C
omp
u
te
r
S
c
i
e
n
c
e
an
d
I
n
for
mati
on
T
e
c
h
n
ol
ogi
e
s
V
ol
.
6
,
N
o
.
3
,
N
ove
m
b
e
r
2025
,
p
p.
34
6
~
35
4
IS
S
N
:
2722
-
3221
,
D
O
I
:
10.
1
1591
/
c
s
i
t
.
v
6
i
3
.
pp
34
6
-
35
4
346
Jou
r
n
al
h
o
m
e
pa
ge
:
ht
t
p:
/
/
i
ae
s
pr
i
m
e
.
c
om
/
i
nd
e
x
.
php
/
c
s
i
t
Pr
e
d
i
c
t
i
v
e
m
o
d
e
l
f
o
r
h
i
g
h
-
r
i
sk
h
e
a
l
t
h
c
a
r
e
c
l
i
e
n
t
s
a
n
d
c
l
a
i
m
s
f
r
e
q
u
e
n
c
y
Le
n
i
as
Zh
ou
,
M
ai
n
fo
r
d
M
u
tan
d
ava
r
i
,
Lu
c
i
a
M
aton
d
or
a
S
c
h
o
o
l
o
f
S
c
i
e
n
c
e
In
fo
rm
a
t
i
o
n
a
n
d
T
e
c
h
n
o
l
o
g
y
,
H
a
ra
re
In
s
t
i
t
u
t
e
o
f
T
e
c
h
n
o
l
o
g
y
,
H
a
ra
re
,
Z
i
m
b
a
b
w
e
A
r
ti
c
l
e
I
n
fo
A
BS
TR
A
C
T
Ar
t
i
c
l
e
h
i
s
t
or
y
:
Re
c
e
i
v
e
d
7
J
un
,
2025
Re
vi
s
e
d
27
J
un
,
2025
A
c
c
e
pt
e
d
J
u
l
3
,
2025
G
l
oba
l
he
a
l
t
h
c
a
r
e
s
p
e
nd
i
ng
s
ur
g
e
d
t
o
a
pp
r
ox
i
m
a
t
e
l
y
U
S
D
9.
8
t
r
i
l
l
i
on
i
n
t
he
a
f
t
e
r
m
a
t
h
of
t
h
e
C
O
V
I
D
-
19
p
a
nd
e
m
i
c
,
i
n
t
e
n
s
i
f
yi
ng
t
he
ne
e
d
f
or
e
f
f
e
c
t
i
ve
r
i
s
k
m
a
n
a
ge
m
e
nt
s
t
r
a
t
e
g
i
e
s
i
n
he
a
l
t
hc
a
r
e
i
ns
u
r
a
n
c
e
.
T
h
i
s
s
t
u
dy
p
r
opo
s
e
s
a
pr
e
d
i
c
t
i
ve
m
o
de
l
de
s
i
gne
d
t
o
i
de
n
t
i
f
y
hi
gh
-
r
i
s
k
c
l
i
e
nt
s
f
or
t
i
m
e
l
y
t
a
r
ge
t
e
d
i
nt
e
r
ve
n
t
i
ons
a
nd
t
o
f
or
e
c
a
s
t
c
l
a
i
m
s
f
r
e
qu
e
nc
y
f
o
r
op
t
i
m
i
z
e
d
r
e
s
our
c
e
a
l
l
oc
a
t
i
on
.
A
r
e
a
l
-
w
or
l
d
c
l
a
i
m
s
da
t
a
s
e
t
f
r
om
a
he
a
l
t
hc
a
r
e
i
ns
u
r
a
nc
e
p
r
ov
i
de
r
w
a
s
u
t
i
l
i
z
e
d
.
B
a
ye
s
i
a
n
op
t
i
m
i
z
a
t
i
on
w
a
s
e
m
p
l
oy
e
d
t
o
e
n
ha
n
c
e
d
a
t
a
l
a
b
e
l
l
i
ng.
A
de
e
p
l
e
a
r
ni
n
g
(
D
L
)
m
o
de
l
w
i
t
h
s
i
gm
oi
d
a
c
t
i
va
t
i
on
w
a
s
u
s
e
d
t
o
c
l
a
s
s
i
f
y
hi
gh
-
r
i
s
k
c
l
i
e
nt
s
,
w
hi
l
e
a
r
e
g
r
e
s
s
i
o
n
m
od
e
l
f
or
e
c
a
s
t
e
d
c
l
a
i
m
s
f
r
e
qu
e
nc
y
.
T
h
e
m
od
e
l
w
a
s
t
r
a
i
n
e
d
a
n
d
va
l
i
da
t
e
d
,
a
nd
ga
ve
a
n
a
c
c
ur
a
c
y
of
9
7
%
,
a
pr
e
c
i
s
i
o
n
of
9
5.
2%
,
a
r
e
c
a
l
l
of
98
.
1
%
a
nd
a
n
F
1
-
s
c
or
e
o
f
9
6.
6%
.
T
he
r
e
s
u
l
t
s
c
onf
i
r
m
e
d
t
he
m
ode
l
’
s
a
c
c
u
r
a
c
y
i
n
i
d
e
nt
i
f
yi
ng
h
i
g
h
-
r
i
s
k
c
l
i
e
n
t
s
a
nd
i
t
s
a
bi
l
i
t
y
t
o
pr
ov
i
de
r
e
l
i
a
bl
e
f
o
r
e
c
a
s
t
i
ng
of
f
ut
ur
e
c
l
a
i
m
s
f
r
e
que
nc
y
.
I
m
po
r
t
a
nt
l
y
,
t
h
e
m
od
e
l
a
l
s
o
p
r
ov
i
de
d
t
he
r
e
a
s
on
be
h
i
n
d
i
t
s
c
l
a
s
s
i
f
i
c
a
t
i
on
d
e
c
i
s
i
on
,
e
nha
nc
i
n
g
t
r
a
ns
p
a
r
e
nc
y
a
n
d
t
r
u
s
t
.
T
hi
s
r
e
s
e
a
r
c
h
pr
ovi
de
s
va
l
u
a
bl
e
d
a
t
a
-
d
r
i
ve
n
i
n
s
i
gh
t
s
t
o
bo
t
h
t
he
he
a
l
t
h
c
a
r
e
i
n
s
u
r
e
r
s
a
n
d
c
l
i
e
n
t
s
,
g
i
v
i
ng
t
he
m
t
he
pow
e
r
t
o
s
t
a
y
a
he
a
d
i
n
m
a
na
gi
n
g
ke
y
r
i
s
k
s
,
w
hi
c
h
u
l
t
i
m
a
t
e
l
y
r
e
du
c
e
s
t
h
e
c
o
s
t
of
h
e
a
l
t
hc
a
r
e
i
ns
u
r
a
nc
e
.
T
h
i
s
w
o
r
k
c
ont
r
i
bu
t
e
d
a
s
c
a
l
a
bl
e
a
nd
i
n
t
e
r
pr
e
t
a
bl
e
s
o
l
ut
i
on
f
or
r
i
s
k
pr
e
di
c
t
i
on
i
n
he
a
l
t
hc
a
r
e
i
ns
ur
a
nc
e
.
K
e
y
w
or
d
s
:
Ba
ye
s
i
a
n
o
p
t
i
m
i
z
a
t
i
on
a
l
gor
i
t
h
m
Cl
a
i
m
s
fre
q
ue
n
c
y
D
e
e
p
l
e
a
rn
i
ng
H
e
a
l
t
hc
a
r
e
i
ns
ura
nc
e
H
i
gh
-
ri
s
k
c
l
i
e
nt
s
P
re
di
c
t
i
ve
m
o
de
l
i
ng
T
hi
s
i
s
an
op
e
n
ac
c
e
s
s
ar
t
i
c
l
e
u
nde
r
t
he
C
C
B
Y
-
SA
l
i
c
e
n
s
e
.
Cor
r
e
s
pon
di
n
g
Au
t
h
or
:
L
e
n
i
a
s
Z
ho
u
S
c
hool
of
S
c
i
e
n
c
e
a
n
d
I
nfor
m
a
t
i
on
T
e
c
hnol
ogy
,
H
a
ra
r
e
I
ns
t
i
t
ut
e
of
T
e
c
hn
ol
og
y
Be
l
v
e
d
e
re
,
H
a
ra
r
e
,
Z
i
m
ba
bw
e
E
m
a
i
l
:
z
ho
ul
e
ni
a
s
@
g
m
a
i
l
.
c
o
m
1.
I
N
TR
O
D
U
C
TI
O
N
In
t
od
a
y’s
dyn
a
m
i
c
bus
i
n
e
s
s
e
n
vi
ron
m
e
nt
,
s
us
t
a
i
n
a
b
i
l
i
t
y
a
nd
profi
t
a
b
i
l
i
t
y
a
r
e
c
l
os
e
l
y
t
i
e
d
t
o
e
ff
e
c
t
i
v
e
ri
s
k
m
a
n
a
ge
m
e
nt
,
p
a
rt
i
c
u
l
a
r
l
y
w
i
t
h
i
n
t
he
he
a
l
t
hc
a
r
e
i
ns
ura
nc
e
s
e
c
t
or
.
T
he
e
a
rl
y
i
d
e
nt
i
fi
c
a
t
i
o
n
of
hi
g
h
-
ri
s
k
c
l
i
e
nt
s
i
s
no
l
ong
e
r
a
l
uxury
but
a
n
e
c
e
s
s
it
y
,
e
na
b
l
i
ng
i
ns
u
re
rs
t
o
m
i
t
i
ga
t
e
fi
n
a
n
c
i
a
l
s
t
r
a
i
n
a
n
d
i
m
prov
e
h
e
a
l
t
h
out
c
o
m
e
s
,
a
l
l
oc
a
t
e
re
s
o
urc
e
s
s
t
ra
t
e
gi
c
a
l
l
y
,
a
n
d
s
e
t
pr
e
m
i
u
m
s
a
c
c
u
ra
t
e
l
y
.
H
ow
e
v
e
r,
t
r
a
di
t
i
on
a
l
ri
s
k
i
de
n
t
i
f
i
c
a
t
i
on
m
e
t
hods
oft
e
n
f
a
l
l
s
hort
i
n
a
ddre
s
s
i
ng
t
h
e
c
om
p
l
e
xi
t
i
e
s
of
m
od
e
rn
h
e
a
l
t
h
c
a
re
i
ns
ura
n
c
e
l
a
nds
c
a
p
e
s
.
T
he
y
t
ypi
c
a
l
l
y
fo
c
us
on
pre
di
c
t
i
ng
hi
gh
-
c
os
t
c
l
i
e
nt
s
w
i
t
hout
of
fe
r
i
ng
i
ns
i
ght
s
i
n
t
o
c
l
a
i
m
s
fre
qu
e
nc
y
,
w
hi
c
h
i
s
e
qu
a
l
l
y
c
r
i
t
i
c
a
l
for
c
o
m
pr
e
he
ns
i
ve
r
i
s
k
prof
i
l
i
ng
a
nd
f
i
na
n
c
i
a
l
p
l
a
nn
i
ng
.
F
u
rt
h
e
rm
ore
,
t
he
y
a
r
e
fr
e
qu
e
nt
l
y
c
ha
l
l
e
nge
d
by
ove
r
fi
t
t
i
n
g,
l
i
m
i
t
e
d
i
n
t
e
rpre
t
a
bi
l
i
t
y,
a
n
d
r
e
l
i
a
n
c
e
on
s
m
a
l
l
or
s
ynt
h
e
t
i
c
da
t
a
s
e
t
s
,
w
hi
c
h
und
e
r
m
i
n
e
t
h
e
i
r
r
e
l
i
a
b
i
l
i
t
y
.
T
hi
s
pa
pe
r
p
rop
os
e
d
a
pr
e
di
c
t
i
v
e
m
od
e
l
t
ha
t
i
n
t
e
gra
t
e
s
a
dva
n
c
e
d
m
a
c
hi
n
e
l
e
a
rn
i
ng
(M
L
)
t
e
c
hn
i
qu
e
s
t
o
e
nha
n
c
e
bo
t
h
t
he
pr
e
c
i
s
i
on
of
ri
s
k
i
d
e
nt
i
fi
c
a
t
i
o
n
a
nd
t
h
e
for
e
c
a
s
t
i
ng
of
c
l
a
i
m
s
f
r
e
qu
e
n
c
y.
By
l
e
v
e
ra
g
i
ng
r
obus
t
d
a
t
a
-
dr
i
ve
n
m
e
t
h
odo
l
ogi
e
s
,
t
he
m
od
e
l
a
i
m
s
t
o
s
uppor
t
m
o
re
i
nfor
m
e
d
de
c
i
s
i
on
-
m
a
ki
ng
a
nd
f
os
t
e
r
r
e
s
i
l
i
e
n
c
e
i
n
h
e
a
l
t
h
c
a
re
i
ns
ur
a
n
c
e
ope
ra
t
i
ons
.
Evaluation Warning : The document was created with Spire.PDF for Python.
Com
pu
t
S
c
i
Inf
T
e
c
h
nol
IS
S
N
:
2722
-
3221
P
r
e
di
c
t
i
v
e
m
o
de
l
f
or
h
i
gh
-
r
i
s
k
h
e
a
l
t
h
c
ar
e
c
l
i
e
nt
s
and
c
l
ai
m
s
f
r
e
qu
e
nc
y
(
L
e
ni
as
Z
hou
)
347
2.
R
ELA
TED
WO
R
K
T
he
r
e
a
re
w
orks
t
h
a
t
w
e
r
e
do
ne
by
o
t
h
e
r
r
e
s
e
a
rc
h
e
rs
o
n
t
he
a
ppl
i
c
a
t
i
on
of
pre
di
c
t
i
ve
a
n
a
l
y
t
i
c
s
w
i
t
hi
n
t
he
d
om
a
i
ns
of
bus
i
n
e
s
s
a
nd
he
a
l
t
hc
a
re
.
T
he
s
e
pri
or
s
t
udi
e
s
e
m
pl
oy
e
d
d
i
ve
rs
e
m
e
t
hodo
l
ogi
e
s
a
nd
e
nc
o
unt
e
re
d
a
r
a
ng
e
of
c
ha
l
l
e
nge
s
.
T
a
b
l
e
1
pre
s
e
nt
s
a
s
ynt
he
s
i
z
e
d
o
ve
rv
i
e
w
o
f
s
e
l
e
c
t
e
d
s
t
ud
i
e
s
a
l
ong
w
i
t
h
t
he
i
r
r
e
s
pe
c
t
i
ve
l
i
m
i
t
a
t
i
ons
.
T
a
b
l
e
1
.
S
u
m
m
a
ry
of
pri
o
r
w
or
ks
on
pr
e
di
c
t
i
ve
a
na
l
yt
i
c
s
i
n
h
e
a
l
t
h
i
ns
ur
a
nc
e
F
o
c
u
s
a
re
a
Re
fe
re
n
c
e
a
u
t
h
o
rs
Co
m
m
o
n
m
e
ri
t
s
Co
m
m
o
n
l
i
m
i
t
a
t
i
o
n
s
P
re
d
i
c
t
i
v
e
a
n
a
l
y
t
i
c
s
i
n
b
u
s
i
n
e
s
s
a
n
d
h
e
a
l
t
h
c
a
r
e
[1
]
–
[3
]
Bro
a
d
a
p
p
l
i
c
a
t
i
o
n
.
Im
p
ro
v
e
d
o
u
t
c
o
m
e
s
.
S
t
ra
t
e
g
i
c
i
n
s
i
g
h
t
s
.
L
a
c
k
o
f
e
m
p
i
ri
c
a
l
v
a
l
i
d
a
t
i
o
n
e
v
i
d
e
n
c
e
.
H
i
g
h
d
e
p
l
o
y
m
e
n
t
c
o
s
t
s
.
D
a
t
a
q
u
a
l
i
t
y
i
s
s
u
e
s
H
e
a
l
t
h
i
n
s
u
ra
n
c
e
c
o
s
t
s
a
n
d
c
l
a
i
m
p
re
d
i
c
t
i
o
n
[3
]
–
[7
]
H
i
g
h
p
re
d
i
c
t
i
o
n
a
c
c
u
ra
c
y
.
