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h
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in
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f
tech
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4
.
0
a
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s
ed
in
ter
c
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b
l
y
[
1
]
–
[
4
]
.
I
m
p
lem
en
tin
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v
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r
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d
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lear
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a
lg
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r
ith
m
s
[
5
]
–
[
7
]
.
Sm
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r
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th
r
o
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g
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th
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f
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r
th
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latio
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ld
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I
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te
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m
s
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f
th
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s
u
s
tain
ab
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d
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v
elo
p
m
en
t
g
o
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(
SDGs
)
o
f
th
e
Un
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Natio
n
s
(
UN)
,
s
m
ar
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ag
r
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in
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n
u
m
b
er
two
,
wh
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is
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r
o
h
u
n
g
er
[
6
]
–
[
8
]
.
Su
cc
ess
in
in
tellig
en
t
ag
r
icu
ltu
r
e,
esp
ec
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in
cr
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p
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ca
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b
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attr
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to
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T
h
u
s
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a
s
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tan
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t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
9
3
8
I
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t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
439
-
4
4
6
440
b
e
m
ad
e
to
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p
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[
9
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,
[
1
0
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.
Dete
r
m
in
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o
f
s
aid
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1
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h
e
d
ec
is
io
n
tr
ee
a
lg
o
r
ith
m
,
a
m
ac
h
i
n
e
lear
n
i
n
g
a
lg
o
r
ith
m
,
was
im
p
lem
en
ted
to
ad
d
r
ess
t
h
e
cr
o
p
'
s
wate
r
r
eq
u
ir
em
en
t
in
th
e
ad
v
an
ce
m
en
t
o
f
s
m
ar
t
ir
r
ig
atio
n
.
T
h
e
th
r
ee
ess
en
tial
p
ar
am
eter
s
th
at
wer
e
u
s
ed
in
th
e
s
y
s
tem
ar
e
h
u
m
id
ity
,
tem
p
er
atu
r
e,
an
d
m
o
is
tu
r
e
.
C
o
r
r
esp
o
n
d
in
g
s
en
s
o
r
s
f
o
r
th
e
s
aid
p
ar
am
ete
r
s
wer
e
d
e
p
lo
y
e
d
f
o
r
test
in
g
.
A
m
icr
o
p
r
o
ce
s
s
o
r
c
o
n
tr
o
lled
t
h
e
s
aid
s
en
s
o
r
s
.
I
n
B
aln
e
’
s
in
v
esti
g
atio
n
,
m
u
ltip
le
lin
ea
r
r
eg
r
ess
io
n
was
u
til
ized
.
Dev
elo
p
in
g
an
a
p
p
lica
tio
n
was
d
ee
m
ed
ap
p
r
o
p
r
iate
wh
ich
c
o
n
ce
n
tr
ate
s
o
n
s
ev
er
al
s
m
ar
t
ag
r
icu
ltu
r
al
p
ar
am
eter
s
lik
e
s
o
il
d
am
p
n
ess
,
th
e
tem
p
er
atu
r
e
o
f
th
e
s
o
il
s
u
r
f
ac
e,
wea
th
er
v
iewp
o
in
t,
i.e
.
,
r
ain
f
all
an
d
tem
p
er
atu
r
e,
a
n
d
s
o
il
n
u
tr
ien
ts
.
W
ith
all
th
e
n
ec
ess
ar
y
p
ar
am
eter
s
,
th
e
in
f
o
r
m
atio
n
in
v
esti
g
atio
n
m
eth
o
d
was th
e
r
e
s
ea
r
c
h
m
eth
o
d
o
lo
g
y
im
p
lo
r
e
d
in
th
e
s
tu
d
y
[
1
2
]
.
A
r
ev
iew
s
tu
d
y
o
n
m
ac
h
in
e
lear
n
in
g
a
n
d
I
o
T
ab
o
u
t
a
g
r
ic
u
ltu
r
al
m
an
a
g
em
en
t
was
co
n
d
u
cted
b
y
Ma
d
u
r
an
g
a
a
n
d
Ab
ey
s
ek
e
r
a
[
1
3
]
.
T
h
e
r
ev
iew
s
tu
d
y
f
o
c
u
s
es
o
n
m
ac
h
in
e
lear
n
in
g
ap
p
lic
atio
n
s
in
I
o
T
-
b
ased
ag
r
icu
ltu
r
e:
p
lan
t
m
an
a
g
em
en
t,
cr
o
p
an
d
y
ield
m
a
n
ag
em
e
n
t,
d
is
ea
s
e
m
an
ag
em
en
t,
wee
d
m
an
ag
em
en
t,
wate
r
m
an
ag
em
en
t,
an
d
an
im
al
tr
ac
k
in
g
.
T
h
ese
ap
p
licatio
n
s
ca
n
b
e
d
o
n
e
u
s
in
g
wid
el
y
u
s
ed
alg
o
r
ith
m
s
lik
e
th
e
n
aïv
e
B
ay
es
alg
o
r
ith
m
,
s
u
p
p
o
r
t
v
ec
t
o
r
r
e
g
r
ess
io
n
,
a
n
d
K
-
n
ea
r
est
n
eig
h
b
o
r
.
T
h
e
s
aid
r
ev
iew
s
tu
d
y
f
u
r
th
er
s
tated
th
at
th
e
h
y
b
r
id
ap
p
licatio
n
o
f
m
ac
h
in
e
lear
n
in
g
a
n
d
I
o
T
will b
en
e
f
it th
e
ad
v
an
ce
s
o
f
ag
r
icu
ltu
r
e
wo
r
ld
wid
e.
Salim
an
d
Mitto
n
[
1
4
]
p
r
o
p
o
s
ed
a
s
y
s
tem
in
co
r
p
o
r
atin
g
m
ac
h
in
e
lear
n
i
n
g
an
d
wir
el
ess
s
en
s
o
r
n
etwo
r
k
s
f
o
r
s
m
ar
t
a
g
r
icu
ltu
r
e
.
T
h
e
lar
g
e
am
o
u
n
t
o
f
d
ata
p
r
o
d
u
ce
d
b
y
th
e
n
etwo
r
k
s
en
s
o
r
s
ca
u
s
es
a
d
ec
r
ea
s
e
in
th
e
s
y
s
tem
'
s
life
s
p
an
;
th
e
r
ef
o
r
e,
th
e
m
ac
h
in
e
lear
n
i
n
g
-
b
ased
d
ata
r
ed
u
ctio
n
alg
o
r
ith
m
was
m
ad
e
.
