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1218
Ar
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Similar
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o
ac
h
es
h
av
e
im
p
r
o
v
e
d
th
e
ac
cu
r
ac
y
o
f
p
r
ed
ictio
n
s
,
b
u
t
th
e
u
n
ce
r
tain
ty
o
f
p
ar
am
eter
is
atio
n
a
n
d
t
h
e
in
co
n
s
is
ten
cy
o
f
n
u
t
r
itio
n
d
ata
s
tr
ea
m
s
in
p
r
ac
tice
s
till
lim
it
th
eir
p
r
ac
tical
p
er
f
o
r
m
an
ce
[
4
]
,
[
1
4
]
,
[
1
5
]
.
Stu
d
ies
h
av
e
tr
ied
to
ad
d
r
ess
th
ese
s
h
o
r
tco
m
in
g
s
b
y
f
u
zz
y
lo
g
ic
-
b
ased
d
ietar
y
d
ec
is
io
n
-
s
u
p
p
o
r
t
s
y
s
tem
s
[
1
6
]
,
[
1
7
]
an
d
h
y
b
r
id
m
o
d
ellin
g
with
a
Kalm
an
f
ilter
to
m
an
a
g
e
n
o
is
y
s
en
s
o
r
d
ata
[
1
8
]
,
[
1
9
]
.
Ho
wev
e
r
,
th
e
co
m
b
in
atio
n
o
f
s
tatis
tical
e
s
tim
at
es
an
d
co
g
n
itiv
e
r
ea
s
o
n
in
g
f
r
am
ewo
r
k
s
r
em
ain
s
co
m
p
u
tatio
n
ally
co
m
p
le
x
an
d
r
eq
u
ir
es c
ar
ef
u
l b
alan
cin
g
to
a
v
o
id
in
ef
f
icien
ci
es in
th
e
s
y
s
tem
[
2
0
]
,
[
2
1
]
.
T
o
ad
d
r
ess
th
ese
g
ap
s
,
th
is
p
ap
er
p
r
o
p
o
s
es
an
ad
a
p
tiv
e
ex
ten
d
ed
Kalm
an
f
ilter
a
n
d
f
u
zz
y
lo
g
ic
(
AE
KFFL)
m
o
d
el
[
2
2
]
th
at
i
n
teg
r
ates
with
th
e
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
to
p
r
o
v
id
e
ac
cu
r
ate,
d
y
n
am
ic,
a
n
d
p
er
s
o
n
alis
ed
d
ietar
y
ad
v
i
ce
t
o
ch
ild
r
en
.
T
h
e
m
o
d
el
s
tab
ilizes
b
io
m
etr
ic
m
ea
s
u
r
em
en
ts
u
s
in
g
an
E
KF
an
d
ap
p
lies
f
u
zz
y
in
f
er
en
ce
to
p
r
o
v
id
e
h
u
m
an
-
lik
e,
in
ter
p
r
eta
b
le
ca
lo
r
ie
g
u
i
d
an
ce
.
T
h
e
m
ain
c
o
n
tr
ib
u
tio
n
o
f
th
is
s
tu
d
y
is
to
d
ev
elo
p
an
d
v
alid
a
te
an
in
teg
r
ated
s
y
s
tem
o
f
d
iet
ar
y
r
ec
o
m
m
en
d
atio
n
s
th
at
(
i)
ad
ap
ts
in
r
ea
l
tim
e
to
p
h
y
s
io
lo
g
ical
an
d
life
s
ty
le
ch
an
g
es,
(
ii)
is
in
ter
p
r
etab
le
f
o
r
ca
r
er
s
an
d
p
r
ac
titi
o
n
e
r
s
,
an
d
(
iii)
d
em
o
n
s
tr
ates
im
p
r
o
v
e
d
g
en
e
r
alis
atio
n
an
d
p
er
s
o
n
alis
atio
n
co
m
p
ar
e
d
to
th
e
b
aselin
e
ap
p
r
o
ac
h
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
p
r
o
o
f
-
of
-
co
n
ce
p
t
ap
p
licatio
n
u
s
es
a
h
y
b
r
id
o
f
I
o
T
an
d
th
e
AE
KFFL
to
im
p
l
em
en
t
th
e
ar
ch
itectu
r
e,
d
esig
n
,
d
e
v
elo
p
,
an
d
test
th
e
s
u
g
g
ested
m
o
d
el
[
2
2
]
.
T
h
is
ex
p
er
im
e
n
tal
s
tu
d
y
h
as
th
e
f
o
llo
win
g
p
ar
am
eter
s
:
2
.
1
.
P
a
ra
m
et
er
des
ig
n
T
h
e
s
tu
d
y
em
p
lo
y
s
a
q
u
a
n
tita
tiv
e,
ex
p
er
im
en
tal,
an
d
ap
p
lied
r
esear
ch
ap
p
r
o
ac
h
t
ar
g
eted
at
im
p
r
o
v
in
g
r
ea
l
-
tim
e
d
ietar
y
m
o
n
ito
r
in
g
u
s
in
g
I
o
T
tech
n
o
lo
g
ies
an
d
AI
-
b
ased
d
ec
is
io
n
-
m
ak
in
g
.