W
i
d
e
m
o
d
e
l
c
o
m
p
a
ri
s
o
n
.
E
t
h
i
c
a
l
A
I
i
n
t
e
g
ra
t
i
o
n
.
O
v
e
rfi
t
t
i
n
g
,
l
i
m
i
t
e
d
i
n
t
e
rp
re
t
a
b
i
l
i
t
y
,
a
n
d
t
h
e
u
s
e
o
f
s
m
a
l
l
a
n
d
s
y
n
t
h
e
t
i
c
d
a
t
a
s
e
t
s
.
Ri
s
k
a
s
s
e
s
s
m
e
n
t
a
n
d
h
i
g
h
u
t
i
l
i
z
a
t
i
o
n
p
re
d
i
c
t
i
o
n
[6
],
[8
]
–
[1
2
]
Re
a
l
-
w
o
rl
d
re
l
e
v
a
n
c
e
,
l
a
rg
e
d
a
t
a
s
e
t
s
,
a
n
d
i
m
p
ro
v
e
d
ri
s
k
p
re
d
i
c
t
i
o
n
.
Re
s
o
u
rc
e
-
i
n
t
e
n
s
i
v
e
,
m
i
s
s
i
n
g
v
a
ri
a
b
l
e
s
,
a
n
d
l
i
m
i
t
e
d
m
o
d
e
l
d
i
v
e
rs
i
t
y
.
F
e
w
r
i
s
k
fa
c
t
o
rs
w
e
re
c
o
n
s
i
d
e
re
d
d
u
e
t
o
s
y
s
t
e
m
l
i
m
i
t
a
t
i
o
n
s
.
N
o
i
s
e
fro
m
o
v
e
rs
a
m
p
l
i
n
g
.
S
y
s
t
e
m
i
c
re
v
i
e
w
s
a
n
d
c
o
m
p
a
ra
t
i
v
e
s
t
u
d
i
e
s
[1
3
]
–
[1
6
]
Co
m
p
re
h
e
n
s
i
v
e
m
o
d
e
l
c
o
v
e
ra
g
e
,
t
i
m
e
s
e
ri
e
s
fo
re
c
a
s
t
i
n
g
,
a
n
d
re
a
l
-
w
o
rl
d
d
a
t
a
u
s
a
g
e
.
L
i
m
i
t
e
d
e
x
p
l
a
i
n
a
b
i
l
i
t
y
.
U
s
e
d
s
h
o
rt
t
i
m
e
fra
m
e
s
a
n
d
h
i
g
h
s
e
n
s
i
t
i
v
i
t
y
t
o
o
u
t
l
i
e
rs
.
H
i
g
h
ri
s
k
o
f
b
i
a
s
.
L
i
m
i
t
e
d
c
l
i
n
i
c
a
l
i
m
p
l
e
m
e
n
t
a
t
i
o
n
.
A
d
v
a
n
c
e
d
A
I
i
n
t
e
g
ra
t
i
o
n
i
n
h
e
a
l
t
h
c
a
re
[1
7
],
[1
8
]
H
i
g
h
a
c
c
u
ra
c
y
a
n
d
re
a
l
-
t
i
m
e
u
p
d
a
t
e
s
.
M
u
l
t
i
m
o
d
a
l
d
a
t
a
i
n
t
e
g
ra
t
i
o
n
.
Re
g
i
o
n
a
l
d
a
t
a
l
i
m
i
t
a
t
i
o
n
s
a
n
d
i
m
b
a
l
a
n
c
e
s
i
n
r
a
re
c
o
n
d
i
t
i
o
n
s
.
F
ra
u
d
d
e
t
e
c
t
i
o
n
i
n
h
e
a
l
t
h
c
a
re
[1
9
]
H
i
g
h
fra
u
d
d
e
t
e
c
t
i
o
n
a
c
c
u
ra
c
y
o
n
t
h
e
u
s
e
o
f
m
o
d
e
l
s
.
S
m
a
l
l
s
a
m
p
l
e
s
i
z
e
.
L
i
m
i
t
e
d
n
u
m
b
e
r
o
f
fe
a
t
u
re
s
.
M
a
n
u
a
l
fe
a
t
u
re
e
n
g
i
n
e
e
ri
n
g
.
H
a
n
d
l
i
n
g
o
f
g
e
n
d
e
r
i
m
b
a
la
n
c
e
i
n
d
a
t
a
s
e
t
s
.
T
i
m
e
s
e
ri
e
s
s
t
a
t
i
s
t
i
c
a
l
a
n
a
l
y
s
i
s
o
f
c
l
a
i
m
s
[2
0
]
–
[2
2
]
Be
t
t
e
r
h
a
n
d
l
i
n
g
o
f
s
k
e
w
e
d
d
a
t
a
.
Ch
a
l
l
e
n
g
e
s
w
i
t
h
c
e
n
s
o
re
d
d
a
t
a
.
Co
u
l
d
n
o
t
b
e
ge
n
e
ra
l
i
z
e
d
.
D
a
t
a
q
u
a
l
i
t
y
i
s
s
u
e
s
.
Re
s
p
o
n
s
i
b
l
e
A
I
i
n
h
e
a
l
t
h
c
a
re
[6
],
[2
3
]
Co
m
p
re
h
e
n
s
i
v
e
m
o
d
e
l
s
e
v
a
l
u
a
t
i
o
n
o
n
e
t
h
i
c
s
U
s
e
d
s
m
a
l
l
s
a
m
p
l
e
s
w
i
t
h
l
i
m
i
t
e
d
v
a
ri
a
b
l
e
s
.
G
e
n
e
ra
l
i
z
e
d
e
t
h
i
c
a
l
d
i
s
c
u
s
s
i
o
n
.
S
c
a
l
a
b
i
l
i
t
y
a
n
d
e
q
u
i
t
y
c
o
n
c
e
r
n
s
.
T
he
re
s
e
a
r
c
h
by
N
w
oke
[1]
a
n
d
N
n
a
m
di
[2]
e
x
a
m
i
n
e
d
t
h
e
a
ppl
i
c
a
t
i
on
of
pre
d
i
c
t
i
v
e
a
n
a
l
y
t
i
c
s
i
n
de
c
i
s
i
on
-
m
a
ki
ng
t
o
i
m
p
rove
h
e
a
l
t
h
c
a
r
e
out
c
o
m
e
s
.
T
he
i
r
f
i
ndi
n
gs
c
a
n
be
g
e
n
e
ra
l
i
z
e
d
t
o
m
os
t
s
c
e
na
r
i
os
be
c
a
us
e
t
h
e
re
w
a
s
a
s
i
gni
f
i
c
a
nt
i
m
prov
e
m
e
n
t
i
n
ou
t
c
o
m
e
s
due
t
o
e
a
r
l
y
de
t
e
c
t
i
o
n
a
nd
i
nt
e
rv
e
nt
i
on
[1],
[2]
.
H
ow
e
ve
r
,
t
h
e
a
ppro
a
c
h
l
a
c
ke
d
“
e
m
pi
r
i
c
a
l
va
l
i
d
a
t
i
on
”
d
ue
t
o
hi
gh
i
m
pl
e
m
e
nt
a
t
i
on
c
os
t
s
[1]
,
[2].
T
he
w
ork
of
T
h
a
kr
e
e
t
a
l
.
[3]
c
e
nt
e
re
d
on
t
he
pre
d
i
c
t
i
o
n
of
i
ns
ura
nc
e
c
os
t
s
a
nd
fr
a
ud
de
t
e
c
t
i
o
n
by
e
m
p
l
oyi
ng
ML
m
od
e
l
s
.
D
e
s
pi
t
e
t
he
h
i
gh
pr
e
di
c
t
i
on
a
c
c
ura
c
y
t
h
a
t
t
he
y
a
t
t
a
i
ne
d
,
t
he
m
ode
l
s
s
uffe
r
e
d
fr
om
ove
rf
i
t
t
i
ng
a
s
w
e
l
l
a
s
a
l
a
c
k
of
i
nt
e
rpre
t
a
bi
l
i
t
y
[3]
,
[20]
.
I
t
i
s
ke
y
t
o
be
a
b
l
e
t
o
kn
ow
how
t
he
m
o
de
l
s
a
r
e
a
rri
v
i
ng
a
t
t
he
i
r
de
c
i
s
i
ons
.
A
no
t
h
e
r,
b
ut
l
e
s
s
e
r
,
c
r
i
t
i
c
i
s
m
o
f
t
he
i
r
a
p
proa
c
h
w
a
s
t
he
r
e
l
i
a
n
c
e
on
s
m
a
l
l
a
nd
s
ynt
he
t
i
c
d
a
t
a
s
e
t
s
,
w
hi
c
h
A
l
a
m
a
nd
P
r
ybut
o
k
[6]
i
d
e
nt
i
fi
e
d
a
s
a
m
a
j
or
fa
c
t
or
for
ove
rfi
t
t
i
ng.
T
h
e
a
ppl
i
c
a
t
i
on
t
o
r
e
a
l
-
w
or
l
d
probl
e
m
s
w
a
s
i
l
l
us
t
ra
t
e
d
i
n
Ru
i
j
t
e
r
e
t
al
.
[
9]
a
nd
L
i
e
t
al
.
[10]
a
s
t
h
e
y
a
pp
l
i
e
d
s
i
z
a
bl
e
da
t
a
s
e
t
s
a
nd
a
c
h
i
e
v
e
d
e
nha
nc
e
d
ri
s
k
p
re
d
i
c
t
i
on
.
Bo
t
h
u
t
i
l
i
z
e
d
ML
m
od
e
l
s
for
r
i
s
k
s
t
r
a
t
i
fi
c
a
t
i
on
t
o
p
i
npo
i
nt
hi
gh
-
n
e
e
d
p
a
t
i
e
n
t
s
.
T
he
a
ut
ho
rs
f
a
c
e
d
c
h
a
l
l
e
nge
s
i
n
d
e
a
l
i
n
g
w
i
t
h
m
i
s
s
i
ng
v
a
ri
a
b
l
e
s
i
n
t
he
re
a
l
-
w
orl
d
m
e
d
i
c
a
l
d
a
t
a
.
T
he
re
a
l
-
w
orl
d
m
e
di
c
a
l
d
a
t
a
r
e
c
o
rds
d
e
m
ons
t
ra
t
e
i
m
ba
l
a
n
c
e
s
be
c
a
us
e
hi
gh
-
c
os
t
s
c
e
n
a
ri
os
a
re
l
e
s
s
fre
q
ue
n
t
t
ha
n
s
t
a
nd
a
rd
a
nd
l
ow
-
c
os
t
s
i
t
u
a
t
i
o
ns
[9]
,
[10
],
[
12]
.
T
h
e
ot
he
r
c
ha
l
l
e
nge
t
h
a
t
i
s
c
o
m
m
o
n
a
m
ong
t
h
e
s
e
r
e
s
e
a
r
c
h
pa
pe
rs
i
s
t
he
i
s
s
ue
of
h
i
gh
c
o
m
pu
t
i
n
g
pow
e
r
t
ha
t
w
a
s
re
q
ui
r
e
d
t
o
run
t
he
m
od
e
l
s
.
T
h
e
s
t
udy
by
A
l
ot
a
i
b
i
[11]
foc
us
e
d
on
t
he
us
e
of
pr
e
d
i
c
t
i
v
e
a
na
l
yt
i
c
s
t
o
i
de
n
t
i
f
y
r
i
s
k
fa
c
t
o
rs
w
i
t
hi
n
o
rga
n
i
z
a
t
i
ons
.
T
hi
s
w
ork
m
a
d
e
re
m
a
rk
a
bl
e
pro
gre
s
s
i
n
p
i
c
k
i
ng
l
e
ga
l
a
n
d
r
e
gul
a
t
o
ry
r
i
s
ks
a
s
w
e
l
l
a
s
i
nfo
rm
a
t
i
on
t
e
c
hno
l
ogy
ri
s
ks
,
b
ut
t
h
e
re
w
a
s
a
ne
e
d
for
l
a
rg
e
r
da
t
a
s
e
t
s
.
A
l
ot
a
i
b
i
[11]
us
e
d
de
c
i
s
i
on
t
r
e
e
s
(D
T
)
,
l
i
ne
a
r
t
ra
ns
for
m
a
t
i
on
(
LT
),
a
nd
n
e
ur
a
l
ne
t
w
ork
s
(N
N
)
a
nd
po
i
nt
e
d
out
t
ha
t
t
he
a
p
proa
c
h
w
a
s
w
ort
h
t
ryi
ng
us
i
ng
o
t
he
r
m
ode
l
s
.
F
ore
c
a
s
t
i
n
g
f
ut
ur
e
he
a
l
t
h
c
l
a
i
m
s
va
l
ue
s
w
a
s
don
e
i
n
t
h
e
w
ork
of
M
a
s
ha
s
h
a
e
t
a
l
[
13]
us
i
ng
a
n
a
ut
or
e
gr
e
s
s
i
ve
i
n
t
e
g
ra
t
e
d
m
ov
i
ng
a
v
e
ra
ge
(
A
RIM
A
)
m
ode
l
.
T
he
y
m
a
n
a
ge
d
t
o
for
e
c
a
s
t
h
e
a
l
t
h
c
a
r
e
t
re
nds
a
n
d
fut
ur
e
c
l
a
i
m
v
a
l
ue
s
fro
m
t
i
m
e
s
e
ri
e
s
da
t
a
.
T
he
y
ha
d
c
h
a
l
l
e
n
ge
s
w
i
t
h
out
l
i
e
rs
.
T
h
e
for
e
c
a
s
t
i
ng
a
ppro
a
c
h
us
e
d
i
n
[1
3]
i
s
i
de
a
l
l
y
a
pp
l
i
c
a
b
l
e
t
o
l
i
ne
a
r
da
t
a
a
nd
m
a
y
n
ot
c
a
pt
ur
e
no
nl
i
n
e
ar
,
c
om
p
l
e
x
p
a
t
t
e
rns
.
M
ode
l
c
o
m
p
a
ri
s
on
w
a
s
a
l
s
o
do
ne
i
n
t
h
e
w
o
rk
of
[9]
a
nd
[14]
.
T
he
w
ork
of
A
l
oy
uni
[14]
fi
rs
t
c
om
p
a
r
e
d
M
L
a
nd
de
e
p
l
e
a
rni
ng
(D
L
)
a
nd
hi
ghl
i
ght
e
d
t
ha
t
M
L
re
q
ui
r
e
d
m
or
e
f
e
a
t
ure
e
ngi
n
e
e
r
i
ng
a
n
d
a
s
s
i
s
t
a
n
c
e
fro
m
t
he
dom
a
i
n
e
xpe
r
t
s
.
O
n
t
he
ot
h
e
r
h
a
nd
,
D
L
c
ou
l
d
l
e
a
rn
fr
om
ra
w
da
t
a
a
nd
w
a
s
fo
und
t
o
b
e
m
ore
e
ffe
c
t
i
ve
on
l
a
rg
e
da
t
a
s
e
t
s
[14]
.
I
n
c
o
m
pa
r
i
ng
c
onvo
l
ut
i
ona
l
ne
ur
a
l
n
e
t
w
o
r
k
(
CN
N
),
s
uppor
t
ve
c
t
or
m
a
c
h
i
n
e
(S
V
M
),
g
e
ne
r
a
t
i
ve
a
dve
rs
a
ri
a
l
n
e
t
w
or
k
(G
A
N
)
,
a
n
d
r
a
ndo
m
f
ore
s
t
(
RF
)
a
c
r
os
s
t
w
e
nt
y
p
ubl
i
c
a
t
i
ons
,
A
l
oyuni
[14]
found
t
h
a
t
CN
N
-
ba
s
e
d
m
ode
l
s
,
e
s
pe
c
i
a
l
l
y
w
h
e
n
c
om
b
i
ne
d
w
i
t
h
e
ns
e
m
bl
e
m
e
t
hods
or
G
A
N
s
,
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2722
-
3221
Com
pu
t
S
c
i
Inf
T
e
c
h
nol
,
V
o
l
.
6
,
N
o
.
