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id
1.
I
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Me
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s
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f
p
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1
]
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B
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p
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it h
as a
h
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o
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ten
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[
2
]
.
Ho
wev
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is
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s
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[
3
]
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d
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n
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s
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lead
in
g
to
f
aster
b
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f
s
p
o
ilag
e
[
4
]
,
[
5
]
.
Path
o
g
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ic
b
ac
ter
ia
p
r
esen
t
in
m
ea
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an
d
th
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ca
n
ca
u
s
e
co
n
s
u
m
er
s
to
b
ec
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m
e
s
ick
[
6
]
,
[
7
]
.
Nea
r
-
in
f
r
a
r
ed
s
p
ec
tr
o
s
co
p
y
(
NI
R
S)
tech
n
o
lo
g
y
ca
n
b
e
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s
ed
to
d
etec
t
th
e
co
m
p
o
s
itio
n
co
n
tai
n
ed
in
b
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f
[
8
]
,
[
9
]
.
NI
R
S
ca
n
d
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t
s
ev
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lecu
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co
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ten
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b
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s
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ch
as
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co
m
p
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n
en
ts
,
tech
n
o
lo
g
ical
p
ar
am
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s
o
r
elec
tr
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n
ic
eq
u
ip
m
en
t
,
m
in
er
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co
n
ten
ts
,
q
u
ality
tr
aits
,
f
atty
ac
id
s
,
an
d
m
an
y
m
o
r
e
[
1
0
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–
[
1
2
]
.
T
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f
r
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elin
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is
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s
in
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m
ac
h
in
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lear
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in
g
[
1
3
]
.
Pre
d
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m
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d
elin
g
o
f
m
ea
t
q
u
ality
attr
ib
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te
s
f
r
o
m
h
u
m
an
s
en
s
in
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lik
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ten
d
er
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ju
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f
lav
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h
as b
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n
d
o
n
e
with
m
ac
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in
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lear
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in
g
[
1
4
]
.
T
h
e
r
a
n
d
o
m
f
o
r
est
alg
o
r
ith
m
[
1
5
]
w
as
u
s
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as
th
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m
ain
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[
2
2
]
.
2.
M
E
T
H
O
D
2
.
1
.
Da
t
a
s
et
T
h
is
s
tu
d
y
u
s
ed
a
d
ataset
o
f
b
ee
f
q
u
ality
p
ar
am
eter
s
f
r
o
m
p
r
ev
i
o
u
s
r
esear
ch
[
2
3
]
.
T
h
e
o
b
ject
ex
am
in
ed
in
th
e
s
tu
d
y
was
f
r
e
s
h
b
ee
f
.
T
h
e
d
ata
ac
q
u
is
itio
n
p
r
o
ce
s
s
was
ca
r
r
ied
o
u
t
in
two
way
s
:
a
d
estru
ctiv
e
m
eth
o
d
u
s
in
g
lab
o
r
ato
r
y
to
o
l
s
an
d
a
n
o
n
-
d
estru
ctiv
e
m
eth
o
d
u
s
in
g
NI
R
S
s
en
s
o
r
s
.
T
h
e
d
estru
ctiv
e
m
eth
o
d
p
r
o
d
u
ce
d
d
ata
o
n
th
e
v
alu
e
o
f
m
ea
t
q
u
ality
p
ar
am
eter
s
,
wh
i
ch
b
ec
am
e
th
e
ta
r
g
et
v
a
r
iab
le
s
o
f
th
e
m
o
d
elin
g
.
T
h
e
n
o
n
-
d
estru
ctiv
e
m
eth
o
d
g
en
er
ate
d
s
p
ec
tr
o
s
co
p
y
d
ata
th
at
s
er
v
ed
as
tr
ain
in
g
d
at
a
in
m
o
d
elin
g
.
An
ex
am
p
le
o
f
m
ea
t
q
u
ality
p
a
r
am
eter
d
ata
ca
n
b
e
o
b
s
er
v
e
d
in
T
ab
le
1
.
T
h
ese
d
ata
h
a
v
e
6
d
ata
c
o
lu
m
n
s
ac
co
r
d
in
g
to
th
e
p
ar
am
eter
s
to
b
e
p
r
e
d
icted
an
d
8
0
r
o
ws o
f
d
ata.
Sp
ec
tr
o
s
co
p
ic
d
ata
ar
e
s
im
ilar
to
s
ig
n
al
d
ata,
b
u
t
in
th
is
s
tu
d
y
th
e
s
p
ec
tr
o
s
co
p
y
d
ata
is
alr
e
ad
y
in
th
e
f
o
r
m
o
f
a
s
p
r
ea
d
s
h
ee
t
f
ile.
T
h
e
d
ata
co
n
s
is
ts
o
f
1
3
6
co
lu
m
n
s
,
with
th
e
co
lu
m
n
n
am
e
b
ein
g
th
e
wav
ele
n
g
th
v
alu
e
o
f
th
e
s
en
s
o
r
in
n
an
o
m
eter
s
(
n
m
)
an
d
co
n
s
is
ts
o
f
7
2
0
r
o
ws
d
ata.
T
h
is
s
p
ec
tr
o
s
co
p
y
d
ata
was
o
b
tain
ed
f
r
o
m
8
0
s
am
p
les
th
at
wer
e
s
ca
n
n
ed
n
in
e
tim
es,
an
d
an
e
x
a
m
p
le
o
f
s
p
ec
tr
o
s
co
p
ic
d
ata
ca
n
b
e
s
ee
n
in
T
ab
le
2
an
d
v
is
u
alize
d
as sh
o
wn
i
n
Fig
u
r
e
1
.
T
ab
le
1
.
E
x
am
p
le
d
ata
f
r
o
m
lab
o
r
ato
r
y
[
2
3
]
N
o
.
S
t
o
r
a
g
e
Ti
m
e
(
h
o
u
r
)
D
r
i
p
L
o
ss
(
%)
C
o
l
o
r
(
L*
)
pH
W
a
t
e
r
M
o
i
s
t
u
r
e
(
%)
TPC
(
c
f
u
/
g
)
1
0
0
.
