I
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t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
d
van
c
e
s
i
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p
p
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S
c
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s
(
I
JA
A
S
)
V
ol
.
14
, N
o.
4
,
D
e
c
e
m
be
r
20
25
, pp.
1241
~
1250
I
S
S
N
:
2252
-
8814
,
D
O
I
:
10.11591/
ij
a
a
s
.
v14.
i
4
.
pp1241
-
1250
1241
Jou
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:
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unt
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A
m
be
dka
r
C
ha
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r
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r
of
e
s
s
or
, A
ndhr
a
U
ni
ve
r
s
i
t
y, V
i
s
a
kha
pa
t
na
m
, I
ndi
a
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
A
pr
17, 2025
R
e
vi
s
e
d
O
c
t
27, 2025
A
c
c
e
pt
e
d
N
ov 9, 2025
Ensuring
the
security
and
privacy
for
the
patient
medical
record
s
and
medical
reports
data
is
a
crucial
challenge
as
cloud
-
based
healt
hcare
technologie
s
become
more
prevale
nt.
For
cloud
-
hosted
medical
data,
internet
of
things
(I
o
T)
and
artificial
intell
igence
(AI)
technologies
s
hows
best solutions for the challenges
in the medical
domain. This study
su
ggests
a
Secure
and
Transparent
Multi
-
Key
Authentication
Framework
that
makes
use
of
AI
.
Using
Z
-
sc
ore
normalization,
the
framework
first
preproc
esses
the
data
before
clustering
to
create
a
multi
-
level
multi
-
key
security
structure.
The
physics
-
informed
triangulation
aggrega
tion
neural
net
work
(PITANN)
model
in
the
study
reduces
computat
ion
costs
by
mini
mizing
overhead,
ensuring
secure
handli
ng
of
location
-
based
and
medic
al
da
ta
for
enhanced
data
classification
and
encryption
effectivenes
s.
A
mult
i
-
key
derivation
of
an
elliptic
curve,
the
ElGamal
cryptography
scheme
is
presented,
which
allows
for
safe
multi
-
key
encryption
with
little
incre
ase
in
the
length
of
the
ciphertext.
This
method
guarantees
safe,
confid
ential
access
to
cloud
-
hosted
encrypted
health
information.
An
envis
ioned
amalgamat
ion
improves
flexibi
lity
by
enhancing
performance
metrics
such
as
speed
of
computation
while
safeguarding
patient
information
through
enhanced
security
measures
and
ensuring
precise
medical
record
int
egrity
within virtu
al healthcare systems.
K
e
y
w
o
r
d
s
:
C
lo
ud
-
ba
s
e
d he
a
lt
hc
a
r
e
E
ll
ip
ti
c
c
ur
ve
c
r
ypt
ogr
a
phy
M
ul
ti
-
ke
y e
nc
r
ypt
io
n
P
hys
ic
s
in
f
or
m
e
d
tr
ia
ngul
a
ti
on
a
ggr
e
ga
ti
on ne
ur
a
l
ne
twor
ks
S
e
c
ur
e
a
ut
he
nt
ic
a
ti
on
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
N
it
a
la
ks
he
s
w
a
r
a
R
a
o K
ol
ukul
a
D
e
pa
r
tm
e
nt
of
C
om
put
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r
S
c
ie
nc
e
a
nd E
ngi
ne
e
r
in
g, S
c
hool
of
T
e
c
hnol
ogy
,
G
I
T
A
M
U
ni
ve
r
s
it
y
V
is
a
kha
pa
tn
a
m
,
I
ndi
a
E
m
a
il
:
kol
ukul
a
ni
tl
a
@
gm
a
il
.c
om
1.
I
N
T
R
O
D
U
C
T
I
O
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An
in
ve
s
t
ig
a
ti
on
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xa
m
in
e
s
h
ow
a
n
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r
t
i
f
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in
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nc
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(
A
I
)
-
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p
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h
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m
p
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m
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c
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is
ks
[
1
]
.
R
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
2252
-
8814
I
nt
J
A
dv A
ppl
S
c
i
,
V
ol
.
14
, N
o.
4
,
D
e
c
e
m
be
r
20
25
:
1241
-
1250
1242
r
o
bus
t
c
r
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og
r
a
phi
c
m
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th
o
ds
e
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ur
e
pr
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l
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om
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r
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nt
s
s
ha
r
e
d
a
m
o
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va
r
io
us
us
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r
s
[
2
]
.
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te
l
li
ge
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t
s
o
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twa
r
e
s
a
f
e
g
ua
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ds
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hr
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ti
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on
m
e
th
ods
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ik
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o
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;
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ls
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d
c
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r
s
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r
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[
3
]
.
E
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ha
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in
g
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p
r
o
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ti
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s
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gni
f
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tl
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s
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a
lo
ngs
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th
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nt
ic
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ti
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m
s
.
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r
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r
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hod
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(
A
I
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m
ode
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ur
e
s
p
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c
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pa
ti
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he
a
lt
h
in
f
o
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m
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n.
A
dva
nc
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d
AI
-
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lt
if
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gn
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bo
ls
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s
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d
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a
f
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y
p
r
ot
oc
o
ls
[
4
]
.
U
s
e
r
s
w
ho
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ut
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c
a
te
t
he
m
s
e
lv
e
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th
r
o
ug
h
r
e
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tr
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d
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th
ods
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nc
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f
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m
a
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om
una
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h
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d
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c
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s
s
.
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o
p
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ds
[
5
]
.
E
ns
ur
in
g
s
e
a
m
le
s
s
a
va
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la
b
il
it
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of
pa
t
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’
s
he
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lt
hc
a
r
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da
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c
t
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on
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ll
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r
uc
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c
in
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.
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he
doc
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p
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n
d
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f
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d
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e
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s
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[
6
]
.
U
ti
li
z
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vi
a
c
lo
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bl
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AI
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none
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pe
r
s
is
t
[
7
]
.
In
de
a
l
in
g
w
i
th
e
le
c
tr
on
ic
he
a
lt
h
r
e
c
o
r
ds
m
a
na
ge
m
e
nt
,
th
e
e
l
li
pt
ic
c
ur
ve
c
r
y
pt
og
r
a
p
hy
(
E
C
C
)
p
r
o
te
c
te
d
c
r
yp
to
gr
a
ph
y
d
is
c
us
s
e
d
be
lo
w
gua
r
a
n
te
e
s
bo
th
s
a
f
e
ty
a
n
d
r
e
li
a
bi
li
ty
.
N
ow
a
da
ys
,
m
a
na
gi
ng
pa
ti
e
nt
da
ta
e
f
f
ic
ie
nt
ly
ha
s
be
c
om
e
m
uc
h
e
a
s
ie
r
due
to
s
ig
ni
f
ic
a
nt
a
dva
nc
e
m
e
nt
s
in
AI
a
lo
ngs
id
e
in
c
r
e
a
s
e
d
us
e
of
c
lo
ud
-
ba
s
e
d
he
a
lt
h
in
f
o
s
to
r
a
ge
s
ys
te
m
s
.
N
e
ve
r
th
e
le
s
s
,
c
onc
e
r
ns
a
bout
da
ta
s
a
f
e
ty
,
c
onf
id
e
nt
ia
li
ty
,
a
nd
una
ut
hor
iz
e
d
us
e
pe
r
s
is
t,
ne
c
e
s
s
it
a
ti
ng
r
obus
t
id
e
nt
if
ic
a
ti
on
a
ut
he
nt
ic
a
ti
on
m
e
th
ods
.
C
om
m
onl
y
e
m
pl
oyi
ng
w
e
a
k
pa
s
s
w
or
d
s
m
a
ke
s
s
ys
te
m
s
s
us
c
e
pt
ib
le
to
th
r
e
a
ts
s
u
c
h
as
id
e
nt
it
y
th
e
f
t
a
nd
phi
s
hi
ng
s
c
a
m
s
[
8]
.
2.
L
I
T
E
R
A
T
U
R
E
S
U
R
V
E
Y
T
he
s
tu
dy
[
9]
e
va
lu
a
te
d
th
e
s
a
f
e
ty
of
pa
ti
e
nt
da
ta
f
il
e
s
,
hi
ghl
ig
ht
in
g
is
s
ue
s
r
e
la
te
d
to
c
onve
nt
io
na
l
w
a
te
r
m
a
r
ks
.
By
e
m
pl
oyi
ng
a
dva
nc
e
d
m
a
c
hi
ne
le
a
r
ni
ng
te
c
hni
que
s
s
uc
h
as
de
e
p
ne
ur
a
l
ne
twor
ks
a
nd
in
c
or
por
a
ti
ng
in
vi
s
ib
le
w
a
te
r
m
a
r
ks
f
or
ve
r
if
ic
a
ti
on
pur
pos
e
s
,
th
e
ir
a
ppr
oa
c
h
e
nha
nc
e
d
bot
h
r
e
s
i
s
ta
nc
e
a
g
a
in
s
t
a
tt
a
c
ks
a
nd
e
ns
ur
e
d
s
e
c
ur
e
id
e
nt
if
ic
a
ti
on
pr
oc
e
s
s
e
s
.