3
,
N
ov
e
m
be
r
2025
:
346
-
354
348
w
e
re
fr
e
que
nt
l
y
us
e
d
for
i
m
a
g
e
-
b
a
s
e
d
di
a
gnos
i
s
w
i
t
h
a
h
i
g
h
a
c
c
ura
c
y
a
nd
s
e
ns
i
t
i
vi
t
y.
H
ybr
i
d
m
ode
l
s
(f
or
e
xa
m
pl
e
,
CN
N
+
M
A
F
W
,
CN
N
+
G
A
N
)
w
e
re
foun
d
t
o
g
i
v
e
i
m
prov
e
d
pe
rf
orm
a
nc
e
a
s
t
h
e
y
c
o
m
bi
n
e
d
fe
a
t
ur
e
s
e
l
e
c
t
i
on
,
o
pt
i
m
i
z
a
t
i
on
,
a
nd
c
l
a
s
s
i
f
i
c
a
t
i
on
t
e
c
h
ni
qu
e
s
.
T
ra
d
i
t
i
o
na
l
m
od
e
l
s
l
i
ke
S
V
M
a
n
d
RF
w
e
re
us
e
d
i
n
c
om
b
i
n
a
t
i
on
w
i
t
h
D
L
for
c
l
a
s
s
i
fi
c
a
t
i
on
t
a
s
ks
or
for
s
t
ru
c
t
ur
e
d
da
t
a
,
but
w
i
t
h
ov
e
rf
i
t
t
i
ng
,
e
xp
l
a
i
na
bi
l
i
t
y
,
a
nd
pre
pro
c
e
s
s
i
n
g
c
ha
l
l
e
ng
e
s
[3]
,
[9]
,
[1
2],
[14
]
.
O
n
t
h
e
re
s
pons
i
bl
e
us
e
of
a
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
g
e
nc
e
(A
I)
i
n
he
a
l
t
hc
a
r
e
,
A
k
t
e
r
e
t
al
.
[
23]
w
a
rns
a
b
out
bi
a
s
t
ha
t
c
a
n
be
a
m
p
l
i
f
i
e
d
by
A
I
m
o
de
l
s
.
T
hi
s
s
t
udy
p
oi
n
t
e
d
out
t
h
a
t
b
i
a
s
m
a
y
l
e
a
d
t
o
u
ne
qu
a
l
t
re
a
t
m
e
nt
or
m
i
s
di
a
gnos
i
s
,
p
a
rt
i
c
ul
a
rl
y
i
n
un
de
rr
e
pr
e
s
e
n
t
e
d
popul
a
t
i
ons
.
T
he
ne
e
d
for
t
h
e
de
ve
l
opm
e
n
t
of
A
I
m
od
e
l
s
t
ha
t
a
re
f
a
i
r
a
nd
i
nc
l
us
i
v
e
w
a
s
e
m
ph
a
s
i
z
e
d
[
17]
.
T
h
e
e
l
e
c
t
ro
ni
c
he
a
l
t
h
r
e
c
ords
(E
H
R
)
c
on
t
a
i
n
s
e
ns
i
t
i
ve
p
e
rs
on
a
l
da
t
a
t
ha
t
r
e
qui
re
s
s
t
ri
c
t
prot
e
c
t
i
o
n
t
o
c
om
pl
y
w
i
t
h
r
e
gu
l
a
t
i
ons
l
i
k
e
t
he
H
e
a
l
t
h
Ins
ur
a
n
c
e
P
ort
a
bi
l
i
t
y
a
nd
A
c
c
oun
t
a
bi
l
i
t
y
A
c
t
(
H
IP
A
A)
a
nd
t
h
e
G
e
ne
r
a
l
D
a
t
a
P
rot
e
c
t
i
o
n
R
e
g
ul
a
t
i
on
(
GDPR
)
[6]
,
[7
],
[18]
.
T
he
s
t
ud
y
by
[3]
a
nd
[9]
a
dv
oc
a
t
e
s
for
e
xpl
a
i
n
a
bl
e
A
I
s
ys
t
e
m
s
t
h
a
t
a
l
l
ow
us
e
rs
t
o
u
nde
rs
t
a
nd
how
d
e
c
i
s
i
ons
a
r
e
m
a
d
e
.
T
he
re
s
e
a
rc
h
e
rs
bot
h
c
onc
l
ud
e
d
by
re
c
o
m
m
e
nd
i
ng
c
o
nt
i
nuous
re
fi
n
e
m
e
nt
a
nd
m
o
ni
t
or
i
n
g
o
f
A
I
s
ys
t
e
m
s
t
o
m
a
i
nt
a
i
n
t
r
us
t
a
nd
e
ff
e
c
t
i
ve
n
e
s
s
.
T
h
e
w
or
k
of
o
t
he
r
r
e
s
e
a
rc
he
rs
on
t
he
s
a
m
e
not
e
hi
gh
l
i
ght
e
d
t
he
n
e
e
d
for
s
t
ruc
t
ure
d
fr
a
m
e
w
orks
a
nd
c
he
c
kl
i
s
t
s
t
o
gui
d
e
re
s
po
ns
i
bl
e
de
pl
oy
m
e
n
t
o
f
A
I
m
o
de
l
s
or
s
ys
t
e
m
s
[17]
,
[
23]
.
3.
M
ET
H
O
D
A
s
ys
t
e
m
at
i
c
a
ppro
a
c
h
w
a
s
us
e
d
t
o
d
e
ve
l
op
a
n
d
e
va
l
ua
t
e
a
pr
e
di
c
t
i
ve
m
o
de
l
for
i
de
n
t
i
fy
i
ng
hi
gh
-
ri
s
k
m
e
d
i
c
a
l
fund
c
l
i
e
nt
s
a
n
d
fo
re
c
a
s
t
i
ng
c
l
a
i
m
s
fre
q
ue
n
c
y.
T
he
proc
e
s
s
i
nv
ol
v
e
d
c
ol
l
e
c
t
i
ng
da
t
a
,
pre
p
roc
e
s
s
i
ng
,
a
nd
e
x
p
l
or
a
t
o
ry
da
t
a
a
na
l
ys
i
s
(
E
D
A
)
,
w
h
i
c
h
i
nc
l
ud
e
d
l
a
be
l
l
i
ng
t
h
e
da
t
a
,
t
r
a
i
n
i
ng
t
he
m
od
e
l
,
va
l
i
d
a
t
i
on
,
a
nd
de
pl
o
ym
e
nt
.
T
he
de
s
i
gn
w
a
s
c
o
rre
l
a
t
i
on
a
l
,
w
i
t
h
a
pr
e
d
i
c
t
i
v
e
purpo
s
e
.
W
hi
l
e
t
r
a
di
t
i
on
a
l
c
orr
e
l
a
t
i
on
a
l
r
e
s
e
a
rc
h
pri
m
a
ri
l
y
s
e
e
ks
t
o
d
e
t
e
rm
i
ne
t
he
e
x
t
e
n
t
of
r
e
l
a
t
i
ons
hi
ps
b
e
t
w
e
e
n
t
w
o
or
m
or
e
va
r
i
a
b
l
e
s
us
i
ng
s
t
a
t
i
s
t
i
c
a
l
d
a
t
a
,
t
he
ul
t
i
m
a
t
e
ob
j
e
c
t
i
ve
h
e
r
e
i
s
t
o
l
e
ve
r
a
g
e
t
h
e
s
e
i
d
e
nt
i
fi
e
d
re
l
a
t
i
ons
hi
ps
t
o
fo
re
c
a
s
t
fu
t
ur
e
out
c
om
e
s
.
T
hi
s
di
d
not
i
nv
ol
v
e
de
s
c
ri
b
i
ng
e
xi
s
t
i
ng
c
onn
e
c
t
i
ons
bu
t
qua
nt
i
fyi
n
g
t
he
m
t
o
e
na
b
l
e
r
e
l
i
a
b
l
e
p
re
d
i
c
t
i
ons
of
h
i
gh
-
r
i
s
k
c
l
i
e
nt
s
a
nd
fore
c
a
s
t
i
ng
c
l
a
i
m
s
fr
e
qu
e
nc
y
.
T
he
i
d
e
a
w
a
s
t
o
c
om
e
up
w
i
t
h
a
n
a
c
c
ur
a
t
e
m
ode
l
t
h
a
t
i
s
i
nt
e
rpre
t
a
bl
e
a
nd
s
c
a
l
a
b
l
e
for
r
e
a
l
-
wo
rl
d
a
ppl
i
c
a
t
i
ons
.
3.
1
.
O
u
r
a
p
p
r
oac
h
T
he
a
ppr
oa
c
h
us
e
d
i
n
t
hi
s
s
t
u
dy
fo
l
l
ow
e
d
a
d
e
du
c
t
i
ve
re
a
s
o
ni
ng
fra
m
e
w
ork.
W
e
b
e
g
a
n
by
e
xa
m
i
n
i
ng
c
ol
l
e
c
t
e
d
da
t
a
va
r
i
a
b
l
e
s
a
nd
c
ons
i
d
e
ri
n
g
t
h
e
e
x
i
s
t
i
ng
t
he
o
ri
e
s
.
T
he
c
onc
e
rn
w
a
s
not
a
bou
t
w
hi
c
h
va
ri
a
bl
e
s
a
re
re
l
a
t
e
d
but
ra
t
he
r
how
s
t
rong
l
y
a
nd
i
n
w
h
a
t
di
re
c
t
i
on
t
he
s
e
r
e
l
a
t
i
ons
hi
ps
e
x
i
s
t
t
o
e
na
b
l
e
t
h
e
ge
ne
r
a
t
i
on
of
r
e
l
i
a
b
l
e
fu
t
ure
pr
e
di
c
t
i
ons
.
F
i
gur
e
1
s
how
s
t
h
e
ove
rvi
e
w
o
f
t
he
r
e
s
e
a
rc
h
w
or
kfl
ow
t
ha
t
w
a
s
fo
l
l
ow
e
d.
3.
2
.
D
atas
e
t
W
e
us
e
d
a
re
a
l
-
w
o
rl
d
d
a
t
a
s
e
t
c
ont
a
i
n
i
ng
fi
v
e
y
e
a
rs
(2020
t
o
2024)
of
he
a
l
t
hc
a
re
c
l
a
i
m
s
.
T
hi
s
da
t
a
s
e
t
i
nc
l
ude
d
p
a
t
i
e
n
t
d
e
m
ogra
phi
c
s
,
t
r
e
a
t
m
e
nt
hi
s
t
ori
e
s
,
a
nd
c
l
a
i
m
ou
t
c
om
e
s
.
T
h
e
d
a
t
a
s
e
t
h
a
d
a
t
ot
a
l
of
t
h
i
rt
y
-
fi
v
e
(35)
fe
a
t
ure
s
a
nd
n
i
ne
hundr
e
d
a
nd
t
hi
r
t
y
-
fo
ur
t
hous
a
n
d
e
i
g
ht
hund
re
d
(934
,
800)
c
l
a
i
m
s
fro
m
on
e
hu
ndre
d
a
nd
t
e
n
t
hous
a
nd
a
nd
t
hr
e
e
(110
,
003)
uni
que
m
e
m
b
e
rs
.
T
h
e
va
l
u
e
of
c
l
a
i
m
s
ov
e
r
t
he
pe
ri
od
a
m
oun
t
e
d
t
o
one
hundre
d
a
nd
t
w
e
nt
y
-
f
our
m
i
l
l
i
o
n
fi
v
e
hu
ndre
d
t
ho
us
a
nd
U
n
i
t
e
d
S
t
a
t
e
s
dol
l
a
rs
(
US
D
124
,
50
0,
00
0
)
.
Z
i
m
b
a
bw
e
ope
ra
t
e
s
und
e
r
a
s
t
ru
c
t
ur
e
d
c
u
r
re
n
c
y
s
ys
t
e
m
w
i
t
h
t
h
e
Z
i
m
b
a
b
w
e
G
o
l
d
(
Z
i
G
)
b
e
i
ng
t
he
pri
m
a
ry
c
urr
e
n
c
y.
T
he
c
om
p
a
ny
t
ha
t
p
rovi
d
e
d
us
w
i
t
h
t
he
d
a
t
a
s
e
t
pr
e
f
e
r
s
t
o
r
e
port
its
fi
n
a
nc
i
a
l
s
i
n
U
S
D
.
If
a
pe
rs
on
pa
ys
for
s
e
rv
i
c
e
s
i
n
l
o
c
a
l
c
u
rre
n
c
y
(
Z
i
G
)
,
t
h
e
a
m
ount
i
s
c
onve
r
t
e
d
t
o
t
h
e
USD
e
qui
v
a
l
e
nt
us
i
ng
t
he
d
a
i
l
y
fore
i
gn
e
x
c
ha
nge
r
a
t
e
of
t
h
a
t
d
a
y
for
re
por
t
i
ng
purpos
e
s
.
T
hi
s
i
s
t
h
e
re
a
s
on
w
hy
our
d
a
t
a
s
e
t
va
l
ue
i
s
s
t
a
t
e
d
i
n
U
S
D
.
T
he
h
a
nd
l
i
ng
of
he
a
l
t
hc
a
r
e
c
l
a
i
m
s
da
t
a
i
s
i
m
port
a
n
t
for
d
e
ve
l
opi
n
g
a
rob
us
t
m
ode
l
.
T
h
e
pre
s
e
nc
e
of
i
rre
g
ul
a
r
t
i
m
e
s
e
ri
e
s
,
hi
g
h
d
i
m
e
ns
i
o
na
l
i
t
y
,
a
s
w
e
l
l
a
s
pr
i
va
c
y
c
on
c
e
r
ns
r
e
qui
r
e
s
c
a
r
e
fu
l
d
a
t
a
m
a
n
a
g
e
m
e
nt
.
F
o
r
t
hi
s
re
a
s
on,
t
he
a
p
proa
c
h
pri
ori
t
i
z
e
d
t
h
orough
d
a
t
a
c
l
e
a
n
i
n
g
a
n
d
s
t
ri
c
t
e
t
hi
c
a
l
h
a
nd
l
i
ng
a
s
m
uc
h
a
s
,
i
f
no
t
m
ore
t
ha
n
,
t
he
m
od
e
l
l
i
ng
pr
oc
e
s
s
i
t
s
e
l
f.
T
h
e
s
uc
c
e
s
s
of
t
h
e
p
re
di
c
t
i
ve
m
od
e
l
h
i
nge
d
on
e
f
fe
c
t
i
ve
ly
m
a
na
gi
ng
a
nd
t
r
a
ns
for
m
i
ng
t
he
c
o
m
pl
e
x,
n
oi
s
y
,
a
nd
s
e
ns
i
t
i
ve
r
e
a
l
-
w
o
rl
d
da
t
a
.
T
h
i
s
a
ppr
oa
c
h
e
x
a
m
i
n
e
d
t
h
e
fi
e
l
ds
a
n
d
groupe
d
t
he
m
a
c
c
ord
i
ng
t
o
t
h
e
i
r
r
e
l
e
va
nc
e
fo
r
pr
e
d
i
c
t
i
v
e
m
o
de
l
l
i
ng
,
a
s
s
how
n
i
n
T
a
bl
e
2
.
T
a
b
l
e
2
pro
vi
d
e
s
a
c
l
e
a
r
a
nd
org
a
n
i
z
e
d
ov
e
rv
i
e
w
o
f
t
h
e
d
a
t
a
fi
e
l
ds
t
ha
t
w
e
r
e
f
e
d
i
nt
o
t
h
e
pr
e
d
i
c
t
i
v
e
m
ode
l
.
T
h
i
s
l
i
s
t
i
n
g
of
f
i
e
l
ds
a
n
d
e
x
pl
a
i
n
i
ng
t
he
i
r
r
e
l
e
va
n
c
e
e
nha
nc
e
s
t
h
e
t
r
a
ns
pa
re
n
c
y
a
nd
r
e
prod
uc
i
bi
l
i
t
y
of
t
he
r
e
s
e
a
rc
h,
a
l
l
ow
i
ng
ot
h
e
r
r
e
s
e
a
rc
h
e
rs
t
o
bu
i
l
d
f
rom
t
h
e
pe
rfor
m
e
d
w
ork
.