T
h
e
s
tu
d
y
u
s
es
s
en
s
o
r
-
b
ased
d
ata
co
llectio
n
,
m
ac
h
in
e
lea
r
n
in
g
te
ch
n
iq
u
es,
an
d
u
s
er
en
g
ag
e
m
en
t
v
ia
a
m
o
b
ile
ap
p
licatio
n
to
im
p
r
o
v
e
m
ea
l r
e
co
m
m
en
d
atio
n
ac
cu
r
ac
y
.
T
h
e
AE
KFFL
[
2
3
]
m
o
d
el
is
u
s
ed
to
f
ilter
an
d
an
ticip
ate
weig
h
t
ch
an
g
es,
en
s
u
r
in
g
th
at
m
ea
l
p
lan
s
ar
e
tailo
r
ed
to
ea
ch
c
h
ild
’
s
n
u
tr
itio
n
al
r
eq
u
ir
em
en
ts
.
T
h
e
r
esear
ch
d
esig
n
is
d
iv
id
ed
in
to
f
o
u
r
m
aj
o
r
p
h
ases
:
(
i
)
I
o
T
-
b
ased
d
ata
co
llectin
g
,
(
ii
)
s
ig
n
al
p
r
o
ce
s
s
in
g
v
ia
E
KF,
(
iii
)
a
d
ap
tiv
e
d
ec
is
io
n
-
m
a
k
in
g
v
ia
f
u
zz
y
lo
g
ic
,
an
d
(
iv
)
m
o
b
ile
ap
p
licatio
n
d
e
p
lo
y
m
en
t.
Fig
u
r
e
1
s
h
o
ws th
e
s
e
q
u
en
ce
o
f
p
h
ases
in
th
is
s
tu
d
y
.
Fig
u
r
e
1
.
Step
s
o
f
th
e
m
ac
h
i
n
e
p
r
o
ce
s
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J I
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o
m
m
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ec
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n
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2252
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7
6
A
r
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t h
yb
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o
f I
o
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ith
a
d
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p
tive
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K
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filt
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N
o
o
r
r
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m
Yu
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o
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)
1219
T
h
e
f
o
r
m
u
la
o
f
I
o
T
d
ata
co
lle
ctio
n
f
o
r
s
en
s
o
r
r
ea
d
in
g
s
,
E
K
F
s
ig
n
al
p
r
o
ce
s
s
in
g
,
f
u
zz
y
lo
g
ic
in
f
er
en
ce
s
y
s
tem
,
an
d
d
ec
is
io
n
r
u
le
is
as f
o
llo
ws:
=
[
ℎ
,
,
]
(
1
)
wh
er
e
ℎ
h
eig
h
t is at
tim
e,
weig
h
t is at
tim
e,
an
d
is
ℎ
2
.
E
KF
s
ig
n
al
p
r
o
ce
s
s
i
n
g
.
Pre
d
ic
t a
n
d
u
p
d
ate
s
tep
f
o
r
s
ig
n
al
d
e
n
o
is
in
g
.
|
−
1
=
(
−
1
,
)
+
(
2
)
|
−
1
=
(
−
1
)
+
(
3
)
=
|
−
1
(
|
−
1
+
)
−
1
(
4
)
|
=
|
−
1
+
(
−
ℎ
(
|
−
1
)
(
5
)
A
f
u
zz
y
lo
g
ic
i
n
f
er
e
n
ce
s
y
s
tem
is
an
in
p
u
t
o
f
clea
n
e
d
o
r
f
ilter
ed
s
ig
n
als
|
,
wh
er
ea
s
th
e
f
u
zz
y
r
u
le
b
ase,
(
ℎ
)
(
ℎ
)
t
h
is
o
u
tp
u
t sh
o
ws
_
=
(
|
)
.
2
.
2
.
Da
t
a
c
o
llect
io
n
T
h
e
d
ata
ac
q
u
is
itio
n
s
y
s
tem
b
ased
o
n
I
o
T
s
en
s
o
r
s
is
ess
en
tia
l to
m
ain
tain
th
e
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
o
f
th
e
AE
KFFL
m
o
d
el.
Du
r
in
g
th
is
p
h
ase,
ca
lib
r
ated
d
ev
ice
s
co
n
n
ec
ted
to
th
e
I
o
T
a
n
d
a
m
o
b
ile
ap
p
licatio
n
co
llect
p
h
y
s
io
lo
g
ical
p
ar
am
et
er
s
in
r
ea
l
tim
e,
s
u
ch
as
h
eig
h
t,
weig
h
t,
an
d
B
MI
.
T
h
ese
s
en
s
o
r
s
co
n
tin
u
o
u
s
ly
p
r
o
v
id
e
d
ata
to
a
ce
n
tr
al
p
r
o
c
ess
in
g
u
n
it,
wh
ich
r
ec
o
r
d
s
an
d
s
to
r
es
th
em
in
a
s
tr
u
ctu
r
ed
d
atab
ase.