D
e
s
pi
te
a
dva
nc
e
m
e
nt
s
in
bl
oc
kc
h
a
in
te
c
hnol
ogy
a
nd
c
r
ypt
ogr
a
phi
c
m
e
th
ods
f
or
e
nha
nc
in
g
s
e
c
ur
it
y,
c
ha
ll
e
nge
s
r
e
la
te
d
to
c
om
pa
ti
bi
li
ty
a
nd
pe
r
f
or
m
a
nc
e
r
e
m
a
in
e
d
unr
e
s
ol
ve
d.
T
h
e
s
tu
dy
[
10]
di
s
c
u
s
s
e
d
c
onc
e
r
ns
r
e
la
te
d
to
s
e
c
ur
in
g
he
a
lt
h
da
ta
th
r
ough
in
te
r
ne
t
of
th
in
gs
(
I
oT
)
te
c
hnol
ogy
in
m
e
di
c
a
l
s
ys
te
m
s
.
P
r
opos
e
d
w
a
s
an
a
nonymou
s
s
ys
te
m
ut
il
iz
in
g
c
r
ypt
ogr
a
phy
f
or
its
s
e
c
ur
it
y
f
e
a
tu
r
e
s
.
T
he
y
e
ns
ur
e
d
e
f
f
e
c
ti
ve
ne
s
s
w
it
hout
c
om
pr
om
is
in
g
s
a
f
e
ty
m
e
a
s
ur
e
s
.
N
e
ve
r
th
e
le
s
s
,
s
c
a
la
bi
li
ty
a
nd
c
om
put
a
ti
ona
l
ove
r
he
a
d c
onc
e
r
ns
r
e
m
a
in
e
d.
I
n
[
11]
in
ve
s
ti
ga
te
d
pr
oc
e
s
s
in
g
of
c
li
ni
c
a
l
not
e
s
us
in
g
bi
di
r
e
c
ti
ona
l
lo
ng
s
hor
t
-
te
r
m
m
e
m
or
y
(
B
iL
S
T
M
)
.
AI
,
m
a
c
hi
ne
le
a
r
ni
ng
a
nd
in
te
r
ne
t
of
th
in
gs
pl
a
ys
v
e
r
y
vi
ta
l
r
ol
e
f
or
pa
ti
e
nt
r
e
c
or
ds
m
oni
to
r
in
g
i
n
m
e
di
c
a
l
dom
a
in
.
I
n
[
12]
de
s
ig
ne
d
a
qua
nt
um
-
s
a
f
e
m
ul
ti
-
f
a
c
to
r
a
ut
he
nt
ic
a
ti
on
m
e
c
ha
ni
s
m
f
or
m
e
di
c
a
l
I
oT
.
T
he
ir
a
ppr
oa
c
h
im
pr
ove
d
s
e
c
ur
it
y
but
e
nc
ount
e
r
e
d
is
s
ue
s
in
c
o
m
pl
e
xi
ty
a
nd
e
f
f
ic
ie
nc
y.
F
ur
th
e
r
r
e
s
e
a
r
c
h
w
a
s
r
e
qui
r
e
d f
or
opt
im
iz
a
ti
on.
In
[
13]
s
ugge
s
te
d
a
s
a
f
e
a
ut
he
nt
ic
a
ti
on
a
ppr
oa
c
h
f
or
c
lo
ud
-
ba
s
e
d
e
le
c
tr
oni
c
he
a
lt
h
r
e
c
or
ds
.
T
he
ir
s
ol
ut
io
n
in
c
r
e
a
s
e
d
s
e
c
ur
it
y
a
nd
m
in
im
iz
e
d
ke
y
e
xc
h
a
nge
,
but
it
ha
d
s
c
a
la
bi
li
ty
is
s
ue
s
.
F
ur
th
e
r
r
e
s
e
a
r
c
h
w
a
s
r
e
qui
r
e
d f
or
r
e
a
l
-
w
or
ld
a
ppl
ic
a
ti
on.
2.1.
P
r
ob
le
m
s
t
at
e
m
e
n
t
H
ig
h
c
om
put
a
ti
ona
l
c
om
pl
e
xi
ty
,
s
c
a
l
a
bi
li
ty
pr
obl
e
m
s
,
a
nd
vul
n
e
r
a
bi
li
ti
e
s
in
ke
y
ge
ne
r
a
ti
on a
r
e
s
om
e
of
th
e
di
f
f
ic
ul
ti
e
s
f
a
c
in
g
he
a
lt
hc
a
r
e
da
t
a
m
a
na
ge
m
e
nt
.
T
he
s
e
a
r
e
a
ddr
e
s
s
e
d
by
s
e
c
ur
e
a
nd
tr
a
ns
p
a
r
e
nt
m
ul
ti
-
ke
y
a
ut
he
nt
ic
a
ti
on
f
or
c
lo
ud
-
hos
te
d
m
e
di
c
a
l
da
ta
us
in
g
AI
,
w
hi
c
h
u
s
e
s
phys
ic
s
-
in
f
or
m
e
d
tr
ia
ngul
a
ti
on
a
ggr
e
ga
ti
on
ne
ur
a
l
ne
twor
k
(
P
I
T
A
N
N
)
ba
s
e
d
c
la
s
s
if
ic
a
ti
on
a
nd
m
ul
ti
le
ve
l
m
ul
ti
-
ke
y
s
e
c
ur
it
y.
S
e
c
ur
e
e
nc
r
ypt
io
n
a
nd
li
tt
le
c
ip
he
r
te
xt
e
xpa
n
s
io
n
a
r
e
gua
r
a
nt
e
e
d
by
a
m
ul
ti
-
ke
y
de
r
iv
a
ti
on
e
ll
ip
ti
c
c
ur
ve
E
lG
a
m
a
l
c
r
ypt
ogr
a
phy te
c
hni
que
.
T
hi
s
a
ppr
oa
c
h e
nh
a
nc
e
s
pr
oduc
ti
vi
ty
,
c
onf
id
e
nt
ia
li
ty
, a
nd a
c
c
ur
a
c
y w
hi
c
h i
s
a
s
e
c
ur
e
a
nd s
c
a
la
bl
e
onl
in
e
h
e
a
lt
h c
a
r
e
a
dm
in
is
tr
a
ti
on t
ool
.
3.
P
R
O
P
O
S
E
D
M
E
T
H
O
D
T
h
e
m
obi
le
he
a
lt
h
(
M
H
E
A
L
T
H
)
a
nd
U
C
I
d
a
t
a
s
e
t
i
s
pr
e
pr
o
c
e
s
s
e
d
us
in
g
Z
-
s
c
or
e
no
r
m
a
li
z
a
ti
o
n,
a
n
d
da
t
a
i
s
c
lu
s
t
e
r
e
d
f
or
m
ul
t
il
e
v
e
l
m
u
lt
i
-
ke
y
s
e
c
ur
i
ty
.
T
o
a
s
s
ur
e
th
e
pr
i
va
c
y
of
th
e
tr
a
n
s
m
i
s
s
i
on
a
nd
s
t
or
a
ge
,
th
e
s
y
s
t
e
m
ut
i
li
s
e
s
P
I
T
A
N
N
f
or
pr
ot
e
c
t
e
d
lo
c
a
ti
o
n
a
nd
m
e
di
c
a
l
d
a
t
a
c
l
a
s
s
if
i
c
a
t
io
n.
T
hi
s
S
e
c
ti
o
n w
il
l
ou
tl
i
ne
ho
w
t
he
de
r
i
va
ti
o
n
of
m
u
lt
i
pl
e
k
e
y
s
p
r
ot
e
c
t
s
th
e
in
f
or
m
a
ti
on
of
t
he
c
lu
s
t
e
r
e
d
d
a
t
a
w
hi
c
h
i
s
s
to
r
e
d
in
th
e
c
lo
ud
us
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
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A
r
ti
fi
c
ia
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te
ll
ig
e
nc
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bas
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d m
ul
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u
r
it
y
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ot
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(
R
av
i
K
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an B
agadi)
1243
e
ll
ip
ti
c
c
ur
v
e
c
r
yp
to
gr
a
phy
w
it
h
s
m
a
l
l
s
iz
e
d
c
ip
he
r
te
xt
e
xp
a
n
s
i
o
n.