It
a
l
s
o
de
m
ons
t
r
a
t
e
d
a
s
t
ro
ng
unde
rs
t
a
nd
i
ng
of
how
he
a
l
t
hc
a
r
e
d
a
t
a
t
r
a
ns
l
a
t
e
s
i
nt
o
m
e
a
n
i
ngfu
l
pre
d
i
c
t
ors
for
bot
h
h
i
gh
-
r
i
s
k
c
l
i
e
nt
i
de
n
t
i
f
i
c
a
t
i
on
a
nd
c
l
a
i
m
s
fr
e
que
nc
y
for
e
c
a
s
t
i
ng
[
24]
.
F
rom
t
h
e
35
fe
a
t
ur
e
s
i
n
t
ha
t
da
t
a
s
e
t
,
ou
r
a
ppr
oa
c
h
us
e
d
t
hre
e
(3
)
c
o
m
pound
f
e
a
t
ur
e
s
,
w
hi
c
h
a
re
di
re
c
t
i
ndi
c
a
t
ors
of
po
t
e
n
t
i
a
l
l
y
hi
gh
-
ri
s
k
c
l
i
e
nt
s
a
nd
c
a
n
a
s
s
i
s
t
i
n
for
e
c
a
s
t
i
ng
c
l
a
i
m
s
fre
que
n
c
y
.
T
h
e
t
hr
e
e
c
om
p
ound
f
e
a
t
ur
e
s
a
r
e
t
he
t
ot
a
l
a
m
oun
t
c
l
a
i
m
e
d,
t
h
e
t
ot
a
l
a
m
ou
nt
r
e
j
e
c
t
e
d
,
a
nd
t
he
nu
m
be
r
of
c
l
a
i
m
s
pe
r
Evaluation Warning : The document was created with Spire.PDF for Python.
Com
pu
t
S
c
i
Inf
T
e
c
h
nol
IS
S
N
:
2722
-
3221
P
r
e
di
c
t
i
v
e
m
o
de
l
f
or
h
i
gh
-
r
i
s
k
h
e
a
l
t
h
c
ar
e
c
l
i
e
nt
s
and
c
l
ai
m
s
f
r
e
qu
e
nc
y
(
L
e
ni
as
Z
hou
)
349
m
ont
h.
T
h
e
c
l
a
i
m
s
t
h
a
t
fi
n
a
n
c
i
a
l
l
y
s
t
r
a
i
n
t
he
h
e
a
l
t
h
c
a
r
e
i
ns
ura
nc
e
prov
i
de
rs
a
re
t
h
e
l
a
r
ge
a
m
oun
t
s
a
nd
t
h
e
fre
qu
e
nc
y
of
t
h
e
s
m
a
l
l
,
c
l
a
i
m
e
d
a
m
o
unt
s
.
T
h
e
re
j
e
c
t
e
d
c
l
a
i
m
s
a
l
s
o
a
ff
e
c
t
t
h
e
c
l
i
e
nt
,
w
ho
i
s
a
c
us
t
om
e
r
of
t
he
he
a
l
t
h
c
a
r
e
i
ns
ur
e
r
a
nd
i
s
of
c
on
c
e
r
n
i
n
fl
a
g
gi
n
g
r
i
s
k.
F
i
gure
1
.
R
e
s
e
a
rc
h
w
ork
fl
ow
T
a
b
l
e
2
.
H
e
a
l
t
h
c
a
r
e
c
l
a
i
m
s
d
a
t
a
f
i
e
l
ds
a
nd
t
h
e
i
r
r
e
l
e
va
n
c
e
t
o
pre
di
c
t
i
ve
m
ode
l
l
i
ng
Ca
t
e
g
o
ry
S
p
e
c
i
fi
c
fi
e
l
d
s
Re
l
e
v
a
n
c
e
fo
r
p
re
d
i
c
t
i
v
e
m
o
d
e
l
l
i
n
g
Cl
i
e
n
t
d
e
m
o
g
ra
p
h
i
c
s
A
g
e
,
g
e
n
d
e
r,
ra
c
e
,
a
d
d
re
s
s
,
d
e
p
ri
v
a
t
i
o
n
i
n
d
e
x
,
a
n
d
m
e
d
i
c
a
l
a
i
d
p
a
c
k
a
g
e
.
Co
rre
l
a
t
e
w
i
t
h
v
a
ri
o
u
s
h
e
a
l
t
h
c
o
n
d
i
t
i
o
n
s
a
n
d
h
e
a
l
t
h
c
a
re
n
e
e
d
s
,
i
n
fl
u
e
n
c
i
n
g
c
l
a
i
m
s
fre
q
u
e
n
c
y
,
a
n
d
ri
s
k
s
t
ra
t
i
fi
c
a
t
i
o
n
.
Cl
a
i
m
d
e
t
a
i
l
s
Cl
a
i
m
t
y
p
e
(
i
n
p
a
t
i
e
n
t
,
o
u
t
p
a
t
i
e
n
t
,
pha
rm
a
c
y
,
ra
d
i
o
l
o
g
y
,
e
n
ro
l
m
e
n
t
)
,
p
ro
v
i
d
e
r
i
d
e
n
t
i
fi
e
r,
a
n
d
d
a
t
e
o
f
s
e
rv
i
c
e
.
D
e
fi
n
e
t
h
e
n
a
t
u
re
a
n
d
t
i
m
i
n
g
o
f
h
e
a
l
t
h
c
a
re
i
n
t
e
ra
c
t
i
o
n
s
,
c
ru
c
i
a
l
fo
r
u
n
d
e
rs
t
a
n
d
i
n
g
s
e
rv
i
c
e
u
t
i
l
i
z
a
t
i
o
n
p
a
t
t
e
rn
s
,
a
n
d
i
d
e
n
t
i
fy
i
n
g
s
p
e
c
i
fi
c
c
l
a
i
m
t
y
p
e
s
fo
r
a
n
a
l
y
s
i
s
.
Cl
i
n
i
c
a
l
i
n
fo
rm
a
t
i
o
n
D
i
a
g
n
o
s
i
s
c
o
d
e
s
,
p
ro
c
e
d
u
re
c
o
d
e
s
,
t
o
t
a
l
n
u
m
b
e
r
o
f
t
i
m
e
s
d
i
a
g
n
o
s
e
d
w
i
t
h
t
a
rg
e
t
c
o
n
d
i
t
i
o
n
,
l
i
fe
s
t
y
l
e
fa
c
t
o
rs
(s
m
o
k
i
n
g
s
t
a
t
u
s
,
b
o
d
y
m
a
s
s
i
n
d
e
x
(
BM
I
)
,
a
n
d
s
p
o
rt
i
n
g
a
c
t
i
v
i
t
i
e
s
).
D
i
re
c
t
i
n
d
i
c
a
t
o
rs
o
f
h
e
a
l
t
h
s
t
a
t
u
s
,
d
i
s
e
a
s
e
b
u
rd
e
n
,
a
n
d
t
y
p
e
s
o
f
s
e
rv
i
c
e
c
o
n
s
u
m
e
d
,
e
s
s
e
n
t
i
a
l
fo
r
ri
s
k
a
s
s
e
s
s
m
e
n
t
a
n
d
p
re
d
i
c
t
i
n
g
fu
t
u
re
c
l
a
i
m
s
.
Co
m
o
r
b
i
d
i
t
y
c
o
u
n
t
s
p
ro
v
i
d
e
a
m
e
a
s
u
re
o
f
p
a
t
i
e
n
t
c
o
m
p
l
e
x
i
t
y
[1
5
]
.
F
i
n
a
n
c
i
a
l
d
a
t
a
Bi
l
l
e
d
a
m
o
u
n
t
,
p
a
i
d
a
m
o
u
n
t
,
re
j
e
c
t
e
d
a
m
o
u
n
t
.
D
i
re
c
t
l
y
i
n
fo
rm
t
h
e
f
i
n
a
n
c
i
a
l
v
a
l
u
e
o
f
c
l
a
i
m
s
,
c
ru
c
i
a
l
fo
r
fo
re
c
a
s
t
i
n
g
c
o
s
t
s
,
a
n
d
i
d
e
n
t
i
fy
i
n
g
p
o
t
e
n
t
i
a
l
l
y
h
i
g
h
-
c
o
s
t
c
l
i
e
n
t
s
.
T
e
m
p
o
r
a
ry
d
a
t
a
N
u
m
b
e
r
o
f
p
re
v
i
o
u
s
v
i
s
i
t
s
,
t
i
m
e
s
e
r
i
e
s
o
f
d
i
a
g
n
o
s
t
i
c
h
i
s
t
o
ry
.
P
ro
v
i
d
e
l
o
n
g
i
t
u
d
i
n
a
l
c
o
n
t
e
x
t
fo
r
u
n
d
e
rs
t
a
n
d
i
n
g
p
a
t
i
e
n
t
b
e
h
a
v
i
o
r
,
d
i
s
e
a
s
e
p
ro
g
re
s
s
i
o
n
,
a
n
d
p
re
d
i
c
t
i
n
g
fu
t
u
re
u
t
i
l
i
z
a
t
i
o
n
p
a
t
t
e
rn
s
[6
]
.
3.
3
.
Lab
e
l
l
i
n
g
p
r
oc
e
s
s
We
e
m
pl
oye
d
a
B
a
y
e
s
i
a
n
op
t
i
m
i
z
a
t
i
on
a
l
gor
i
t
h
m
t
o
e
n
h
a
nc
e
d
a
t
a
l
a
be
l
l
i
ng,
a
l
l
ow
i
ng
e
ff
i
c
i
e
n
t
e
xpl
o
ra
t
i
on
of
p
a
r
a
m
e
t
e
r
s
pa
c
e
s
a
n
d
e
nh
a
nc
i
ng
t
he
qu
a
l
i
t
y
of
l
a
b
e
l
e
d
da
t
a
fo
r
s
u
bs
e
qu
e
nt
m
od
e
l
i
ng.
I
n
t
hi
s
c
a
s
e
,
B
a
y
e
s
i
a
n
opt
i
m
i
z
a
t
i
on
w
a
s
be
i
ng
us
e
d
b
e
yon
d
i
t
s
t
ra
d
i
t
i
on
a
l
r
ol
e
;
i
t
s
pr
i
nc
i
pl
e
s
(s
urro
ga
t
e
a
nd
a
c
qu
i
s
i
t
i
on
fun
c
t
i
on)
w
e
r
e
a
ppl
i
e
d
t
o
s
e
l
e
c
t
d
a
t
a
p
oi
n
t
s
f
or
l
a
be
l
l
i
ng
i
n
a
c
t
i
ve
l
e
a
r
ni
ng
.
A
n
a
c
q
ui
s
i
t
i
on
func
t
i
on
c
he
c
ks
for
un
l
a
b
e
l
e
d
d
a
t
a
t
h
a
t
w
i
l
l
b
e
m
os
t
be
n
e
fi
c
i
a
l
t
o
l
a
b
e
l
n
e
xt
for
a
ML
m
od
e
l
.
S
uppos
e
you
ha
ve
a
hu
ge
da
t
a
s
e
t
but
c
a
n
onl
y
a
fford
t
o
h
a
v
e
a
s
m
a
l
l
fra
c
t
i
on
of
i
t
l
a
be
l
e
d
.
T
h
e
a
c
qui
s
i
t
i
o
n
fu
nc
t
i
on
gu
i
de
s
i
n
s
e
l
e
c
t
i
ng
t
h
e
one
s
t
o
l
a
be
l
ne
x
t
a
nd
he
nc
e
l
a
b
e
l
s
t
h
e
w
h
ol
e
da
t
a
s
e
t
.
Ba
ye
s
i
a
n
op
t
i
m
i
z
a
t
i
on
,
i
n
t
h
e
c
on
t
e
x
t
o
f
"
l
a
b
e
l
l
i
ng
a
he
a
d
,
"
i
s
a
"
s
m
a
r
t
"
c
ur
a
t
or
of
d
a
t
a
w
h
e
re
t
he
hi
gh
-
r
i
s
k
h
e
a
l
t
h
c
a
r
e
c
l
i
e
n
t
s
a
re
i
d
e
nt
i
fi
e
d
.
I
t
fu
rt
h
e
r
d
i
re
c
t
s
t
he
c
os
t
l
y
hu
m
a
n
l
a
be
l
l
i
ng
e
ffo
rt
t
o
t
h
e
m
os
t
va
l
u
a
bl
e
a
n
d
i
nfor
m
a
t
i
ve
d
a
t
a
p
oi
n
t
s
,
r
e
s
ul
t
i
ng
i
n
a
m
o
re
a
c
c
ur
a
t
e
a
nd
e
ffi
c
i
e
nt
DL
m
o
de
l
f
or
ri
s
k
c
l
a
s
s
i
fi
c
a
t
i
on
a
t
a
s
i
gn
i
fi
c
a
n
t
l
y
l
ow
l
a
b
e
l
l
i
ng
c
os
t
.
T
h
e
m
e
m
be
rs
w
i
t
hi
n
t
he
d
a
t
a
s
e
t
w
e
r
e
l
a
be
l
l
e
d
a
s
h
i
gh
-
r
i
s
k
i
f
a
ny
of
t
h
e
fo
l
l
ow
i
ng
c
o
ndi
t
i
ons
w
e
r
e
m
e
t
:
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2722
-
3221
Com
pu
t
S
c
i
Inf
T
e
c
h
nol
,
V
o
l
.
6
,
N
o
.
3
,
N
ov
e
m
be
r
2025
:
346
-
354
350
−
T
ot
a
l
c
l
a
i
m
e
d
a
m
ount
>
op
t
i
m
i
z
e
d
t
hre
s
ho
l
d
(t
yp
i
c
a
l
l
y
a
rou
nd
75
-
90
%
p
e
r
c
e
n
t
i
l
e
)
,
−
T
ot
a
l
re
j
e
c
t
e
d
a
m
ount
>
op
t
i
m
i
z
e
d
t
hr
e
s
ho
l
d
,
−
Cl
a
i
m
s
P
e
r
M
ont
h
>
op
t
i
m
i
z
e
d
t
hre
s
ho
l
d
,
O
t
he
rw
i
s
e
,
m
e
m
be
rs
w
e
r
e
l
a
be
l
l
e
d
l
ow
-
ri
s
k
.
Ba
ye
s
i
a
n
opt
i
m
i
z
a
t
i
on
w
a
s
ut
i
l
i
z
e
d
t
o
a
ut
o
m
a
t
i
c
a
l
l
y
fi
nd
t
h
e
be
s
t
qua
n
t
i
l
e
t
hr
e
s
hol
ds
.
T
hi
s
a
ppro
a
c
h
i
m
pro
ve
d
r
e
l
i
a
bi
l
i
t
y
a
nd
r
e
duc
e
d
t
h
e
n
e
e
d
fo
r
s
ub
j
e
c
t
i
ve
m
a
nua
l
t
hr
e
s
hol
d
s
e
t
t
i
ngs
.
A
s
a
r
e
s
ul
t
,
t
h
e
m
od
e
l
de
c
i
s
i
ons
a
r
e
f
ul
l
y
d
a
t
a
-
dr
i
ve
n
[2
5]
.
3.
4
.
C
l
as
s
i
f
i
c
a
ti
on
p
r
o
c
e
s
s
A
DL
m
ode
l
w
a
s
us
e
d
,
a
s
i
m
pl
e
,
e
ff
i
c
i
e
nt
f
e
e
dforw
a
rd
NN
.
T
h
e
ne
t
w
ork
s
t
ru
c
t
ur
e
w
i
t
h
f
i
v
e
d
e
ns
e
l
a
y
e
rs
of
“
n
eu
r
ons
”
w
a
s
us
e
d
,
s
t
a
rt
i
ng
w
i
t
h
t
w
o
hi
d
d
e
n
l
a
y
e
rs
t
h
a
t
pr
oc
e
s
s
t
he
i
npu
t
d
a
t
a
us
i
ng
a
c
om
m
on
a
c
t
i
v
a
t
i
on
f
unc
t
i
on
c
a
l
l
e
d
r
e
c
t
i
fi
e
d
l
i
ne
a
r
u
ni
t
(
R
e
L
U
)
,
w
hi
c
h
t
e
a
c
he
s
t
h
e
n
e
t
w
or
k
t
o
l
e
a
rn
c
o
m
pl
e
x
re
l
a
t
i
ons
hi
ps
[
14]
.