I
n
ad
d
itio
n
to
p
h
y
s
io
lo
g
ical
d
ata,
th
e
s
y
s
tem
in
co
r
p
o
r
ates
en
v
ir
o
n
m
e
n
tal
an
d
b
e
h
av
io
u
r
al
in
f
o
r
m
ati
o
n
co
llected
th
r
o
u
g
h
h
u
m
an
in
ter
v
e
n
tio
n
.
A
m
o
b
il
e
in
ter
f
ac
e
allo
ws
p
ar
e
n
ts
to
m
o
n
ito
r
t
h
eir
c
h
ild
’
s
f
o
o
d
p
r
ef
er
en
ce
s
,
d
ietar
y
r
estrictio
n
s
,
aller
g
ies,
an
d
ea
ti
n
g
p
atter
n
s
.
T
h
is
d
u
al
in
p
u
t
tech
n
iq
u
e
en
s
u
r
es
th
at
th
e
m
o
d
el
n
o
t
o
n
ly
r
ep
r
esen
ts
th
e
cu
r
r
en
t
n
u
t
r
itio
n
al
s
itu
atio
n
o
f
th
e
ch
ild
b
u
t
also
ad
ap
ts
to
th
e
in
d
iv
id
u
al
d
ietar
y
h
ab
its
an
d
p
r
e
f
er
en
ce
s
,
w
h
ich
im
p
r
o
v
es
th
e
p
er
s
o
n
alis
atio
n
an
d
q
u
ality
o
f
th
e
d
ec
is
io
n
.
T
h
e
c
o
llectio
n
p
h
ase
s
h
all
b
e
g
u
id
ed
b
y
et
h
ical
g
u
id
elin
es,
s
u
ch
as
in
f
o
r
m
e
d
co
n
s
en
t,
d
ata
p
r
o
te
ctio
n
,
an
d
s
ec
u
r
e
m
ea
n
s
o
f
tr
an
s
f
er
.
Data
an
o
m
alies
o
r
m
i
s
s
in
g
d
ata
s
h
all
b
e
h
an
d
led
b
y
s
tatis
tical
im
p
u
tatio
n
o
r
f
lag
g
e
d
f
o
r
h
u
m
an
v
er
if
i
ca
tio
n
.
2
.
3
.
M
o
del t
ra
ini
ng
a
nd
inte
g
ra
t
io
n
T
h
e
m
ain
AE
KFFL
m
o
d
el
c
o
m
b
in
es
th
e
E
KF
f
o
r
r
ea
l
-
ti
m
e
s
ig
n
al
f
ilter
in
g
an
d
a
f
u
z
zy
in
f
er
en
ce
s
y
s
tem
(
FIS)
f
o
r
ad
a
p
tiv
e
d
e
cisi
o
n
-
m
ak
in
g
.
Sig
n
al
p
r
o
ce
s
s
in
g
with
E
KF
f
o
llo
ws
th
e
p
r
ed
ictio
n
-
co
r
r
ec
tio
n
cy
cle
d
escr
ib
ed
in
(
2
)
-
(
4
)
,
r
esu
ltin
g
in
ex
ce
llen
t
ac
cu
r
ac
y
ev
en
with
n
o
is
y
s
en
s
o
r
r
ea
d
in
g
s
.
T
h
e
f
u
zz
y
in
f
er
en
ce
e
n
g
in
e
ac
ce
p
ts
f
ilter
ed
d
ata
x
(t|
t)
.
T
h
e
f
u
zz
y
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
an
d
r
u
le
s
ets
ar
e
b
u
ilt
u
s
in
g
p
ae
d
iatr
ic
n
u
tr
itio
n
r
ec
o
m
m
en
d
atio
n
s
a
n
d
ex
p
e
r
t
k
n
o
wled
g
e
to
en
ab
le
s
em
an
tic
r
ea
s
o
n
in
g
.
E
x
am
p
les o
f
f
u
zz
y
s
ets
ar
e
B
MI
(
lo
w,
n
o
r
m
al,
h
ig
h
)
,
weig
h
t
c
h
an
g
e
r
at
e
(
s
lo
w,
m
o
d
er
ate,
f
ast),
an
d
ca
lo
r
ie
r
eq
u
ir
em
e
n
t
o
u
tp
u
t
(
l
o
w,
m
ed
iu
m
,
h
ig
h
)
.
2
.
4
.
B
a
s
eline
m
o
del im
plementa
t
io
n a
nd
co
nfig
ura
t
io
n
Fo
u
r
b
aselin
e
s
y
s
tem
s
wer
e
im
p
lem
en
ted
to
e
n
ab
le
a
f
ai
r
an
d
s
y
s
tem
atic
co
m
p
ar
is
o
n
with
th
e
p
r
o
p
o
s
ed
AE
KFFL
m
o
d
el.