0
0
%
2
9
.
0
9
5
.
4
5
7
6
.
7
8
%
2
1
,
8
0
2
.
1
8
2
1
3
.
9
7
%
3
2
.
4
8
5
.
5
2
7
6
.
9
2
%
7
2
,
8
9
5
.
3
8
3
2
6
.
4
6
%
3
5
.
6
8
5
.
3
2
7
5
.
5
1
%
1
1
0
,
2
0
4
.
6
7
…
…
…
…
…
…
…
80
7
1
7
.
0
4
%
3
1
.
2
7
5
.
5
9
7
4
.
8
6
%
2
,
4
5
9
,
8
0
1
.
1
5
T
ab
le
2
.
NI
R
S d
ata
ex
am
p
le
H
o
u
r
W
a
v
e
l
e
n
g
t
h
(
n
m)
2
5
5
6
.
2
4
2
5
3
9
.
3
5
…
1
3
5
1
.
3
5
1
3
4
6
.
6
1
0
2
.
2
7
2
.
3
7
…
1
.
4
1
1
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3
5
1
1
.
9
6
2
.
0
8
…
1
.
7
2
1
.
9
5
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1
.
7
8
1
.
8
6
…
1
.
6
2
1
.
5
3
3
2
.
1
2
2
.
2
2
…
1
.
8
9
1
.
8
8
4
2
.
1
1
2
.
2
8
…
1
.
8
5
1
.
7
7
5
1
.
7
1
1
.
8
6
…
2
.
0
1
2
.
1
1
6
2
.
1
3
2
.
3
0
…
2
.
8
6
2
.
5
5
7
2
.
8
1
2
.
9
1
…
3
.
7
9
4
.
0
1
Fig
u
r
e
1
.
NI
R
S
d
ata
p
lo
ttin
g
Evaluation Warning : The document was created with Spire.PDF for Python.
C
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m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
E
ffects o
f h
yp
erp
a
r
a
mete
r
tu
n
in
g
o
n
r
a
n
d
o
m
fo
r
est r
eg
r
es
s
o
r
in
th
e
b
ee
f
q
u
a
lity
…
(
R
id
w
a
n
R
a
a
fi'u
d
i
n
)
161
2
.
2
.
E
x
perim
ent
a
l scena
rio
I
n
th
is
s
t
u
d
y
,
tw
o
s
c
e
n
a
r
i
o
s
we
r
e
p
r
o
p
o
s
e
d
i
n
v
o
lv
in
g
R
FR
wi
th
d
e
f
a
u
l
t
p
ar
am
et
er
s
an
d
t
h
e
a
p
p
lic
ati
o
n
o
f
h
y
p
e
r
p
a
r
a
m
et
e
r
s
,
as
s
h
o
wn
in
F
ig
u
r
e
2
.
T
h
e
m
o
d
e
li
n
g
p
r
o
ce
s
s
was
ca
r
r
ie
d
o
u
t
a
lte
r
n
a
tel
y
o
n
ea
c
h
m
ea
t
q
u
ali
ty
p
a
r
a
m
e
te
r
.
A
c
o
m
p
ar
at
iv
e
d
is
t
r
i
b
u
ti
o
n
o
f
tr
ai
n
i
n
g
a
n
d
tes
ti
n
g
d
at
a
o
f
7
t
o
2
,
as s
h
o
w
n
in
T
a
b
le
3
.
O
r
gi
na
l
N
I
R
S
D
a
t
a
C
ol
or
R
F
R
de
f
a
ul
t
pa
r
a
m
e
t
e
r
P
e
r
f
or
m
a
nc
e
E
va
l
ua
t
i
on
1
2
4
D
r
i
p l
os
s
pH
S
t
or
a
ge
t
i
m
e
T
P
C
W
a
t
e
r
m
oi
s
t
ur
e
R
F
R
w
i
t
h
hyp
e
r
pa
r
a
m
e
t
e
r
t
uni
ng
3
Fig
u
r
e
2
.
E
x
p
er
im
e
n
tal
s
ce
n
ar
io
T
ab
le
3
.
Data
s
et
d
is
tr
ib
u
tio
n
D
a
t
a
s
e
t
A
mo
u
n
t
o
f
d
a
t
a
P
e
r
c
e
n
t
a
g
e
(
%)
Tr
a
i
n
i
n
g
d
a
t
a
5
6
0
7
7
.
8
Te
st
i
n
g
d
a
t
a
1
6
0
2
2
.
2
S
u
m
7
2
0
1
0
0
.
0
2
.
3
.
H
y
perpa
ra
m
e
t
er
t
un
ing
co
nfig
ura
t
io
n
I
n
th
is
s
t
u
d
y
,
t
h
e
R
an
d
o
m
iz
ed
Sea
r
c
h
C
V
m
et
h
o
d
was
u
s
e
d
[
1
9
]
.
R
a
n
d
o
m
i
ze
d
Se
ar
ch
C
V
p
r
o
v
i
d
es
b
o
th
'
f
it'
a
n
d
'
s
co
r
e'
m
e
th
o
d
s
.
Ad
d
i
ti
o
n
all
y
,
it
s
u
p
p
o
r
ts
'
s
co
r
e
_
s
a
m
p
les'
,
'
p
r
ed
ict'
,
'
p
r
e
d
i
ct
_
p
r
o
b
a'
,
'
d
e
cisi
o
n
_
f
u
n
cti
o
n
'
,
'
tr
a
n
s
f
o
r
m
'
,
a
n
d
'
in
v
e
r
s
e
_
t
r
a
n
s
f
o
r
m
'
,
p
r
o
v
id
ed
th
ese
m
et
h
o
d
s
ar
e
a
v
ai
la
b
l
e
in
t
h
e
esti
m
at
o
r
u
t
ili
ze
d
.
T
h
e
esti
m
at
o
r
'
s
p
a
r
a
m
et
er
s
u
ti
liz
e
d
f
o
r
im
p
l
em
en
ti
n
g
t
h
ese
te
ch
n
i
q
u
es
a
r
e
f
i
n
e
-
t
u
n
e
d
t
h
r
o
u
g
h
c
r
o
s
s
-
v
ali
d
a
te
d
ex
p
l
o
r
at
io
n
ac
r
o
s
s
v
ar
io
u
s
p
ar
am
ete
r
c
o
n
f
i
g
u
r
a
ti
o
n
s
.