T
he
f
i
n
a
l
l
a
y
e
r
,
w
hi
c
h
m
a
ke
s
t
he
a
c
t
u
a
l
pre
di
c
t
i
ons
,
us
e
d
a
s
i
gm
oi
d
a
c
t
i
v
a
t
i
on
func
t
i
o
n.
T
he
s
i
gm
oi
d
a
c
t
i
va
t
i
on
fun
c
t
i
on
g
i
ve
s
t
he
ov
e
ra
l
l
r
e
s
ul
t
of
e
i
t
he
r
hi
gh
ri
s
k
or
l
ow
-
ri
s
k
be
c
a
us
e
i
t
s
qu
a
s
he
s
i
t
s
out
pu
t
i
nt
o
a
nu
m
b
e
r
b
e
t
w
e
e
n
0
a
nd
1
,
w
hi
c
h
c
a
n
b
e
di
r
e
c
t
l
y
i
nt
e
rpre
t
e
d
a
s
a
pr
oba
b
i
l
i
t
y
[14]
.
T
he
da
t
a
s
e
t
of
934
,
800
m
e
di
c
a
l
c
l
a
i
m
s
i
ns
t
a
nc
e
s
w
a
s
r
a
ndo
m
l
y
s
e
p
a
ra
t
e
d
i
nt
o
a
n
e
va
l
ua
t
i
o
n
d
a
t
a
s
e
t
of
280
,
440
(30
%
)
m
e
d
i
c
a
l
c
l
a
i
m
i
ns
t
a
nc
e
s
a
nd
654
,
360
(70%
)
m
e
di
c
a
l
c
l
a
i
m
i
ns
t
a
n
c
e
s
for
t
r
a
i
n
i
ng
t
he
m
ode
l
.
T
he
m
o
de
l
us
e
d
t
h
e
A
d
a
m
op
t
i
m
i
z
e
r
,
w
h
i
c
h
i
s
l
i
k
e
a
s
m
a
rt
t
e
a
c
he
r
t
h
a
t
a
d
j
us
t
s
h
ow
m
uc
h
t
h
e
n
e
t
w
ork
l
e
a
r
ns
from
e
a
c
h
m
i
s
t
a
k
e
t
o
m
a
k
e
t
r
a
i
n
i
ng
f
a
s
t
e
r
a
nd
m
ore
e
ffi
c
i
e
n
t
[
14]
.
T
h
e
t
ra
i
ni
ng
w
a
s
don
e
ove
r
3
0
i
t
e
ra
t
i
ons
(e
poc
hs
)
,
a
nd
a
ft
e
r
t
h
e
t
r
a
i
n
i
ng
,
t
h
e
p
e
rfor
m
a
n
c
e
w
a
s
c
h
e
c
k
e
d
us
i
ng
c
o
m
pl
e
t
e
l
y
ne
w
,
uns
e
e
n
d
a
t
a
t
o
g
i
ve
a
t
rue
m
e
a
s
u
re
of
re
a
l
-
w
or
l
d
a
c
c
ur
a
c
y.
3.
5
.
M
od
e
l
i
n
t
e
r
f
ac
e
T
o
a
l
l
ow
us
e
rs
t
o
i
nt
e
ra
c
t
w
i
t
h
t
he
m
o
de
l
a
nd
us
e
i
t
a
s
a
ri
s
k
c
l
a
s
s
i
fi
e
r
t
oo
l
,
w
e
c
r
e
a
t
e
d
a
us
e
r
i
nt
e
rfa
c
e
(U
I)
da
s
hb
oa
rd
,
a
s
s
how
n
i
n
F
i
gure
2
.
T
he
da
s
hboa
rd
s
t
r
e
a
m
l
i
ne
s
t
he
p
roc
e
s
s
o
f
da
t
a
s
e
t
m
a
n
a
ge
m
e
nt
,
m
o
de
l
t
ra
i
ni
ng
,
a
nd
c
l
a
s
s
i
f
i
c
a
t
i
on
.
T
h
e
da
s
hb
o
a
rd
a
bs
t
ra
c
t
s
a
w
a
y
t
h
e
und
e
rl
y
i
ng
c
ode
,
m
a
k
i
ng
t
he
pow
e
rful
DL
m
od
e
l
a
c
c
e
s
s
i
b
l
e
t
o
us
e
rs
w
ho
m
a
y
not
ha
ve
pr
ogra
m
m
i
ng
e
xp
e
rt
i
s
e
,
a
l
l
ow
i
ng
t
he
m
t
o
l
e
v
e
ra
g
e
i
t
fo
r
i
d
e
nt
i
fy
i
ng
h
i
gh
-
r
i
s
k
c
l
i
e
nt
s
a
nd
fo
re
c
a
s
t
i
ng
c
l
a
i
m
s
fr
e
qu
e
nc
y
.
T
hi
s
m
a
k
e
s
t
he
t
ool
a
c
c
e
s
s
i
bl
e
t
o
a
b
roa
d
e
r
a
ud
i
e
nc
e
.
F
i
gure
2
.
R
i
s
k
c
l
a
s
s
i
f
i
e
r
da
s
hb
oa
rd
T
he
da
s
hb
oa
rd
prov
i
d
e
s
a
us
e
r
-
f
ri
e
ndl
y
i
n
t
e
rfa
c
e
t
o
m
o
ni
t
o
r
t
h
e
s
t
a
t
us
of
t
he
m
o
de
l
;
upl
o
a
d
c
l
a
i
m
fi
l
e
s
i
n
CS
V
fo
rm
a
t
or
m
a
nu
a
l
l
y
e
n
t
e
r
t
he
c
l
a
i
m
d
e
t
a
i
l
s
us
i
n
g
m
a
nu
a
l
c
l
a
s
s
i
fi
c
a
t
i
on
.
It
f
e
a
t
ure
s
p
i
pe
l
i
n
e
s
t
a
t
us
vi
s
ua
l
i
z
a
t
i
on
for
f
e
a
t
ure
e
ngi
n
e
e
ri
ng
,
Ba
ye
s
i
a
n
opt
i
m
i
z
a
t
i
on
,
DL
m
od
e
l
t
ra
i
ni
ng
,
a
n
d
c
onf
i
rm
s
i
f
t
he
t
ra
i
ne
d
fi
l
e
h
a
s
be
e
n
s
a
v
e
d
s
u
c
c
e
s
s
ful
l
y.
I
t
ha
s
a
c
t
i
on
opt
i
ons
for
e
i
t
he
r
re
t
ra
i
ni
n
g
t
he
m
od
e
l
or
m
a
nu
a
l
l
y
c
l
a
s
s
i
fy
i
ng
t
he
c
l
a
i
m
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
Com
pu
t
S
c
i
Inf
T
e
c
h
nol
IS
S
N
:
2722
-
3221
P
r
e
di
c
t
i
v
e
m
o
de
l
f
or
h
i
gh
-
r
i
s
k
h
e
a
l
t
h
c
ar
e
c
l
i
e
nt
s
and
c
l
ai
m
s
f
r
e
qu
e
nc
y
(
L
e
ni
as
Z
hou
)
351
4.
R
ES
U
LTS
A
N
D
D
I
S
C
U
S
S
I
O
N
4.
1
.
Tr
ai
n
i
n
g
r
e
s
u
l
ts
T
he
DL
m
ode
l
pe
r
form
e
d
e
xc
e
p
t
i
on
a
l
l
y
w
e
l
l
,
c
o
rre
c
t
l
y
c
l
a
s
s
i
fyi
n
g
99
.
86%
o
f
uns
e
e
n
h
e
a
l
t
h
c
a
r
e
c
l
a
i
m
s
i
n
t
he
t
e
s
t
da
t
a
s
e
t
a
s
e
i
t
h
e
r
hi
gh
-
ri
s
k
or
l
ow
-
r
i
s
k.
T
hi
s
s
how
e
d
a
ve
r
y
s
t
r
ong
pre
di
c
t
i
ve
c
a
pa
bi
l
i
t
y
.
T
he
s
c
a
l
e
r
w
a
s
s
uc
c
e
s
s
ful
l
y
s
a
ve
d
,
e
ns
uri
n
g
t
h
a
t
t
he
m
od
e
l
c
a
n
n
ow
be
de
p
l
oy
e
d
a
nd
us
e
d
w
i
t
hout
t
h
e
ne
e
d
t
o
be
r
e
t
r
a
i
ne
d
.
Bo
t
h
t
r
a
i
n
i
ng
a
nd
v
a
l
i
da
t
i
on
a
c
c
ur
a
c
y
s
t
e
a
di
l
y
i
nc
r
e
a
s
e
d
a
nd
s
t
a
bi
l
i
z
e
d
a
round
9
8
-
99
.
8%
.
T
h
e
t
ra
i
ni
ng
a
nd
va
l
i
d
a
t
i
on
l
os
s
d
e
c
r
e
a
s
e
d
s
i
gni
fi
c
a
n
t
l
y
w
i
t
hou
t
ov
e
rf
i
t
t
i
ng
,
i
ndi
c
a
t
i
ng
t
ha
t
t
he
m
ode
l
i
s
w
e
l
l
ge
ne
r
a
l
i
z
e
d
.
F
i
gur
e
3
s
how
s
a
s
c
r
e
e
ns
h
ot
of
t
h
e
m
od
e
l
t
ra
i
ni
ng
re
s
u
l
t
s
,
w
h
e
re
X
-
a
xi
s
re
pre
s
e
nt
s
t
h
e
e
po
c
h
re
fe
rs
t
o
t
he
nu
m
b
e
r
of
t
i
m
e
s
t
he
d
a
t
a
s
e
t
ha
s
p
a
s
s
e
d
t
hroug
h
t
he
N
N
a
nd
Y
-
a
xi
s
s
how
s
m
ode
l
a
c
c
ura
c
y
a
s
a
pe
rc
e
nt
a
g
e
of
c
orr
e
c
t
pr
e
di
c
t
i
ons
.
F
i
gu
re
3
.
DL
m
od
e
l
t
r
a
i
n
i
ng
c
ur
ve
s
4.
2
.
C
on
fu
s
i
on
matr
i
x
T
he
c
onfus
i
on
m
a
t
ri
x
e
v
a
l
u
a
t
e
s
t
h
e
m
o
de
l
’s
a
b
i
l
i
t
y
t
o
c
o
rre
c
t
l
y
c
l
a
s
s
i
fy
h
i
gh
-
r
i
s
k
a
nd
l
ow
-
ri
s
k
m
e
m
be
rs
a
s
p
re
s
e
nt
e
d
i
n
F
i
gu
re
4
.
T
h
e
m
ode
l
p
re
d
i
c
t
e
d
18
,
602
c
l
i
e
nt
s
a
s
l
ow
-
ri
s
k
(0)
w
h
e
n
t
he
y
w
e
r
e
a
c
t
ua
l
l
y
“
l
ow
-
ri
s
k
”
(0)
.
T
hi
s
i
s
a
n
e
xc
e
l
l
e
n
t
p
e
rfor
m
a
nc
e
i
m
prov
e
m
e
nt
in
i
d
e
nt
i
fy
i
ng
i
ndi
vi
du
a
l
s
w
h
o
a
re
ge
nui
ne
l
y
no
t
hi
gh
ri
s
k
.
T
he
m
od
e
l
i
n
c
orr
e
c
t
l
y
p
re
d
i
c
t
e
d
43
c
l
i
e
nt
s
a
s
“
h
i
gh
-
r
i
s
k
”
(
1)
w
he
n
t
h
e
y
w
e
re
a
c
t
ua
l
l
y
“
l
ow
-
ri
s
k
”
(0)
.
T
he
s
e
a
re
t
yp
e
1
e
rrors
.
T
hi
s
m
a
y
l
e
a
d
t
o
u
nne
c
e
s
s
a
ry
i
nt
e
rv
en
t
i
ons
for
m
e
m
b
e
rs
w
ho
don’t
n
e
e
d
s
uc
h
r
e
s
our
c
e
a
l
l
oc
a
t
i
ons
.
W
h
i
l
e
43
i
s
a
r
e
l
a
t
i
v
e
l
y
l
ow
nu
m
be
r
c
o
m
p
a
re
d
t
o
t
rue
ne
g
a
t
i
ve
s
,
t
h
e
i
m
p
a
c
t
o
f
f
a
l
s
e
pos
i
t
i
ve
s
de
p
e
nds
on
t
h
e
r
e
s
pe
c
t
i
ve
c
os
t
of
s
u
c
h
m
i
s
c
l
a
s
s
i
fi
c
a
t
i
on
.
T
he
m
o
de
l
pr
e
di
c
t
e
d
3
c
l
i
e
nt
s
a
s
“
l
ow
-
r
i
s
k”
w
hi
l
e
t
he
y
w
e
re
“
h
i
gh
-
r
i
s
k”
(1).
T
h
e
s
e
a
r
e
t
ype
11
e
rrors
.
T
he
s
e
a
re
t
h
e
m
os
t
c
ri
t
i
c
a
l
e
rrors
i
n
t
h
i
s
c
ont
e
x
t
.
F
or
h
e
a
l
t
hc
a
re
,
a
fa
l
s
e
ne
g
a
t
i
ve
m
e
a
ns
t
ha
t
a
hi
gh
-
r
i
s
k
i
n
di
v
i
dua
l
go
e
s
un
not
i
c
e
d
,
pot
e
nt
i
a
l
l
y
l
e
a
d
i
ng
t
o
a
dve
rs
e
he
a
l
t
h
ou
t
c
o
m
e
s
,
h
i
gh
f
ut
ur
e
c
os
t
s
du
e
t
o
de
l
a
ye
d
i
nt
e
rv
e
nt
i
ons
,
or
m
i
s
s
e
d
oppor
t
uni
t
i
e
s
f
or
pre
ve
n
t
a
t
i
ve
c
a
re
.
T
h
e
fa
c
t
t
ha
t
t
he
n
um
b
e
r
i
s
ve
ry
l
ow
i
s
a
s
t
rong
p
os
i
t
i
ve
for
t
h
e
m
od
e
l
.
T
he
m
o
de
l
a
l
s
o
p
re
d
i
c
t
e
d
14
,
3
53
c
l
i
e
nt
s
a
s
“
hi
g
h
-
ri
s
k
”
w
h
e
n
t
he
y
w
e
re
a
c
t
u
a
l
l
y
hi
gh
-
r
i
s
k.
T
h
i
s
hi
gh
nu
m
b
e
r
s
how
s
gre
a
t
pe
rf
orm
a
n
c
e
for
t
he
m
ode
l
.
T
hi
s
i
s
t
h
e
pr
i
m
a
ry
obj
e
c
t
i
v
e
of
t
hi
s
m
ode
l
i
s
t
o
i
d
e
nt
i
fy
hi
gh
-
ri
s
k
c
l
i
e
nt
s
.
F
i
gure
4
.
M
od
e
l
c
onfus
i
on
m
a
t
ri
x
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2722
-
3221
Com
pu
t
S
c
i
Inf
T
e
c
h
nol
,
V
o
l
.
6
,
N
o
.
3
,
N
ov
e
m
be
r
2025
:
346
-
354
352
4.
2
.
1
.
H
i
gh
ac
c
u
r
a
c
y
O
ve
ra
l
l
,
m
os
t
pre
d
i
c
t
i
o
ns
f
a
l
l
i
n
t
o
t
h
e
t
ru
e
pos
i
t
i
v
e
a
nd
t
ru
e
ne
g
a
t
i
ve
c
a
t
e
g
ori
e
s
.
T
hi
s
i
nd
i
c
a
t
e
s
a
ve
ry
l
ow
r
a
t
e
o
f
m
i
s
c
l
a
s
s
i
f
i
c
a
t
i
on
.
S
uc
h
r
e
s
ul
t
s
a
r
e
c
ons
i
s
t
e
n
t
w
i
t
h
t
he
hi
g
h
-
t
e
s
t
a
c
c
ur
a
c
y
(0
.
9986
%)
r
e
por
t
e
d
e
a
r
l
i
e
r
i
n
s
e
c
t
i
on
4
.
