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C
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Dec
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CC B
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C
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m
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San
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Dep
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Ag
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s
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T
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Facu
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Ag
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T
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n
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lo
g
y
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Un
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as B
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awijay
a
10
-
1
1
Vete
r
an
St.,
Keta
wan
g
g
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e,
L
o
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k
war
u
,
Ma
lan
g
6
5
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4
5
,
E
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av
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I
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d
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esia
E
m
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im
am
s
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to
s
o
@
u
b
.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
Ar
tific
ial
i
n
tellig
en
ce
(
AI
)
h
as
em
er
g
ed
as
a
tr
an
s
f
o
r
m
ativ
e
f
o
r
ce
ac
r
o
s
s
d
iv
er
s
e
s
ec
to
r
s
.
I
ts
im
p
lem
en
tatio
n
h
as
r
ev
o
l
u
tio
n
ized
in
d
u
s
tr
ies
b
y
en
ab
lin
g
r
ea
l
-
tim
e
d
ata
an
aly
s
is
,
p
atter
n
r
ec
o
g
n
itio
n
,
p
r
ed
ictio
n
,
th
er
eb
y
s
u
p
p
o
r
tin
g
m
o
r
e
in
tellig
en
t
d
ec
is
io
n
-
m
a
k
in
g
[
1
]
–
[
4
]
.
I
n
th
e
a
g
r
o
i
n
d
u
s
tr
ial
s
ec
to
r
m
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k
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d
b
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s
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ality
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m
u
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ac
to
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n
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k
s
,
an
d
h
ig
h
lev
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o
f
u
n
ce
r
tain
ty
AI
h
as
th
e
p
o
t
en
tial
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tr
an
s
f
o
r
m
co
n
v
en
tio
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p
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s
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b
y
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tin
g
ef
f
icien
c
y
,
r
esil
ien
ce
,
an
d
s
u
s
tain
ab
ilit
y
[
1
]
,
[
5
]
,
[
6
]
.
Sch
o
la
r
s
an
d
p
r
ac
titi
o
n
er
s
alik
e
h
a
v
e
in
cr
ea
s
in
g
ly
ac
k
n
o
wled
g
ed
t
h
e
v
alu
e
o
f
AI
i
n
o
p
tim
izin
g
ag
r
icu
ltu
r
al
p
r
o
d
u
ctio
n
[
7
]
,
im
p
r
o
v
in
g
q
u
ality
co
n
tr
o
l
[
8
]
,
an
d
s
tr
en
g
th
e
n
in
g
s
u
p
p
ly
c
h
ain
p
er
f
o
r
m
an
ce
[
9
]
–
[
1
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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A
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2261
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1
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.
I
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with
AI
e
n
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ANN)
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m
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[
1
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–
[
2
1
]
.
T
h
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teg
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Paltrin
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[
2
2
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DNNs)
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Kam
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[
2
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ML
P)
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Gh
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[
2
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ased
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t
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u
p
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ly
ch
ain
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E
x
is
tin
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s
tu
d
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h
av
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s
u
f
f
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tly
ex
p
lo
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h
o
w
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m
o
d
els
ca
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s
u
p
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r
t
r
is
k
ev
alu
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in
th
is
s
ec
to
r
,
p
ar
ticu
lar
ly
b
y
in
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r
atin
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tr
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ctu
r
ed
r
is
k
ass
ess
m
en
t
f
r
am
ewo
r
k
s
s
u
ch
as
FME
A
with
s
u
p
er
v
is
ed
ANNs
.
T
h
is
s
tu
d
y
ad
d
r
ess
es
th
is
g
ap
b
y
im
p
lem
en
tin
g
a
n
ANN
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B
P
to
class
if
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s
u
s
tain
ab
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elate
d
r
is
k
s
in
th
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co
f
f
ee
s
u
p
p
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c
h
ain
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o
f
f
er
in
g
a
n
o
v
el
i
n
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r
atio
n
o
f
tr
ad
itio
n
al
r
is
k
ass
es
s
m
en
t
(
FME
A)
with
m
o
d
er
n
AI
-
b
ased
class
if
icatio
n
.
T
h
is
co
n
tr
ib
u
tio
n
ex
te
n
d
s
th
e
a
p
p
licatio
n
o
f
ML
i
n
ag
r
icu
ltu
r
e
b
y
f
o
c
u
s
in
g
o
n
th
e
co
f
f
ee
in
d
u
s
tr
y
an
d
p
r
o
v
id
in
g
a
s
tr
u
ctu
r
ed
m
o
d
el
f
o
r
e
n
h
an
c
in
g
r
is
k
ev
alu
atio
n
ac
cu
r
ac
y
an
d
d
ec
is
io
n
-
m
ak
in
g
.
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
ad
o
p
ts
two
m
eth
o
d
o
lo
g
ical
ap
p
r
o
ac
h
es:
th
e
ex
p
er
im
en
tal
m
eth
o
d
an
d
th
e
d
ata
-
d
r
iv
en
m
eth
o
d
.
T
h
e
ex
p
e
r
im
en
tal
m
eth
o
d
is
u
s
ed
to
id
e
n
tify
an
d
an
aly
ze
r
is
k
s
in
th
e
co
f
f
ee
a
g
r
o
in
d
u
s
tr
y
s
u
p
p
l
y
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ain
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wh
ile
th
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ata
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d
r
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elies
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n
AI
,
s
p
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if
ica
lly
s
u
p
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v
is
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ANN,
to
an
al
y
z
e
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d
class
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y
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A
d
etailed
r
esear
ch
m
eth
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d
d
e
s
ig
n
is
p
r
esen
ted
in
Fig
u
r
e
1
.
2
.
1
.