T
h
e
alg
o
r
ith
m
was
b
ased
o
n
d
ata
f
r
o
m
th
e
en
v
ir
o
n
m
e
n
t
th
at
b
en
ef
itted
ag
r
icu
ltu
r
e
in
t
h
e
lo
n
g
r
u
n
.
M
o
r
eo
v
er
,
t
h
e
s
aid
m
ac
h
in
e
lear
n
in
g
al
g
o
r
it
h
m
aid
ed
in
r
ed
u
cin
g
th
e
en
e
r
g
y
c
o
n
s
u
m
p
tio
n
o
f
th
e
wir
el
ess
s
en
s
o
r
n
etwo
r
k
.
MA
T
L
AB
was
u
s
ed
to
v
alid
ate
th
e
d
ev
elo
p
ed
alg
o
r
ith
m
,
r
esu
ltin
g
in
a
d
ata
r
ed
u
ctio
n
o
f
7
0
%
b
ein
g
s
en
t.
T
h
e
s
y
s
tem
was r
ated
to
h
av
e
ex
ce
llen
t a
cc
u
r
ac
y
.
I
n
th
e
s
tu
d
y
co
n
d
u
cted
b
y
Ab
r
ah
am
et
a
l.
[
1
5
]
,
two
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
wer
e
c
o
m
p
ar
ed
an
d
im
p
lem
en
ted
i
n
th
e
ap
p
licatio
n
-
b
ased
m
o
n
ito
r
in
g
o
f
f
ar
m
co
n
d
itio
n
s
an
d
in
th
e
class
if
icati
o
n
o
f
a
n
im
als in
th
e
ag
r
icu
ltu
r
al
f
ield
.
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NN)
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
alg
o
r
ith
m
s
wer
e
co
m
p
ar
ed
in
th
e
m
atter
o
f
clas
s
if
icatio
n
,
esp
ec
ially
f
o
r
im
a
g
e
in
p
u
ts
.
T
h
e
C
NN
was
co
n
clu
d
ed
to
p
er
f
o
r
m
well
co
m
p
ar
ed
to
th
e
SVM
alg
o
r
ith
m
.
T
h
e
in
p
u
ts
f
r
o
m
th
e
class
if
icatio
n
s
d
o
n
e
b
y
m
ac
h
in
e
lear
n
i
n
g
wer
e
in
co
r
p
o
r
ated
in
to
th
e
wate
r
an
d
s
p
r
in
k
ler
m
o
n
ito
r
in
g
s
y
s
tem
.
Ma
x
im
izin
g
ir
r
ig
atio
n
f
o
r
c
r
o
p
p
r
o
d
u
ctio
n
u
s
in
g
v
ar
i
o
u
s
m
ac
h
in
e
-
lear
n
in
g
alg
o
r
ith
m
s
was
th
e
m
ain
f
o
cu
s
o
f
th
e
s
tu
d
y
p
er
f
o
r
m
ed
b
y
Fre
d
j
et
a
l.
[
1
6
]
.
T
h
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
g
r
ad
ie
n
t
b
o
o
s
t
r
e
g
r
ess
o
r
,
d
ec
is
io
n
tr
ee
,
XGB
R
eg
r
ess
o
r
,
an
d
r
an
d
o
m
f
o
r
est
,
wh
ich
wer
e
p
r
o
g
r
am
m
ed
th
r
o
u
g
h
Py
th
o
n
,
wer
e
co
m
p
ar
e
d
to
p
r
ed
ict
th
e
am
o
u
n
t
o
f
wate
r
n
ee
d
ed
b
y
t
h
e
cr
o
p
f
o
r
o
p
tim
al
p
r
o
d
u
ctiv
ity
o
f
ir
r
ig
atio
n
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
w
as
ev
alu
ated
th
r
o
u
g
h
th
e
u
s
e
o
f
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
MSE
)
,
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
,
an
d
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
.
T
h
e
s
tu
d
y
co
n
clu
d
e
d
th
at
XGB
R
eg
r
es
s
io
n
was
th
e
m
o
s
t
ef
f
icien
t a
lg
o
r
ith
m
,
wh
ile
r
an
d
o
m
f
o
r
es
t
was th
e
least e
f
f
icien
t.
An
o
th
er
s
tu
d
y
c
o
n
ce
n
t
r
atin
g
o
n
th
e
d
e
v
elo
p
m
e
n
t
o
f
in
te
llig
en
t
ir
r
ig
atio
n
was
ac
co
m
p
lis
h
ed
b
y
B
h
an
u
et
a
l.
[
1
7
]
.
T
h
e
in
tellig
en
t
ir
r
ig
atio
n
was
im
p
lem
en
te
d
th
r
o
u
g
h
th
e
T
h
in
g
Sp
ea
k
cl
o
u
d
,
an
I
o
T
C
lo
u
d
.
I
t
u
tili
ze
d
th
e
n
aïv
e
B
ay
es
alg
o
r
ith
m
to
class
if
y
th
e
s
en
s
ed
d
a
ta
f
r
o
m
p
a
r
am
eter
s
lik
e
atm
o
s
p
h
er
ic
tem
p
e
r
atu
r
e
d
ata,
h
u
m
i
d
ity
d
ata,
s
o
il
m
o
i
s
tu
r
e,
an
d
s
o
il
tem
p
er
at
u
r
e.
W
h
en
th
e
s
en
s
ed
d
ata
is
b
elo
w
th
e
th
r
esh
o
ld
,
t
h
e
s
y
s
tem
will
em
ail
th
e
u
s
er
an
d
p
r
o
m
p
t
a
n
ap
p
r
o
p
r
iate
r
es
p
o
n
s
e.
T
h
e
n
aïv
e
B
ay
es
a
lg
o
r
ith
m
g
av
e
7
6
.
4
7
%
ac
cu
r
ac
y
in
te
r
m
s
o
f
class
if
icatio
n
.
P
a
d
a
r
i
a
n
et
a
l
.
[
1
8
]
r
e
v
i
e
w
e
d
s
e
v
e
r
a
l
w
o
r
k
s
o
f
l
it
e
r
a
t
u
r
e
o
n
t
h
e
a
p
p
l
i
c
at
i
o
n
o
f
m
a
c
h
i
n
e
l
e
a
r
n
in
g
a
s
a
t
o
o
l
i
n
t
h
e
p
r
o
g
r
e
s
s
o
f
a
g
r
i
c
u
lt
u
r
e
,
e
s
p
e
c
i
al
l
y
i
n
s
o
i
l
s
ci
e
n
c
e
.