1.
4.
2
.
2
.
M
i
n
i
mi
z
i
n
g
c
r
i
ti
c
a
l
e
r
r
or
s
T
he
m
os
t
c
r
uc
i
a
l
a
s
p
e
c
t
for
i
d
e
nt
i
fy
i
ng
h
i
gh
-
r
i
s
k
c
l
i
e
n
t
s
i
s
m
i
n
i
m
i
z
i
ng
f
a
l
s
e
n
e
ga
t
i
v
e
s
,
a
s
m
i
s
s
i
ng
a
hi
gh
-
r
i
s
k
i
ndi
vi
du
a
l
c
a
n
ha
v
e
s
i
gni
fi
c
a
n
t
c
ons
e
que
n
c
e
s
.
W
i
t
h
o
nl
y
3
fa
l
s
e
ne
g
a
t
i
ve
s
,
t
he
m
ode
l
s
h
ow
e
d
out
s
t
a
ndi
n
g
p
e
rfor
m
a
nc
e
i
n
t
hi
s
re
g
a
r
d
.
T
hi
s
m
e
a
ns
ve
r
y
f
e
w
ge
n
ui
n
e
l
y
hi
g
h
-
ri
s
k
c
l
i
e
nt
s
a
r
e
s
l
i
pp
i
ng
t
hrou
gh
t
he
c
r
a
c
ks
.
4.
2
.
3
.
M
an
age
ab
l
e
fa
l
s
e
p
os
i
ti
v
e
s
W
hi
l
e
t
h
e
r
e
a
re
43
fa
l
s
e
pos
i
t
i
v
e
s
,
t
h
e
num
be
r
re
m
a
i
ns
re
l
a
t
i
ve
l
y
s
m
a
l
l
.
T
h
e
i
r
i
m
p
a
c
t
s
hou
l
d
be
c
ons
i
d
e
re
d
c
a
r
e
ful
l
y
i
n
t
he
ov
e
ra
l
l
e
v
a
l
u
a
t
i
on
.
T
hi
s
i
s
e
s
p
e
c
i
a
l
l
y
t
ru
e
w
h
e
n
w
e
i
gh
e
d
a
g
a
i
ns
t
t
h
e
b
e
n
e
fi
t
s
of
c
orre
c
t
l
y
i
d
e
nt
i
fyi
ng
o
ve
r
14
,
000
h
i
gh
-
r
i
s
k
c
l
i
e
n
t
s
a
nd
ne
a
rl
y
19
,
000
l
ow
-
r
i
s
k
c
l
i
e
n
t
s
.
4.
3
.
O
p
e
r
at
i
on
al
r
e
s
u
l
ts
T
he
f
ol
l
ow
i
ng
out
pu
t
i
s
s
how
n
i
n
F
i
gure
5
,
de
m
o
n
s
t
r
a
t
e
s
t
h
e
re
a
l
-
w
orl
d
a
pp
l
i
c
a
t
i
on
of
t
he
DL
m
o
de
l
for
he
a
l
t
hc
a
r
e
ri
s
k
i
d
e
nt
i
fi
c
a
t
i
o
n
a
nd
for
e
c
a
s
t
i
ng
c
l
a
i
m
s
f
re
que
nc
y
.
T
he
m
o
de
l
i
n
ge
s
t
e
d
ne
w
c
l
a
i
m
d
a
t
a
s
e
a
m
l
e
s
s
l
y
,
w
i
t
h
a
n
o
pt
i
on
t
o
u
pl
o
a
d
a
ba
t
c
h
i
n
CS
V
fo
rm
a
t
.
T
h
e
m
od
e
l
a
u
t
om
a
t
i
c
a
l
l
y
p
roc
e
s
s
e
d
da
t
a
,
gi
vi
ng
a
ri
s
k
c
a
t
e
go
ry
t
o
t
he
m
e
m
b
e
r
.
T
h
e
m
ode
l
t
h
e
n
s
a
ve
d
t
he
re
s
ul
t
,
m
a
ki
ng
t
h
e
m
a
v
a
i
l
a
b
l
e
fo
r
fur
t
he
r
a
n
a
l
ys
i
s
or
i
nt
e
gra
t
i
o
n
t
o
o
t
he
r
s
ys
t
e
m
s
,
a
nd
t
h
a
t
’s
a
n
“
a
c
t
i
ona
b
l
e
ou
t
p
ut
”
.
T
he
m
ode
l
a
l
s
o
g
a
ve
t
h
e
c
l
a
i
m
s
fr
e
qu
e
n
c
y
ba
s
e
d
on
t
he
a
v
a
i
l
a
b
l
e
da
t
a
,
“
pre
d
i
c
t
e
d
c
l
a
i
m
s
pe
r
m
on
t
h
,
”
offe
ri
ng
a
m
ore
di
r
e
c
t
qu
a
n
t
i
t
a
t
i
ve
for
e
c
a
s
t
.
T
he
“
e
xp
l
a
n
a
t
i
on
”
p
a
rt
c
l
a
r
i
fi
e
s
t
h
e
r
a
t
i
on
a
l
e
.
T
h
i
s
e
xp
l
a
na
t
i
on
f
or
t
h
e
d
e
c
i
s
i
on
e
nh
a
n
c
e
s
t
he
pra
c
t
i
c
a
l
ut
i
l
i
t
y
of
t
he
m
ode
l
,
a
l
l
ow
i
ng
us
e
rs
t
o
und
e
rs
t
a
nd
w
hy
a
c
l
i
e
n
t
ha
s
b
e
e
n
f
l
a
gg
e
d
a
s
h
i
gh
-
r
i
s
k
,
w
h
i
c
h
i
s
i
m
po
r
t
a
nt
for
t
rus
t
a
n
d
e
ff
e
c
t
i
v
e
i
n
t
e
rv
e
n
t
i
on
i
n
he
a
l
t
hc
a
re
.
W
e
us
e
d
a
m
a
nua
l
,
rul
e
-
b
a
s
e
d
e
xpl
a
n
a
t
i
on
a
pp
roa
c
h
du
e
t
o
i
t
s
c
l
a
ri
t
y
a
nd
s
i
m
p
l
i
c
i
t
y
,
a
s
i
t
e
xpl
a
i
ns
t
he
p
re
d
i
c
t
i
ons
i
n
a
w
a
y
hu
m
a
ns
c
a
n
un
de
rs
t
a
nd
.
T
hi
s
e
m
pow
e
rs
t
he
us
e
rs
t
o
a
c
t
o
n
t
he
s
e
i
ns
i
gh
t
s
w
i
t
h
gre
a
t
e
r
c
onf
i
d
e
nc
e
,
a
s
s
ho
w
n
i
n
t
h
e
da
s
hb
oa
rd
r
e
s
ul
t
i
n
F
i
gure
6
.
F
i
gure
5
.
M
od
e
l
i
nt
e
ra
c
t
i
on
out
c
om
e
s
i
n
re
a
l
-
w
or
l
d
a
pp
l
i
c
a
t
i
ons
F
i
gure
6
.
D
a
s
hboa
rd
c
l
a
s
s
i
fi
c
a
t
i
on
r
e
s
ul
t
5.
C
O
N
C
LU
S
I
O
N
T
he
c
om
p
re
h
e
ns
i
v
e
w
ork
und
e
rt
a
ke
n
t
o
c
o
m
e
up
w
i
t
h
a
pr
e
di
c
t
i
ve
m
ode
l
for
i
d
e
nt
i
fyi
ng
hi
gh
-
ri
s
k
he
a
l
t
h
c
a
r
e
c
l
i
e
nt
s
a
nd
fo
re
c
a
s
t
i
ng
c
l
a
i
m
s
fre
que
n
c
y
h
a
s
y
i
e
l
d
e
d
e
x
c
e
p
t
i
o
na
l
l
y
pro
m
i
s
i
ng
re
s
u
l
t
s
,
d
i
re
c
t
l
y
a
ddre
s
s
i
ng
a
l
l
t
hre
e
c
ore
o
bj
e
c
t
i
ve
s
.
T
he
pr
i
m
a
ry
ob
j
e
c
t
i
ve
o
f
i
de
n
t
i
f
yi
ng
h
i
gh
-
r
i
s
k
c
l
i
e
nt
s
h
a
s
be
e
n
a
c
hi
e
v
e
d
w
i
t
h
out
s
t
a
n
di
ng
a
c
c
ura
c
y
,
a
s
e
v
i
de
nc
e
d
by
bot
h
t
e
s
t
i
ng
a
nd
op
e
ra
t
i
on
a
l
r
e
s
ul
t
s
.
A
ddi
t
i
o
na
l
l
y
,
i
t
s
u
c
c
e
s
s
ful
l
y
fore
c
a
s
t
e
d
c
l
a
i
m
s
f
re
qu
e
n
c
y
a
nd
pro
vi
d
e
d
a
n
e
x
pl
a
na
t
i
on
for
t
he
pre
d
i
c
t
i
ons
.
T
he
s
e
re
s
u
l
t
s
a
r
e
w
e
l
l
s
uppor
t
e
d
Evaluation Warning : The document was created with Spire.PDF for Python.
Com
pu
t
S
c
i
Inf
T
e
c
h
nol
IS
S
N
:
2722
-
3221
P
r
e
di
c
t
i
v
e
m
o
de
l
f
or
h
i
gh
-
r
i
s
k
h
e
a
l
t
h
c
ar
e
c
l
i
e
nt
s
and
c
l
ai
m
s
f
r
e
qu
e
nc
y
(
L
e
ni
as
Z
hou
)
353
by
t
h
e
da
t
a
a
nd
a
l
i
gn
w
i
t
h
p
ri
or
re
s
e
a
r
c
h
,
p
a
rt
i
c
u
l
a
r
l
y
i
n
hi
ghl
i
gh
t
i
ng
t
h
e
t
hr
e
e
c
o
m
pou
nd
f
e
a
t
ur
e
s
(
t
o
t
a
l
a
m
ou
nt
c
l
a
i
m
e
d,
t
o
t
a
l
a
m
ount
r
e
j
e
c
t
e
d
,
a
nd
c
l
a
i
m
s
fre
q
ue
n
c
y
)
a
s
t
h
e
c
ri
t
i
c
a
l
i
ndi
c
a
t
ors
of
hi
gh
-
ri
s
k
be
h
a
v
i
ors
i
n
h
e
a
l
t
h
c
a
r
e
fra
u
d
d
e
t
e
c
t
i
on
.
T
h
e
m
ode
l
’s
p
re
c
i
s
i
o
n
s
urp
a
s
s
e
d
t
ypi
c
a
l
e
xp
e
c
t
a
t
i
ons
for
s
u
c
h
a
c
o
m
pl
e
x
pre
di
c
t
i
ve
t
a
s
k,
re
ve
a
l
i
ng
i
t
s
p
ot
e
nt
i
a
l
for
pro
a
c
t
i
v
e
r
i
s
k
m
a
na
g
e
m
e
n
t
i
n
h
e
a
l
t
h
c
a
r
e
i
ns
ura
nc
e
.
T
h
i
s
s
how
s
pot
e
n
t
i
a
l
t
o
i
m
pr
ove
h
e
a
l
t
h
ou
t
c
o
m
e
s
a
nd
m
i
t
i
g
a
t
e
fut
u
re
he
a
l
t
hc
a
re
c
os
t
s
.
T
he
ot
h
e
r
no
t
a
b
l
e
c
o
nt
r
i
but
i
on
i
s
t
he
i
n
t
e
gr
a
t
i
on
of
Ba
ye
s
i
a
n
opt
i
m
i
z
a
t
i
on
t
o
e
nh
a
n
c
e
t
h
e
pe
rf
orm
a
nc
e
of
DL
m
ode
l
s
o
n
r
e
a
l
-
w
or
l
d
h
e
a
l
t
h
c
a
re
c
l
a
i
m
s
da
t
a
.
T
h
e
e
m
p
ha
s
i
s
o
n
e
x
pl
a
i
n
a
bi
l
i
t
y
fur
t
he
r
pre
s
e
nt
s
t
hi
s
a
s
a
s
i
gni
f
i
c
a
nt
c
on
c
e
pt
u
a
l
a
dv
a
nc
e
m
e
n
t
i
n
t
he
a
ppl
i
c
a
t
i
on
of
DL
t
o
he
a
l
t
hc
a
r
e
a
n
a
l
y
t
i
c
s
.
F
ut
ur
e
r
e
s
e
a
rc
h
c
oul
d
e
xp
l
or
e
t
h
e
i
nt
e
gr
a
t
i
on
of
a
dva
n
c
e
d
c
os
t
i
n
g
m
e
c
ha
ni
s
m
s
t
o
s
uppor
t
t
he
de
pl
oy
m
e
nt
of
a
pr
e
d
i
c
t
i
v
e
m
ode
l
-
as
-
a
-
s
e
r
vi
c
e
(P
M
a
a
S
)
pl
a
t
form
.
T
h
i
s
w
i
l
l
a
l
l
ow
us
a
g
e
by
i
nd
i
vi
d
ua
l
s
a
nd
s
m
a
l
l
t
o
m
e
d
i
um
-
s
i
z
e
d
bus
i
ne
s
s
e
nt
e
rpri
s
e
s
w
ho
h
a
ve
i
ni
t
i
a
l
i
nv
e
s
t
m
e
nt
c
a
p
a
c
i
t
y
l
i
m
i
t
a
t
i
ons
.
F
U
N
D
I
N
G
I
N
F
O
R
M
A
TI
O
N
T
he
a
u
t
hors
s
t
a
t
e
t
ha
t
no
fu
ndi
ng
w
a
s
i
n
vol
v
e
d
.
A
U
TH
O
R
C
O
N
TR
I
BU
TI
O
N
S
TA
T
EM
EN
T
T
hi
s
j
our
na
l
us
e
s
t
h
e
Cont
ri
bu
t
or
Rol
e
s
T
a
x
ono
m
y
(
C
Re
di
T
)
t
o
re
c
ogn
i
z
e
i
nd
i
vi
du
a
l
a
ut
hor
c
ont
r
i
bu
t
i
ons
,
r
e
du
c
e
a
ut
hors
h
i
p
di
s
pu
t
e
s
,
a
nd
fa
c
i
l
i
t
a
t
e
c
o
l
l
a
bora
t
i
on
.
N
ame
of
A
u
th
o
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
L
e
n
i
a
s
Z
ho
u
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
M
a
i
nfor
d
M
u
t
a
n
da
v
a
ri
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
L
uc
i
a
M
a
t
ondor
a
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
ft
w
a
re
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
rm
a
l
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
s
o
u
rc
e
s
D
:
D
a
t
a
Cu
ra
t
i
o
n
O
:
W
ri
t
i
n
g
-
O
ri
g
i
n
a
l
D
ra
ft
E
:
W
ri
t
i
n
g
-
Re
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
s
u
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
rv
i
s
i
o
n
P
:
P
ro
j
e
c
t
a
d
m
i
n
i
s
t
ra
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
s
i
t
i
o
n
C
O
N
F
LI
C
T
O
F
I
N
T
ER
ES
T
S
TA
T
EM
EN
T
A
ut
hors
s
t
a
t
e
no
c
onf
l
i
c
t
of
i
n
t
e
r
e
s
t
.
D
A
TA
A
V
A
I
LA
BI
LI
TY
T
he
d
a
t
a
t
h
a
t
s
up
port
t
he
fi
nd
i
ngs
of
t
hi
s
s
t
ud
y
a
r
e
a
v
a
i
l
a
bl
e
on
re
qu
e
s
t
fr
om
t
he
c
orr
e
s
pond
i
ng
a
ut
ho
r,
[
L
Z
]
.
T
h
e
d
a
t
a
,
w
h
i
c
h
c
on
t
a
i
n
i
nfor
m
a
t
i
on
t
h
a
t
c
oul
d
c
o
m
pr
om
i
s
e
t
he
p
ri
v
a
c
y
of
r
e
s
e
a
rc
h
pa
rt
i
c
i
pa
n
t
s
,
a
r
e
not
pub
l
i
c
l
y
a
va
i
l
a
bl
e
d
ue
t
o
c
e
rt
a
i
n
r
e
s
t
ri
c
t
i
ons
.