E
x
perim
ent
a
l
m
et
ho
d
T
h
e
ex
p
er
im
e
n
tal
m
eth
o
d
is
d
esig
n
ed
to
s
y
s
tem
atica
lly
id
en
tify
an
d
an
al
y
ze
p
o
te
n
tial
r
is
k
s
af
f
ec
tin
g
th
e
co
f
f
ee
a
g
r
o
-
i
n
d
u
s
tr
y
.
T
h
is
ap
p
r
o
ac
h
in
v
o
lv
es
b
o
th
q
u
alitativ
e
an
d
q
u
a
n
titativ
e
d
ata
co
llectio
n
a
n
d
ca
teg
o
r
izatio
n
b
ased
o
n
s
u
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n
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d
im
en
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io
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s
.
T
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e
ar
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s
ev
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al
k
ey
s
tep
s
,
in
clu
d
in
g
a
s
f
o
llo
ws
:
i)
I
d
en
tific
atio
n
o
f
p
o
ten
tial
r
is
k
s
:
th
e
in
itial
p
h
ase
in
v
o
lv
es
id
en
tify
in
g
v
ar
io
u
s
r
is
k
s
th
a
t
m
ay
im
p
ac
t
th
e
co
f
f
ee
ag
r
o
-
in
d
u
s
tr
y
.
T
h
is
is
ac
h
iev
ed
th
r
o
u
g
h
ex
p
er
t
co
n
s
u
ltatio
n
s
,
liter
atu
r
e
r
ev
iews,
an
d
f
ield
o
b
s
er
v
atio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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ii)
Gr
o
u
p
in
g
r
is
k
s
b
y
s
u
s
tain
ab
ili
ty
asp
ec
ts
:
id
en
tifie
d
r
is
k
s
ar
e
ca
teg
o
r
ized
in
to
s
u
s
tain
ab
ilit
y
d
im
en
s
io
n
s
s
u
ch
as
s
o
cial,
ec
o
n
o
m
ic,
an
d
en
v
ir
o
n
m
en
tal
asp
ec
ts
.
T
h
is
class
if
icatio
n
f
ac
ilit
ates
a
h
o
lis
tic
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
r
is
k
s
'
b
r
o
ad
er
im
p
licatio
n
s
.
iii)
C
o
llectio
n
o
f
S
-
O
-
D
v
al
u
es:
f
o
r
ea
ch
r
is
k
,
th
r
ee
cr
itical
p
ar
am
eter
s
ar
e
m
ea
s
u
r
e
d
b
y
5
5
r
esp
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n
d
en
ts
.
T
h
ese
in
clu
d
e
s
ev
er
ity
(
S):
t
h
e
p
o
ten
tial
im
p
ac
t
o
f
t
h
e
r
is
k
;
o
cc
u
r
r
en
ce
(
O)
:
t
h
e
lik
elih
o
o
d
o
f
th
e
r
is
k
o
cc
u
r
r
in
g
;
an
d
d
etec
tio
n
(
D
)
:
t
h
e
p
r
o
b
ab
ilit
y
o
f
d
etec
tin
g
t
h
e
r
is
k
b
ef
o
r
e
it c
au
s
es h
ar
m
.
iv
)
C
alcu
latio
n
o
f
r
is
k
p
r
io
r
ity
n
u
m
b
er
(
R
PN)
:
u
s
in
g
th
e
S
-
O
-
D
v
alu
es,
th
e
R
PN
i
s
ca
lcu
lated
f
o
r
ea
ch
r
is
k
.
T
h
e
R
PN q
u
an
tifie
s
th
e
cr
iticality
o
f
r
is
k
s
,
en
ab
lin
g
p
r
i
o
r
itizatio
n
.
v)
C
las
s
if
icatio
n
o
f
r
is
k
s
b
ased
o
n
R
PN:
r
is
k
s
ar
e
th
en
clas
s
if
ied
in
to
f
iv
e
p
r
i
o
r
ity
lev
els
ac
co
r
d
in
g
to
th
eir
R
PN
s
co
r
es,
g
u
id
in
g
tar
g
eted
r
is
k
m
itig
atio
n
s
tr
ateg
ies.
T
h
e
class
if
icatio
n
tar
g
ets
a
r
e
as
f
o
llo
ws:
v
er
y
lo
w
=
R
PN
1
–
4
0
;
l
o
w
=
R
PN
4
1
–
1
0
0
;
m
o
d
er
ate
=
R
PN
1
0
1
–
2
0
0
;
h
i
g
h
=
R
PN
2
0
1
–
5
0
0
;
an
d
v
er
y
h
ig
h
=
R
PN
5
0
1
–
1
0
0
0
.
T
h
is
c
lass
if
icatio
n
h
elp
s
in
p
r
io
r
itizi
n
g
r
is
k
s
f
r
o
m
least
to
m
o
s
t
cr
i
tical,
allo
win
g
f
o
r
ef
f
icie
n
t a
llo
ca
tio
n
o
f
r
eso
u
r
ce
s
an
d
m
itig
atio
n
ef
f
o
r
ts
[
2
6
]
,
[
2
7
]
.
Fig
u
r
e
1
.
R
esear
ch
m
eth
o
d
d
e
s
ig
n
2
.
2
.
Da
t
a
-
driv
en
m
et
ho
d
T
h
e
d
ata
-
d
r
iv
e
n
m
eth
o
d
em
p
lo
y
ed
a
s
u
p
er
v
is
ed
ANN
ap
p
r
o
ac
h
to
class
if
y
r
is
k
ca
teg
o
r
ies
in
th
e
co
f
f
ee
s
u
p
p
ly
c
h
ain
u
s
in
g
a
s
u
p
er
v
is
ed
lear
n
in
g
f
r
am
ewo
r
k
.
T
h
e
d
ev
elo
p
m
en
t
p
r
o
ce
s
s
co
n
s
is
ted
o
f
s
ev
er
al
s
tag
es:
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
,
m
o
d
el
d
esig
n
,
tr
ain
i
n
g
,
a
n
d
e
v
alu
atio
n
.