T
h
e
r
e
v
i
e
w
is
c
o
m
p
r
e
h
e
n
s
i
v
e
i
n
t
h
a
t
i
t
d
et
e
r
m
i
n
e
d
t
h
e
p
e
r
f
o
r
m
a
n
c
e
o
f
t
h
e
b
a
s
i
c
m
a
c
h
in
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
u
p
t
o
h
y
b
r
i
d
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
,
w
i
t
h
a
f
o
c
u
s
o
n
s
o
i
l
a
n
a
l
y
s
is
.
N
e
u
r
a
l
n
e
t
w
o
r
k
s
,
S
V
M
,
a
n
d
r
an
d
o
m
f
o
r
e
s
t
s
p
e
r
f
o
r
m
e
d
b
e
t
t
e
r
t
h
a
n
s
i
m
p
l
e
r
a
l
g
o
r
it
h
m
s
s
u
c
h
a
s
p
a
r
t
i
a
l
l
e
as
t
s
q
u
a
r
e
r
e
g
r
e
s
s
i
o
n
,
p
r
i
n
c
i
p
a
l
c
o
m
p
o
n
e
n
t
r
e
g
r
e
s
s
i
o
n
,
m
u
l
ti
p
l
e
l
i
n
e
a
r
r
e
g
r
e
s
s
i
o
n
,
a
n
d
K
-
n
e
a
r
es
t
n
ei
g
h
b
o
r
.
T
h
e
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
a
i
d
e
d
t
h
e
a
d
v
a
n
c
e
m
e
n
t
o
f
s
o
i
l
s
c
i
e
n
c
e
i
n
m
o
d
e
l
i
n
g
c
a
t
e
g
o
r
i
c
al
a
n
d
c
o
n
ti
n
u
o
u
s
s
o
il
p
r
o
p
e
r
t
i
es
.
T
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Ad
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b
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l
.
[
1
9
]
.
Dete
r
m
in
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o
f
th
e
s
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m
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is
tu
r
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co
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m
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Ma
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Jo
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Mo
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tical
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W
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Dash
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[
2
0
]
co
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class
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Far
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[
2
1
]
c
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is
[
2
2
]
–
[
2
8
]
.
T
h
is
s
tu
d
y
aim
s
to
d
etec
t
s
o
il
p
H
lev
el,
s
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m
ac
r
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tr
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,
i
.
e.
,
n
itr
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en
,
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s
,
p
o
ta
s
s
iu
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d
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icr
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tr
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e.
g
.
,
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o
n
,
co
p
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,
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d
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s
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m
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ith
m
.
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r
s
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H
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th
e
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test
k
its
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iv
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Dep
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tm
e
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t
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f
Ag
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icu
ltu
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v
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e
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p
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f
o
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th
e
m
ac
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in
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alg
o
r
ith
m
b
ased
o
n
t
h
e
s
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cc
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l
p
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f
o
r
m
an
ce
o
f
th
e
p
r
o
ce
d
u
r
es.
Dev
elo
p
in
g
a
n
ap
p
r
o
p
r
iate
ass
ay
th
at
im
p
lo
r
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co
lo
r
im
etr
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p
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ce
s
s
f
o
r
s
o
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m
icr
o
n
u
tr
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is
a
m
u
s
t
f
o
r
ir
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n
,
co
p
p
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,
a
n
d
zin
c.
T
h
e
d
etec
tio
n
o
f
th
e
s
aid
p
ar
a
m
eter
s
p
r
im
ar
ily
r
elies
o
n
th
e
im
ag
e
p
r
o
d
u
ce
d
f
r
o
m
th
e
co
lo
r
im
etr
i
c
p
r
o
ce
s
s
es.
Af
ter
s
u
cc
ess
f
u
lly
p
er
f
o
r
m
in
g
th
e
ch
em
ical
p
r
o
ce
s
s
es,
th
e
in
p
u
ts
d
eter
m
in
e
th
e
ap
p
r
o
p
r
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cr
o
p
an
d
f
e
r
tili
ze
r
r
ec
o
m
m
en
d
atio
n
b
ased
o
n
t
h
e
s
o
il sam
p
le.
2.
M
ACH
I
N
E
L
E
AR
NING
Dev
elo
p
in
g
a
n
ap
p
r
o
p
r
iate
ass
ay
th
at
im
p
lo
r
es
a
co
lo
r
im
etr
ic
p
r
o
ce
s
s
f
o
r
s
o
il
m
icr
o
n
u
tr
ien
t
s
is
a
m
u
s
t
f
o
r
ir
o
n
,
co
p
p
er
,
a
n
d
zin
c
.
T
h
e
im
ag
e
p
r
o
d
u
ce
d
f
r
o
m
th
e
co
lo
r
im
etr
ic
p
r
o
ce
s
s
es
p
lay
s
a
p
iv
o
t
al
r
o
le
in
d
etec
tin
g
th
e
p
ar
am
ete
r
s
.
T
h
e
in
p
u
ts
d
e
ter
m
in
in
g
t
h
e
a
p
p
r
o
p
r
iate
c
r
o
p
an
d
f
e
r
tili
ze
r
r
ec
o
m
m
en
d
ati
o
n
,
c
o
n
s
id
er
in
g
th
e
s
o
il
s
am
p
le,
ar
e
f
o
llo
wed
af
t
er
s
u
cc
ess
f
u
lly
p
er
f
o
r
m
in
g
th
e
ch
em
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p
r
o
ce
s
s
es.
T
h
is
s
e
ctio
n
d
is
cu
s
s
es
th
e
ad
v
an
tag
es
o
f
u
s
in
g
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
co
m
p
ar
ed
to
th
e
co
n
v
en
tio
n
al
m
eth
o
d
o
f
d
eter
m
in
in
g
th
e
lev
el
o
f
s
o
il
p
ar
am
ete
r
s
.
Fu
r
th
er
m
o
r
e
,
s
ev
er
al
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
ca
n
b
e
em
p
lo
y
ed
to
ac
h
ie
v
e
th
is
s
tu
d
y
'
s
g
o
als ar
e
co
m
p
ar
ed
.
2
.
1
.