T
h
e
r
u
le
-
b
ased
ca
lcu
lato
r
was
d
ev
elo
p
e
d
f
o
llo
win
g
th
e
W
H
O
d
ietar
y
r
ef
e
r
en
ce
tab
les
an
d
B
MI
class
if
icatio
n
r
u
les,
r
ep
r
esen
tin
g
a
co
n
v
en
tio
n
al
n
o
n
-
a
d
ap
tiv
e
n
u
tr
itio
n
m
eth
o
d
.
T
h
e
ANN
-
b
ased
n
u
tr
itio
n
est
im
ato
r
s
er
v
ed
as
a
m
ac
h
in
e
-
l
ea
r
n
in
g
b
aselin
e
an
d
was
im
p
lem
en
ted
u
s
in
g
a
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
tr
ain
e
d
o
n
b
io
m
etr
ic
an
d
n
u
tr
itio
n
al
f
ea
tu
r
es.
A
co
m
m
er
cial
m
o
b
ile
d
iet
ap
p
licatio
n
alg
o
r
ith
m
was
in
clu
d
e
d
to
r
ef
l
ec
t
wid
ely
ad
o
p
ted
ap
p
-
b
ased
to
o
ls
th
at
r
ely
o
n
m
an
u
al
f
o
o
d
lo
g
g
in
g
an
d
s
tatic
n
u
tr
ien
t
l
o
o
k
-
u
p
tab
les.
Fin
al
ly
,
a
wea
r
ab
le
-
in
teg
r
ated
s
y
s
t
em
alg
o
r
it
h
m
was
c
o
n
s
tr
u
cte
d
u
s
in
g
I
o
T
s
en
s
o
r
d
ata
(
wei
g
h
t,
s
tep
co
u
n
t,
h
ea
r
t
r
ate)
to
em
u
late
m
o
d
er
n
h
ea
lth
-
tr
ac
k
in
g
s
y
s
tem
s
th
at
p
r
o
v
id
e
d
y
n
am
ic
b
u
t
non
-
c
o
n
tex
tu
al
ca
lo
r
ic
esti
m
ates.
All
m
o
d
els
wer
e
ev
alu
at
ed
u
s
in
g
an
8
0
/2
0
tr
ain
–
test
s
p
lit
an
d
ass
e
s
s
ed
u
s
in
g
r
o
o
t
m
ea
n
s
q
u
a
r
e
er
r
o
r
(
R
MSE
)
to
en
s
u
r
e
co
n
s
is
ten
cy
ac
r
o
s
s
ex
p
er
im
en
tal
co
n
d
itio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
5
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
1
2
1
7
-
1
2
2
5
1220
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
d
escr
ib
es
th
e
r
esu
lts
o
f
tr
ain
in
g
th
e
AE
KFFL
with
an
I
o
T
in
teg
r
atio
n
m
o
d
el
(
AE
KFFL
-
I
o
T
)
an
d
co
m
p
ar
in
g
its
p
er
f
o
r
m
an
ce
to
b
aselin
e
n
u
tr
itio
n
esti
m
atio
n
s
y
s
tem
s
,
in
clu
d
in
g
th
e
alg
o
r
ith
m
o
f
a
r
u
le
-
b
ased
ca
lc
u
lato
r
,
a
m
ac
h
i
n
e
le
ar
n
in
g
-
b
ased
s
y
s
tem
ANN,
a
Mo
b
ile
Diet
Ap
p
,
an
d
a
wea
r
ab
le
-
in
teg
r
ated
s
y
s
te
m
.
T
h
e
ev
alu
atio
n
c
o
n
ce
n
tr
ated
o
n
im
p
o
r
tan
t le
ar
n
in
g
m
ea
s
u
r
es,
m
o
d
el
p
e
r
f
o
r
m
an
ce
,
an
d
g
e
n
er
alis
atio
n
.
3
.
1
.
T
ra
ini
ng
a
nd
m
o
del int
eg
ra
t
io
n per
f
o
r
m
a
nce
T
h
e
AE
KFFL
-
I
o
T
m
o
d
el
was
tr
ain
ed
u
s
in
g
a
d
ataset
o
f
3
0
0
an
o
n
y
m
is
ed
ch
ild
n
u
tr
itio
n
p
r
o
f
iles
th
at
co
n
tain
ed
b
o
th
s
en
s
o
r
d
ata
an
d
m
an
u
ally
en
ter
ed
f
o
o
d
in
ta
k
e
r
ec
o
r
d
s
.
T
h
e
d
ataset
was
s
p
lit
in
to
8
0
%
tr
ain
in
g
an
d
2
0
%
test
in
g
.
T
r
ain
in
g
an
d
test
in
g
u
s
in
g
R
M
SE.
T
h
e
R
MSE
m
ea
s
u
r
e
is
co
m
m
o
n
ly
u
s
ed
to
d
eter
m
in
e
th
e
ac
cu
r
ac
y
o
f
a
p
r
ed
ictiv
e
m
o
d
e
l
[
2
4
]
.
I
t
co
m
p
u
tes
th
e
er
r
o
r
’
s
av
er
ag
e
m
ag
n
itu
d
e
b
etwe
en
p
r
ed
icted
an
d
ac
tu
al
v
alu
es.