U
n
l
i
k
e
Gr
i
d
S
ea
r
c
h
C
V
,
w
h
ic
h
tes
ts
e
v
e
r
y
p
ar
am
et
er
v
al
u
e,
R
a
n
d
o
m
i
ze
d
Se
ar
c
h
C
V
s
el
ec
ts
a
p
r
e
d
et
e
r
m
i
n
ed
n
u
m
b
er
o
f
p
a
r
a
m
e
te
r
c
o
n
f
i
g
u
r
at
io
n
s
r
an
d
o
m
ly
f
r
o
m
s
p
e
ci
f
ie
d
d
is
t
r
i
b
u
ti
o
n
s
.
T
h
e
q
u
an
t
it
y
o
f
c
o
n
f
ig
u
r
a
ti
o
n
s
t
est
ed
is
d
e
te
r
m
in
ed
b
y
n
_
i
te
r
.
W
h
e
n
a
ll
p
a
r
a
m
et
er
s
ar
e
lis
te
d
,
s
am
p
li
n
g
wit
h
o
u
t
r
e
p
eti
tio
n
o
cc
u
r
s
.
C
o
n
v
e
r
s
e
ly
,
if
a
n
y
p
ar
am
ete
r
is
d
ef
in
ed
as
a
d
i
s
tr
i
b
u
ti
o
n
,
s
a
m
p
li
n
g
wit
h
r
e
p
l
ac
e
m
e
n
t
is
e
m
p
l
o
y
e
d
.
I
t'
s
a
d
v
is
a
b
l
e
t
o
u
tili
ze
c
o
n
ti
n
u
o
u
s
d
is
t
r
i
b
u
ti
o
n
s
f
o
r
c
o
n
ti
n
u
o
u
s
p
a
r
am
ete
r
s
[
2
2
]
.
Fo
r
th
e
u
s
e
o
f
R
FR
b
y
d
ef
a
u
lt,
th
er
e
ar
e
s
till
alg
o
r
ith
m
p
a
r
am
eter
s
s
et,
wh
ile
th
e
d
ef
a
u
lt
p
ar
am
eter
s
ettin
g
s
ca
n
b
e
s
ee
n
i
n
T
a
b
le
4
.
Me
an
w
h
ile,
f
o
r
h
y
p
e
r
p
ar
a
m
eter
s
etu
p
,
th
er
e
is
ac
tu
ally
n
o
s
tan
d
a
r
d
r
ef
er
e
n
ce
f
o
r
h
o
w
m
an
y
p
a
r
am
eter
co
m
b
in
atio
n
s
was
u
s
ed
,
b
u
t
u
s
u
ally
,
th
e
m
o
r
e
p
ar
am
eter
co
m
b
in
atio
n
s
ar
e
u
s
ed
,
th
e
lo
n
g
er
th
e
tr
ain
i
n
g
p
r
o
ce
s
s
in
m
ac
h
in
e
lear
n
in
g
.
T
h
e
s
etu
p
f
o
r
th
e
co
m
b
in
atio
n
o
f
h
y
p
er
p
ar
am
eter
tu
n
i
n
g
in
th
is
s
tu
d
y
ca
n
b
e
s
ee
n
i
n
T
ab
le
5
.
T
ab
le
4
.
Def
a
u
lt p
ar
am
eter
o
f
R
F
R
[
2
4
]
P
a
r
a
me
t
e
r
D
a
t
a
t
y
p
e
D
e
f
a
u
l
t
v
a
l
u
e
n
_
e
st
i
ma
t
o
r
s
1
0
0
c
r
i
t
e
r
i
o
n
sq
u
a
r
e
d
_
e
r
r
o
r
max
_
d
e
p
t
h
N
o
n
e
mi
n
_
sa
mp
l
e
s
_
s
p
l
i
t
2
mi
n
_
sa
mp
l
e
s
_
l
e
a
f
1
mi
n
_
w
e
i
g
h
t
_
f
r
a
c
t
i
o
n
_
l
e
a
f
f
l
o
a
t
0
.
0
max
_
f
e
a
t
u
r
e
s
i
n
t
o
r
f
l
o
a
t
1
.
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
7
2
2
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3
2
2
1
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
,
Vo
l.
6
,
No
.
2
,
J
u
ly
20
25
:
159
-
1
6
8
162
T
ab
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5
.
Hy
p
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p
ar
a
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eter
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ig
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P
a
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a
me
t
e
r
V
a
l
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s
n
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e
st
i
ma
t
o
r
s
[
2
0
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4
0
0
,
6
0
0
,
8
0
0
,
1
0
0
0
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1
2
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1
4
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6
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max
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2
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4
.
M
o
del e
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t
i
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n
T
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i
n
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g
r
o
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t
m
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a
n
s
q
u
a
r
e
e
r
r
o
r
(
R
MS
E
)
a
n
d
R
-
s
q
u
a
r
e
d
(
R
²
)
t
o
m
e
a
s
u
r
e
i
ts
p
e
r
f
o
r
m
a
n
c
e
.
R
M
S
E
a
n
d
R
²
a
r
e
u
s
e
d
t
o
a
s
s
es
s
h
o
w
w
e
l
l
t
h
e
m
o
d
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l
p
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d
i
c
t
s
d
a
ta
.
T
h
e
s
el
e
c
ti
o
n
o
f
R
M
S
E
a
n
d
R
²
a
s
e
v
a
l
u
a
ti
o
n
m
e
t
r
i
c
s
is
b
a
s
e
d
o
n
t
h
e
i
r
a
b
i
l
i
t
y
t
o
p
r
o
v
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d
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c
o
m
p
r
e
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e
n
s
i
v
e
u
n
d
e
r
s
t
a
n
d
i
n
g
o
f
t
h
e
m
o
d
e
l
'
s
p
r
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d
i
c
t
i
o
n
a
c
c
u
r
a
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y
.