D
a
t
a
a
c
qu
i
s
it
i
on
f
ol
lo
w
e
d
b
y
pr
e
pr
o
c
e
s
s
i
ng
,
s
e
c
ur
e
m
u
lt
i
pl
e
k
e
y
g
e
n
e
r
a
ti
o
n
s
a
nd
th
e
e
n
c
r
ypt
io
n
of
d
a
ta
a
r
e
s
how
n
in
th
e
s
c
he
m
a
ti
c
r
e
pr
e
s
e
nt
a
ti
on
a
s
pe
r
F
ig
ur
e
1
.
I
n
da
ta
c
ol
le
c
ti
on
in
it
ia
ll
y
it
is
c
ol
le
c
te
d
two
da
ta
s
e
ts
na
m
e
ly
M
H
E
A
L
T
H
a
nd
U
C
I
.
I
n
s
e
c
ur
e
m
ul
ti
ke
y
ge
ne
r
a
ti
on
it
is
u
s
e
d
E
ll
ip
ti
c
c
ur
ve
E
lG
a
m
a
l
c
r
ypt
ogr
a
ph
y
.
F
or
da
ta
e
nc
r
ypt
io
n
to
c
onve
r
t
pl
a
in
te
xt
to
c
ip
he
r
t
e
xt
, i
t
is
us
e
d
P
I
T
A
N
N
.
F
ig
ur
e
1. O
ve
r
a
ll
s
c
he
m
a
ti
c
r
e
pr
e
s
e
nt
a
ti
on of
pr
opos
e
d m
e
th
od
ol
ogy
3.1.
D
at
a
c
ol
le
c
t
io
n
T
he
M
H
E
A
L
T
H
da
ta
s
e
t
is
b
a
s
e
d
upon
a
c
ti
vi
ty
r
e
c
ogni
ti
on
a
nd
he
a
lt
hc
a
r
e
w
it
h
U
C
I
m
a
c
hi
ne
le
a
r
ni
ng
r
e
pos
it
or
ie
s
da
ta
s
e
ts
[
14]
,
[
15]
.
T
he
s
e
da
ta
s
e
ts
c
ont
a
in
r
ic
h
in
f
or
m
a
ti
on
th
a
t
c
a
n
be
us
e
d
f
or
c
r
e
a
ti
ng
pr
e
di
c
ti
on
m
ode
l
on
hi
gh
-
qua
li
ty
m
e
di
c
a
l
in
f
or
m
a
ti
on.
T
he
pr
oc
e
s
s
in
g
e
ns
ur
e
s
th
a
t
th
e
s
e
da
ta
a
r
e
a
c
c
ur
a
te
,
r
e
li
a
bl
e
,
a
nd
c
ont
r
ib
ut
e
to
a
dva
nc
e
qua
li
ty
m
e
di
c
a
l
s
ol
ut
io
ns
.
T
he
M
H
E
A
L
T
H
da
ta
s
e
t
is
a
n
op
e
n
da
ta
s
e
t
a
nd
it
is
s
p
e
c
if
ic
a
ll
y
de
s
ig
ne
d
f
or
hum
a
n
a
c
ti
vi
ty
r
e
c
ogni
ti
on
und
e
r
w
e
a
r
a
bl
e
s
e
ns
or
s
s
c
e
na
r
io
s
.
T
hi
s
da
ta
s
e
t
is
ut
il
iz
e
d
to
pr
om
ot
e
s
c
ie
nt
if
ic
r
e
s
e
a
r
c
h
a
c
ti
vi
ti
e
s
in
m
obi
le
he
a
l
th
m
oni
to
r
in
g
a
nd
a
c
ti
vi
ty
id
e
nt
if
ic
a
ti
on.
T
he
U
C
I
m
a
c
hi
ne
le
a
r
ni
ng
r
e
pos
it
or
y
s
e
r
ve
s
a
s
a
f
unda
m
e
nt
a
l
r
e
s
o
ur
c
e
f
or
num
e
r
ous
da
ta
s
e
ts
ut
il
iz
e
d
in
m
a
c
hi
ne
le
a
r
ni
ng
r
e
s
e
a
r
c
h
a
c
ti
vi
ti
e
s
a
nd
te
s
ti
ng
pur
pos
e
s
.
T
he
U
ni
ve
r
s
it
y
of
C
a
li
f
or
ni
a
,
I
r
vi
ne
hos
ts
th
is
r
e
pos
it
or
y
w
hi
c
h
de
li
ve
r
s
a
br
oa
d
s
pe
c
tr
um
of
da
t
a
s
e
t
s
e
n
c
om
pa
s
s
in
g
c
la
s
s
if
ic
a
ti
on,
r
e
gr
e
s
s
io
n,
c
lu
s
te
r
in
g
,
a
nd
ti
m
e
s
e
r
ie
s
a
na
ly
s
is
doma
in
s
.
3.2.
P
r
e
p
r
oc
e
s
s
in
g
P
r
e
pr
oc
e
s
s
in
g
A
I
-
ope
r
a
te
d
a
ut
he
nt
ic
a
ti
on
a
nd
s
e
c
ur
e
ke
y
m
a
n
a
ge
m
e
nt
f
or
or
ga
ni
z
e
d
m
e
di
c
a
l
da
ta
gua
r
a
nt
e
e
s
.
P
r
e
pr
oc
e
s
s
in
g
in
f
or
m
a
ti
on
ha
s
be
e
n
s
uppl
ie
d
a
s
s
ta
te
d
be
lo
w
.
I
n
Z
-
s
c
or
e
nor
m
a
li
z
a
ti
on
,
th
e
pr
e
pr
oc
e
s
s
in
g
ph
a
s
e
of
nor
m
a
li
z
a
ti
on
in
vol
ve
s
br
e
a
ki
ng
th
e
da
t
a
in
to
num
e
r
ic
a
l
pr
ope
r
ti
e
s
th
a
t
c
a
n
b
e
u
s
e
d
to
c
onve
r
t
da
ta
va
lu
e
s
in
to
a
s
pe
c
if
ic
r
a
nge
.
W
he
n
nor
m
a
li
z
in
g
da
ta
,
m
a
ny
te
c
hni
que
s
a
r
e
c
om
m
onl
y
us
e
d, s
uc
h
a
s
de
c
im
a
l
s
c
a
li
ng,
Z
-
s
c
or
e
ge
ne
r
a
li
z
a
ti
on
a
nd
m
in
im
um
-
m
a
xi
m
um
nor
m
a
li
z
a
ti
on.
In
(
1
)
s
how
s
how
Z
-
s
c
or
e
nor
m
a
li
z
a
ti
on f
r
om
a
tt
r
ib
ut
e
to
in
to
a
pr
e
vi
ous
ly
unknown r
a
nge
t
r
a
ns
f
or
m
s
t
o a
va
lu
e
[
16]
.
′
=
−
(
)
(
1)
W
he
r
e
′
r
e
s
ul
t
of
nor
m
a
li
z
a
ti
on
va
lu
e
of
.
is
th
e
va
lu
e
to
be
nor
m
a
li
z
e
d
in
a
tt
r
ib
ut
e
w
hi
c
h
is
th
e
m
e
a
n va
lu
e
of
a
tt
r
ib
ut
e
a
nd
(
)
is
t
he
s
ta
nd
a
r
d de
vi
a
ti
on f
or
a
tt
r
ib
u
te
.
3.3.
P
h
ys
ic
s
in
f
or
m
e
d
t
r
ia
n
gu
la
t
io
n
aggr
e
gat
io
n
n
e
u
r
al
n
e
t
w
or
k
s
P
hys
ic
a
l
r
ul
e
s
a
r
e
in
c
or
por
a
te
d
in
to
ne
ur
a
l
ne
twor
k
a
r
c
hi
te
c
tu
r
e
to
s
ol
ve
pa
r
ti
a
l
di
f
f
e
r
e
nc
e
e
qua
ti
ons
(
P
D
E
s
)
,
known
a
s
phys
ic
s
-
in
f
or
m
e
d
ne
ur
a
l
ne
twor
ks
(
P
I
N
N
S
)
[
17]
.
B
ut
w
he
n
it
c
om
e
s
to
ha
ndl
in
g
c
om
pl
e
x
ge
om
e
tr
ic
a
nd
odd
dom
a
in
s
,
t
r
a
di
ti
ona
l
pi
ns
of
te
n
s
tr
uggl
e
w
it
h
a
c
c
ur
a
c
y
a
nd
e
f
f
ic
ie
nc
y.