T
h
e
tr
ain
in
g
r
esu
lts
,
as sh
o
wn
in
Fig
u
r
e
2
,
a
r
e
s
u
m
m
ar
is
ed
b
elo
w
.
T
h
e
m
o
d
el
u
s
ed
E
KF
in
g
to
d
ec
r
ea
s
e
in
p
u
t
n
o
is
e
an
d
d
r
i
f
t,
r
esu
ltin
g
in
f
aster
co
n
v
er
g
e
n
ce
an
d
m
o
r
e
s
tab
le
lear
n
in
g
.
Fu
zz
y
lo
g
ic
r
u
les,
lear
n
t
an
d
twea
k
ed
with
ex
p
er
t
in
p
u
t,
allo
we
d
f
o
r
r
ea
s
o
n
in
g
in
am
b
ig
u
o
u
s
o
r
b
o
r
d
er
lin
e
i
n
s
tan
ce
s
,
fo
r
e
x
am
p
le,
wh
en
B
MI
an
d
weig
h
t
ch
an
g
es we
r
e
o
n
ju
d
g
m
en
t th
r
esh
o
ld
s
.
Fig
u
r
e
2
.
AE
KFFL
-
I
o
T
m
o
d
el
m
etr
ics
3
.
2
.
Co
m
pa
riso
n wit
h o
t
her
f
o
o
d nutr
it
io
n sy
s
t
em
s
A
co
m
p
ar
ativ
e
ev
alu
atio
n
wa
s
p
er
f
o
r
m
ed
u
s
in
g
id
e
n
tical
t
est
d
ata
ac
r
o
s
s
all
s
y
s
tem
s
.
T
h
e
m
etr
ics
ev
alu
ated
in
clu
d
e
R
MSE
f
o
r
ca
lo
r
ie
p
r
ed
ictio
n
,
m
o
d
el
g
e
n
er
aliza
tio
n
(
d
if
f
e
r
en
ce
b
etwe
en
tr
ain
in
g
an
d
test
R
MSE
)
,
ad
ap
tab
ilit
y
(
r
u
le
tu
n
i
n
g
o
r
lear
n
in
g
ca
p
ab
ilit
y
)
,
an
d
s
y
s
tem
co
m
p
lex
ity
.
Fig
u
r
e
3
s
h
o
ws
th
e
co
m
p
ar
is
o
n
o
f
tr
ain
in
g
an
d
test
in
g
R
MSE
,
wh
ich
in
d
icate
s
th
e
A
E
K
F
FL
-
I
o
T
s
h
o
ws
th
e
f
astes
t
ex
ec
u
tio
n
tr
ain
in
g
tim
e,
2
1
.
4
3
an
d
2
2
.
3
5
in
test
in
g
,
wh
ile
th
e
lo
wes
t
is
th
e
r
u
le
-
b
ased
ca
lcu
lato
r
,
3
0
.
1
2
f
o
r
t
r
ain
in
g
a
n
d
3
8
.
4
1
f
o
r
test
in
g
.
T
h
e
R
MSE
in
d
icate
s
th
at
th
e
lo
wer
v
alu
e
is
b
etter
.
Fig
u
r
e
3
.
T
r
ain
in
g
a
n
d
test
in
g
R
MSE
co
m
p
ar
is
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
A
r
ec
en
t h
yb
r
id
o
f I
o
T w
ith
a
d
a
p
tive
ex
ten
d
ed
K
a
lma
n
filt
er fu
z
z
y
lo
g
ic
…
(
N
o
o
r
r
eza
m
Yu
s
o
p
)
1221
Me
an
wh
ile,
Fig
u
r
e
4
r
e
p
r
es
en
ts
th
e
g
en
e
r
aliza
tio
n
an
d
s
y
s
tem
ca
p
ab
ilit
ies
co
m
p
ar
is
o
n
m
o
d
el.
T
h
e
r
esu
lt
s
h
o
ws
th
at
g
e
n
er
al
izatio
n
p
r
o
v
id
es
AE
KF
F
L
-
I
o
T
0
.
9
2
th
e
h
i
g
h
est
p
er
f
o
r
m
a
n
ce
,
an
d
Mo
b
ile
Diet
Ap
p
8
.
3
6
is
t
h
e
lo
west
p
er
f
o
r
m
an
ce
.
Nex
t,
a
d
a
p
tab
ilit
y
s
h
o
ws
th
e
r
esu
lt
with
th
e
h
ig
h
est
3
an
d
0
is
th
e
r
u
le
-
b
ased
ca
l
cu
lato
r
.
Fo
r
I
o
T
i
n
teg
r
atio
n
,
th
e
h
ig
h
est
is
A
E
KF
F
L
-
I
o
T
an
d
th
e
wea
r
ab
le
-
in
teg
r
ated
s
y
s
tem
.
Fin
ally
,
f
o
r
p
er
s
o
n
aliza
tio
n
,
th
e
s
y
s
tem
ca
p
ab
ilit
y
is
h
ig
h
est
f
o
r
AE
KFFL
-
I
o
T
,
f
o
llo
wed
b
y
th
e
wea
r
ab
le
-
in
teg
r
ated
s
y
s
tem
,
th
e
m
o
b
ile
d
iet
ap
p
,
an
d
th
e
r
u
le
-
b
ased
ca
lcu
lato
r
.