R
M
SE
w
a
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k
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t
e
r
p
r
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t
.
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MS
E
i
s
m
o
r
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s
e
n
s
it
i
v
e
t
o
l
a
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g
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e
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r
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l
at
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t
h
e
s
q
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a
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m
e
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n
o
f
e
r
r
o
r
s
,
w
h
i
c
h
m
a
k
e
s
i
t
s
u
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ta
b
l
e
f
o
r
d
e
t
e
ct
i
n
g
m
o
d
e
l
s
w
it
h
s
i
g
n
i
f
i
c
a
n
t
p
r
e
d
i
cti
o
n
e
r
r
o
r
s
[
2
5
]
.
Me
an
wh
ile,
R
²
was
ch
o
s
en
b
e
ca
u
s
e
it
was
ab
le
to
s
h
o
w
th
e
p
r
o
p
o
r
tio
n
o
f
v
ar
ian
ce
f
r
o
m
th
e
d
ata
th
at
th
e
m
o
d
el
co
u
ld
e
x
p
lain
.
R²
is
a
co
m
m
o
n
ly
u
s
ed
ev
alu
atio
n
m
etr
ic
in
r
eg
r
ess
io
n
b
ec
au
s
e
it
g
iv
es
an
id
ea
o
f
h
o
w
well
th
e
m
o
d
el
f
its
ag
ai
n
s
t
th
e
d
ata
[
2
6
]
.
T
h
e
h
ig
h
er
th
e
R
²
v
alu
e,
th
e
b
etter
th
e
m
o
d
el
is
ab
le
to
ac
co
u
n
t
f
o
r
v
a
r
iatio
n
s
in
th
e
d
ata.
T
h
e
R
MSE
f
o
r
m
u
la
ca
n
b
e
s
ee
n
in
(
1
)
an
d
f
o
r
m
u
la
R
²
ca
n
b
e
s
ee
n
in
(
2
)
.
R
M
SE
=
√
1
∑
=
1
(
−
ˆ
)
2
(
1
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2
=
1
−
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=
1
(
−
ˆ
)
2
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=
1
(
−
‾
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2
(
2
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W
h
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=
ac
tu
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ata,
ˆ
=
p
r
ed
i
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d
ata,
̅
=
m
ea
n
o
f
ac
tu
al
d
a
ta
,
an
d
n
=
n
u
m
b
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o
f
d
ata
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
m
o
d
el
was
ev
alu
ated
u
s
in
g
R
MSE
an
d
R
²
to
as
s
es
s
it
s
p
r
ed
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p
er
f
o
r
m
an
ce
.
Hy
p
e
r
p
ar
am
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tu
n
in
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r
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lted
in
th
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b
est
s
et
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f
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u
r
atio
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s
,
as
in
d
icate
d
b
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th
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s
m
allest
R
M
SE
v
a
lu
e
an
d
g
r
ea
ter
R
²
test
in
g
r
esu
lts
f
o
r
ea
ch
m
ea
t
q
u
ality
p
ar
am
eter
.
As
a
co
m
p
ar
is
o
n
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d
to
s
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th
e
ef
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t
o
f
h
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p
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p
ar
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eter
tu
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in
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th
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m
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d
elin
g
ac
cu
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ac
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r
esu
lts
co
m
p
ar
e
th
e
r
esu
lts
o
f
R
FR
with
th
e
d
ef
au
lt
co
n
f
ig
u
r
atio
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an
d
th
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r
esu
lts
o
f
h
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p
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ar
am
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tu
n
in
g
.
T
h
e
r
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lts
o
f
t
h
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d
e
f
au
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t
co
n
f
ig
u
r
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n
an
d
u
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in
g
h
y
p
er
p
ar
am
eter
tu
n
in
g
ca
n
b
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s
ee
n
in
T
a
b
le
6
.
B
ased
o
n
th
e
d
ata
in
T
a
b
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6
,
it
c
an
b
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s
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n
th
at
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p
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f
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r
m
a
n
ce
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f
th
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m
o
d
elin
g
im
p
r
o
v
e
d
f
r
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m
th
e
o
n
e
u
s
in
g
th
e
d
ef
au
lt
c
o
n
f
i
g
u
r
atio
n
to
th
e
r
esu
lt
o
f
h
y
p
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r
p
ar
am
ete
r
tu
n
in
g
.
T
h
e
in
c
r
ea
s
e
ca
n
b
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s
ee
n
f
r
o
m
th
e
s
m
aller
R
MSE
v
alu
e
an
d
th
e
in
cr
ea
s
i
n
g
R
²
v
alu
e.
T
h
e
in
cr
ea
s
e
in
p
er
f
o
r
m
an
ce
in
t
h
e
in
cr
ea
s
in
g
R
²
v
alu
e
with
an
a
v
er
ag
e
in
c
r
ea
s
e
o
f
0
.
0
9
9
7
o
r
1
4
%
ca
n
b
e
s
ee
n
i
n
T
ab
le
7
.
R
²
d
ef
au
lt
s
h
o
ws
th
e
r
esu
lts
o
f
th
e
m
o
d
el
ev
alu
ati
o
n
f
o
r
all
m
ea
t
q
u
ality
p
ar
am
eter
s
,
wh
ile
R
2
h
y
p
er
p
ar
a
m
eter
is
th
e
ev
alu
atio
n
r
esu
lts
o
f
th
e
r
an
d
o
m
f
o
r
est
m
o
d
el
th
at
h
as im
p
lem
e
n
ted
h
y
p
er
p
ar
am
eter
tu
n
in
g
.
T
ab
le
6
.
Pre
d
ictio
n
r
esu
lts
B
e
e
f
q
u
a
l
i
t
y
p
a
r
a
me
t
e
r
D
e
f
a
u
l
t
c
o
n
f
i
g
u
r
a
t
i
o
n
H
y
p
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r
p
a
r
a
me
t
e
r
t
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n
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n
g
R
M
S
E
R²
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M
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R²
c
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l
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r
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.
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1
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q
u
ar
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v
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f
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l
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(
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w
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m
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st
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0
.