T
o
de
a
l
w
it
h
th
e
s
e
D
a
t
a
C
o
l
l
e
c
t
i
o
n
M
H
E
A
L
T
H
U
C
I
P
r
e
p
r
o
c
e
s
s
i
n
g
w
i
t
h
Z
-
s
c
o
r
e
m
e
t
h
o
d
P
r
e
p
r
o
c
e
s
s
i
n
g
M
u
l
t
i
K
e
y
D
e
r
i
v
a
t
i
o
n
E
l
l
i
p
t
i
c
C
u
r
v
e
E
l
G
a
m
a
l
c
r
y
p
t
o
g
r
a
p
h
y
S
e
c
u
r
e
M
u
l
t
i
K
e
y
G
e
n
e
r
a
t
e
P
h
y
s
i
c
s
I
n
f
o
r
m
e
d
T
r
i
a
n
g
u
l
a
t
i
o
n
A
g
g
r
e
g
a
t
i
o
n
N
e
u
r
a
l
N
e
t
w
o
r
k
s
(
P
I
T
A
N
N
)
E
n
c
r
y
p
t
i
o
n
f
o
r
d
a
t
a
S
e
c
u
r
e
d
a
t
a
s
t
o
r
e
i
n
c
l
o
u
d
D
a
t
a
i
s
c
l
u
s
t
e
r
e
d
f
o
r
m
u
l
t
i
l
e
v
e
l
m
u
l
t
i
k
e
y
s
e
c
u
r
i
t
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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14
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4
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be
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20
25
:
1241
-
1250
1244
is
s
ue
s
,
w
e
pr
ovi
de
P
I
T
A
N
N
,
a
ne
w
s
tr
uc
tu
r
e
th
a
t
im
pr
ove
s
P
I
N
N
s
us
in
g
tr
ia
ngl
e
-
ba
s
e
d
a
ggr
e
ga
ti
on
a
nd
dom
a
in
di
s
c
r
e
ti
ona
r
y m
e
th
ods
. T
he
pur
po
s
e
of
t
he
pi
t
is
t
o s
ol
v
e
P
D
E
i
n ge
ne
r
a
l
a
s
s
how
n by (
2)
.
(
)
=
(
)
∈
Ω
(
2)
W
he
r
e
is
a
di
f
f
e
r
e
nt
ia
l
ope
r
a
to
r
,
(
)
is
th
e
s
ol
ut
io
n
f
unc
ti
on,
a
nd
(
)
r
e
pr
e
s
e
nt
s
s
our
c
e
te
r
m
s
or
e
xt
e
r
na
l
f
or
c
e
s
.
I
n
t
r
ia
ngul
a
ti
on
,
P
I
T
A
N
N
us
e
s
D
e
l
a
una
y
tr
ia
ngul
a
ti
on
to
e
xt
r
a
c
t
a
c
ol
le
c
ti
on
of
non
-
ove
r
la
ppi
ng
tr
ia
ngul
a
r
e
le
m
e
nt
s
f
r
om
th
e
c
om
put
in
g
dom
a
in
Ω
.
T
hi
s
m
a
ke
s
it
pos
s
ib
le
to
e
xt
r
a
c
t
f
e
a
tu
r
e
s
lo
c
a
ll
y
a
nd
im
pr
ove
s
th
e
ne
ur
a
l
ne
twor
k'
s
a
bi
li
ty
to
e
f
f
ic
ie
nt
ly
le
a
r
n
in
tr
ic
a
te
bounda
r
y
in
te
r
a
c
ti
ons
[
18]
.
F
or
e
a
c
h
tr
ia
ngul
a
r
e
le
m
e
nt
, w
e
a
ppr
oxi
m
a
te
t
he
s
ol
ut
io
n
(
)
us
in
g a
s
e
t
of
ba
s
is
f
unc
ti
ons
s
how
n i
n (
3)
.
(
)
≈
∑
(
)
=
1
(
3)
W
he
r
e
(
)
is
a
s
pe
c
if
ic
s
pa
ti
a
l
poi
nt
,
th
e
a
ppr
oxi
m
a
te
d
s
ol
ut
io
n
f
unc
ti
on,
is
th
e
to
ta
l
num
be
r
of
node
s
,
(
)
is
th
e
s
ha
pe
f
unc
ti
on
a
s
s
o
c
ia
te
d
w
it
h
th
e
tr
ia
ngul
a
ti
on,
w
hi
c
h
a
r
e
us
e
d
to
in
te
r
pol
a
te
th
e
s
ol
ut
io
n
w
it
hi
n
e
a
c
h
tr
ia
ngul
a
r
e
le
m
e
nt
,
is
th
e
noda
l
v
a
lu
e
o
f
th
e
s
ol
ut
io
n
f
unc
ti
on
a
t
th
e
tr
ia
ngul
a
ti
on
node
.
I
n
a
ggr
e
g
a
ti
on
,
P
I
T
A
N
N
a
ggr
e
ga
te
s
lo
c
a
l
s
ol
ut
io
ns
de
r
iv
e
d
f
r
om
in
di
vi
dua
l
tr
ia
ngul
a
r
e
le
m
e
nt
s
r
a
th
e
r
th
a
n t
r
e
a
ti
ng t
he
e
nt
i
r
e
doma
in
a
s
a
s
in
gl
e
e
nt
it
y
[
19
]
. T
he
f
in
a
l
s
ol
ut
io
n
(
)
is
c
om
put
e
d a
s
a
w
e
ig
ht
e
d s
um
a
s
s
how
n i
n (
4)
.
(
)
=
∑
(
)
=
1
(
4)
W
he
r
e
is
t
he
a
ggr
e
ga
ti
on w
e
ig
ht
s
l
e
a
r
ne
d dur
in
g t
r
a
in
in
g,
is
t
he
numbe
r
of
s
uc
h e
le
m
e
nt
s
u
s
e
d i
n t
he
a
ggr
e
ga
ti
on pr
oc
e
s
s
.
3.4.
M
u
lt
i
k
e
y d
e
r
iv
at
io
n
e
ll
ip
t
ic
c
u
r
ve
E
lG
am
al
c
r
yp
t
ogr
a
p
h
y
E
C
C
is
a
publi
c
-
ke
y c
r
ypt
os
ys
te
m
t
ha
t
us
e
s
e
ll
ip
ti
c
c
ur
ve
s
ove
r
f
in
it
e
f
ie
ld
s
, w
hi
c
h ha
ve
a
n a
lg
e
br
a
ic
s
tr
uc
tu
r
e
.
S
hor
te
r
ke
y
le
ngt
hs
a
nd
r
obus
t
s
e
c
ur
it
y
m
a
ke
it
e
f
f
e
c
ti
ve
in
c
ont
e
xt
s
w
it
h
li
m
it
e
d
r
e
s
our
c
e
s
,
s
uc
h
m
obi
le
de
vi
c
e
s
a
nd
I
nt
e
r
ne
t
of
T
hi
ngs
pl
a
tf
or
m
s
.
K
now
n
f
or
it
s
a
s
ym
m
e
tr
ic
e
nc
r
ypt
io
n,
th
e
E
l
G
a
m
a
l
c
r
ypt
os
ys
te
m
ba
s
e
s
it
s
s
e
c
ur
it
y
on
th
e
di
f
f
ic
ul
ty
of
s
ol
vi
n
g
th
e
di
s
c
r
e
te
lo
ga
r
it
hm
p
r
obl
e
m
.
T
oge
th
e
r
,
th
e
y
im
pr
ove
s
e
c
ur
it
y
a
nd
c
om
put
a
ti
ona
l
e
f
f
ic
ie
nc
y
in
e
ll
ip
ti
c
c
ur
v
e
E
lG
a
m
a
l
(
E
C
-
E
l
G
a
m
a
l)
e
nc
r
ypt
io
n.
T
he
m
a
th
e
m
a
ti
c
a
l
c
h
a
r
a
c
te
r
is
ti
c
s
of
e
ll
ip
ti
c
c
ur
ve
s
a
s
gi
ve
n
by
th
e
f
ol
lo
w
in
g
(
5)
w
hi
c
h
s
e
r
ve
a
s
th
e
f
ounda
ti
on f
or
E
C
C
.
2
=
3
+
+
(
5)
W
he
r
e
,
a
r
e
th
e
c
oor
di
na
te
s
of
a
poi
nt
on
th
e
e
ll
ip
ti
c
c
ur
ve
a
nd
,
a
r
e
th
e
poi
nt
s
w
hi
c
h
f
or
m
s
th
e
s
pe
c
if
ic
s
ha
pe
of
th
e
c
ur
ve
.
T
he
EC
-
E
lG
a
m
a
l
C
r
ypt
os
y
s
te
m
E
C
-
E
l
G
a
m
a
l
is
a
m
odi
f
ic
a
ti
on
o
f
th
e
E
lG
a
m
a
l
c
r
ypt
os
ys
te
m
th
a
t
us
e
s
e
ll
ip
ti
c
c
ur
ve
poi
nt
m
ul
ti
pl
ic
a
ti
on
in
pl
a
c
e
of
m
odul
a
r
a
r
it
hm
e
ti
c
[
20]
.