T
h
e
m
o
d
el
was
im
p
lem
en
ted
u
s
in
g
th
e
T
en
s
o
r
Flo
w
lib
r
ar
y
with
t
h
e
K
er
as API
in
Py
th
o
n
.
i)
I
n
p
u
t
d
ata
g
at
h
er
in
g
:
r
elev
an
t
r
is
k
-
r
elate
d
d
ata
ar
e
g
ath
er
ed
f
r
o
m
t
h
e
co
f
f
ee
a
g
r
o
-
i
n
d
u
s
tr
y
s
u
p
p
ly
ch
ai
n
an
d
p
r
e
p
ar
ed
f
o
r
f
u
r
t
h
er
an
aly
s
is
.
T
h
is
d
ata
s
er
v
es a
s
th
e
f
o
u
n
d
atio
n
f
o
r
tr
ain
i
n
g
th
e
A
I
m
o
d
el.
ii)
Data
p
r
e
-
p
r
o
ce
s
s
in
g
:
th
e
r
aw
d
ataset
was
f
ir
s
t
c
lean
ed
an
d
p
r
ep
ar
ed
f
o
r
m
o
d
elin
g
.
C
ateg
o
r
ical
v
ar
iab
les,
in
clu
d
in
g
r
is
k
ca
teg
o
r
ies
an
d
s
p
ec
if
ic
r
is
k
ty
p
es,
wer
e
en
co
d
ed
u
s
in
g
L
a
b
elE
n
co
d
er
.
T
o
f
ac
ilit
ate
m
u
lti
-
class
class
if
icatio
n
,
th
e
t
ar
g
et
v
ar
ia
b
le
was
tr
an
s
f
o
r
m
e
d
in
to
a
o
n
e
-
h
o
t
e
n
co
d
e
d
f
o
r
m
at.
Nu
m
er
ical
f
ea
tu
r
es,
p
ar
ticu
lar
ly
th
e
R
PN,
wer
e
n
o
r
m
alize
d
u
s
in
g
t
h
e
Stan
d
ar
d
Scaler
to
en
s
u
r
e
all
f
ea
tu
r
es
co
n
tr
ib
u
ted
eq
u
ally
d
u
r
in
g
m
o
d
el
tr
ain
in
g
.
T
h
e
en
tire
d
ataset
was
th
en
s
p
lit
in
to
t
r
ain
in
g
(
7
0
%)
,
v
alid
atio
n
(
1
5
%),
an
d
test
s
ets
(
1
5
%).
T
h
is
s
tr
atif
ied
d
iv
is
io
n
en
s
u
r
ed
a
b
alan
ce
d
r
e
p
r
esen
tatio
n
o
f
ea
c
h
r
is
k
ca
teg
o
r
y
ac
r
o
s
s
all
s
u
b
s
ets.
iii)
Mo
d
el
d
esig
n
:
th
e
ANN
m
o
d
el
was
co
n
s
tr
u
cted
u
s
in
g
a
s
e
q
u
en
tial
ar
ch
itectu
r
e
co
m
p
o
s
ed
o
f
m
u
ltip
le
f
u
lly
co
n
n
ec
ted
(
Den
s
e)
lay
er
s
.
T
o
en
h
an
ce
tr
ain
i
n
g
s
tab
ilit
y
an
d
r
ed
u
ce
i
n
ter
n
al
c
o
v
ar
iate
s
h
if
t,
B
atch
No
r
m
aliza
tio
n
lay
er
s
wer
e
in
co
r
p
o
r
ated
af
ter
d
e
n
s
e
lay
er
s
.
Ad
d
itio
n
ally
,
d
r
o
p
o
u
t
lay
er
s
wer
e
ap
p
lied
to
m
itig
ate
o
v
er
f
itti
n
g
b
y
r
an
d
o
m
ly
d
is
ab
lin
g
a
f
r
ac
tio
n
o
f
n
e
u
r
o
n
s
d
u
r
i
n
g
tr
ai
n
in
g
.
T
h
e
o
u
tp
u
t
lay
er
co
n
s
is
ted
o
f
f
i
v
e
n
o
d
es
co
r
r
esp
o
n
d
in
g
to
th
e
f
iv
e
p
r
e
d
ef
in
ed
r
is
k
ca
teg
o
r
ies.
A
s
o
f
t
m
ax
ac
tiv
atio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
r
tifi
cia
l in
tellig
en
ce
-
b
a
s
ed
r
is
k
a
s
s
es
s
men
t in
a
g
r
o
-
in
d
u
s
tr
y
u
s
in
g
s
u
p
ervis
ed
…
(
I
ma
m
S
a
n
to
s
o
)
2263
f
u
n
ctio
n
was
ap
p
lied
to
th
is
lay
er
to
g
e
n
er
ate
a
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
ac
r
o
s
s
all
ca
teg
o
r
ies.
T
h
e
class
with
th
e
h
ig
h
est
p
r
o
b
a
b
ilit
y
was
s
elec
ted
a
s
th
e
f
in
al
p
r
ed
ictio
n
.
T
h
e
n
etwo
r
k
ar
ch
it
ec
tu
r
e
o
f
th
is
r
esear
ch
ca
n
b
e
s
ee
n
in
Fig
u
r
e
2
.
iv
)
Mo
d
el
tr
ain
in
g
:
th
e
m
o
d
el
was
co
m
p
iled
u
s
in
g
th
e
ca
teg
o
r
ical_
cr
o
s
s
en
tr
o
p
y
lo
s
s
f
u
n
ctio
n
,
ap
p
r
o
p
r
iat
e
f
o
r
m
u
lti
-
class
class
if
icatio
n
task
s
.
T
h
e
Ad
a
m
o
p
tim
izer
,
wh
ich
in
ter
n
ally
u
s
es
BP
,
wa
s
em
p
lo
y
e
d
to
u
p
d
ate
weig
h
ts
an
d
b
iases
d
u
r
in
g
tr
ain
in
g
.