Fig
u
r
e
4
.
Gen
e
r
aliza
tio
n
an
d
s
y
s
tem
ca
p
ab
ilit
ies co
m
p
ar
is
o
n
m
o
d
el
T
ab
le
1
s
h
o
ws
th
e
co
m
p
ar
ati
v
e
an
aly
s
is
o
f
AE
KFFL
with
I
o
T
.
T
h
e
co
m
p
ar
ativ
e
a
n
aly
s
is
clea
r
ly
s
h
o
ws
th
at
th
e
AE
KFFL
-
I
o
T
m
o
d
el
o
u
t
p
er
f
o
r
m
s
all
o
th
er
s
y
s
tem
s
ac
r
o
s
s
m
u
ltip
le
ev
al
u
atio
n
d
im
en
s
io
n
s
,
s
p
ec
if
ically
g
en
er
aliza
tio
n
,
ad
ap
tiv
ity
,
I
o
T
in
te
g
r
atio
n
,
an
d
p
er
s
o
n
aliza
tio
n
.
T
ab
le
1
s
h
o
w
s
th
at
th
e
r
esu
lt
o
f
AE
KFFL
-
I
o
T
s
h
o
ws
th
at
th
e
tr
ain
in
g
R
MSE
is
2
1
.
4
3
,
th
e
t
esti
n
g
R
MSE
i
s
2
2
.
3
5
,
th
e
g
e
n
er
aliza
tio
n
g
ap
is
0
.
9
2
,
a
d
ap
tab
ilit
y
is
h
i
g
h
,
I
o
T
in
teg
r
atio
n
is
f
u
ll,
a
n
d
p
e
r
s
o
n
a
lizatio
n
is
also
h
ig
h
.
T
ab
le
1
.
T
h
e
co
m
p
a
r
ativ
e
an
al
y
s
is
o
f
AE
KFFL
with
I
o
T
S
y
st
e
m
Tr
a
i
n
R
M
S
E
Te
st
R
M
S
E
G
e
n
e
r
a
l
i
z
a
t
i
o
n
G
a
p
A
d
a
p
t
i
v
i
t
y
I
o
T
i
n
t
e
g
r
a
t
i
o
n
P
e
r
so
n
a
l
i
z
a
t
i
o
n
A
EK
F
F
L
-
I
o
T
2
1
.
4
3
2
2
.
3
5
0
.
9
2
H
i
g
h
F
u
l
l
H
i
g
h
R
u
l
e
-
b
a
se
d
c
a
l
c
u
l
a
t
o
r
3
0
.
1
2
3
8
.
4
1
8
.
2
9
N
o
n
e
N
o
n
e
Lo
w
ML
-
b
a
se
d
s
y
s
t
e
m (A
N
N
)
2
3
.
9
1
3
0
.
6
7
6
.
7
6
M
e
d
i
u
m
P
a
r
t
i
a
l
M
e
d
i
u
m
M
o
b
i
l
e
d
i
e
t
A
p
p
2
5
.
4
4
3
3
.
8
0
8
.
3
6
Lo
w
N
o
n
e
M
e
d
i
u
m
W
e
a
r
a
b
l
e
-
i
n
t
e
g
r
a
t
e
d
S
y
s
2
2
.
1
8
2
8
.
4
0
6
.
2
2
M
e
d
i
u
m
F
u
l
l
M
e
d
i
u
m
3.
3
.
Dis
cus
s
io
n
As
s
h
o
wn
in
Fig
u
r
es
2
-
4
,
th
e
s
tu
d
y
d
em
o
n
s
tr
ates
th
at
co
m
b
in
in
g
th
e
I
o
T
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r
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k
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As
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Fig
u
r
e
4
,
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e
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in
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in
g
s
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em
o
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tr
ate
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A
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atio
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h
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e
x
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en
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n
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ap
(
0
.
9
2
)
in
d
icate
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th
at
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h
ib
its
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is
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tem
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em
o
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ates
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ig
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r
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r
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Fu
r
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r
m
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,
th
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m
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el
co
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v
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d
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(
1
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p
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s
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an
d
t
r
ain
ed
ef
f
icien
tly
(
7
.
2
m
in
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s
u
r
p
ass
in
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p
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s
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,
r
esil
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ce
,
an
d
ad
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tatio
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to
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an
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h
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Sp
ec
if
ically
,
th
e
E
KF
is
in
s
t
r
u
m
en
tal
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en
h
a
n
cin
g
an
d
s
tab
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in
g
b
io
m
etr
ic
d
ata
o
b
ta
in
ed
f
r
o
m
s
en
s
o
r
s
[
2
5
]
,
in
cl
u
d
in
g
weig
h
t
tr
en
d
s
an
d
B
MI
v
ar
iatio
n
s
.