M
a
chine
lea
rning
v
s
co
nv
ent
io
na
l m
et
ho
d
T
h
e
c
o
n
v
en
ti
o
n
al
m
et
h
o
d
o
f
d
et
e
r
m
i
n
i
n
g
t
h
e
s
o
il
p
ar
am
ete
r
le
v
e
ls
d
e
p
e
n
d
s
o
n
t
h
e
co
lo
r
-
m
atc
h
i
n
g
ab
i
lit
y
o
f
t
h
e
u
s
e
r
s
o
f
s
o
il
test
k
its
s
u
b
j
ec
t
ed
t
o
d
i
f
f
er
e
n
t
li
g
h
ti
n
g
c
o
n
d
iti
o
n
s
,
t
h
u
s
r
el
y
i
n
g
o
n
t
h
e
o
p
ti
ca
l
p
e
r
c
e
p
ti
o
n
o
f
t
h
e
f
a
r
m
e
r
s
o
r
o
th
e
r
in
te
r
est
ed
i
n
d
i
v
i
d
u
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.
Af
te
r
p
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f
o
r
m
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g
th
e
p
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cr
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d
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r
o
ce
s
s
f
r
o
m
t
h
e
s
o
il
t
est
k
it
,
th
e
in
d
i
v
i
d
u
al
m
u
s
t
c
o
m
p
ar
e
t
h
e
c
o
l
o
r
p
r
o
d
u
c
e
d
b
y
t
h
e
p
r
o
ce
s
s
t
o
t
h
e
c
o
l
o
r
c
h
a
r
t
,
as
co
lo
r
c
h
ar
t
s
a
r
e
s
ta
n
d
a
r
d
in
s
o
i
l
test
i
n
g
.
T
h
is
c
an
b
e
at
tr
i
b
u
t
ed
to
a
s
u
b
j
ec
t
iv
e
p
r
o
ce
s
s
o
f
d
et
e
r
m
in
ati
o
n
,
w
h
ic
h
c
an
b
e
eli
m
i
n
at
e
d
b
y
o
p
ti
m
iz
in
g
th
e
m
a
ch
in
e.
A
n
y
m
is
i
n
te
r
p
r
et
ed
v
al
u
es
o
f
t
h
e
l
ev
el
o
f
s
o
il
p
ar
am
ete
r
s
c
an
ca
u
s
e
s
o
il
p
o
ll
u
tio
n
o
r
a
b
u
s
e
,
t
h
u
s
less
e
n
i
n
g
c
r
o
p
a
n
d
y
ie
ld
p
r
o
d
u
cti
o
n
.
P
r
o
d
u
ci
n
g
a
c
o
n
s
id
er
a
b
l
e
a
m
o
u
n
t
o
f
d
a
ta
s
et
t
o
b
e
t
r
a
in
ed
is
a
r
eq
u
i
r
em
en
t
to
en
s
u
r
e
th
e
v
a
li
d
it
y
a
n
d
ac
c
u
r
a
cy
o
f
class
i
f
i
ca
t
io
n
a
n
d
r
eg
r
ess
io
n
u
p
o
n
im
p
l
em
e
n
ta
ti
o
n
.
I
t
is
i
m
p
lie
d
to
h
a
v
e
im
a
g
es
,
co
n
s
i
d
e
r
ed
th
e
s
i
g
n
if
i
ca
n
t
d
at
a
s
u
b
jec
te
d
t
o
t
r
a
in
in
g
,
f
r
o
m
v
ar
i
o
u
s
l
ig
h
t
in
g
en
v
i
r
o
n
m
e
n
ts
t
o
e
lim
in
at
e
co
n
s
i
d
e
r
a
ti
o
n
s
f
o
r
is
s
u
es f
r
o
m
lig
h
t
in
g
.
T
h
e
d
ev
ic
e
wil
l
i
n
d
e
p
en
d
e
n
t
ly
d
ete
r
m
i
n
e
t
h
e
s
ai
d
l
e
v
els
u
s
i
n
g
m
a
ch
in
e
lea
r
n
i
n
g
a
n
d
ta
k
e
i
n
t
o
c
o
n
s
id
er
ati
o
n
t
h
e
b
est
a
m
o
n
g
t
h
e
s
e
lec
t
ed
m
ac
h
i
n
e
l
ea
r
n
i
n
g
a
lg
o
r
it
h
m
s
.
2
.
2
.
P
r
o
s
pect
ed
m
a
chine le
a
rning
a
lg
o
rit
hm
2
.
2
.
1
.
Na
ïv
e
B
a
y
es a
lg
o
rit
hm
T
h
e
n
aïv
e
B
ay
es
al
g
o
r
ith
m
is
a
m
ac
h
in
e
lea
r
n
in
g
tech
n
iq
u
e
th
at
class
if
ies
d
ata
u
s
in
g
s
tatis
tic
al
m
o
d
els
to
esti
m
ate
th
e
lik
elih
o
o
d
o
f
a
class
h
av
in
g
a
p
ar
ticu
lar
attr
ib
u
te.
I
t
ap
p
lies
B
ay
es
'
T
h
eo
r
em
,
ass
u
m
in
g
in
d
ep
en
d
en
ce
a
m
o
n
g
f
ea
t
u
r
es,
to
p
r
ed
ict
o
u
tco
m
es.
Ad
d
iti
o
n
ally
,
t
h
e
alg
o
r
ith
m
s
ee
k
s
o
p
tim
al
class
if
icatio
n
ac
r
o
s
s
all
class
e
s
an
d
th
eir
co
r
r
esp
o
n
d
in
g
attr
ib
u
tes
[
2
9
]
–
[
3
2
]
.
2
.
2
.
2
.
Su
pp
o
rt
v
ec
t
o
r
m
a
chi
ne
a
lg
o
rit
hm
SVM
is
a
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
th
at
u
s
es
k
er
n
el
f
u
n
ctio
n
s
f
o
r
r
e
g
r
ess
io
n
an
d
class
if
icatio
n
task
s
.
I
t
s
o
lv
es
q
u
ad
r
atic
o
p
tim
izatio
n
p
r
o
b
lem
s
to
id
en
tify
th
e
b
est
h
y
p
er
p
lan
e
f
o
r
s
ep
ar
atin
g
d
ata
in
to
d
is
tin
ct
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
439
-
4
4
6
442
class
es.
T
h
e
alg
o
r
ith
m
f
o
cu
s
es
o
n
a
s
u
b
s
et
o
f
s
u
p
p
o
r
t
v
ec
to
r
s
,
a
s
m
all
p
o
r
tio
n
o
f
th
e
tr
ain
in
g
d
ata,
f
o
r
d
ec
is
io
n
-
m
ak
in
g
[
3
3
]
–
[
3
6
]
.
2
.
2
.
3
.