K
e
y
ge
ne
r
a
ti
on
,
s
e
le
c
t
a
n
e
ll
ip
ti
c
c
ur
ve
ove
r
a
f
in
it
e
f
ie
ld
.C
hoos
e
a
ba
s
e
poi
nt
on
w
it
h
a
la
r
ge
pr
im
e
or
de
r
.
S
e
le
c
t
a
pr
iv
a
te
ke
y
,
a
r
a
ndom
in
te
ge
r
.
C
om
put
e
th
e
p
ubl
ic
ke
y
=
.
E
nc
r
ypt
io
n:
r
e
pr
e
s
e
nt
th
e
pl
a
in
te
xt
m
e
s
s
a
ge
a
s
a
poi
nt
on
th
e
c
ur
ve
.
C
hoos
e
a
r
a
ndom
in
t
e
ge
r
k
.C
om
put
e
th
e
c
ip
he
r
te
xt
a
s
a
pa
ir
of
poi
nt
s
(
1
,
2
)
:
1
=
2
=
+
.
I
n
D
e
c
r
ypt
io
n
,
C
om
put
e
=
2
−
1
.
W
he
r
e
1
,
2
is
th
e
f
ir
s
t
a
nd
s
e
c
ond
c
om
pone
nt
of
th
e
c
ip
he
r
te
xt
.
M
is
th
e
pl
a
in
te
xt
m
e
s
s
a
ge
.
k
is
th
e
r
a
ndom
in
te
ge
r
.
is
publ
ic
ke
y.
is
a
ba
s
e
poi
nt
of
c
ur
ve
[
21]
–
[
25]
.
3.5.
C
om
p
u
t
at
io
n
al
c
om
p
le
xi
t
y
T
he
pr
opos
e
d
P
I
T
A
N
N
m
ode
l
pe
r
f
or
m
s
tr
a
in
in
g
w
it
h
a
c
o
m
pl
e
xi
ty
of
a
ppr
ox
im
a
te
ly
O
(
n
×
t
)
,
w
he
r
e
n
i
s
th
e
num
be
r
of
tr
a
in
in
g
s
a
m
pl
e
s
a
nd
t
is
th
e
nu
m
be
r
of
tr
ia
ngul
a
r
e
le
m
e
nt
s
us
e
d
in
dom
a
in
di
s
c
r
e
ti
z
a
ti
on.
T
he
E
C
C
-
E
lG
a
m
a
l
e
nc
r
ypt
io
n
s
te
p
pe
r
f
o
r
m
s
poi
nt
m
ul
ti
pl
ic
a
ti
on
on
e
ll
ip
ti
c
c
ur
ve
s
w
it
h
c
om
pl
e
xi
ty
O
(
k
lo
g
k
)
,
w
he
r
e
k
is
th
e
ke
y
s
iz
e
.
O
ve
r
a
ll
,
th
e
c
om
bi
ne
d
f
r
a
m
e
w
or
k
a
c
hi
e
ve
s
a
ne
a
r
-
li
ne
a
r
c
om
pl
e
xi
ty
w
it
h
r
e
s
pe
c
t
to
da
ta
s
e
t
s
i
z
e
,
m
a
ki
ng
it
f
e
a
s
ib
le
f
or
la
r
ge
-
s
c
a
le
he
a
lt
hc
a
r
e
d
a
ta
pr
oc
e
s
s
in
g
on
m
ode
r
n c
lo
ud pla
tf
or
m
s
[
26]
.
4.
R
E
S
U
L
T
S
A
N
D
D
I
S
C
U
S
S
I
O
N
S
T
he
s
y
s
te
m
r
e
qui
r
e
s
a
n
I
nt
e
l
C
or
e
i3
pr
oc
e
s
s
or
,
32G
B
R
A
M
,
a
nd
a
1
T
B
S
S
D
f
or
opt
im
a
l
pe
r
f
or
m
a
nc
e
.
H
ig
h
-
s
pe
e
d
in
te
r
ne
t
is
ne
e
d
e
d
s
o
th
a
t
th
e
c
lo
ud
w
or
ks
e
f
f
ic
ie
nt
ly
,
w
hi
le
G
oogl
e
C
lo
ud
S
to
r
a
ge
Evaluation Warning : The document was created with Spire.PDF for Python.
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(
R
av
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agadi)
1245
pr
ovi
de
s
th
e
ne
c
e
s
s
a
r
y
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ta
s
to
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a
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.
I
t
c
a
n
r
un
on
W
in
dow
s
11
a
nd
us
e
s
P
yt
hon
3.12
a
lo
ng
w
it
h
m
a
c
hi
ne
le
a
r
ni
ng
a
nd
e
nc
r
ypt
io
n
li
b
r
a
r
ie
s
li
ke
T
e
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or
F
lo
w
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P
yT
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c
h,
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ik
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le
a
r
n,
N
um
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y,
P
a
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s
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C
r
ypt
ogr
a
phy
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a
nd
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yC
r
ypt
odome
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P
os
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r
e
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Q
L
is
us
e
d
a
s
th
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ta
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s
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ut
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w
hi
le
th
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c
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vi
c
e
s
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r
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pr
ovi
de
d
by
G
oogl
e
C
lo
ud
C
om
put
e
.
P
a
nda
pow
e
r
is
a
s
ugge
s
te
d
pow
e
r
s
ys
te
m
a
na
ly
s
i
s
to
ol
th
a
t
pr
ovi
de
s
c
om
pl
e
te
c
om
put
a
ti
ona
l
c
a
pa
bi
li
ti
e
s
. H
e
a
r
t
di
s
e
a
s
e
pr
e
di
c
ti
on a
nd a
na
ly
s
is
i
s
s
how
n
a
nd pr
e
s
e
nt
e
d i
n
[
27]
.
4.1.
C
om
p
ar
is
on
an
al
ys
is
T
h
e
T
a
bl
e
1
pr
e
s
e
nt
s
a
c
om
p
a
r
i
s
o
n
of
t
he
pe
r
f
or
m
a
n
c
e
of
c
onv
ol
u
ti
o
na
l
n
e
ur
a
l
n
e
t
w
or
k
(
C
N
N
)
,
r
a
n
dom
f
or
e
s
t
(
R
F
)
,
a
n
d
t
he
s
u
gg
e
s
te
d
M
H
E
A
L
T
H
d
a
t
a
s
e
t.
R
F
a
c
hi
e
v
e
s
a
c
c
ur
a
c
y
of
9
5.5
6%
,
w
it
h
pr
e
c
i
s
io
n,
r
e
c
a
l
l,
a
n
d
F
1
-
s
c
o
r
e
v
a
lu
e
s
of
93
.
50
%
,
91
.8
3%
a
nd
93
.2
5%
,
r
e
s
p
e
c
ti
v
e
l
y. C
N
N
i
m
pr
ov
e
s
w
it
h
90
.4%
a
c
c
ur
a
c
y.
T
h
e
M
H
E
A
L
T
H
d
a
t
a
s
e
t
ou
tp
e
r
f
or
m
s
bot
h,
a
c
hi
e
vi
ng
9
9.
21%
a
c
c
ur
a
c
y,
w
it
h
pr
e
c
i
s
io
n
,
r
e
c
a
ll
,
a
nd
F
1
-
s
c
or
e
s
of
99.
36%
,
9
9.6
%
,
a
n
d
99.
14%
, r
e
s
p
e
c
ti
v
e
l
y.
M
H
E
A
L
T
H
s
h
ow
s
s
u
pe
r
i
or
pe
r
f
or
m
a
n
c
e
a
c
r
o
s
s
a
ll
m
e
tr
i
c
s
.
T
a
bl
e
1
.
M
H
E
A
L
T
H
d
a
ta
s
e
t
c
om
pa
r
is
on of
e
xi
s
ti
ng me
th
ods
w
it
h pr
opos
e
d m
ode
l
M
e
t
hods
A
c
c
ur
a
c
y %
P
r
e
c
i
s
i
on %
R
e
c
a
l
l
%
F1
-
s
c
or
e
%
RF
95.56
93.50
91.83
93.25
C
N
N
[
23]
90.4
96
95.3
94.56
P
r
opos
e
d
99.21
99.36
99.6
99.14
T
a
bl
e
2
s
how
s
c
om
p
a
r
e
s
th
e
pe
r
f
or
m
a
nc
e
of
lo
gi
s
ti
c
r
e
gr
e
s
s
i
on
(
L
R
)
,
B
iL
S
T
M
,
a
nd
th
e
pr
opos
e
d
U
C
I
da
ta
s
e
t.