B
y
co
n
tin
u
o
u
s
ly
m
in
im
is
in
g
s
tate
-
esti
m
atio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
2
5
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7
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I
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h
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,
Vo
l.
1
5
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
1
2
1
7
-
1
2
2
5
1222
er
r
o
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,
t
h
e
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p
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ile
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wh
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s
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n
o
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v
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id
ab
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Me
an
wh
ile,
th
e
f
u
zz
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ic
co
m
p
o
n
en
t
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r
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v
id
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th
e
co
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tex
tu
al
in
tellig
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ce
r
eq
u
ir
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f
o
r
p
er
s
o
n
alize
d
d
ietar
y
r
e
co
m
m
en
d
atio
n
s
.
Un
lik
e
ANN
-
b
ased
s
y
s
tem
s
th
at
p
r
o
d
u
ce
n
u
m
e
r
ical
p
r
ed
ictio
n
s
with
o
u
t
in
ter
p
r
etab
ilit
y
,
f
u
z
zy
r
u
les
in
co
r
p
o
r
ate
h
u
m
an
-
li
k
e
r
ea
s
o
n
in
g
[
2
6
]
,
[
2
7
]
,
ad
ju
s
tin
g
ca
lo
r
ic
r
e
co
m
m
en
d
atio
n
s
b
ased
o
n
life
s
ty
le
f
ac
to
r
s
an
d
p
h
y
s
io
lo
g
ic
al
co
n
d
itio
n
s
.
T
h
is
en
h
an
ce
s
p
er
s
o
n
aliza
tio
n
an
d
en
ab
les th
e
s
y
s
tem
to
ad
ap
t a
s
th
e
ch
ild
’
s
h
ea
lth
p
r
o
f
ile
e
v
o
lv
es.
T
h
ese
f
in
d
in
g
s
d
em
o
n
s
tr
ate
th
at
AE
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h
an
I
o
T
f
r
a
m
ewo
r
k
ef
f
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y
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d
r
ess
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im
p
o
r
tan
t
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s
in
tr
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al
n
u
tr
itio
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tem
s
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s
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ch
as
m
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n
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y
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en
s
o
r
d
ata
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d
p
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o
v
id
in
g
co
n
te
x
tu
alize
d
s
u
g
g
esti
o
n
s
.
T
h
e
m
o
d
el
’
s
h
ig
h
ac
cu
r
ac
y
an
d
r
ap
id
co
n
v
er
g
e
n
ce
m
ak
e
it
s
u
itab
le
f
o
r
r
ea
l
-
tim
e
im
p
lem
en
tatio
n
in
m
o
b
ile
h
ea
lth
ap
p
licatio
n
s
.
T
h
u
s
,
in
ac
cu
r
ate
n
u
tr
itio
n
al
esti
m
ates
ca
n
lead
to
m
aln
u
tr
itio
n
o
r
ex
ce
s
s
iv
e
ca
lo
r
ie
in
tak
e,
wh
ic
h
af
f
ec
t
s
ch
ild
r
en
’
s
d
ev
el
o
p
m
e
n
t
[
2
8
]
,
wh
er
ea
s
a
d
ap
tiv
e
m
ea
l
p
lan
n
in
g
in
cr
ea
s
es
ad
h
er
en
ce
to
n
u
tr
itio
n
al
n
ee
d
s
an
d
r
ed
u
ce
s
d
ef
icien
cies
in
ess
en
tial
n
u
tr
ien
ts
[
2
9
]
.
T
h
is
alig
n
s
with
th
e
s
tu
d
y
’
s
g
o
al
o
f
d
ev
el
o
p
in
g
a
r
esp
o
n
s
iv
e,
in
tellig
en
t d
ietar
y
a
d
v
is
o
r
tailo
r
ed
to
th
e
in
d
iv
i
d
u
al
n
ee
d
s
o
f
ch
ild
r
en
b
y
in
teg
r
atin
g
AI
a
n
d
I
o
T
tech
n
o
lo
g
y
.
T
h
e
r
esu
lts
p
r
esen
ted
in
T
ab
l
e
1
h
ig
h
lig
h
t
th
e
AE
KFFL
-
I
o
T
f
r
am
ewo
r
k
’
s
p
o
ten
tial
as
a
v
iab
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d
s
u
p
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io
r
alter
n
ativ
e
f
o
r
r
ea
l
-
tim
e,
p
er
s
o
n
alize
d
n
u
tr
itio
n
al
ass
ess
m
en
t
in
r
eso
u
r
ce
-
lim
i
ted
o
r
d
ata
-
s
ca
r
ce
s
ettin
g
s
.
T
h
e
s
y
s
tem
co
llects
co
n
tin
u
o
u
s
p
h
y
s
io
lo
g
ical
an
d
d
ietar
y
d
ata
th
r
o
u
g
h
I
o
T
-
e
n
ab
le
d
s
en
s
o
r
n
etwo
r
k
s
,
wh
ich
ar
e
th
en
an
al
y
ze
d
b
y
E
KF
to
m
in
im
ize
n
o
is
e
a
n
d
d
r
i
f
t,
co
m
m
o
n
is
s
u
es
in
em
b
ed
d
ed
s
en
s
in
g
d
ev
ices
[
3
0
]
.