Dec
is
io
n t
re
e
a
lg
o
rit
h
m
T
h
e
d
ec
is
io
n
tr
ee
alg
o
r
ith
m
is
a
n
o
n
p
ar
am
etr
ic
s
u
p
er
v
i
s
ed
lear
n
in
g
m
eth
o
d
wid
ely
u
s
ed
f
o
r
class
if
icatio
n
an
d
r
e
g
r
ess
io
n
task
s
.
I
t
m
ak
es
n
o
ass
u
m
p
tio
n
s
ab
o
u
t
th
e
d
ata
d
is
tr
ib
u
tio
n
an
d
b
u
ild
s
a
tr
ee
s
tr
u
ctu
r
e
b
y
r
ec
u
r
s
iv
ely
s
p
litt
in
g
th
e
d
ata
b
ased
o
n
f
ea
tu
r
e
v
alu
es.
T
h
is
alg
o
r
ith
m
is
tr
ain
e
d
o
n
lab
eled
d
ata
to
class
if
y
n
ew,
u
n
s
ee
n
in
s
tan
ce
s
ac
cu
r
ately
[
3
7
]
–
[
4
0
]
.
3.
M
AT
E
R
I
AL
S AN
D
M
E
T
H
O
DS
T
h
is
s
ec
tio
n
o
u
tlin
es
th
e
r
esear
ch
m
eth
o
d
o
lo
g
ies
th
at
will
b
e
em
p
lo
y
ed
th
r
o
u
g
h
o
u
t
th
e
s
tu
d
y
.
I
t
d
etails
th
e
s
p
ec
if
ic
ap
p
r
o
ac
h
es
an
d
tech
n
iq
u
es
u
s
ed
to
en
s
u
r
e
th
e
ac
cu
r
ate
ac
h
iev
em
en
t
o
f
r
esear
ch
o
b
jectiv
es.
Ad
d
itio
n
ally
,
it h
ig
h
lig
h
ts
th
e
m
ater
ials
an
d
to
o
ls
ess
en
tial f
o
r
m
ee
tin
g
t
h
e
s
tu
d
y
'
s
g
o
als.
3
.
1
.
Resea
rc
h
m
e
t
ho
do
lo
g
y
T
h
e
r
esear
ch
m
et
h
o
d
o
lo
g
y
im
p
lo
r
ed
i
n
th
is
s
tu
d
y
is
m
a
in
ly
q
u
a
n
titativ
e
an
d
co
u
p
le
d
with
an
ex
p
er
im
en
tal
r
esear
c
h
d
esig
n
.
T
h
e
ap
p
r
o
ac
h
in
ex
p
er
im
en
ta
l
r
esear
ch
d
esig
n
s
tar
ted
with
d
ata
co
llectio
n
an
d
was
f
o
llo
wed
b
y
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
,
e.
g
.
,
d
ata
clea
n
s
in
g
.
Sev
er
al
d
ata
wer
e
in
v
o
lv
e
d
in
th
e
m
o
d
el
tr
ain
in
g
,
f
o
llo
wed
af
ter
th
e
d
ata
h
ad
b
e
en
p
r
e
-
p
r
o
ce
s
s
ed
s
in
ce
it
m
u
s
t
ca
p
tu
r
e
th
e
n
ec
ess
ar
y
d
ata
f
ea
tu
r
es.
T
h
e
m
o
d
el
u
s
ed
in
class
if
icatio
n
an
d
r
e
g
r
ess
io
n
will
b
e
th
e
p
r
im
ar
y
p
r
o
d
u
ct
o
f
m
o
d
el
tr
ain
in
g
.
T
h
e
d
e
v
elo
p
ed
m
o
d
el
was
test
ed
to
ass
ess
it
s
ac
cu
r
ac
y
,
g
i
v
en
th
e
o
th
er
d
ata
to
b
e
test
ed
f
o
r
class
if
icatio
n
an
d
r
eg
r
ess
io
n
.
T
h
e
e
x
p
er
im
e
n
tal
r
esear
ch
d
esig
n
co
n
clu
d
es
in
t
h
e
m
o
d
el
ev
al
u
atio
n
as
it
is
ass
ess
ed
th
r
o
u
g
h
s
ev
er
al
k
ey
p
er
f
o
r
m
an
ce
in
d
icato
r
s
s
et
b
y
th
e
r
ese
ar
ch
er
s
an
d
o
th
er
s
tan
d
ar
d
s
a
v
ailab
le.
Op
tim
a
l
f
ea
tu
r
e
s
elec
tio
n
is
o
n
e
o
f
th
e
tr
en
d
in
g
r
esear
ch
m
eth
o
d
o
l
o
g
ies
u
s
ed
in
m
ac
h
in
e
lear
n
in
g
an
d
is
u
s
u
ally
d
o
n
e
u
n
d
er
th
e
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
p
o
tio
n
o
f
ex
p
er
im
en
tal
r
esear
ch
.
T
h
r
o
u
g
h
th
is
r
esear
ch
m
et
h
o
d
o
lo
g
y
,
in
c
o
r
p
o
r
atin
g
th
is
s
elec
tio
n
,
th
e
r
esear
ch
ca
n
o
b
s
er
v
e
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
i
n
an
o
p
tim
ized
f
ash
io
n
.
3
.
2
.
M
et
ho
do
lo
g
y
pro
ce
s
s
f
lo
wcha
rt
T
h
e
m
et
h
o
d
o
l
o
g
y
p
r
o
c
ess
f
l
o
wch
ar
ts
p
r
ese
n
t
th
e
d
i
r
ec
ti
o
n
i
n
t
h
e
ac
h
i
e
v
e
m
e
n
t
o
f
t
h
e
o
b
je
c
tiv
es
o
f
th
e
s
tu
d
y
.
C
h
e
m
i
ca
l
a
n
d
el
ec
t
r
o
n
ic
d
es
ig
n
we
r
e
t
h
e
m
ai
n
p
r
o
ce
s
s
es
i
n
t
h
e
f
l
o
w
ch
a
r
t,
as
r
e
f
l
ec
t
e
d
i
n
Fi
g
u
r
es
1
an
d
2
.
T
h
ese
m
ain
p
r
o
ce
s
s
es we
r
e
o
b
s
er
v
ed
in
d
eter
m
in
in
g
s
o
il
p
H,
m
ac
r
o
n
u
tr
ien
ts
,
an
d
m
icr
o
n
u
t
r
ien
ts
.
Fig
u
r
e
1
.