L
R
a
c
hi
e
ve
s
a
c
c
ur
a
c
y
of
88.14%
,
w
it
h
pr
e
c
is
io
n
,
r
e
c
a
ll
,
a
nd
F
1
-
s
c
or
e
va
lu
e
s
of
88%
,
89.10%
a
nd
88.36%
,
r
e
s
pe
c
ti
ve
ly
.
B
iL
S
T
M
im
pr
ove
s
w
it
h
93.4%
a
c
c
ur
a
c
y.
T
he
U
C
I
d
a
ta
s
e
t
out
pe
r
f
or
m
s
bot
h,
a
c
hi
e
vi
ng
99.52%
a
c
c
ur
a
c
y,
w
it
h
pr
e
c
is
io
n,
r
e
c
a
ll
,
a
nd
F
1
-
s
c
or
e
s
of
99.46%
,
99.34%
,
a
nd
99.25%
,
r
e
s
pe
c
ti
ve
ly
.
U
C
I
s
how
s
s
up
e
r
io
r
pe
r
f
or
m
a
nc
e
a
c
r
os
s
a
ll
m
e
tr
ic
s
.
T
a
bl
e
3
s
how
s
th
e
e
r
r
or
va
lu
e
s
f
or
th
e
pr
opos
e
d m
e
th
od c
om
pa
r
e
d t
o t
he
e
xi
s
ti
ng me
th
od.
T
a
bl
e
2. U
C
I
da
ta
s
e
t
c
om
pa
r
is
on of
e
xi
s
ti
ng me
th
ods
w
it
h pr
opos
e
d m
ode
l
M
e
t
hods
A
c
c
ur
a
c
y
%
P
r
e
c
i
s
i
on
%
R
e
c
a
l
l
%
F1
-
s
c
or
e
%
L
R
[
24]
88.14
88
89.10
88.36
B
i
L
S
T
M
[
25]
93.4
96.9
91.7
94.23
P
r
opos
e
d
99.52
99.46
99.34
99.25
T
a
bl
e
3. E
r
r
or
va
lu
e
f
or
pr
opos
e
d w
it
h e
xi
s
ti
ng
M
e
t
hods
R
s
qua
r
e
d e
r
r
or
M
S
E
R
M
S
E
D
N
N
[
17]
0.52
0.61
0.45
G
N
N
[
19]
0.41
0.47
0.55
P
r
opos
e
d
0.33
0.29
0.36
F
ig
ur
e
2
pr
e
s
e
nt
s
th
e
tr
a
in
in
g
a
nd
va
li
da
ti
on
pe
r
f
or
m
a
nc
e
ov
e
r
100
e
poc
h
s
.
F
or
th
e
M
H
E
A
L
T
H
da
ta
s
e
t,
(
a
)
s
how
s
a
tr
a
in
in
g
a
c
c
ur
a
c
y
of
0.99
a
nd
a
te
s
ti
ng
a
c
c
ur
a
c
y
of
0.98,
in
di
c
a
ti
ng
a
s
tr
ong
f
it
,
w
hi
le
(
b)
r
e
por
ts
a
tr
a
in
in
g
lo
s
s
of
0.79
a
nd
a
te
s
ti
ng
lo
s
s
of
0.80.
S
im
il
a
r
ly
,
f
or
th
e
U
C
I
da
ta
s
e
t,
(
c
)
s
how
s
a
tr
a
in
in
g
a
c
c
ur
a
c
y
of
0.99 a
nd
a
te
s
ti
ng
a
c
c
ur
a
c
y
of
0.83,
w
hi
le
(
d)
r
e
por
ts
a
tr
a
in
in
g
lo
s
s
of
0.80
a
nd a
te
s
ti
ng
lo
s
s
of
0.75, de
m
ons
tr
a
ti
ng good ge
ne
r
a
li
z
a
ti
on.
F
ig
ur
e
3
s
how
s
th
e
c
ih
phe
r
te
xt
e
xpa
n
s
io
n
r
a
ti
o
in
(
a
)
e
nc
r
ypt
io
n
ti
m
e
a
nd
(
b)
de
c
r
ypt
io
n
ti
m
e
w
hi
c
h
c
om
pa
r
e
s
th
e
c
lu
s
te
r
'
s
e
n
c
r
ypt
in
g
a
nd
de
c
r
ypt
io
n
ti
m
e
s
.
T
he
c
l
us
te
r
r
out
in
e
ly
pe
r
f
or
m
s
be
tt
e
r
th
a
n
th
e
ot
he
r
s
in
bot
h
m
e
tr
ic
s
.
B
ot
h
e
nc
r
ypt
io
n
a
nd
de
c
r
ypt
io
n
dur
a
ti
ons
in
c
r
e
a
s
e
li
ne
a
r
ly
w
it
h
da
ta
s
iz
e
,
a
nd
th
e
c
lu
s
te
r
m
a
in
ta
in
s
i
ts
hi
ghe
s
t
e
f
f
ic
ie
nc
y t
hr
oughout.
4.2. L
im
it
at
io
n
s
W
hi
le
th
e
pr
opos
e
d
P
I
T
A
N
N
–
E
C
C
-
E
lG
a
m
a
l
f
r
a
m
e
w
or
k
de
m
o
ns
tr
a
te
s
e
xc
e
ll
e
nt
pe
r
f
or
m
a
nc
e
on
th
e
M
H
E
A
L
T
H
a
nd U
C
I
da
ta
s
e
ts
, s
e
ve
r
a
l
li
m
it
a
ti
ons
r
e
m
a
in
. F
ir
s
t,
t
he
a
ppr
oa
c
h doe
s
not
c
ur
r
e
nt
ly
a
ddr
e
s
s
pos
t
-
qua
nt
um
c
r
ypt
ogr
a
phi
c
th
r
e
a
ts
;
f
ut
ur
e
w
or
k
s
houl
d
c
ons
id
e
r
la
tt
ic
e
-
ba
s
e
d
or
c
ode
-
ba
s
e
d
c
r
ypt
ogr
a
phy
to
m
it
ig
a
te
qua
nt
um
a
tt
a
c
k
s
.
S
e
c
ond,
a
c
tu
a
l
c
li
ni
c
a
l
da
ta
s
e
ts
w
e
r
e
not
e
v
a
lu
a
te
d
b
e
c
a
u
s
e
of
a
c
c
e
s
s
ib
il
it
y
li
m
it
a
ti
ons
,
a
nd
th
e
r
e
f
or
e
f
ur
th
e
r
va
li
da
ti
on
is
ne
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[
1]
A
.
S
.
R
a
j
put
,
A
.
A
ga
r
w
a
l
,
a
nd
K
.
B
.
R
a
j
a
,
“
A
r
obus
t
m
ul
t
i
-
ke
y
a
ut
hor
i
t
y
s
ys
t
e
m
f
or
pr
i
va
c
y
-
pr
e
s
e
r
vi
ng
di
s
t
r
i
but
i
on
a
nd
a
c
c
e
s
s
c
ont
r
ol
of
he
a
l
t
hc
a
r
e
da
t
a
,”
C
om
put
e
r
C
om
m
uni
c
at
i
ons
, vol
. 225, pp. 195
–
204,
2024, doi
:
10.1016/
j
.c
om
c
om
.2024.07.005.
[
2]
B
.
S
.
R
a
j
a
nd
S
.
V
e
nugopa
l
a
c
ha
r
,
“
M
ul
t
i
-
da
t
a
m
ul
t
i
-
us
e
r
e
nd
t
o
e
nd
e
nc
r
ypt
i
on
f
or
e
l
e
c
t
r
oni
c
he
a
l
t
h
r
e
c
or
ds
da
t
a
s
e
c
ur
i
t
y
i
n
c
l
oud,”
W
i
r
e
l
e
s
s
P
e
r
s
onal
C
o
m
m
uni
c
at
i
ons
, vol
. 125, no. 3, pp. 2413
–
2441, 20
22, doi
:
10.1007/
s
11277
-
022
-
09666
-
2.
[
3]
T
.
H
a
r
i
t
ha
a
nd
A
.
A
ni
t
ha
,
“
M
ul
t
i
-
l
e
ve
l
s
e
c
ur
i
t
y
i
n
h
e
a
l
t
hc
a
r
e
by
i
nt
e
gr
a
t
i
ng
l
a
t
t
i
c
e
-
ba
s
e
d
a
c
c
e
s
s
c
ont
r
ol
a
nd
bl
oc
kc
ha
i
n
-
ba
s
e
d
s
m
a
r
t
c
ont
r
a
c
t
s
s
y
s
t
e
m
,”
I
E
E
E
A
c
c
e
s
s
, vol
. 11, pp. 114322
–
114340, 2023, doi
:
10.1109/
A
C
C
E
S
S
.2023.3324740.
[
4]
J
.
A
.
A
l
z
ubi
,
O
.
A
.