T
h
e
d
ef
au
lt
lear
n
in
g
r
ate
p
r
o
v
id
ed
b
y
T
en
s
o
r
Fl
o
w
was
u
s
ed
th
r
o
u
g
h
o
u
t.
Mo
d
el
tr
ain
in
g
w
as
co
n
d
u
cted
with
a
b
atch
s
ize
o
f
3
2
o
v
er
a
m
ax
im
u
m
o
f
5
0
0
ep
o
ch
s
.
An
E
ar
ly
Sto
p
p
in
g
m
ec
h
an
is
m
wi
th
a
p
atien
ce
o
f
1
0
0
e
p
o
ch
s
was
im
p
lem
en
ted
to
h
alt
tr
ai
n
in
g
w
h
en
th
e
v
alid
atio
n
lo
s
s
f
ailed
to
im
p
r
o
v
e,
th
er
eb
y
p
r
ev
en
tin
g
o
v
e
r
f
itti
n
g
.
A
Mo
d
elC
h
ec
k
p
o
in
t
ca
llb
ac
k
was
also
u
s
ed
to
s
av
e
th
e
m
o
d
el
with
th
e
lo
west v
alid
atio
n
lo
s
s
d
u
r
in
g
tr
ain
in
g
.
v)
E
r
r
o
r
ev
alu
atio
n
:
f
o
llo
win
g
tr
ain
in
g
,
th
e
b
est
m
o
d
el
was
e
v
alu
ated
u
s
in
g
th
e
test
d
atase
t.
T
h
e
m
o
d
el
o
u
tp
u
t,
ex
p
r
ess
ed
as
a
v
e
cto
r
o
f
p
r
o
b
ab
ilit
ies
f
r
o
m
th
e
s
o
f
tm
ax
lay
er
,
was
co
n
v
er
ted
to
d
is
cr
ete
class
lab
els
u
s
in
g
th
e
ar
g
m
a
x
f
u
n
ctio
n
.
Mo
d
el
p
er
f
o
r
m
an
ce
was
ass
es
s
ed
th
r
o
u
g
h
v
ar
io
u
s
m
etr
ics,
in
clu
d
in
g
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
a
n
d
F1
-
s
co
r
e,
all
d
er
iv
e
d
f
r
o
m
th
e
class
if
icatio
n
r
ep
o
r
t.
Ad
d
itio
n
ally
,
a
co
n
f
u
s
io
n
m
atr
ix
was
g
en
er
ated
to
p
r
o
v
id
e
f
u
r
th
e
r
in
s
ig
h
t
in
to
th
e
m
o
d
el’
s
class
if
icatio
n
p
er
f
o
r
m
an
c
e
ac
r
o
s
s
ca
teg
o
r
ies.
I
f
p
er
f
o
r
m
a
n
ce
d
id
n
o
t
m
ee
t
ex
p
ec
tatio
n
s
,
s
ev
er
al
asp
ec
ts
co
u
ld
b
e
r
ev
i
s
ed
,
in
clu
d
in
g
f
ea
tu
r
e
en
g
in
ee
r
in
g
,
m
o
d
el
ar
c
h
itectu
r
e,
o
r
h
y
p
er
p
ar
a
m
eter
t
u
n
in
g
.
Fig
u
r
e
2
.
Netwo
r
k
a
r
ch
itectu
r
e
ANN
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
P
o
t
ent
ia
l r
is
k
s
in co
f
f
ee
a
g
ro
-
ind
us
t
ry
T
h
e
id
en
tific
atio
n
r
esu
lts
r
ev
ea
led
3
5
p
o
ten
tial
r
is
k
s
with
in
th
e
co
f
f
ee
s
u
p
p
ly
ch
ai
n
;
ea
ch
lin
k
ed
to
o
n
e
o
r
m
o
r
e
o
f
s
ev
en
s
u
s
tain
ab
ilit
y
d
im
en
s
io
n
s
.
So
cial
r
is
k
s
r
elate
to
th
e
welf
a
r
e
o
f
s
u
p
p
ly
c
h
ain
ac
t
o
r
s
;
ec
o
n
o
m
ic
r
is
k
s
co
n
ce
r
n
f
in
a
n
c
ial
s
tab
ilit
y
an
d
p
r
o
f
itab
ilit
y
;
e
n
v
ir
o
n
m
en
tal
r
is
k
s
in
v
o
lv
e
th
r
ea
ts
to
ec
o
s
y
s
tem
s
an
d
lo
n
g
-
te
r
m
s
u
s
tain
ab
ilit
y
;
tech
n
o
lo
g
ical
r
is
k
s
h
ig
h
lig
h
t
lim
itatio
n
s
in
ad
o
p
tio
n
an
d
in
f
r
astru
ctu
r
e;
o
p
er
atio
n
al
r
is
k
s
p
er
tain
t
o
p
r
o
d
u
ctio
n
an
d
d
is
tr
ib
u
tio
n
ch
allen
g
es;
en
ter
p
r
is
e
r
is
k
s
r
ef
lect
s
tr
ateg
ic
an
d
m
an
ag
er
ial
is
s
u
es;
an
d
q
u
ality
r
is
k
s
in
v
o
lv
e
th
e
co
n
s
is
ten
cy
an
d
co
m
p
lian
ce
o
f
f
in
al
p
r
o
d
u
cts.
Fu
ll
d
etail
s
ar
e
p
r
esen
ted
in
T
a
b
le
1
.
E
ac
h
i
d
en
tifie
d
r
is
k
was
th
en
e
n
co
d
e
d
with
n
u
m
er
ical
v
alu
es
f
r
o
m
1
to
3
5
.