Me
th
o
d
o
lo
g
y
p
r
o
ce
s
s
f
lo
wch
ar
t f
o
r
d
eter
m
in
atio
n
o
f
s
o
il p
H
an
d
m
ac
r
o
n
u
tr
ien
ts
Fig
u
r
e
2
.
Me
th
o
d
o
lo
g
y
p
r
o
ce
s
s
f
lo
wch
ar
t f
o
r
d
eter
m
in
atio
n
o
f
s
o
il m
ac
r
o
n
u
tr
ien
ts
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
Ma
ch
in
e
lea
r
n
in
g
f
o
r
th
e
d
ete
ctio
n
o
f so
il p
H,
ma
cro
n
u
tr
ien
ts
,
…
(
Jo
h
n
J
o
s
h
u
a
Mo
n
ta
ñ
ez)
443
3
.
2
.
1
.
So
il pH
a
nd
m
a
cr
o
nu
t
rient
s
T
h
e
s
o
il
test
k
it
is
e
s
s
en
tial
in
d
eter
m
in
in
g
s
o
il
p
H
an
d
m
ac
r
o
n
u
tr
ie
n
ts
.
T
h
is
s
p
ec
if
ies
th
e
p
r
o
ce
d
u
r
e
to
b
e
f
o
llo
wed
to
p
r
e
p
ar
e
s
o
il
s
am
p
les.
Af
ter
s
u
cc
ess
f
u
lly
g
at
h
er
in
g
s
o
il
s
am
p
les,
th
e
s
o
il
te
s
ts
f
o
llo
wed
,
wh
ich
co
n
clu
d
e
d
th
e
ch
em
ical
p
r
o
ce
s
s
es,
i.e
.
,
p
H,
n
itro
g
en
,
p
o
tass
iu
m
,
an
d
p
h
o
s
p
h
o
r
u
s
test
s
.
C
a
p
tu
r
in
g
th
e
im
ag
es
g
en
er
ated
f
r
o
m
th
e
s
o
il
test
p
er
f
o
r
m
a
n
ce
o
b
s
er
v
e
d
in
th
e
test
tu
b
es
in
itiates
th
e
elec
tr
o
n
ics
p
r
o
ce
s
s
es.
T
h
e
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
p
o
r
tio
n
in
clu
d
e
s
clea
n
in
g
an
d
ass
ess
in
g
th
e
d
a
ta
to
s
ee
if
it
s
u
its
th
e
m
ac
h
i
n
e
lear
n
in
g
al
g
o
r
ith
m
p
ar
am
eter
s
.
Sev
er
al
im
ag
es
m
u
s
t
m
ee
t
th
e
n
ec
ess
ar
y
am
o
u
n
t
n
ee
d
e
d
f
o
r
th
e
m
o
d
el
tr
ai
n
in
g
o
f
th
e
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
.
Mo
d
el
tr
ai
n
in
g
u
tili
ze
s
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
i.e
.
,
n
aïv
e
B
ay
es
alg
o
r
ith
m
,
SVM
alg
o
r
ith
m
,
an
d
d
ec
is
io
n
tr
ee
alg
o
r
it
h
m
,
w
h
er
e
th
e
d
ata
we
r
e
s
u
b
jecte
d
to
af
t
er
s
u
cc
ess
f
u
l
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
.
A
c
o
m
p
ar
is
o
n
o
f
th
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
'
s
p
er
f
o
r
m
an
ce
was
o
b
s
er
v
ed
t
o
d
ete
r
m
in
e
wh
ich
o
f
th
e
th
r
ee
m
ac
h
in
e
lea
r
n
in
g
alg
o
r
ith
m
s
is
co
n
s
i
d
er
e
d
th
e
h
ig
h
est
-
p
er
f
o
r
m
i
n
g
alg
o
r
it
h
m
in
class
if
icatio
n
an
d
r
eg
r
ess
io
n
.
L
astl
y
,
u
p
o
n
p
r
o
p
er
d
eter
m
in
atio
n
o
f
th
e
s
o
il
p
H,
n
itro
g
en
,
p
h
o
s
p
h
o
r
u
s
,
an
d
p
o
tass
iu
m
lev
els,
th
e
lev
els s
er
v
ed
as in
p
u
t i
n
th
e
cr
o
p
a
n
d
f
e
r
tili
ze
r
r
ec
o
m
m
e
n
d
atio
n
s
o
f
t
h
e
s
tu
d
y
.
3
.
2
.
2
.
So
il m
icro
nu
t
rient
s
I
n
d
eter
m
i
n
in
g
s
o
il
m
ic
r
o
n
u
tr
ien
ts
,
i.e
.
,
ir
o
n
,
co
p
p
er
,
an
d
zin
c
,
n
o
r
ea
d
ily
av
ailab
le
to
o
ls
o
r
d
e
v
ices
in
co
r
p
o
r
ate
co
lo
r
im
etr
ic
p
r
o
ce
s
s
es.
Ob
s
er
v
in
g
v
ar
io
u
s
ch
em
ical
p
r
o
ce
s
s
es
r
eq
u
ir
es
th
e
ec
o
n
o
m
ical
d
eter
m
in
atio
n
o
f
th
e
s
aid
elem
en
ts
o
r
co
m
p
o
u
n
d
s
in
th
e
s
o
il
s
am
p
le.
Af
ter
th
e
ch
em
ical
p
r
o
ce
s
s
es
h
av
e
b
ee
n
d
o
n
e,
a
n
ass
ess
m
en
t
o
f
th
e
en
d
p
r
o
d
u
ct
f
r
o
m
th
e
ch
em
ical
p
r
o
ce
s
s
es
m
u
s
t
b
e
d
o
n
e
to
d
ete
r
m
in
e
wh
eth
er
it
is
ap
p
licab
le
f
o
r
elec
tr
o
n
ic
im
p
le
m
en
tatio
n
.
Af
ter
f
in
is
h
in
g
th
e
n
ec
ess
ar
y
ad
ju
s
tm
en
t
o
f
th
e
el
ec
tr
o
n
ic
p
a
r
am
eter
s
,
th
e
ev
alu
atio
n
o
f
th
e
elec
tr
o
n
ic
im
p
lem
en
tatio
n
f
o
llo
wed
,
wh
ich
was
m
o
r
e
c
o
m
p
r
eh
en
s
i
v
e
th
a
n
co
m
p
ar
in
g
ex
is
tin
g
s
tan
d
ar
d
s
f
o
u
n
d
i
n
th
e
in
d
u
s
tr
y
an
d
g
o
v
er
n
m
en
tal
in
s
tr
u
m
en
talities
.
3
.