A
l
z
ubi
,
M
.
B
e
s
e
i
s
o,
A
.
K
.
B
ud
a
t
i
,
a
nd
K
.
S
ha
nka
r
,
“
O
pt
i
m
a
l
m
ul
t
i
pl
e
ke
y
-
ba
s
e
d
hom
om
or
phi
c
e
nc
r
ypt
i
on
w
i
t
h
de
e
p
ne
ur
a
l
ne
t
w
or
ks
t
o
s
e
c
ur
e
m
e
di
c
a
l
da
t
a
t
r
a
ns
m
i
s
s
i
on
a
nd
di
a
gnos
i
s
,”
E
x
pe
r
t
Sy
s
t
e
m
s
,
vol
.
39,
no.
4,
2022,
doi
:
10.1111/
e
xs
y.12879.
[
5]
S
.
G
a
ya
t
hr
i
a
nd
S
.
G
ow
r
i
,
“
C
U
N
A
:
A
pr
i
va
c
y
pr
e
s
e
r
vi
ng
m
e
di
c
a
l
r
e
c
or
ds
s
t
or
a
ge
i
n
c
l
oud
e
nvi
r
onm
e
nt
us
i
ng
de
e
p
e
nc
r
ypt
i
on,
”
M
e
as
ur
e
m
e
nt
:
Se
ns
o
r
s
, vol
. 24, 2022, doi
:
10.1016/
j
.m
e
a
s
e
n.2022.100528.
[
6]
C
.
L
.
C
he
n,
P
.
T
.
H
ua
ng,
Y
.
Y
.
D
e
ng,
H
.
C
.
C
he
n,
a
nd
Y
.
C
.
W
a
ng,
“
A
s
e
c
ur
e
e
l
e
c
t
r
oni
c
m
e
di
c
a
l
r
e
c
or
d
a
ut
hor
i
z
a
t
i
on
s
ys
t
e
m
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i
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c
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a
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oud
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c
e
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r
i
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a
ut
he
nt
i
c
a
t
i
on
pr
ot
oc
ol
f
or
t
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he
a
l
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i
c
y
a
t
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r
i
but
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-
ba
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e
d
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nc
r
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on
w
i
t
h
f
a
s
t
de
c
r
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i
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f
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pe
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t
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a
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r
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:
a
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e
p
l
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a
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ni
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-
ba
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t
e
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m
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t
y
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nha
nc
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d
l
i
ght
w
e
i
ght
a
nd
a
nonym
i
t
y
-
pr
e
s
e
r
vi
ng
us
e
r
a
ut
he
nt
i
c
a
t
i
on
s
c
he
m
e
f
or
I
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ba
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e
d
he
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l
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hc
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,”
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a
l
no
t
e
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f
or
e
f
f
i
c
i
e
nt
di
a
gnos
i
s
w
i
t
h
f
e
e
dba
c
k
a
t
t
e
nt
i
on
–
ba
s
e
d
B
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L
S
T
M
,”
M
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di
c
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E
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ul
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f
a
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us
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r
a
ut
he
nt
i
c
a
t
i
on
pr
ot
oc
ol
f
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c
l
oud
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s
s
i
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c
ur
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a
ut
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i
c
a
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i
on
pr
ot
oc
ol
f
or
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-
he
a
l
t
h
r
e
c
or
ds
i
n
c
l
oud
w
i
t
h
a
ne
w
ke
y
ge
ne
r
a
t
i
on
m
e
t
hod
a
nd
m
i
ni
m
i
z
e
d
ke
y
e
xc
ha
nge
,”
J
our
nal
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K
i
ng
Saud
U
ni
v
e
r
s
i
t
y
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C
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, 2024. [
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v
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bl
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:
ht
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ps
:
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w
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.ka
ggl
e
.c
om
/
da
t
a
s
e
t
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/
m
he
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l
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.
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K
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ggl
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U
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M
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, 2024. [
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v
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:
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ps
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/
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w
w
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da
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D
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uc
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ur
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m
or
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c
a
l
f
i
l
t
e
r
i
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nd l
oc
a
l
Z
-
s
c
or
e
nor
m
a
l
i
z
a
t
i
on f
or
i
nf
r
a
r
e
d
s
m
a
l
l
t
a
r
ge
t
de
t
e
c
t
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on a
ga
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t
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a
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Se
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“
C
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t
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ng
m
ode
l
m
i
s
s
p
e
c
i
f
i
c
a
t
i
on
i
n
phys
i
c
s
-
i
nf
or
m
e
d
ne
ur
a
l
ne
t
w
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ks
(
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N
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)
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m
a
t
i
c
c
om
pa
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i
s
on
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n
c
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put
a
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pa
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ba
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pt
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E
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e
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e
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f
E
l
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G
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l
gor
i
t
hm
f
or
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pe
e
c
h
s
i
gna
l
s
e
nc
r
ypt
i
on
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nd
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c
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a
r
ni
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m
e
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hod
f
o
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a
ut
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a
t
i
c
S
M
S
s
pa
m
c
l
a
s
s
i
f
i
c
a
t
i
on:
p
e
r
f
or
m
a
nc
e
of
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e
a
r
ni
ng
a
l
gor
i
t
hm
s
on
i
ndi
ge
nous
da
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s
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t
,”
C
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u
r
r
e
n
c
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and
C
om
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“
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i
t
:
m
obi
l
e
he
a
l
t
h
d
a
t
a
f
or
pr
e
di
c
t
i
ng
a
t
hl
e
t
i
c
s
f
i
t
ne
s
s
u
s
i
ng
m
a
c
hi
ne
l
e
a
r
ni
ng
m
ode
l
s
,”
i
n
2022
2nd
I
nt
e
r
nat
i
onal
Se
m
i
nar
on
M
ac
hi
ne
L
e
ar
ni
ng,
O
pt
i
m
i
z
at
i
on,
an
d
D
at
a Sc
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e
nc
e
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I
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w
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f
-
a
t
t
e
nt
i
on
m
ode
l
f
or
hum
a
n
a
c
t
i
vi
t
y
r
e
c
ogni
t
i
on
us
i
ng
w
e
a
r
a
bl
e
s
e
ns
or
,”
I
E
E
E
J
our
nal
of
T
r
ans
l
at
i
onal
E
ngi
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e
r
i
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h and M
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“
P
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i
va
c
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s
e
r
va
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e
de
r
a
t
e
d l
e
a
r
ni
ng i
n he
a
l
t
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a
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,”
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uni
t
i
e
s
a
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ha
l
l
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a
pa
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a
,
a
nd
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D
uvvi
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“
P
r
e
di
c
t
i
ve
a
na
l
yt
i
c
s
of
he
a
r
t
di
s
e
a
s
e
pr
e
s
e
nc
e
w
i
t
h
f
e
a
t
ur
e
i
m
por
t
a
nc
e
ba
s
e
d
on
m
a
c
hi
ne
l
e
a
r
ni
ng
a
l
gor
i
t
h
m
s
,”
I
ndone
s
i
an
J
our
nal
of
E
l
e
c
t
r
i
c
al
E
ngi
ne
e
r
i
ng
and C
om
put
e
r
Sc
i
e
n
c
e
, vol
. 32, no. 2, pp. 1070
–
1077, 2023, doi
:
10.11591/
i
j
e
e
c
s
.v32.i
2.pp1070
-
1077.
B
I
O
G
R
A
P
H
I
E
S
O
F
A
U
T
H
O
R
S
Ravi
Kiran
Bagadi
is
an
Associate
Professor
in
the
Departmen
t
of
Computer
Scienc
e
Engine
ering
at
GITAM
School
of
Techn
ology,
Visakh
apatna
m.
With
over
14
year
s
of
e
xperience,
he
holds
a
Ph.D.,
M.Tech,
and
B.
Tech
in
Computer
Scie
nce
and
Engineering.
He
has
published
extensively
in
international
journals
and
conferences,
focusing
on
areas
such
as
computer
vision
and im
age processi
ng.
He can be contacted at email:
rbagadi@
gitam.edu
.
Neelima
Santoshi
K
oraganji
received
her
B.Tech
(CS
and
SE)
from
GITAM
College
of
Enginee
ring
and
M.Tech
(CST)
from
Andhra
Univ
ersity
a
nd
pursuing
her
Ph.D
.
at
Andhra
University.
She
has
19
years
of
teaching
experience
and
is
currently
working
as
an
Assistant
Professor
in
the
Computer
Science
and
Engineering
departm
ent,
GITAM
Deemed
to
be
University,
Visakhapatnam.
She
is
passionate
to
work
with
the
young
minds.
She
is
the
life
member
of
Computer
Society
of
India
(CSI).
Her
current
research
in
terest
includes
quantum
computi
ng,
artificial
intell
igence,
machine
l
earning,
deep
le
arning
an
d
cloud
computing
.