T
h
e
S
-
O
-
D
s
co
r
es,
alo
n
g
with
th
e
R
PN
f
o
r
ea
ch
r
is
k
,
a
r
e
p
r
o
v
id
ed
in
th
e
s
u
p
p
lem
en
tar
y
f
ile,
wh
ich
s
er
v
ed
as
th
e
d
ataset
f
o
r
tr
ain
in
g
an
d
ev
al
u
atin
g
th
e
ANN
m
o
d
el.
I
n
to
tal,
th
e
r
e
s
ea
r
ch
er
will
o
b
tain
1
,
9
2
5
d
at
a
s
ets,
co
n
s
is
tin
g
o
f
1
,
9
2
5
S
-
O
-
D
s
co
r
e
co
m
b
in
atio
n
s
f
o
r
ea
ch
r
is
k
an
d
th
eir
co
r
r
e
s
p
o
n
d
in
g
1
,
9
2
5
R
PN v
alu
es.
3
.
2
.
M
o
del t
ra
ini
ng
re
s
ult
B
ased
o
n
Fig
u
r
e
3
,
t
h
e
g
r
ap
h
o
f
lo
s
s
o
v
er
ep
o
c
h
s
as
s
h
o
w
n
in
Fig
u
r
e
3
(
a
)
a
n
d
th
e
g
r
ap
h
o
f
ac
cu
r
ac
y
o
v
er
ep
o
c
h
s
as
s
h
o
wn
in
Fig
u
r
e
3
(
b
)
,
th
e
tr
ain
in
g
r
esu
lts
o
f
th
e
m
o
d
el
ca
n
b
e
s
u
m
m
ar
ize
d
as
f
o
llo
ws.
Prio
r
to
tr
ain
in
g
,
th
e
d
ataset
u
n
d
er
we
n
t
p
r
e
-
p
r
o
ce
s
s
in
g
an
d
was
d
iv
id
ed
in
to
1
,
3
4
7
tr
ain
in
g
d
ata,
2
8
9
v
alid
atio
n
d
ata,
an
d
2
8
9
test
d
ata.
I
n
itially
,
b
o
th
th
e
tr
ain
i
n
g
lo
s
s
an
d
v
alid
atio
n
lo
s
s
d
ec
r
ea
s
ed
s
h
ar
p
ly
d
u
r
i
n
g
th
e
f
ir
s
t
5
0
ep
o
c
h
s
,
d
r
o
p
p
in
g
f
r
o
m
ap
p
r
o
x
im
ately
1
.
7
–
1
.
8
to
ar
o
u
n
d
0
.
4
–
0
.
6
.
T
h
is
in
d
icate
s
th
at
th
e
m
o
d
el
q
u
ick
l
y
lear
n
ed
th
e
f
u
n
d
am
en
tal
p
atter
n
s
in
th
e
d
ata.
Af
ter
ep
o
c
h
5
0
,
tr
ain
in
g
lo
s
s
co
n
tin
u
ed
to
d
ec
lin
e
g
r
ad
u
ally
t
o
ar
o
u
n
d
0
.
3
b
y
ep
o
ch
5
0
0
,
with
m
in
o
r
f
lu
ct
u
atio
n
s
lik
ely
d
u
e
to
m
in
i
-
b
atch
g
r
ad
ien
t
d
escen
t.
T
h
e
v
alid
atio
n
lo
s
s
r
ea
ch
ed
its
lo
west
p
o
in
t
(
~0
.
1
5
–
0
.
2
)
b
etwe
en
e
p
o
ch
s
1
0
0
an
d
2
0
0
,
th
e
n
r
em
ain
e
d
r
e
lativ
ely
s
tab
le
with
n
o
s
ig
n
if
ican
t
u
p
war
d
t
r
en
d
,
s
u
g
g
esti
n
g
th
at
th
e
m
o
d
el
d
id
n
o
t su
f
f
er
f
r
o
m
s
ev
er
e
o
v
e
r
f
itti
n
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
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tell
,
Vo
l.
1
5
,
No
.
3
,
J
u
n
e
2
0
2
6
:
2
2
6
0
-
2
2
6
8
2264
T
ab
le
1
.
Po
ten
tial r
is
k
s
in
co
f
f
ee
ag
r
o
-
i
n
d
u
s
tr
y
R
i
s
k
d
i
m
e
n
s
i
o
n
C
o
d
e
R
i
s
k
i
n
d
i
c
a
t
o
r
R
i
s
k
I
D
S
o
c
i
a
l
S1
I
n
f
o
r
mat
i
o
n
se
c
u
r
i
t
y
a
n
d
c
o
n
t
r
a
c
t
s
1
S2
G
o
v
e
r
n
m
e
n
t
p
o
l
i
c
y
c
h
a
n
g
e
s
2
S3
Lo
n
g
su
p
p
l
y
c
h
a
i
n
3
S4
W
o
r
k
a
c
c
i
d
e
n
t
s
4
Ec
o
n
o
mi
c
F1
I
n
c
o
me
u
n
c
e
r
t
a
i
n
t
y
5
F2
H
i
g
h
s
t
o
r
a
g
e
c
o
st
s
6
F3
P
r
i
c
e
f
l
u
c
t
u
a
t
i
o
n
s
7
F4
H
i
g
h
t
r
a
n
s
p
o
r
t
a
t
i
o
n
c
o
s
t
s
8
F5
I
n
f
l
a
t
i
o
n
/
c
u
r
r
e
n
c
y
d
e
p
r
e
c
i
a
t
i
o
n
9
En
v
i
r
o
n
m
e
n
t
a
l
L1
P
e
st
/
d
i
sea
s
e
i
n
f
e
st
a
t
i
o
n
10
L2
U
n
p
r
e
d
i
c
t
a
b
l
e
w
e
a
t
h
e
r
11
L3
W
a
st
e
p
r
o
c
e
ssi
n
g
e
r
r
o
r
s
12
L4
C
a
r
b
o
n
e
mi
ss
i