3
.
P
er
f
o
r
m
a
nce
m
e
t
rics o
f
m
a
chine le
a
rning
a
lg
o
rit
hm
T
h
e
ass
ess
m
en
t
o
f
th
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
ca
n
b
e
ac
h
iev
ed
u
s
in
g
f
o
u
r
p
er
f
o
r
m
an
c
e
m
etr
ics:
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e.
T
h
ese
ar
e
th
e
u
s
u
al
p
er
f
o
r
m
a
n
ce
m
etr
ics
to
ass
e
s
s
th
e
cla
s
s
if
icatio
n
ca
p
ab
ilit
ies
o
f
th
e
th
r
ee
s
elec
ted
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
.
L
is
ted
as
f
o
llo
ws
is
th
e
co
r
r
e
s
p
o
n
d
in
g
eq
u
atio
n
:
=
(
+
)
/
(
+
+
+
)
(
1
)
=
/
(
+
)
(
2
)
=
/
(
+
)
(
3
)
1
−
=
/
(
+
1
/
2
(
+
)
)
(
4
)
wh
er
e
T
P
is
th
e
n
u
m
b
er
o
f
tr
u
e
p
o
s
itiv
es,
FP
is
th
e
n
u
m
b
e
r
o
f
f
alse
p
o
s
itiv
es,
an
d
FN
is
th
e
n
u
m
b
er
o
f
f
alse
n
eg
ativ
es.
3
.
4
.
M
a
t
er
ia
ls
us
ed
So
il
test
k
it
.
I
t
is
a
k
it
d
ev
elo
p
ed
b
y
th
e
B
u
r
ea
u
o
f
s
o
ils
an
d
wate
r
o
f
th
e
Dep
ar
tm
e
n
t
o
f
A
g
r
icu
ltu
r
e,
wh
ich
co
n
tain
s
ch
em
icals
th
at
f
ield
tech
n
ician
s
an
d
f
ar
m
er
s
u
s
e
to
s
tu
d
y
th
e
s
o
il
s
am
p
les
f
r
o
m
th
e
f
ar
m
lan
d
.
Usi
n
g
th
ese
s
o
il
te
s
t
k
its
,
t
im
e
-
ef
f
icien
t
d
eter
m
in
atio
n
o
f
th
e
n
u
tr
ien
ts
will
b
e
o
b
s
er
v
ed
.
T
h
e
s
o
il
test
k
it
in
clu
d
es
th
e
p
r
o
ce
d
u
r
e
o
f
p
r
o
p
e
r
s
o
il
s
am
p
lin
g
,
th
e
p
r
o
ce
d
u
r
es
f
o
r
th
e
v
ar
io
u
s
s
o
il
test
s
,
an
d
cr
o
p
a
n
d
f
er
tili
ze
r
r
ec
o
m
m
en
d
atio
n
s
,
as
d
ep
icted
in
Fig
u
r
e
3
.
I
t
is
co
s
t
-
ef
f
icien
t
s
in
ce
th
e
f
ar
m
er
s
ca
n
r
e
ad
ily
d
eter
m
in
e
th
e
lev
els
o
f
s
o
il p
ar
am
eter
s
with
o
u
t p
ay
in
g
lab
o
r
at
o
r
y
f
ee
s
.
Fig
u
r
e
3
.
So
il
test
k
it f
r
o
m
th
e
Dep
ar
tm
en
t o
f
Ag
r
icu
ltu
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
5
:
439
-
4
4
6
444
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
co
m
p
a
r
ativ
e
an
aly
s
is
o
f
t
h
e
d
if
f
er
e
n
t
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
u
tili
zin
g
th
e
a
b
o
v
e
m
en
tio
n
ed
f
o
u
r
p
e
r
f
o
r
m
an
ce
m
etr
ics
is
s
h
o
wca
s
ed
in
Fig
u
r
e
4
.
R
eg
ar
d
in
g
th
e
p
e
r
f
o
r
m
an
ce
m
etr
ics,
th
e
SVM
wa
s
co
n
s
id
er
ed
th
e
h
ig
h
est
-
p
er
f
o
r
m
in
g
m
ac
h
in
e
lear
n
in
g
,
with
g
ar
n
er
e
d
s
co
r
es
o
f
0
.
8
6
2
f
o
r
ac
cu
r
ac
y
,
0
.
8
8
9
f
o
r
p
r
ec
is
io
n
,
0
.
8
6
4
f
o
r
r
ec
all,
an
d
0
.
8
9
1
f
o
r
F1
-
s
co
r
e.
T
h
e
SV
M
o
u
tr
an
k
ed
th
e
o
th
er
two
m
a
ch
in
e
lear
n
i
n
g
i
n
all
p
er
f
o
r
m
an
ce
m
etr
ics.
T
h
e
n
aï
v
e
B
ay
es
alg
o
r
ith
m
r
an
k
ed
s
e
co
n
d
a
n
d
last
f
o
r
th
e
d
ec
is
io
n
tr
ee
.
T
h
er
e
f
o
r
e,
th
e
SVM
alg
o
r
ith
m
was
co
n
s
id
er
ed
th
e
h
ig
h
est
-
p
e
r
f
o
r
m
in
g
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
a
m
o
n
g
th
e
th
r
ee
.
T
h
e
s
y
s
tem
co
r
r
ec
tly
m
ap
p
ed
th
e
s
o
il sam
p
le'
s
ap
p
r
o
p
r
iate
cr
o
p
a
n
d
f
er
tili
ze
r
r
ec
o
m
m
en
d
atio
n
s
.
Fig
u
r
e
4
.
C
o
m
p
a
r
is
o
n
o
f
th
e
p
er
f
o
r
m
a
n
ce
m
etr
ics o
f
t
h
e
th
r
e
e
-
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
5.
CO
NCLU
SI
O
N
I
d
en
tific
atio
n
o
f
s
o
il
p
H
an
d
m
ac
r
o
n
u
tr
ien
ts
,
i.e
.
,
n
itro
g
en
,
p
h
o
s
p
h
o
r
u
s
,
p
o
tass
iu
m
,
an
d
m
icr
o
n
u
tr
ie
n
ts
,
i.e
.
,
zin
c,
ir
o
n
,
an
d
c
o
p
p
er
,
is
ess
en
tial
in
m
ain
tain
in
g
s
o
il
p
r
o
d
u
ctiv
it
y
.