She
can be cont
acted at em
ail:
bvp.neelima@
gmail.com
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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A
r
ti
fi
c
ia
l
in
te
ll
ig
e
nc
e
-
bas
e
d m
ul
ti
-
k
e
y
s
e
c
u
r
it
y
f
or
p
r
ot
e
c
te
d an
d
…
(
R
av
i
K
ir
an B
agadi)
1249
Bandreddi
Venkata
Seshuku
mari
is
an
Associate
Professor
in
t
he
Department
of
Information
Technology
at
VNRVJIET,
boasts
a
remarkable
aca
demic
career
marked
by
dedication
to
both
teaching
and
research.
She
was
awarded
a
Ph.D.
(C
SE)
and
M.Tech.
(CSE)
,
and
B.Tech.
(CS
and
IT)
from
JNTUH.
She
has
20
y
ears
of
teaching
experience
and
10
years
of
dedicated
research
experience.
She
has
published
30
p
apers
in
estee
med
journals
like
ACM
,
Elsevier
and
Springer,
and
Inder
Science.
She
has
served
in
various
departmental
roles
like
HOD,
M.Tech.,
and
various
administrative
roles,
demonstrating
her
commitment
to
studen
t
development. She can be contac
ted at email: seshukumari_bv@
vnrvjiet.in.
Kavya
Ramya
Sree
Karuturi
pursuing
Ph.D
.
in
Aditya
University
,
Surampalem,
Andhrapradesh,
India.
She
received
her
Masters
Degree
M.
Tech
in
Inf
ormation
Technology
in
2018
from
SRKR
Engineering
College,
Bhimavaram.
Now,
she
is
working
as
an
Assistant
Profes
sor
in
the
depar
tment
of
Artific
ial
Intelli
gence
and
Machi
ne
Lear
ning
at
Aditya
University,
Surampalem,
Andhra
Pradesh,
India.
She
has
e
xperienc
e
more
than
7
years
in
teaching
and
4
years
as
Software
Engineer
in
Industry.
Her
curre
nt
research
on
machine
learning,
deep
learning,
computer
vision
and
image
processing
.
She
c
an
be
cont
acted
at
email:
kavyaramyasreek@adityauniversity.in.
Sirees
ha
Abotula
pursuing
her
Ph.D
.
in
Andhra
University,
Visakhapatnam
India.
She
received
her
Master’s
degree
M.
Tech
in
Computer
Science
and
Systems
Engineering in 2010 from Andhra University. Now, she i
s working as an Assistant
Profes
sor
in
the depar
tment of AI
and
DS at GITAM
University Vis
akhapatnam A
ndhra Pradesh India. S
he
has
more
than
18
years
of
teaching
and
6
years
of
research
experienc
e.
She
is
the
life
member
of
IAENG.
Her
current
research
interest
includes
AI,
machine
l
earning,
deep
learning,
software
engineering
,
IoT,
and
cloud
computing
.
She
can
b
e
contacted
at
email:
sabotula85@gmail.com
.
Bodapati
Venkata
Rajanna
is
currently
working
as
an
Associat
e
Professor
in
Department
of
Electrical
and
Electronics
Engineering
at
MLR
I
nstitute
of
Technology,
Hyderabad,
India.
He
received
B.Tech.
degree
in
Electrical
and
Elect
ronics
Engineering
from
Chirala
Enginee
ring
College,
JNTU,
Kakinad
a,
India,
in
2010,
M.
Tech.
degree
in
Power
Electronics
and
Drives
from
Koneru
Lakshmaiah
Education
Found
ation,
Guntur,
India,
in
2015
and
Ph.D.
in
Electrical
and
Electronics
Engineering
at
Koneru
Lakshmaiah
Education
Foundati
on,
Guntur
,
India,
in
2021.
His
curre
nt
resea
rch
include
s
,
dynamic
m
odeling
of
batteries
for
renewable
energy
storage,
battery
management
syste
ms
(BMS)
for
electric
vehicles
and
portable
electronics
applications,
renewable
energy
sources
integration
with
battery
energy
storage
systems
(BESS),
smart
metering
and
smart
grids,
micro
-
grids,
automati
c
meter
reading
(AMR)
devices,
GSM/GP
RS
and
power
line
carrier
(PLC)
communi
cation,
and
various
modulat
ion
techniqu
es
such
as
QPSK,
B
PSK,
ASK,
FSK,
OOK,
and GMSK
. He can be
contacted
at email
: rajannab
v2012@
gmail.co
m.
Mahalak
shmi
Annava
rapu
completed
her
B.Tech
(Comput
e
r
Science
and
Engineering)
from
Chirala
Engineering
College,
Chirala
affiliated
to
JNTU,
Kakinada.
She
completed
her
M.
Tech
(Computer
Science
and
Engineering)
fro
m
Avanthi
Institut
e
of
Engineering
and
Technology
affiliated
to
JNTU,
Hyderabad.
She
ha
d
e
xperience
in
different
academic
and
adminis
trative
roles
at
various
aca
demic
institutes
for
more
than
9
years.
Currentl
y
working
a
s
an
Assistant
Professor
at
RVR
and
JC
College
of
Engineering
(Autonomou
s) in Departm
ent of Com
puter
Science and B
u
siness S
yst
em, Guntur.
She had t
wo
patents.
She
attended
and
presented
papers
in
different
conferences,
workshops
and
symposiums.
She
publ
ished
various
papers
in
different
international
a
nd
national
journals.
She
can be cont
acted at em
ail:
mahalakshmi.
valluri09@gmail.com.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
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I
nt
J
A
dv A
ppl
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i
,
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ol
.
14
, N
o.
4
,
D
e
c
e
m
be
r
20
25
:
1241
-
1250
1250
Nitalaksheswa
ra
Rao
Koluku
la
obtained
his
Ph.D.
in
Computer
Science
and
Systems
Engine
ering
at
Andhra
Univer
sity
Visakh
apatna
m
India.
H
e
received
his
Master’s
degree
M.
Tech
in
Computer
Science
and
engineering
in
2009.
N
ow,
he
is
an
Assistant
Profes
sor
in
depar
tment
of
CSE
at
GITAM
Univer
sity
Visakh
apatna
m
Andhra
Prade
sh
India.
His
current
research
interest
includes
AI,
machine
learning,
deep
learning
software
engineerin
g,
data
engineerin
g,
and
quality
assurance
.
He
can
be
contacted
at
email:
kolukulanitla@
gmail.com.
Jayasree
Pinajala
is
currently
a
Research
Scholar
and
Pursuin
g
her
Ph.D
.
at
Godavari
Global
University,
Rajamahendravaram.
She
Completed
her
B.
Tech
(Information
Technology)
in
2011from
VRS
and
YRN
College
of
Engineering
a
nd
Technology,
Chirala
affiliated
to JNT
U, Kakinad
a. She co
mple
ted her M.
Tech (Computer Science and Engineering)
in
2013
from
Narasaraope
ta
Engineering
College
affiliated
to
JN
TU,
Kakinada.
She
had
Experience
in
different
academic
and
administrative
roles
at
various
aca
demic
institutes
for
more
than
7
years.
Currently
working
as
an
Assistant
Professor
a
t
Chaitanya
Enginering
College
Visakha
patnam
in
Depar
tment
of
Computer
Science
and
Engineering.
She
attended
and
presented
papers
in
different
conferences,
workshops
and
sym
posiums.
She
published
various
papers
in
different
international
and
national
journals.
She
ca
n
be
contacted
at
email:
jayasree
p4@
gmail.com.
James
Stephen
Meka
is
a
respected
academician,
cur
rently
serving
as
the
National
Chair
Professor
at
the
Dr.
B.R.
Ambedka
r
Chair,
Andhr
a
University,
under
the
Ministry
of
Social
Justice
and
Empowerment,
Government
of
India.
With
over
22
years
of
teaching
and
resear
ch
experie
nce,
and
more
than
11
years
in
administrative
roles,
he
has
made
significant
contributions
to
the
academic
and
researc
h
landscape
of
th
e
Institutions,
he
served
.
During his tenu
re as the
Registrar of Andhra
University,
his
leadersh
ip
as the De
an of
the A.U.
Trans
-
disciplinary
Research
Hub
(TDR
-
HUB)
was
marked
by
his
efforts
to
foster
research
beyond
the
confines
of
the
university,
extending
opportunities
to
a
ffiliated
institutions.
He
played
a
pioneering
role
in
establishing
standard
protocols
in
line
with
UGC
guidelines,
contribu
ting
to
the
research
growth
of
young
scholars.
He
can
be
contacted
at
email:
jamesstephe
nm@
gmail.com.
Evaluation Warning : The document was created with Spire.PDF for Python.