o
n
s
13
L5
N
a
t
u
r
a
l
d
i
sas
t
e
r
s
14
L6
F
i
r
e
s
15
Te
c
h
n
o
l
o
g
y
T1
P
o
o
r
f
a
r
mi
n
g
p
r
a
c
t
i
c
e
s
16
T2
I
mp
r
o
p
e
r
p
l
a
n
t
i
n
g
t
e
c
h
n
i
q
u
e
s
17
T3
Eq
u
i
p
me
n
t
f
a
i
l
u
r
e
18
T4
R
a
w
m
a
t
e
r
i
a
l
d
a
m
a
g
e
19
O
p
e
r
a
t
i
o
n
a
l
O1
C
o
n
t
r
a
c
t
n
o
n
-
c
o
mp
l
i
a
n
c
e
20
O2
H
a
r
v
e
s
t
i
n
g
e
r
r
o
r
s
21
O3
U
n
c
e
r
t
a
i
n
r
a
w
ma
t
e
r
i
a
l
st
o
c
k
22
O4
Li
mi
t
e
d
w
o
r
k
f
o
r
c
e
23
O5
R
a
w
m
a
t
e
r
i
a
l
d
e
l
a
y
s
24
O6
P
a
c
k
a
g
i
n
g
e
r
r
o
r
s
25
En
t
e
r
p
r
i
se
E1
H
i
g
h
c
o
m
p
e
t
i
t
i
o
n
26
E2
U
n
st
a
b
l
e
m
a
r
k
e
t
d
e
ma
n
d
27
E3
R
e
c
o
r
d
i
n
g
e
r
r
o
r
s (
p
u
r
c
h
a
se
/
sa
l
e
s)
28
Q
u
a
l
i
t
y
Q1
Lo
w
r
a
w
ma
t
e
r
i
a
l
q
u
a
l
i
t
y
29
Q2
Li
mi
t
e
d
t
e
c
h
n
o
l
o
g
y
a
n
d
i
n
n
o
v
a
t
i
o
n
30
Q3
P
o
o
r
p
a
c
k
a
g
i
n
g
31
Q4
P
e
st
d
a
m
a
g
e
t
o
p
r
o
d
u
c
t
32
Q5
P
r
o
d
u
c
t
r
e
j
e
c
t
i
o
n
33
Q6
V
a
r
i
a
b
l
e
b
e
a
n
q
u
a
l
i
t
y
34
Q7
Q
u
a
l
i
t
y
l
o
ss
d
u
r
i
n
g
s
t
o
r
a
g
e
35
(
a)
(
b
)
Fig
u
r
e
3
.
Gr
a
p
h
o
f
(
a)
lo
s
s
o
v
er
ep
o
ch
s
a
n
d
(
b
)
ac
cu
r
ac
y
o
v
er
ep
o
ch
s
I
n
ter
m
s
o
f
ac
cu
r
ac
y
,
t
h
e
tr
ain
in
g
ac
cu
r
ac
y
im
p
r
o
v
ed
f
r
o
m
a
r
o
u
n
d
2
0
%
at
e
p
o
ch
0
to
ap
p
r
o
x
im
ately
8
0
%
b
y
ep
o
ch
1
0
0
,
ev
e
n
tu
all
y
r
ea
ch
in
g
a
b
o
u
t
9
2
%
b
y
e
p
o
ch
5
0
0
.
Valid
atio
n
ac
cu
r
ac
y
i
n
cr
ea
s
ed
ev
en
m
o
r
e
r
ap
id
ly
,
r
ea
c
h
in
g
a
p
p
r
o
x
im
at
ely
9
0
%
b
y
ep
o
ch
5
0
an
d
s
tab
ilizin
g
b
etwe
en
9
4
–
9
7
%
af
ter
ep
o
ch
1
0
0
.
I
n
ter
esti
n
g
ly
,
th
e
v
alid
atio
n
p
er
f
o
r
m
a
n
ce
s
lig
h
tly
ex
ce
ed
e
d
tr
ain
in
g
p
e
r
f
o
r
m
an
ce
,
in
d
icat
in
g
th
at
th
e
m
o
d
el
ac
h
iev
ed
g
o
o
d
g
en
er
aliza
tio
n
an
d
d
id
n
o
t m
e
r
ely
m
em
o
r
ize
th
e
tr
ain
in
g
d
ata
.
Ov
er
all,
th
e
m
o
d
el
ap
p
ea
r
s
to
h
av
e
co
n
v
er
g
ed
b
y
ar
o
u
n
d
ep
o
ch
2
0
0
,
with
o
n
l
y
m
ar
g
in
al
im
p
r
o
v
em
e
n
ts
in
lo
s
s
an
d
ac
c
u
r
ac
y
th
er
ea
f
ter
.
T
h
u
s
,
th
e
u
s
e
o
f
ea
r
ly
s
to
p
p
in
g
b
etwe
en
e
p
o
ch
s
1
5
0
an
d
2
0
0
co
u
ld
b
e
c
o
n
s
id
er
ed
to
r
e
d
u
c
e
co
m
p
u
tatio
n
al
tim
e
with
o
u
t
co
m
p
r
o
m
is
in
g
p
er
f
o
r
m
a
n
ce
.
I
n
co
n
cl
u
s
io
n
,
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
r
tifi
cia
l in
tellig
en
ce
-
b
a
s
ed
r
is
k
a
s
s
es
s
men
t in
a
g
r
o
-
in
d
u
s
tr
y
u
s
in
g
s
u
p
ervis
ed
…
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I
ma
m
S
a
n
to
s
o
)
2265
s
u
p
er
v
is
ed
n
eu
r
al
n
etwo
r
k
m
o
d
el
d
em
o
n
s
tr
ated
s
tr
o
n
g
lear
n
in
g
a
n
d
g
e
n
er
aliza
tio
n
ca
p
ab
ilit
ies,
ac
h
iev
in
g
ap
p
r
o
x
im
ately
9
5
%
v
alid
atio
n
ac
cu
r
ac
y
a
n
d
a
v
alid
atio
n
lo
s
s
o
f
ar
o
u
n
d
0
.
