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ons
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N
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K
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M
us
i
a
l
,
a
nd
B
.
G
a
br
ys
,
“
O
n
t
a
ki
ng
a
dva
nt
a
ge
of
oppor
t
uni
s
t
i
c
m
e
t
a
-
know
l
e
dge
t
o
r
e
duc
e
c
onf
i
gur
a
t
i
on
s
pa
c
e
s
f
or
a
ut
om
a
t
e
d
m
a
c
hi
ne
l
e
a
r
ni
ng,”
E
x
pe
r
t
S
y
s
t
e
m
s
w
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t
h
A
ppl
i
c
at
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ons
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Z
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Z
ha
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Y
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L
i
,
S
.
Y
a
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Z
.
Z
ha
ng,
a
nd
Y
.
L
e
i
,
“
C
od
e
-
a
w
a
r
e
f
a
ul
t
l
oc
a
l
i
z
a
t
i
on
w
i
t
h
pr
e
-
t
r
a
i
ni
ng
a
nd
i
nt
e
r
pr
e
t
a
bl
e
m
a
c
hi
ne
l
e
a
r
ni
ng,”
E
x
pe
r
t
Sy
s
t
e
m
s
w
i
t
h A
ppl
i
c
at
i
ons
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K
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F
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l
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ppou,
G
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A
i
f
a
nt
i
s
,
G
.
A
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P
a
pa
kos
t
a
s
,
a
nd
G
.
E
.
T
s
e
kour
a
s
,
“
S
t
r
uc
t
ur
e
l
e
a
r
ni
ng
a
nd
hype
r
pa
r
a
m
e
t
e
r
opt
i
m
i
z
a
t
i
on
us
i
ng
a
n
a
ut
om
a
t
e
d m
a
c
hi
ne
l
e
a
r
ni
ng
(
A
ut
oM
L
)
pi
pe
l
i
ne
,”
I
nf
or
m
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i
on
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K
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L
e
f
e
vr
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e
t
al
.
,
“
M
ode
l
O
p
s
f
or
e
nha
nc
e
d
de
c
i
s
i
on
-
m
a
ki
ng
a
nd
gove
r
na
nc
e
i
n
e
m
e
r
ge
nc
y
c
ont
r
ol
r
oom
s
,”
E
nv
i
r
onm
e
nt
Sy
s
t
e
m
s
and D
e
c
i
s
i
ons
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N
. F
ong a
nd
S
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.
-
O
r
e
, “
C
on
s
i
de
r
a
t
i
ons
f
or
c
om
pl
i
a
n
c
e
w
i
t
h
t
he
i
nf
or
m
a
t
i
on s
e
c
ur
i
t
y pol
i
c
y
(
i
n S
pa
ni
s
h:
C
ons
i
de
r
a
c
i
one
s
pa
r
a
e
l
c
um
pl
i
m
i
e
nt
o
de
l
a
pol
í
t
i
c
a
de
s
e
gur
i
da
d
de
l
a
i
nf
or
m
a
c
i
ón
)
,”
R
e
v
i
s
t
a
I
be
r
i
c
a
de
Si
s
t
e
m
a
s
e
T
e
c
nol
ogi
a
s
de
I
nf
or
m
ac
c
ao
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