R
EF
ER
EN
C
ES
[
1]
J
.
N
w
o
k
e
,
“
H
e
a
l
t
h
c
a
re
d
a
t
a
a
n
a
l
y
t
i
c
s
a
n
d
p
re
d
i
c
t
i
v
e
m
o
d
e
l
l
i
n
g
:
e
n
h
a
n
c
i
n
g
o
u
t
c
o
m
e
s
i
n
re
s
o
u
rc
e
a
l
l
o
c
a
t
i
o
n
,
d
i
s
e
a
s
e
p
re
v
a
l
e
n
c
e
a
n
d
h
i
g
h
-
ri
s
k
p
o
p
u
l
a
t
i
o
n
s
,
”
In
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
H
e
a
l
t
h
S
c
i
e
n
c
e
s
,
v
o
l
.
7
,
n
o
.
7
,
p
p
.
1
–
3
5
,
2
0
2
4
,
d
o
i
:
1
0
.
4
7
9
4
1
/
i
j
h
s
.
2
2
4
5
.
[2
]
M
.
N
n
a
m
d
i
,
“
P
re
d
i
c
t
i
v
e
a
n
a
l
y
t
i
c
s
i
n
h
e
a
l
t
h
c
a
re
,
”
R
e
s
e
a
r
c
h
G
a
t
e
,
A
p
r.
2
0
2
4
.
[O
n
l
i
n
e
].
A
v
a
i
l
a
b
l
e
:
h
t
t
p
s
:
/
/
w
w
w
.
re
s
e
a
rc
h
g
a
t
e
.
n
e
t
/
p
u
b
l
i
c
a
t
i
o
n
/
3
7
9
4
7
8
1
9
6
_
P
re
d
i
c
t
i
v
e
_
A
n
a
l
y
t
i
c
s
_
i
n
_
H
e
a
l
t
h
c
a
re
.
[3
]
V
.
P
.
T
h
a
k
re
,
R.
D
.
P
o
u
l
,
a
n
d
A
.
D
.
S
a
w
a
rk
a
r,
“
P
re
d
i
c
t
i
v
e
p
re
c
i
s
i
o
n
:
u
n
r
a
v
e
l
i
n
g
h
e
a
l
t
h
i
n
s
u
ra
n
c
e
c
l
a
i
m
p
a
t
t
e
rn
s
w
i
t
h
l
o
g
i
s
t
i
c
re
g
re
s
s
i
o
n
a
n
d
d
e
c
i
s
i
o
n
t
re
e
s
,
”
Cu
r
e
u
s
J
o
u
r
n
a
l
o
f
Co
m
p
u
t
e
r
S
c
i
e
n
c
e
,
2
0
2
5
,
d
o
i
:
1
0
.
7
7
5
9
/
s
4
4
3
8
9
-
0
2
5
-
0
3
0
1
0
-
y.
[4
]
A
.
A
.
A
d
e
s
i
n
a
,
T
.
V
.
Iy
e
l
o
l
u
,
a
n
d
P
.
O
.
P
a
u
l
,
“
L
e
v
e
ra
g
i
n
g
p
re
d
i
c
t
i
v
e
a
n
a
l
y
t
i
c
s
fo
r
s
t
ra
t
e
g
i
c
d
e
c
i
s
i
o
n
-
m
a
k
i
n
g
:
e
n
h
a
n
c
i
n
g
b
u
s
i
n
e
s
s
p
e
rfo
rm
a
n
c
e
t
h
ro
u
g
h
d
a
t
a
-
d
ri
v
e
n
i
n
s
i
g
h
t
s
,
”
W
o
r
l
d
J
o
u
r
n
a
l
o
f
A
d
v
a
n
c
e
d
R
e
s
e
a
r
c
h
a
n
d
R
e
v
i
e
ws
,
v
o
l
.
2
2
,
n
o
.
3
,
p
p
.
1
9
2
7
–
1
9
3
4
,
J
u
n
.
2
0
2
4
,
d
o
i
:
1
0
.
3
0
5
7
4
/
w
j
a
rr.
2
0
2
4
.
2
2
.
3
.
1
9
6
1
.
[5
]
R.
S
o
l
i
m
a
n
,
“
RW
D
1
0
7
b
e
t
t
e
r
u
s
e
o
f
re
a
l
-
w
o
rl
d
d
a
t
a
t
h
ro
u
g
h
p
re
d
i
c
t
i
v
e
a
n
a
l
y
t
i
c
s
:
a
k
n
o
w
l
e
d
g
e
-
to
-
w
i
s
d
o
m
c
o
n
c
e
p
t
u
a
l
fra
m
e
w
o
rk
fo
r
e
v
i
d
e
n
c
e
-
ba
s
e
d
p
r
a
c
t
i
c
e
,
”
V
a
l
u
e
i
n
H
e
a
l
t
h
,
v
o
l
.
2
5
,
n
o
.
1
2
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
v
a
l
.
2
0
2
2
.
0
9
.
2
3
3
2
.
[6
]
A
.
A
l
a
m
a
n
d
V
.
R.
P
ry
b
u
t
o
k
,
“
U
s
e
o
f
re
s
p
o
n
s
i
b
l
e
a
rt
i
fi
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
t
o
p
re
d
i
c
t
h
e
a
l
t
h
i
n
s
u
ra
n
c
e
c
l
a
i
m
s
i
n
t
h
e
U
S
A
u
s
i
n
g
m
a
c
h
i
n
e
l
e
a
rn
i
n
g
a
l
g
o
ri
t
h
m
s
,
”
E
x
p
l
o
r
a
t
i
o
n
o
f
D
i
g
i
t
a
l
H
e
a
l
t
h
T
e
c
h
n
o
l
o
g
i
e
s
,
p
p
.
3
0
–
4
5
,
2
0
2
4
,
d
o
i
:
1
0
.
3
7
3
4
9
/
e
d
h
t
.
2
0
2
4
.
0
0
0
0
9
.
[7
]
B.
H
a
rt
m
a
n
,
R.
O
w
e
n
,
a
n
d
Z
.
G
i
b
b
s
,
“
P
re
d
i
c
t
i
n
g
h
i
g
h
-
c
o
s
t
h
e
a
l
t
h
i
n
s
u
ra
n
c
e
m
e
m
b
e
rs
t
h
ro
u
g
h
b
o
o
s
t
e
d
t
re
e
s
a
n
d
o
v
e
rs
a
m
p
l
i
n
g
:
a
n
a
p
p
l
i
c
a
t
i
o
n
u
s
i
n
g
t
h
e
H
CCI
d
a
t
a
b
a
s
e
,
”
No
r
t
h
A
m
e
r
i
c
a
n
A
c
t
u
a
r
i
a
l
J
o
u
r
n
a
l
,
p
p
.
1
–
9
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
8
0
/
1
0
9
2
0
2
7
7
.
2
0
2
0
.
1
7
5
4
2
4
2
.
[8
]
D
.
A
.
V
a
l
l
e
ro
,
“
P
re
d
i
c
t
i
n
g
r
i
s
k
s
i
n
a
n
i
n
c
re
a
s
i
n
g
l
y
c
o
m
p
l
e
x
w
o
rl
d
,
”
E
n
v
i
r
o
n
m
e
n
t
a
l
S
y
s
t
e
m
s
S
c
i
e
n
c
e
,
p
p
.
8
9
–
1
3
3
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
b
9
7
8
-
0
-
12
-
8
2
1
9
5
3
-
9
.
0
0
0
0
6
-
4.
[9
]
U
.
W
.
d
e
R
u
i
j
t
e
r
e
t
a
l
.
,
“
P
re
d
i
c
t
i
o
n
m
o
d
e
l
s
fo
r
fu
t
u
re
h
i
g
h
-
n
e
e
d
h
i
g
h
-
c
o
s
t
h
e
a
l
t
h
c
a
re
u
s
e
:
a
s
y
s
t
e
m
a
t
i
c
re
v
i
e
w
,
”
J
o
u
r
n
a
l
o
f
G
e
n
e
r
a
l
In
t
e
r
n
a
l
M
e
d
i
c
i
n
e
,
v
o
l
.
3
7
,
n
o
.
7
,
p
p
.
1
7
6
3
–
1
7
7
0
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
6
0
6
-
0
2
1
-
0
7
3
3
3
-
z.
[1
0
]
Z
.
L
i
e
t
a
l
.
,
“
D
e
v
e
l
o
p
i
n
g
a
m
o
d
e
l
t
o
p
re
d
i
c
t
h
i
g
h
h
e
a
l
t
h
c
a
re
u
t
i
l
i
z
a
t
i
o
n
a
m
o
n
g
p
a
t
i
e
n
t
s
i
n
a
N
e
w
Y
o
rk
Ci
t
y
s
a
fe
t
y
n
e
t
s
y
s
t
e
m
,
”
M
e
d
i
c
a
l
Ca
r
e
,
v
o
l
.
6
1
,
n
o
.
2
,
p
p
.
1
0
2
–
1
0
8
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
9
7
/
M
L
R.
0
0
0
0
0
0
0
0
0
0
0
0
1
8
0
7
.
[1
1
]
E
.
M
.
A
l
o
t
a
i
b
i
,
“
R
i
s
k
a
s
s
e
s
s
m
e
n
t
u
s
i
n
g
p
re
d
i
c
t
i
v
e
a
n
a
l
y
t
i
c
s
,
”
In
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
P
r
o
f
e
s
s
i
o
n
a
l
B
u
s
i
n
e
s
s
R
e
v
i
e
w
,
v
o
l
.
8
,
n
o
.
5
,
2
0
2
3
,
d
o
i
:
1
0
.
2
6
6
6
8
/
b
u
s
i
n
e
s
s
re
v
i
e
w
/
2
0
2
3
.
v
8
i
5
.
1
7
2
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2722
-
3221
Com
pu
t
S
c
i
Inf
T
e
c
h
nol
,
V
o
l
.
6
,
N
o
.
3
,
N
ov
e
m
be
r
2025
:
346
-
354
354
[1
2
]
S
.
T
.
M
o
t
u
ru
,
W
.
G
.
J
o
h
n
s
o
n
,
a
n
d
H
.
L
i
u
,
“
P
re
d
i
c
t
i
v
e
ri
s
k
m
o
d
e
l
l
i
n
g
fo
r
f
o
re
c
a
s
t
i
n
g
h
i
g
h
-
c
o
s
t
p
a
t
i
e
n
t
s
:
a
re
a
l
-
w
o
rl
d
a
p
p
l
i
c
a
t
i
o
n
u
s
i
n
g
m
e
d
i
c
a
i
d
d
a
t
a
,
”
In
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
B
i
o
m
e
d
i
c
a
l
E
n
g
i
n
e
e
r
i
n
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
3
,
n
o
.
1
–
2
,
p
p
.
1
1
4
–
1
3
2
,
2
0
1
0
,
d
o
i
:
1
0
.
1
5
0
4
/
IJ
BE
T
.
2
0
1
0
.
0
2
9
6
5
4
.
[1
3
]
M
.
M
a
s
h
a
s
h
a
,
P
.
M
u
t
i
z
e
,
a
n
d
F
.
M
a
z
u
n
g
a
,
“
D
i
s
t
ri
b
u
t
i
o
n
a
n
d
p
a
t
t
e
rn
o
f
a
n
i
n
s
u
ra
n
c
e
h
e
a
l
t
h
c
l
a
i
m
s
y
s
t
e
m
:
a
t
i
m
e
s
e
ri
e
s
a
p
p
ro
a
c
h
,
”
T
a
n
z
a
n
i
a
J
o
u
r
n
a
l
o
f
S
c
i
e
n
c
e
,
v
o
l
.
4
8
,
n
o
.
1
,
p
p
.
1
3
–
2
1
,
2
0
2
2
,
d
o
i
:
1
0
.
4
3
1
4
/
t
j
s
.
v
4
8
i
1
.
2
.
[1
4
]
S
.
A
.
A
l
o
y
u
n
i
,
“
A
s
y
s
t
e
m
a
t
i
c
re
v
i
e
w
o
n
m
a
c
h
i
n
e
l
e
a
rn
i
n
g
a
n
d
d
e
e
p
l
e
a
rn
i
n
g
b
a
s
e
d
p
re
d
i
c
t
i
v
e
m
o
d
e
l
s
fo
r
h
e
a
l
t
h
i
n
fo
rm
a
t
i
c
s
,
”
J
o
u
r
n
a
l
o
f
P
h
a
r
m
a
c
e
u
t
i
c
a
l
R
e
s
e
a
r
c
h
In
t
e
r
n
a
t
i
o
n
a
l
,
p
p
.
1
8
3
–
1
9
4
,
2
0
2
1
,
d
o
i
:
1
0
.
9
7
3
4
/
j
p
ri
/
2
0
2
1
/
v
3
3
i
4
7
b
3
3
1
1
2
.
[1
5
]
E
.
E
.
A
g
u
,
A
.
O
.
A
b
h
u
l
i
m
e
n
,
A
.
N
.
O
.
-
O
s
a
fi
e
l
e
,
O
.
S
.
O
s
u
n
d
a
re
,
I.
A
.
A
d
e
n
i
ra
n
,
a
n
d
C.
P
.
E
fu
n
n
i
y
i
,
“
U
t
i
l
i
z
i
n
g
A
I
-
d
ri
v
e
n
p
re
d
i
c
t
i
v
e
a
n
a
l
y
t
i
c
s
t
o
re
d
u
c
e
c
re
d
i
t
ri
s
k
a
n
d
e
n
h
a
n
c
e
fi
n
a
n
c
i
a
l
i
n
c
l
u
s
i
o
n
,
”
In
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
F
r
o
n
t
l
i
n
e
R
e
s
e
a
r
c
h
i
n
M
u
l
t
i
d
i
s
c
i
p
l
i
n
a
r
y
S
t
u
d
i
e
s
,
v
o
l
.
3
,
n
o
.
2
,
p
p
.
2
0
–
2
9
,
2
0
2
4
,
d
o
i
:
1
0
.
5
6
3
5
5
/
i
j
frm
s
.
2
0
2
4
.
3
.
2
.
0
0
2
6
.
[1
6
]
H
.
A
b
d
u
l
a
z
e
e
m
,
S
.
W
h
i
t
e
l
a
w
,
G
.
S
c
h
a
u
b
e
rg
e
r,
a
n
d
S
.
J
.
K
l
u
g
,
“
A
s
y
s
t
e
m
a
t
i
c
re
v
i
e
w
o
f
c
l
i
n
i
c
a
l
h
e
a
l
t
h
c
o
n
d
i
t
i
o
n
s
p
re
d
i
c
t
e
d
b
y
m
a
c
h
i
n
e
l
e
a
rn
i
n
g
d
i
a
g
n
o
s
t
i
c
a
n
d
p
ro
g
n
o
s
t
i
c
m
o
d
e
l
s
t
ra
i
n
e
d
o
r
v
a
l
i
d
a
t
e
d
u
s
i
n
g
re
a
l
-
w
o
rl
d
p
ri
m
a
ry
h
e
a
l
t
h
c
a
re
d
a
t
a
,
”
P
L
O
S
O
NE
,
v
o
l
.
1
8
,
n
o
.
9
,
p
.
e
0
2
7
4
2
7
6
,
S
e
p
.
2
0
2
3
,
d
o
i
:
1
0
.
1
3
7
1
/
j
o
u
rn
a
l
.
p
o
n
e
.
0
2
7
4
2
7
6
.
[1
7
]
G
.
T
s
e
e
t
a
l
.
,
“
H
e
a
l
t
h
c
a
re
b
i
g
d
a
t
a
i
n
H
o
n
g
K
o
n
g
:
d
e
v
e
l
o
p
m
e
n
t
a
n
d
i
m
p
l
e
m
e
n
t
a
t
i
o
n
o
f
a
rt
i
fi
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
-
e
n
h
a
n
c
e
d
p
r
e
d
i
c
t
i
v
e
m
o
d
e
l
s
fo
r
ri
s
k
s
t
ra
t
i
fi
c
a
t
i
o
n
,
”
Cu
r
r
e
n
t
P
r
o
b
l
e
m
s
i
n
Ca
r
d
i
o
l
o
g
y
,
v
o
l
.