2
.
T
h
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
m
o
d
e
l
is
f
u
r
th
e
r
e
v
alu
ated
u
s
in
g
a
co
n
f
u
s
io
n
m
atr
ix
.
T
h
e
co
n
f
u
s
i
o
n
m
at
r
ix
f
o
r
th
e
test
in
g
d
ata,
as
illu
s
tr
ated
in
Fig
u
r
e
4
,
i
n
d
icate
s
th
at
th
e
m
o
d
el
s
u
cc
ess
f
u
lly
c
lass
if
ied
n
ea
r
ly
all
s
am
p
les
co
r
r
ec
tly
.
Of
th
e
9
7
class
0
s
am
p
les,
9
6
wer
e
p
r
ed
icted
ac
cu
r
ately
,
with
o
n
ly
1
m
is
class
if
icatio
n
.
Fo
r
class
1
,
6
5
o
u
t
o
f
6
6
s
am
p
l
es
wer
e
co
r
r
ec
tly
class
if
ied
,
wh
ile
1
s
am
p
le
was
m
i
s
clas
s
if
ied
.
All
6
0
clas
s
2
s
am
p
les
wer
e
id
en
tifie
d
p
e
r
f
ec
tly
with
o
u
t
a
n
y
er
r
o
r
s
.
I
n
class
3
,
5
2
o
u
t
o
f
5
3
s
am
p
l
es
wer
e
ac
cu
r
ately
p
r
ed
icted
,
with
ju
s
t
1
m
is
cla
s
s
if
icatio
n
.
C
la
s
s
4
ac
h
iev
ed
p
er
f
ec
t
ac
cu
r
ac
y
,
with
all
1
3
s
am
p
les
co
r
r
ec
tly
class
if
ied
.
B
ased
o
n
th
ese
r
esu
lts
,
th
e
p
r
ec
is
io
n
a
n
d
r
ec
all
f
o
r
ea
c
h
class
r
an
g
ed
f
r
o
m
9
6
.
8
%
–
1
0
0
%,
an
d
th
e
F1
-
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NC
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[
1
]
E.
E
l
b
a
si
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a
l
.
,
“
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[
2
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P
.
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A
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[
5
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M
.
Ja
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.
H
.
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0
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[
2
0
]
L.
-
W
.
W
o
n
g
,
G
.
W
.
-
H
.
Ta
n
,
K
.
-
B
.
O
o
i
,
B
.
L
i
n
,
a
n
d
Y
.
K
.
D
w
i
v
e
d
i
,
“
A
r
t
i
f
i
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i
a
l
i
n
t
e
l
l
i
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n
c
e
-
d
r
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v
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r
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s
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ma
n
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g
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m
e
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t
f
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h
a
n
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su
p
p
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h
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i
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g
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y
:
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d
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p
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b
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d
u
a
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t
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M
-
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N
a
n
a
l
y
s
i
s,”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
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f
Pro
d
u
c
t
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Re
s
e
a
r
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h
,
v
o
l
.
6
2
,
n
o
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5
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p
p
.
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5
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5
–
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5
5
5
,
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5
4
3
.
2
0
2
2
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0
6
3
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9
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[
2
1
]
N
.
N
e
z
a
m
o
d
d
i
n
i
,
A
.
G
h
o
l
a
mi
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a
n
d
F
.
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q
l
a
n
,
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r
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s
k
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b
a
se
d
o
p
t
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m
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z
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t
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f
r
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mew
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f
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n
t
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d
s
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p
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a
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n
s
u
s
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n
g
g
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t
i
c
a
l
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i
t
h
m
a
n
d
a
r
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f
i
c
i
a
l
n
e
u
r
a
l
n
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t
w
o
r
k
s,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
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d
u
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t
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s
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v
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l
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5
,
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l
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0
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6
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.
i
j
p
e
.
2
0
1
9
.
1
0
7
5
6
9
.
[
2
2
]
N
.
P
a
l
t
r
i
n
i
e
r
i
,
L.
C
o
mf
o
r
t
,
a
n
d
G
.
R
e
n
i
e
r
s,
“
Le
a
r
n
i
n
g
a
b
o
u
t
r
i
s
k
:
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
f
o
r
r
i
s
k
a
ssessm
e
n
t
,
”
S
a
f
e
t
y
S
c
i
e
n
c
e
,
v
o
l
.
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1
8
,
p
p
.
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7
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–
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8
6
,
O
c
t
.
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0
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9
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d
o
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0
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1
0
1
6
/
j
.
ssc
i
.
2
0
1
9
.
0
6
.
0
0
1
.
[
2
3
]
G
.
K
a
my
s
h
o
v
a
,
“
N
e
u
r
a
l
n
e
t
w
o
r
k
m
o
d
e
l
s
i
n
r
e
d
u
c
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g
t
h
e
r
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sk
s
o
f
i
r
r
i
g
a
t
e
d
a
g
r
i
c
u
l
t
u
r
e
,
”
E
3
S
We
b
o
f
C
o
n
f
e
ren
c
e
s
,
v
o
l
.
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1
7
,
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e
c
.
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0
2
0
,
d
o
i
:
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0
.
1
0
5
1
/
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3
s
c
o
n
f
/
2
0
2
0
2
1
7
1
0
0
0
4
.
[
2
4
]
F
.
D
.
K
a
k
h
k
i
,
S
.
A
.
F
r
e
e
m
a
n
,
a
n
d
G
.
A
.