5
4
4
0
.
7
3
9
0
.
1
9
5
36
Evaluation Warning : The document was created with Spire.PDF for Python.
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
E
ffects o
f h
yp
erp
a
r
a
mete
r
tu
n
in
g
o
n
r
a
n
d
o
m
fo
r
est r
eg
r
es
s
o
r
in
th
e
b
ee
f
q
u
a
lity
…
(
R
id
w
a
n
R
a
a
fi'u
d
i
n
)
163
T
h
e
r
esu
lts
o
f
th
e
im
p
r
o
v
em
e
n
t
u
s
in
g
h
y
p
er
p
a
r
am
eter
tu
n
i
n
g
o
n
th
e
R
FR
ar
e
v
is
u
alize
d
to
co
m
p
a
r
e
th
e
r
esu
lts
b
y
u
s
in
g
t
h
e
d
e
f
au
lt
p
ar
am
eter
s
an
d
b
y
u
s
in
g
h
y
p
er
p
ar
am
eter
tu
n
i
n
g
.
Fo
r
th
e
m
ea
t
co
lo
r
q
u
ality
p
ar
am
eter
s
,
th
e
p
r
ed
ictio
n
co
m
p
ar
is
o
n
r
esu
lts
ca
n
b
e
s
ee
n
in
Fig
u
r
e
s
3
to
8
.
I
n
Fig
u
r
e
3
,
we
ca
n
s
ee
a
co
m
p
ar
is
o
n
o
f
th
e
p
r
ed
ictio
n
r
esu
lts
f
o
r
th
e
co
lo
r
p
a
r
am
ete
r
,
wh
er
e
in
Fig
u
r
e
3
(
a)
,
th
e
y
ello
w
co
lo
r
,
wh
ich
r
ep
r
esen
ts
th
e
p
r
ed
ictio
n
d
ata
,
ap
p
ea
r
s
q
u
ite
f
ar
f
r
o
m
th
e
ac
tu
al
d
ata
lin
e,
wh
ich
is
b
lu
e.
Me
an
wh
ile,
th
e
r
esu
lts
o
f
th
e
ap
p
licatio
n
o
f
h
y
p
er
p
a
r
am
eter
tu
n
in
g
s
h
o
we
d
an
in
cr
ea
s
e
in
ac
c
u
r
ac
y
b
y
s
ee
in
g
th
at
th
e
o
r
an
g
e
lin
e
in
Fig
u
r
e
3
(
b
)
is
clo
s
er
to
th
e
b
lu
e
lin
e,
wh
ich
m
ea
n
s
th
at
th
e
p
r
e
d
ictio
n
er
r
o
r
v
alu
e
is
s
m
aller
with
a
lar
g
er
R
2
v
alu
e.
T
h
e
v
al
u
e
o
f
R
2
u
s
in
g
t
h
e
d
e
f
au
lt
p
ar
am
eter
is
0
.
7
8
9
a
n
d
th
en
in
cr
ea
s
es
to
0
.
8
8
5
a
f
ter
ap
p
ly
in
g
h
y
p
er
p
a
r
am
eter
tu
n
i
n
g
.
O
v
er
all,
it
ca
n
b
e
c
o
n
cl
u
d
ed
th
at
h
y
p
er
p
ar
am
eter
tu
n
in
g
h
as
a
p
o
s
itiv
e
im
p
ac
t
o
n
th
e
im
p
r
o
v
em
en
t
o
f
th
e
m
o
d
el'
s
ac
cu
r
ac
y
in
p
r
ed
ictin
g
th
e
c
o
lo
r
(
L
*
)
v
al
u
e,
as
s
ee
n
f
r
o
m
th
e
in
cr
ea
s
e
in
th
e
R
2
v
alu
e
an
d
t
h
e
s
h
ap
e
o
f
th
e
p
r
ed
ictio
n
lin
e
th
at
is
clo
s
er
to
th
e
d
ata
p
atter
n
.
An
in
cr
ea
s
e
in
R
2
o
f
0
.
0
9
6
o
r
an
in
c
r
ea
s
e
o
f
1
2
%.
(
a)
(
b
)
Fig
u
r
e
3
.
C
o
m
p
a
r
is
o
n
o
f
co
lo
r
(
L
*
)
p
r
ed
ictio
n
r
esu
lts
: (
a)
d
e
f
au
lt p
ar
am
eter
s
an
d
(
b
)
h
y
p
er
p
ar
am
eter
tu
n
in
g
T
h
e
p
r
e
d
icti
o
n
r
es
u
lts
f
o
r
th
e
d
r
ip
lo
s
s
q
u
a
lit
y
p
a
r
a
m
et
er
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
4
,
w
h
e
r
e
F
ig
u
r
e
4
(
a)
s
h
o
ws
a
g
r
a
p
h
o
f
t
h
e
p
r
e
d
i
cti
o
n
r
es
u
lts
w
it
h
a
n
R
2
v
al
u
e
o
f
0
.
8
3
9
wit
h
t
h
e
o
r
a
n
g
e
li
n
e
s
t
il
l
lo
o
k
i
n
g
f
r
u
ct
u
o
u
s
,
wh
i
ch
is
s
til
l
f
ar
f
r
o
m
t
h
e
ac
t
u
al
d
ata
o
n
t
h
e
b
l
u
e
li
n
e
.
W
h
i
le
in
Fi
g
u
r
e
4
(
b
)
,
w
h
i
c
h
h
as
a
p
p
li
ed
h
y
p
er
p
ar
am
ete
r
tu
n
i
n
g
,
it
ca
n
b
e
s
e
en
th
at
t
h
e
r
e
is
a
c
h
a
n
g
e
w
h
e
r
e
th
e
o
r
an
g
e
li
n
e
is
c
lo
s
er
to
t
h
e
b
l
u
e
c
o
l
o
r
o
f
t
h
e
a
ct
u
al
d
a
ta
wit
h
a
n
R
2
v
al
u
e
o
f
0
.
9
3
1
o
r
0
.
0
9
2
g
r
ea
t
e
r
t
h
a
n
b
ef
o
r
e
.