C
o
lo
r
im
etr
y
an
d
s
p
ec
tr
o
m
etr
y
a
r
e
ch
e
m
ical
p
r
o
ce
s
s
es
th
at
h
elp
d
eter
m
in
e
th
e
lev
el
o
f
s
o
il
p
ar
a
m
eter
s
.
C
r
ea
tin
g
"in
-
s
itu
"
d
ev
ices
is
v
ital
f
o
r
f
ar
m
er
s
an
d
o
th
e
r
in
ter
ested
p
ar
ties
in
s
o
il
an
aly
s
is
.
C
r
o
p
an
d
f
er
tili
ze
r
r
ec
o
m
m
en
d
atio
n
s
ar
e
ess
en
tial
in
m
ax
im
izin
g
cr
o
p
y
ield
s
an
d
s
o
il
s
tatu
s
.
Ma
ch
in
e
l
ea
r
n
in
g
is
o
p
tim
ally
u
s
ed
in
s
e
v
er
al
ap
p
licatio
n
s
,
esp
ec
ially
in
im
ag
e
p
r
o
ce
s
s
in
g
,
to
d
eter
m
in
e
th
e
s
tatu
s
o
f
s
o
il
p
ar
am
eter
s
.
I
t
is
s
u
b
jecte
d
to
s
ev
er
al
tr
ain
in
g
im
ag
es
f
o
r
p
r
o
p
er
id
en
tific
ati
o
n
o
f
th
e
lev
el.
Var
io
u
s
m
ac
h
in
e
-
lear
n
in
g
tech
n
i
q
u
es
ca
n
b
e
im
p
lo
r
e
d
,
b
u
t
th
e
SVM
is
co
n
s
id
er
ed
b
est.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
e
au
th
o
r
s
ex
p
r
ess
th
eir
g
r
atitu
d
e
to
th
e
B
ico
l
State
C
o
lle
g
e
o
f
Ap
p
lied
Scien
ce
s
an
d
T
ec
h
n
o
lo
g
y
f
o
r
p
u
b
lis
h
in
g
th
is
p
a
p
er
.
RE
F
E
R
E
NC
E
S
[
1
]
X
.
Y
a
n
g
e
t
a
l
.
,
“
A
su
r
v
e
y
o
n
sm
a
r
t
a
g
r
i
c
u
l
t
u
r
e
:
d
e
v
e
l
o
p
me
n
t
mo
d
e
s
,
t
e
c
h
n
o
l
o
g
i
e
s
,
a
n
d
s
e
c
u
r
i
t
y
a
n
d
p
r
i
v
a
c
y
c
h
a
l
l
e
n
g
e
s,
”
I
EEE
/
C
A
A
J
o
u
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fi
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S
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t
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leg
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g
in
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rin
g
(C
En
g
).
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is
a
stu
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e
n
t
o
f
th
e
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trai
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h
t
M
a
ste
rs
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rsity
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th
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ti
o
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l
E
n
g
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rin
g
Un
i
v
e
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.
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is
d
e
sig
n
a
ted
a
s
th
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In
tellec
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ro
p
e
rt
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it
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a
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g
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r
a
n
d
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g
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se
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rc
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Co
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rd
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to
r
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se
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rc
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in
tere
sts
a
re
e
lec
tro
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ics
e
n
g
in
e
e
rin
g
,
si
g
n
a
l
p
ro
c
e
ss
in
g
,
c
o
m
p
u
ter
v
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n
,
a
rti
ficia
l
i
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telli
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c
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n
g
i
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rin
g
e
d
u
c
a
ti
o
n
,
a
n
d
in
tellec
t
u
a
l
p
ro
p
e
rty
.
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is
a
lso
a
n
a
ss
o
c
iate
m
e
m
b
e
r
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f
th
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g
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rin
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a
n
d
In
d
u
strial
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se
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rc
h
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o
f
th
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ti
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l
Re
se
a
rc
h
Co
u
n
c
il
o
f
th
e
P
h
il
i
p
p
in
e
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jj
fm
o
n
tan
e
z
@a
ste
a
n
.
b
isc
a
st.ed
u
.
p
h
.
J
e
ffr
e
y
S
a
r
m
iento
fin
ish
e
d
h
is
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c
h
e
lo
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f
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ie
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c
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d
M
a
ste
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f
S
c
ien
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i
n
Co
m
p
u
ter E
n
g
i
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e
e
rin
g
a
t
t
h
e
Ba
tan
g
a
s S
tate
Un
iv
e
rsit
y
.
He
fin
ish
e
d
h
is Do
c
to
r
o
f
E
n
g
i
n
e
e
rin
g
m
a
jo
r
in
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m
p
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ter
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g
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ri
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g
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th
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h
n
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ica
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n
stit
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te
o
f
th
e
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h
il
ip
p
i
n
e
s.
He
is
th
e
p
ro
g
ra
m
c
h
a
ir
f
o
r
m
u
lt
i
p
le
p
ro
g
ra
m
s,
in
c
l
u
d
i
n
g
M
S
Cp
E
(M
a
st
e
r
o
f
S
c
ien
c
e
i
n
Co
m
p
u
ter
En
g
i
n
e
e
rin
g
)
,
M
S
AI
(
M
a
ste
r
o
f
S
c
ien
c
e
in
Artifi
c
ial
I
n
telli
g
e
n
c
e
),
a
n
d
M
En
g
-
Cp
E
(M
a
ste
r
o
f
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g
i
n
e
e
rin
g
in
Co
m
p
u
ter
E
n
g
i
n
e
e
rin
g
)
a
t
t
h
e
Ba
tan
g
a
s
S
tate
Un
iv
e
rsity
.
His
re
se
a
rc
h
in
tere
sts
a
re
c
o
m
p
u
ter
e
n
g
in
e
e
rin
g
,
a
p
p
l
i
e
d
m
a
c
h
in
e
lea
rn
in
g
,
P
y
th
o
n
(
p
r
o
g
ra
m
m
in
g
lan
g
u
a
g
e
),
a
n
d
a
p
p
li
e
d
a
rti
ficia
l
in
telli
g
e
n
c
e
.
He
is also
a
n
a
ss
o
c
iate
m
e
m
b
e
r
o
f
th
e
En
g
in
e
e
rin
g
a
n
d
I
n
d
u
strial
Re
se
a
rc
h
Div
isio
n
o
f
th
e
Na
ti
o
n
a
l
Re
se
a
rc
h
Co
u
n
c
il
o
f
th
e
P
h
il
i
p
p
in
e
s.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jeffre
y
.
sa
rm
ien
to
@g
.
b
a
tstate
-
u
.
e
d
u
.
p
h
.
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