4
9
,
n
o
.
1
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
p
c
a
rd
i
o
l
.
2
0
2
3
.
1
0
2
1
6
8
.
[1
8
]
P
.
S
.
K
e
w
a
l
c
h
a
n
d
,
“
A
I
i
n
h
e
a
l
t
h
c
a
re
,
”
In
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
A
d
v
a
n
c
e
d
R
e
s
e
a
r
c
h
i
n
S
c
i
e
n
c
e
,
Co
m
m
u
n
i
c
a
t
i
o
n
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
4
,
n
o
.
2
,
p
p
.
5
4
8
–
5
5
4
,
2
0
2
4
.
[O
n
l
i
n
e
]
A
v
a
i
l
a
b
l
e
:
h
t
t
p
s
:
/
/
i
j
a
rs
c
t
.
c
o
.
i
n
/
P
a
p
e
r1
5
2
8
5
.
p
d
f
[1
9
]
E
.
N
a
b
ra
w
i
a
n
d
A
.
A
l
a
n
a
z
i
,
“
F
ra
u
d
d
e
t
e
c
t
i
o
n
i
n
h
e
a
l
t
h
c
a
re
i
n
s
u
ra
n
c
e
c
l
a
i
m
s
u
s
i
n
g
m
a
c
h
i
n
e
l
e
a
rn
i
n
g
,
”
R
i
s
k
s
,
v
o
l
.
1
1
,
n
o
.
9
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
ri
s
k
s
1
1
0
9
0
1
6
0
.
[2
0
]
C.
C
ro
w
l
e
y
,
J
.
P
e
rl
o
ff,
A
.
S
t
u
c
k
,
a
n
d
R.
M
e
c
h
a
n
i
c
,
“
Ch
a
l
l
e
n
g
e
s
i
n
p
re
d
i
c
t
i
n
g
fu
t
u
re
h
i
g
h
-
c
o
s
t
p
a
t
i
e
n
t
s
fo
r
c
a
re
m
a
n
a
g
e
m
e
n
t
i
n
t
e
rv
e
n
t
i
o
n
s
,
”
B
M
C
H
e
a
l
t
h
S
e
r
v
i
c
e
s
R
e
s
e
a
r
c
h
,
v
o
l
.
2
3
,
n
o
.
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
8
6
/
s
1
2
9
1
3
-
0
2
3
-
0
9
9
5
7
-
9.
[2
1
]
D
.
M
w
e
m
b
e
,
B.
J
o
n
e
s
,
W
.
Ch
a
g
w
i
z
a
,
a
n
d
S
.
N
g
w
e
n
y
a
,
“
A
s
t
a
t
i
s
t
i
c
a
l
a
n
a
l
y
s
i
s
o
f
t
i
m
e
t
o
a
c
l
a
i
m
:
a
c
a
s
e
o
f
Z
i
m
b
a
b
w
e
’s
h
e
a
l
t
h
i
n
s
u
ra
n
c
e
c
l
i
e
n
t
e
l
e
,
”
In
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
A
p
p
l
i
e
d
B
u
s
i
n
e
s
s
a
n
d
M
a
n
a
g
e
m
e
n
t
S
c
i
e
n
c
e
s
,
v
o
l
.
3
,
n
o
.
2
,
p
p
.
2
6
7
–
2
8
8
,
2
0
2
2
,
d
o
i
:
1
0
.
4
7
5
0
9
/
IJ
A
BM
S
.
2
0
2
2
.
v
0
3
i
0
2
.
0
7
.
[2
2
]
C.
A
.
A
rd
a
g
n
a
,
P
.
Ce
ra
v
o
l
o
,
a
n
d
E
.
D
a
m
i
a
n
i
,
“
Bi
g
d
a
t
a
a
n
a
l
y
t
i
c
s
a
s
-
a
-
s
e
rv
i
c
e
:
i
s
s
u
e
s
a
n
d
c
h
a
l
l
e
n
g
e
s
,
”
i
n
2
0
1
6
IE
E
E
In
t
e
r
n
a
t
i
o
n
a
l
Co
n
f
e
r
e
n
c
e
o
n
B
i
g
D
a
t
a
(B
i
g
D
a
t
a
)
,
I
E
E
E
,
D
e
c
.
2
0
1
6
,
p
p
.
3
6
3
8
–
3
6
4
4
.
d
o
i
:
1
0
.
1
1
0
9
/
Bi
g
D
a
t
a
.
2
0
1
6
.
7
8
4
1
0
2
9
.
[2
3
]
S
.
A
k
t
e
r,
Y
.
K
.
D
w
i
v
e
d
i
,
K
.
B
i
s
w
a
s
,
K
.
M
i
c
h
a
e
l
,
R.
J
.
Ba
n
d
a
ra
,
a
n
d
S
.
S
a
j
i
b
,
“
A
d
d
re
s
s
i
n
g
a
l
g
o
ri
t
h
m
i
c
b
i
a
s
i
n
A
I
-
d
ri
v
e
n
c
u
s
t
o
m
e
r
m
a
n
a
g
e
m
e
n
t
,
”
J
o
u
r
n
a
l
o
f
G
l
o
b
a
l
In
f
o
r
m
a
t
i
o
n
M
a
n
a
g
e
m
e
n
t
,
v
o
l
.
2
9
,
n
o
.
6
,
2
0
2
1
,
d
o
i
:
1
0
.
4
0
1
8
/
J
G
IM
.
2
0
2
1
1
1
0
1
.
o
a
3
.
[2
4
]
I.
K
a
p
u
n
g
u
,
E
.
E
v
a
re
s
t
,
N
.
S
h
a
b
a
n
,
a
n
d
A
.
J
.
M
w
a
k
i
s
i
s
i
l
e
,
“
M
o
d
e
l
l
i
n
g
a
n
d
f
o
re
c
a
s
t
i
n
g
c
l
a
i
m
p
a
y
m
e
n
t
s
o
f
T
a
n
z
a
n
i
a
n
a
t
i
o
n
a
l
h
e
a
l
t
h
i
n
s
u
ra
n
c
e
fu
n
d
,
”
T
a
n
z
a
n
i
a
J
o
u
r
n
a
l
o
f
S
c
i
e
n
c
e
,
v
o
l
.
4
9
,
n
o
.
4
,
p
p
.
9
1
1
–
9
2
0
,
O
c
t
.
2
0
2
3
,
d
o
i
:
1
0
.
4
3
1
4
/
t
j
s
.
v
4
9
i
4
.
1
2
.
[2
5
]
M
.
N
a
l
l
u
ri
,
M
.
P
e
n
t
e
l
a
,
a
n
d
N
.
R.
E
l
u
ri
,
“
A
s
c
a
l
a
b
l
e
t
re
e
b
o
o
s
t
i
n
g
s
y
s
t
e
m
:
X
G
b
o
o
s
t
,
”
In
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
R
e
s
e
a
r
c
h
S
t
u
d
i
e
s
i
n
S
c
i
e
n
c
e
,
E
n
g
i
n
e
e
r
i
n
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
7
,
n
o
.
1
2
,
p
p
.
3
6
–
5
1
,
2
0
2
0
,
d
o
i
:
1
0
.
2
2
2
5
9
/
2
3
4
9
-
4
7
6
X
.
0
7
1
2
0
0
5
.
BI
O
G
R
A
P
H
I
ES
O
F
A
U
T
H
O
R
S
L
e
n
i
a
s
Z
h
ou
i
s
a
n
M
T
e
c
h
S
t
ude
nt
i
n
C
l
o
ud
C
o
m
pu
t
i
ng
a
t
H
a
r
a
r
e
I
ns
t
i
t
u
t
e
o
f
T
e
c
hn
ol
o
gy
(
H
I
T
)
,
Z
i
m
ba
bw
e
,
a
h
ol
d
e
r
of
a
B
a
c
he
l
or
of
S
c
i
e
nc
e
H
onor
s
D
e
g
r
e
e
i
n
I
n
f
or
m
a
t
i
on
S
ys
t
e
m
s
f
r
o
m
t
he
W
o
m
e
n
’
s
U
n
i
v
e
r
s
i
t
y
i
n
A
f
r
i
c
a
,
a
nd
a
hol
de
r
of
a
F
u
l
l
T
e
c
h
no
l
og
i
c
a
l
D
i
pl
o
m
a
i
n
T
e
l
e
c
o
m
m
un
i
c
a
t
i
on
s
a
nd
E
l
e
c
t
r
o
ni
c
s
E
ng
i
ne
e
r
i
ng
f
r
o
m
t
he
C
i
t
y
a
nd
G
ui
l
d
I
ns
t
i
t
u
t
e
,
L
o
ndon
.
H
e
i
s
a
C
e
r
t
i
f
i
e
d
I
nf
o
r
m
a
t
i
o
n
S
y
s
t
e
m
s
A
ud
i
t
or
(
C
I
S
A
)
w
i
t
h
25
y
e
a
r
s
of
e
xpe
r
i
e
n
c
e
i
n
t
e
c
h
ni
c
a
l
ope
r
a
t
i
on
s
a
nd
t
e
c
hn
i
c
a
l
a
ud
i
t
s
.
H
e
ha
s
l
e
d
s
e
ve
r
a
l
pr
oj
e
c
t
s
i
n
t
e
l
e
c
om
m
un
i
c
a
t
i
on
s
,
I
T
,
a
nd
a
u
di
t
.
H
i
s
br
o
a
d
r
e
s
e
a
r
c
h
i
n
t
e
r
e
s
t
s
c
o
ve
r
t
op
i
c
s
r
e
l
a
t
i
n
g
t
o
A
I
,
c
ybe
r
s
e
c
u
r
i
t
y
,
I
oT
,
c
l
oud
c
o
m
p
ut
i
ng
,
a
nd
ne
t
w
o
r
k
e
n
gi
n
e
e
r
i
ng
.
H
e
c
a
n
be
c
o
nt
a
c
t
e
d
a
t
e
m
a
i
l
:
z
hou
l
e
ni
a
s
@
gm
a
i
l
.
c
o
m
.
M
ai
n
f
o
r
d
M
u
t
an
d
avar
i
i
s
a
P
hD
S
c
hol
a
r
a
t
S
R
M
I
S
T
U
n
i
v
e
r
s
i
t
y,
I
nd
i
a
,
a
nd
a
L
e
c
t
ur
e
r
a
nd
P
o
s
t
gr
a
dua
t
e
S
t
u
di
e
s
C
oo
r
d
i
na
t
o
r
a
t
t
he
H
a
r
a
r
e
I
ns
t
i
t
u
t
e
o
f
T
e
c
hno
l
og
y
(
H
I
T
)
,
Z
i
m
ba
bw
e
.
W
i
t
h
a
d
va
nc
e
d
de
gr
e
e
s
i
n
C
o
m
pu
t
e
r
S
c
i
e
nc
e
a
nd
S
t
r
a
t
e
gy
a
n
d
I
nn
ova
t
i
on,
h
i
s
r
e
s
e
a
r
c
h
s
p
a
ns
d
a
t
a
a
na
l
yt
i
c
s
,
c
ybe
r
s
e
c
u
r
i
t
y
,
I
oT
,
A
I
,
a
n
d
c
l
o
ud
c
o
m
pu
t
i
ng
.
H
e
i
s
a
m
e
m
be
r
o
f
H
I
T
’
s
C
ybe
r
s
e
c
u
r
i
t
y
a
n
d
A
I
r
e
s
e
a
r
c
h
gr
oup
s
a
nd
a
c
t
i
v
e
l
y
c
on
t
r
i
bu
t
e
s
t
o
na
t
i
ona
l
I
C
T
s
t
a
nd
a
r
d
s
t
hr
ough
t
he
S
t
a
nda
r
d
s
A
s
s
oc
i
a
t
i
on
of
Z
i
m
b
a
bw
e
.
M
i
nf
o
r
d
ha
s
p
ub
l
i
s
he
d
w
i
de
l
y
o
n
t
op
i
c
s
s
uc
h
a
s
da
t
a
l
o
s
s
p
r
e
ve
n
t
i
on
s
ys
t
e
m
s
,
d
i
gi
t
a
l
l
e
a
r
n
i
ng
i
n
f
r
a
s
t
r
u
c
t
u
r
e
,
a
n
d
e
-
h
e
a
l
t
h
s
e
c
u
r
i
t
y
.
H
i
s
w
or
k
br
i
dge
s
a
c
a
de
m
i
c
r
e
s
e
a
r
c
h
w
i
t
h
i
ndu
s
t
r
y
a
pp
l
i
c
a
t
i
ons
,
f
oc
u
s
i
ng
o
n
pr
a
c
t
i
c
a
l
d
i
g
i
t
a
l
s
o
l
u
t
i
o
ns
f
or
e
duc
a
t
i
on
,
t
e
l
e
c
om
m
u
ni
c
a
t
i
o
ns
,
a
n
d
he
a
l
t
h
c
a
r
e
i
n
Z
i
m
ba
bw
e
.
H
e
i
s
a
l
s
o
i
nv
ol
v
e
d
i
n
c
ur
r
i
c
u
l
u
m
de
ve
l
op
m
e
nt
,
po
s
t
g
r
a
dua
t
e
s
upe
r
v
i
s
i
on
,
a
nd
b
ui
l
d
i
ng
a
c
a
de
m
i
c
-
i
nd
us
t
r
y
pa
r
t
ne
r
s
h
i
p
s
.
H
e
c
a
n
be
c
ont
a
c
t
e
d
a
t
e
m
a
i
l
:
m
m
t
a
n
da
v
a
r
i
@g
m
a
i
l
.
c
o
m
.
L
u
c
i
a
M
at
on
d
or
a
i
s
a
gr
a
dua
t
e
w
i
t
h
a
B
T
e
c
h
a
nd
M
T
e
c
h
i
n
S
o
f
t
w
a
r
e
E
ng
i
ne
e
r
i
n
g
f
r
o
m
H
a
r
a
r
e
I
ns
t
i
t
u
t
e
o
f
T
e
c
hno
l
ogy
.
S
h
e
w
or
k
e
d
i
n
t
h
e
i
n
dus
t
r
y
f
or
a
p
e
r
i
od
of
a
y
e
a
r
a
s
a
S
ys
t
e
m
S
uppo
r
t
O
f
f
i
c
e
r
a
n
d
t
h
e
n
c
a
m
e
ba
c
k
t
o
H
a
r
a
r
e
I
ns
t
i
t
ut
e
o
f
T
e
c
h
nol
ogy
a
nd
s
t
a
r
t
e
d
he
r
M
a
s
t
e
r
o
f
T
e
c
hn
ol
ogy
i
n
S
of
t
w
a
r
e
E
ngi
ne
e
r
i
ng
i
n
t
he
I
nf
o
r
m
a
t
i
on
S
e
c
u
r
i
t
y
a
nd
A
s
s
ur
a
nc
e
de
pa
r
t
m
e
nt
.
S
he
a
i
m
e
d
t
o
i
m
pr
ove
he
r
know
l
e
d
ge
a
s
w
e
l
l
a
s
h
e
r
e
x
pe
r
t
i
s
e
i
n
t
he
f
i
e
l
d
of
s
of
t
w
a
r
e
e
ngi
ne
e
r
i
ng
.
S
he
l
ov
e
s
m
e
nt
o
r
i
ng
a
nd
gu
i
d
i
ng
s
t
ude
n
t
s
,
e
s
pe
c
i
a
l
l
y
t
he
gi
r
l
c
h
i
l
d
,
t
o
e
m
pow
e
r
t
he
m
.
S
he
i
s
a
t
e
c
h
e
nt
h
us
i
a
s
t
a
n
d
l
o
ve
s
r
e
s
e
a
r
c
hi
n
g
t
h
e
i
m
p
a
c
t
of
n
e
w
t
e
c
hno
l
og
i
e
s
i
n
e
du
c
a
t
i
o
n
s
ys
t
e
m
s
.
S
he
c
a
n
be
c
o
nt
a
c
t
e
d
a
t
e
m
a
i
l
:
I
m
a
t
ond
or
a
@h
i
t
.
a
c
.
z
w
.
Evaluation Warning : The document was created with Spire.PDF for Python.