I
n
t
h
e
p
r
e
d
i
cti
o
n
o
f
d
r
ip
l
o
s
s
q
u
al
ity
,
R
2
i
n
c
r
e
ase
d
b
y
1
1
%
,
a
n
d
i
t w
as
als
o
s
ee
n
t
h
at
th
e
r
e
w
as
less
d
ev
iat
io
n
o
f
t
h
e
o
r
a
n
g
e
c
o
l
o
r
li
n
e
c
o
m
p
a
r
e
d
to
Fig
u
r
e
4
(
a
)
.
(
a)
(
b
)
F
i
g
u
r
e
4
.
C
o
m
p
a
r
is
o
n
o
f
d
r
i
p
l
o
s
s
(
%
)
p
r
e
d
i
c
t
i
o
n
r
es
u
l
ts
:
(
a
)
d
e
f
a
u
l
t
p
a
r
a
m
e
t
e
r
s
a
n
d
(
b
)
h
y
p
e
r
p
a
r
a
m
e
t
e
r
t
u
n
i
n
g
T
h
e
c
o
m
p
ar
is
o
n
o
f
p
r
e
d
i
cti
o
n
r
es
u
lts
in
th
e
p
H
q
u
ali
ty
p
ar
am
e
te
r
c
an
b
e
s
ee
n
in
F
ig
u
r
e
5
,
wh
er
e
Fig
u
r
e
5
(
a
)
is
a
g
r
a
p
h
t
h
at
s
h
o
ws
t
h
e
p
r
e
d
ic
ti
o
n
r
es
u
l
ts
u
s
i
n
g
t
h
e
d
e
f
a
u
lt
p
a
r
a
m
e
te
r
wit
h
a
n
R
2
v
al
u
e
o
f
0
.
7
3
4
,
wh
i
ch
is
c
o
n
s
i
d
e
r
e
d
s
m
al
l
b
ec
au
s
e
it
is
b
el
o
w
0
.
8
.
W
h
i
le
F
ig
u
r
e
5
(
b
)
s
h
o
ws
t
h
e
r
es
u
lts
o
f
p
H
q
u
al
it
y
p
r
ed
ict
io
n
af
t
er
a
p
p
l
y
i
n
g
h
y
p
er
p
a
r
a
m
e
te
r
tu
n
i
n
g
w
it
h
an
R
2
v
a
lu
e
o
f
0
.
8
4
3
,
wit
h
th
is
v
al
u
e,
it
ca
n
b
e
c
o
n
s
i
d
e
r
e
d
th
at
h
y
p
er
p
ar
am
ete
r
t
u
n
in
g
c
a
n
h
av
e
a
s
ig
n
if
i
ca
n
t
i
m
p
ac
t.
A
n
in
c
r
ea
s
e
i
n
t
h
e
R
2
v
al
u
e
o
f
0
.
1
0
9
o
r
1
5
%
.
T
h
is
in
c
r
ea
s
e
c
a
n
s
m
o
o
t
h
t
h
e
o
r
an
g
e
l
in
e,
w
h
i
ch
is
t
h
e
p
r
ed
ict
io
n
d
at
a,
a
n
d
t
h
e
v
al
u
es
g
et
cl
o
s
er
to
t
h
e
ac
tu
al
d
at
a.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
3
2
2
1
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
,
Vo
l.
6
,
No
.
2
,
J
u
ly
20
25
:
159
-
1
6
8
164
(
a)
(
b
)
Fig
u
r
e
5
.
C
o
m
p
a
r
is
o
n
o
f
p
H
v
alu
e
p
r
ed
ictio
n
r
esu
lts
:
(
a)
d
ef
au
lt p
ar
am
eter
s
an
d
(
b
)
h
y
p
er
p
ar
am
eter
tu
n
in
g
T
h
e
p
r
ed
ictio
n
o
f
th
e
s
to
r
ag
e
tim
e
q
u
ality
p
ar
am
eter
is
s
h
o
wn
in
Fig
u
r
e
6
,
s
p
ec
if
ically
in
th
e
ac
tu
al
d
ata
tim
e
p
r
e
d
ictio
n
d
ata;
it
l
o
o
k
s
lik
e
a
lad
d
er
b
ec
au
s
e
t
h
er
e
is
a
g
r
o
u
p
o
f
d
ata
in
th
e
s
am
e
tim
e
p
er
io
d
,
n
am
ely
at
th
e
s
am
e
h
o
u
r
as
th
e
d
if
f
e
r
en
ce
o
f
1
h
o
u
r
t
o
th
e
d
ata
g
r
o
u
p
b
e
f
o
r
e
an
d
af
ter
.
Actu
al
d
ata
is
a
d
ata
in
ter
v
al
p
e
r
1
h
o
u
r
ac
co
r
d
in
g
to
th
e
d
ata
c
o
llectio
n
tech
n
iq
u
e.
I
n
Fig
u
r
e
6
(
a)
,
it
ca
n
b
e
s
ee
n
th
at
t
h
er
e
a
r
e
s
till
m
an
y
o
r
a
n
g
e
lin
es
th
at
ar
e
to
wer
in
g
o
r
to
o
lo
w
d
o
wn
with
an
R
2
v
alu
e
o
f
0
.
9
0
9
.
T
h
e
d
ef
au
lt
r
esu
lt
o
f
th
is
p
ar
am
eter
ca
n
b
e
c
o
n
s
id
er
ed
v
er
y
h
ig
h
b
ec
au
s
e
it
h
as
ex
ce
ed
ed
0
.
9
,
b
u
t
h
y
p
er
p
a
r
am
eter
tu
n
in
g
is
s
till
ap
p
lied
to
s
ee
th
e
p
er
f
o
r
m
a
n
ce
r
esu
lts
.
I
n
Fig
u
r
e
6
(
b
)
,
th
e
g
r
a
p
h
s
h
o
ws
th
e
p
r
e
d
ictio
n
r
esu
lts
af
ter
ap
p
l
y
in
g
h
y
p
er
p
ar
am
eter
t
u
n
in
g
,
with
t
h
e
R
2
r
esu
lt
b
ein
g
0
.