M
o
s
h
e
r
,
“
U
s
e
o
f
n
e
u
r
a
l
n
e
t
w
o
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k
s
t
o
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d
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n
t
i
f
y
s
a
f
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t
y
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r
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v
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t
i
o
n
p
r
i
o
r
i
t
i
e
s
i
n
a
g
r
o
-
m
a
n
u
f
a
c
t
u
r
i
n
g
o
p
e
r
a
t
i
o
n
s
w
i
t
h
i
n
c
o
m
m
e
r
c
i
a
l
g
r
a
i
n
e
l
e
v
a
t
o
r
s
,
”
A
p
p
l
i
e
d
S
c
i
e
n
c
e
s
,
v
o
l
.
9
,
n
o
.
2
1
,
N
o
v
.
2
0
1
9
,
d
o
i
:
1
0
.
3
3
9
0
/
a
p
p
9
2
1
4
6
9
0
.
[
2
5
]
S
.
G
h
a
f
f
a
r
i
a
n
,
M
.
V.
D.
V
o
o
r
t
,
J
.
V
a
l
e
n
t
e
,
B
.
Te
k
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n
e
r
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o
g
a
n
,
a
n
d
Y
.
D.
M
e
y
,
“
M
a
c
h
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n
e
l
e
a
r
n
i
n
g
-
b
a
se
d
f
a
r
m
r
i
s
k
man
a
g
e
me
n
t
:
a
sy
st
e
ma
t
i
c
ma
p
p
i
n
g
r
e
v
i
e
w
,
”
C
o
m
p
u
t
e
rs
a
n
d
E
l
e
c
t
r
o
n
i
c
s i
n
Ag
r
i
c
u
l
t
u
re
,
v
o
l
.
1
9
2
,
Ja
n
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
m
p
a
g
.
2
0
2
1
.
1
0
6
6
3
1
.
[
2
6
]
D
.
H
.
S
t
a
ma
t
i
s
,
F
a
i
l
u
re
m
o
d
e
a
n
d
e
f
f
e
c
t
a
n
a
l
y
s
i
s:
F
ME
A
f
r
o
m
t
h
e
o
ry
t
o
e
x
e
c
u
t
i
o
n
,
M
i
l
w
a
u
k
e
e
,
W
i
s
c
o
n
s
i
n
:
Q
u
a
l
i
t
y
P
r
e
ss
,
2
0
0
3
.
[
2
7
]
J.
B
.
B
o
w
l
e
s
a
n
d
C
.
E.
P
e
l
á
e
z
,
“
F
u
z
z
y
l
o
g
i
c
p
r
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o
r
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t
i
z
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t
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f
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sy
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f
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e
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d
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c
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l
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y
a
n
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l
y
si
s,
”
Re
l
i
a
b
i
l
i
t
y
E
n
g
i
n
e
e
r
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n
g
&
S
y
st
e
m
S
a
f
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t
y
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v
o
l
.
5
0
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o
.
2
,
p
p
.
2
0
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1
3
,
J
a
n
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1
9
9
5
,
d
o
i
:
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0
.
1
0
1
6
/
0
9
5
1
-
8
3
2
0
(
9
5
)
0
0
0
6
8
-
D.
[
2
8
]
J.
A
.
R
o
d
r
i
g
u
e
s,
A
.
M
a
r
t
i
n
s,
M
.
M
e
n
d
e
s,
J.
T.
F
a
r
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n
h
a
,
R
.
J.
G
.
M
a
t
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u
s,
a
n
d
A
.
J.
M
.
C
a
r
d
o
s
o
,
“
A
u
t
o
m
a
t
i
c
r
i
sk
a
ssessm
e
n
t
f
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r
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n
d
u
st
r
i
a
l
a
sse
t
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si
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g
u
n
s
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p
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v
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s
e
d
a
n
d
s
u
p
e
r
v
i
se
d
l
e
a
r
n
i
n
g
,
”
E
n
e
r
g
i
e
s
,
v
o
l
.
1
5
,
n
o
.
2
4
,
D
e
c
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
e
n
1
5
2
4
9
3
8
7
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Im
a
m
S
a
n
to
s
o
is
a
p
r
o
f
e
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o
r
a
n
d
e
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p
e
rt
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n
a
g
r
o
i
n
d
u
strial
sy
ste
m
s
a
n
d
m
a
n
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g
e
m
e
n
t.
He
e
a
rn
e
d
h
is
d
o
c
t
o
ra
te
in
Ag
r
o
in
d
u
str
ial
Tec
h
n
o
l
o
g
y
fro
m
I
P
B
Un
iv
e
rsity
i
n
2
0
0
5
.
His
e
x
p
e
rti
se
in
c
l
u
d
e
s
r
isk
-
b
a
se
d
a
n
a
ly
sis,
d
e
c
isio
n
su
p
p
o
rt
sy
ste
m
s,
in
telli
g
e
n
t
p
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rfo
rm
a
n
c
e
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teg
ies
,
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n
d
stra
teg
ic
p
lan
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n
g
in
a
g
ro
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d
u
stry
.
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is
a
c
ti
v
e
ly
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n
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d
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n
su
sta
in
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str
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rc
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n
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in
ter
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ry
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ti
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s.
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n
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e
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0
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h
e
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s
in
it
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t
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ficia
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telli
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in
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strial
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m
s.
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c
a
n
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o
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tac
ted
a
t
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a
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:
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m
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b
.
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c
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i
d
.
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z
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m
Wa
fi’u
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d
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re
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m
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g
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Un
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rsitas
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wijay
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,
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o
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n
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0
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c
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n
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Ma
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in
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5
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telli
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h
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c
a
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tac
ted
a
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m
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m
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u
li
d
i
n
a
@s
tu
d
e
n
t.
u
b
.
a
c
.
id
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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2
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c
a
n
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c
o
n
tac
ted
a
t
e
m
a
il
:
r
o
fiq
@
u
b
.
a
c
.
id
.
Evaluation Warning : The document was created with Spire.PDF for Python.