9
5
7
.
W
ith
th
e
R
2
v
alu
e,
th
is
m
o
d
el
ca
n
b
e
s
aid
to
b
e
clo
s
e
to
p
er
f
e
ct
in
p
r
ed
ictin
g
q
u
ality
with
th
e
s
to
r
ag
e
tim
e
p
a
r
a
m
eter
.
T
h
e
d
if
f
er
en
ce
in
R
2
i
n
cr
ea
s
es
u
s
in
g
th
e
d
ef
au
lt p
ar
a
m
eter
,
an
d
af
ter
a
p
p
ly
in
g
th
e
h
y
p
er
p
ar
a
m
eter
tu
n
in
g
,
it is
0
.
0
4
8
o
r
5
%.
(
a)
(
b
)
F
i
g
u
r
e
6
.
C
o
m
p
a
r
i
s
o
n
o
f
s
t
o
r
a
g
e
t
i
m
e
(
h
o
u
r
)
p
r
e
d
i
c
t
i
o
n
r
e
s
u
l
t
s
:
(
a
)
d
e
f
a
u
l
t
p
a
r
a
m
e
t
e
r
s
a
n
d
(
b
)
h
y
p
e
r
p
a
r
a
m
e
t
e
r
t
u
n
i
n
g
T
h
e
n
ex
t
p
r
e
d
ictio
n
r
esu
lt
is
ab
o
u
t
b
io
lo
g
ical
m
ea
t
q
u
a
lity
p
ar
am
eter
s
,
n
am
ely
esti
m
atin
g
th
e
n
u
m
b
er
o
f
b
ac
ter
ial
co
lo
n
ies
u
s
in
g
th
e
T
PC
m
eth
o
d
with
co
lo
n
y
f
o
r
m
i
n
g
u
n
its
p
er
g
r
am
(
cf
u
/g
)
with
o
u
t
p
ay
in
g
atten
tio
n
to
th
e
ty
p
e
o
f
m
icr
o
b
i
o
lo
g
y
.
I
n
Fig
u
r
e
7
(
a)
,
th
e
r
esu
lts
o
f
th
e
m
atch
b
etw
ee
n
th
e
ac
tu
al
d
ata
an
d
th
e
p
r
e
d
icted
d
ata
ar
e
s
h
o
wn
.
T
h
er
e,
it
ca
n
b
e
s
ee
n
th
at
th
e
p
r
ed
ictio
n
r
esu
lts
ar
e
q
u
ite
g
o
o
d
f
o
r
s
m
all
v
alu
es
b
u
t
s
ee
m
to
b
e
m
u
ch
d
if
f
er
en
t
in
th
e
ac
tu
al
d
ata
with
h
ig
h
v
alu
es.
T
h
is
is
b
e
ca
u
s
e,
in
d
ee
d
,
th
e
m
o
d
elin
g
ac
c
u
r
ac
y
with
an
R
2
v
alu
e
is
0
.
8
4
5
f
o
r
m
o
d
elin
g
with
d
ef
au
lt
p
ar
am
eter
s
,
an
d
th
e
d
is
tr
ib
u
tio
n
o
f
ac
tu
al
d
ata
is
m
o
r
e
at
s
m
a
ll
v
alu
es.
W
h
ile
th
e
ac
cu
r
a
cy
r
esu
lts
af
ter
ap
p
ly
i
n
g
th
e
R
2
v
alu
e
tu
n
in
g
h
y
p
er
p
ar
am
eter
is
0
.
9
0
3
,
as sh
o
wn
in
Fig
u
r
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7
(
b
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,
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is
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lt
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iv
es a
n
in
cr
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f
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0
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8
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r
7
%.
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h
e
n
ex
t
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p
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ictio
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r
th
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wate
r
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u
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i
n
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u
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ter
Evaluation Warning : The document was created with Spire.PDF for Python.
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
E
ffects o
f h
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R
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a
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)
165
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y
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s
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lt p
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(
a)
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b
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Fig
u
r
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.
C
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m
p
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r
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f
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PC
(
cf
u
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p
r
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r
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lts
: (
a)
d
ef
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lt
p
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d
(
b
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h
y
p
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r
p
ar
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(
a)
(
b
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F
i
g
u
r
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8
.
C
o
m
p
a
r
i
s
o
n
o
f
w
a
t
e
r
m
o
i
s
t
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r
e
(
%
)
p
r
e
d
i
c
t
i
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n
r
e
s
u
l
t
s
:
(
a
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d
e
f
a
u
l
t
p
a
r
a
m
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t
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r
s
a
n
d
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h
y
p
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p
a
r
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g
B
ased
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ata
p
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a)
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d
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b
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v
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in
Fig
u
r
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s
3
t
o
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,
it
ca
n
b
e
s
ee
n
th
at
th
e
o
r
a
n
g
e
lin
e
in
ea
ch
f
ig
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r
e
(
b
)
ca
n
b
etter
f
o
llo
w
th
e
b
lu
e
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p
atter
n
.
T
h
e
cl
o
s
er
th
e
o
r
an
g
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is
to
th
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b
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lin
e,
th
e
g
r
ea
ter
th
e
R
2
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alu
e
o
r
th
e
s
m
aller
th
e
er
r
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r
v
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o
f
t
h
e
p
r
e
d
ictio
n
r
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lts
.
T
h
e
in
cr
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s
e
in
th
e
R
2
v
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m
ea
n
s
th
at
th
e
ef
f
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t
o
f
h
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p
er
p
a
r
am
eter
tu
n
in
g
o
n
th
e
R
FR
ca
n
r
u
n
well.
T
h
e
p
u
r
p
o
s
e
o
f
h
y
p
e
r
p
ar
a
m
eter
tu
n
in
g
is
to
s
elec
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th
e
b
est
s
et
o
r
s
et
o
f
p
a
r
am
eter
s
in
th
e
R
FR
.
T
h
e
b
est
p
ar
am
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is
also
s
h
o
wn
as
t
h
e
h
ig
h
est
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r
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m
th
e
r
an
d
o
m
p
ar
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elec
tio
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p
r
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ce
s
s
.
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h
e
b
est
p
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r
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m
eter
s
p
r
o
d
u
ce
d
f
r
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m
th
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ar
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s
h
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wn
in
T
ab
le
8
.
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e
r
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f
th
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y
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eter
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if
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e
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ch
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ea
t q
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ality
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tar
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et.
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e
d
if
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e
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t p
ar
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in
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m
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B
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
3
2
2
1
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
,
Vo
l.
6
,
No
.
2
,
J
u
ly
20
25
:
159
-
1
6
8
166
4.
CO
NCLU
SI
O
N
T
h
e
r
esu
lts
o
f
t
h
is
s
tu
d
y
p
r
o
v
e
th
at
th
e
u
s
e
o
f
h
y
p
e
r
p
ar
am
e
ter
tu
n
in
g
ca
n
im
p
r
o
v
e
th
e
p
e
r
f
o
r
m
a
n
ce
o
f
th
e
R
FR
alg
o
r
ith
m
.
T
h
e
p
e
r
f
o
r
m
a
n
ce
im
p
r
o
v
em
e
n
t
was
m
ea
s
u
r
ed
b
y
ev
alu
atin
g
an
i
n
cr
ea
s
e
in
R
2
v
alu
es
f
o
r
all
b
ee
f
f
r
esh
n
ess
q
u
ality
p
r
ed
ictio
n
tar
g
ets.
T
h
e
a
v
er
ag
e
in
cr
ea
s
e
in
R
2
f
r
o
m
all
p
r
ed
ict
io
n
r
esu
lts
o
f
m
ea
t
q
u
ality
p
ar
am
eter
s
is
0
.
0
9
9
7
,
o
r
an
in
cr
ea
s
e
o
f
1
4
%
f
r
o
m
th
e
R
2
v
alu
e
with
th
e
d
ef
au
lt
p
ar
am
eter
.
T
h
e
s
tu
d
y
'
s
r
esu
lts
o
n
th
e
ap
p
licatio
n
o
f
h
y
p
er
p
ar
am
eter
tu
n
in
g
s
h
o
w
th
at
n
o
t
all
p
ar
a
m
eter
c
o
n
f
ig
u
r
atio
n
s
af
f
ec
t
m
ea
t
q
u
ality
p
r
ed
ictio
n
m
o
d
elin
g
.
T
h
is
s
tu
d
y
s
h
o
wed
t
h
at
th
e
“
m
in
_
s
am
p
les_
leaf
”
o
r
t
h
e
m
i
n
im
u
m
n
u
m
b
er
o
f
s
am
p
les
th
at
m
u
s
t
b
e
p
r
esen
t
in
ea
ch
leaf
n
o
d
e
an
d
b
o
o
ts
tr
ap
p
ar
am
eter
s
d
id
n
o
t
s
h
o
w
an
y
d
if
f
er
e
n
ce
in
th
e
r
esu
lts
o
f
m
ea
t
q
u
ality
p
r
ed
ic
tio
n
,
wh
ich
m
e
an
s
th
at
th
e
R
FR
p
ar
am
eter
d
id
n
o
t
af
f
ec
t
t
h
e
h
y
p
er
p
ar
a
m
eter
tu
n
in
g
p
r
o
ce
s
s
.
So
,
in
th
e
ap
p
licatio
n
o
f
th
e
p
r
ed
ictio
n
m
o
d
el,
R
FR
an
d
h
y
p
er
p
a
r
am
et
er
tu
n
in
g
m
u
s
t
b
e
ad
ju
s
ted
to
t
h
e
p
a
r
am
eter
s
g
e
n
er
ated
b
y
h
y
p
e
r
p
ar
am
eter
tu
n
in
g
to
b
e
r
e
-
tr
ain
e
d
in
m
o
d
elin
g
.
E
ac
h
o
f
th
e
h
ig
h
est
p
r
ed
ictio
n
r
esu
lts
o
n
a
ll
m
ea
t
q
u
ality
p
ar
am
ete
r
s
was
af
f
ec
ted
b
y
d
if
f
er
e
n
t
R
FR
p
a
r
am
eter
s
ac
co
r
d
in
g
to
th
e
r
esu
lts
o
f
th
e
b
est
p
ar
am
eter
o
u
tp
u
t
f
r
o
m
th
e
h
y
p
er
p
ar
am
eter
tu
n
in
g
iter
atio
n
p
r
o
c
ess
.
Fo
r
f
u
tu
r
e
wo
r
k
,
h
y
p
er
p
ar
am
eter
t
u
n
in
g
m
eth
o
d
s
s
u
ch
as
Gr
id
C
V
ca
n
a
ls
o
b
e
u
s
ed
to
ex
p
lo
r
e
f
u
r
t
h
er
th
e
a
b
ilit
y
o
f
h
y
p
er
p
ar
am
eter
tu
n
in
g
to
im
p
r
o
v
e
alg
o
r
ith
m
p
e
r
f
o
r
m
an
ce
.
T
h
e
r
esu
lts
o
f
th
is
s
tu
d
y
ca
n
a
ls
o
b
e
co
n
tin
u
e
d
b
y
co
m
b
in
in
g
h
y
p
e
r
p
ar
am
eter
tu
n
in
g
with
s
ev
er
al
p
r
ep
r
o
ce
s
s
m
eth
o
d
s
,
s
u
ch
as
f
ea
tu
r
e
s
elec
tio
n
an
d
NI
R
S
d
ata
tr
an
s
f
o
r
m
atio
n
.
T
h
e
co
m
b
i
n
a
tio
n
o
f
s
ev
er
al
m
eth
o
d
s
,
ea
ch
o
f
wh
ich
h
as
b
ee
n
p
r
o
v
en
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
alg
o
r
ith
m
,
is
ex
p
ec
ted
t
o
b
e
ab
le
to
im
p
r
o
v
e
m
o
r
e.
A
n
ex
am
p
le
o
f
ap
p
ly
in
g
c
o
m
b
in
atio
n
s
is
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[
1
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.
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4
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b
.
a
c
.
id
.
Evaluation Warning : The document was created with Spire.PDF for Python.