W
h
en
co
m
b
in
ed
with
f
u
zz
y
lo
g
ic
-
b
ased
in
f
er
en
ce
a
n
d
f
e
d
er
ated
lea
r
n
in
g
m
ec
h
a
n
is
m
s
,
AE
KFFL
-
I
o
T
p
r
o
v
id
es
b
o
t
h
ef
f
icien
t
lo
ca
l
p
r
o
ce
s
s
in
g
an
d
p
r
iv
ac
y
-
p
r
eser
v
in
g
m
o
d
el
o
p
tim
izatio
n
,
m
ak
i
n
g
it
well
-
s
u
ited
f
o
r
u
s
e
in
ch
ild
h
ea
lth
ca
r
e
m
o
n
i
to
r
in
g
s
y
s
tem
s
,
esp
ec
ially
i
n
r
u
r
al
o
r
u
n
d
er
s
er
v
ed
co
m
m
u
n
ities
with
lim
ited
h
ea
lth
ca
r
e
r
eso
u
r
ce
s
[
3
1
]
.
Fu
r
th
er
m
o
r
e
,
b
y
ex
ce
e
d
in
g
tr
ad
itio
n
al
n
u
tr
itio
n
s
y
s
tem
s
in
ter
m
s
o
f
p
r
ed
ictio
n
ac
cu
r
ac
y
,
g
en
er
alis
atio
n
,
an
d
co
n
tex
t
u
a
l
r
ea
s
o
n
in
g
,
AE
KFFL
-
I
o
T
c
o
n
s
id
er
ab
ly
ad
v
an
ce
s
th
e
s
tate
o
f
th
e
ar
t
in
I
o
T
-
in
teg
r
ated
s
m
ar
t
h
ea
lth
ca
r
e.
T
h
e
h
y
b
r
id
ar
c
h
itectu
r
e,
wh
ich
s
tr
ateg
ically
co
m
b
in
e
s
m
ac
h
in
e
lear
n
in
g
,
pr
o
b
a
b
ilis
tic
esti
m
at
io
n
,
an
d
ex
p
lain
ab
le
f
u
zz
y
r
u
le
lo
g
ic,
s
h
o
ws
g
r
ea
t
p
r
o
m
is
e
f
o
r
s
ca
lab
le,
ad
ap
tiv
e,
an
d
in
ter
p
r
etab
le
d
ec
is
io
n
s
u
p
p
o
r
t
in
c
r
itical
d
o
m
ain
s
th
at
r
e
q
u
ir
e
b
o
th
r
ea
l
-
tim
e
r
esp
o
n
s
iv
en
ess
an
d
m
o
d
el
tr
an
s
p
ar
en
cy
[
3
2
]
,
[
3
3
]
.
Ho
wev
er
,
th
er
e
ar
e
a
n
u
m
b
er
o
f
p
r
o
b
lem
s
with
th
e
p
r
o
p
o
s
ed
m
o
d
el.
D
o
m
ain
e
x
p
er
tis
e
is
s
till
n
ee
d
ed
to
cr
ea
te
f
u
zz
y
r
u
le
b
ases
,
an
d
lar
g
e
-
s
ca
le
ad
o
p
tio
n
m
ay
r
e
q
u
ir
e
r
ec
alib
r
atio
n
ac
r
o
s
s
d
if
f
er
en
t
d
em
o
g
r
ap
h
ic
g
r
o
u
p
s
.
Fu
r
th
er
m
o
r
e,
lo
w
-
p
o
wer
m
o
b
ile
s
ce
n
ar
io
s
m
ay
h
av
e
co
m
p
u
tatio
n
al
co
s
ts
ev
en
th
o
u
g
h
th
e
E
KF
im
p
r
o
v
es
r
o
b
u
s
tn
ess
.
Desp
ite
th
ese
co
n
s
id
er
atio
n
s
,
th
e
p
r
im
ar
y
a
d
v
an
tag
e
o
f
AE
KFFL
lie
s
in
its
in
teg
r
atio
n
o
f
E
KF
’
s
ad
ap
tiv
e
f
ilter
in
g
with
f
u
zz
y
lo
g
ic
’
s
in
ter
p
r
e
tiv
e
r
ea
s
o
n
in
g
,
allo
win
g
th
e
m
o
d
el
to
s
u
r
p
ass
co
n
v
en
tio
n
al
ANN
-
b
ased
n
u
t
r
itio
n
esti
m
ato
r
s
an
d
s
tatic,
r
u
l
e
-
b
ased
s
y
s
tem
s
.
4.
CO
NCLU
SI
O
N
I
n
co
n
clu
s
io
n
,
th
is
s
tu
d
y
s
u
c
ce
s
s
f
u
lly
d
esig
n
ed
,
im
p
lem
en
ted
,
an
d
ev
alu
ate
d
th
e
AE
KFFL
m
o
d
el
in
teg
r
ated
with
I
o
T
tech
n
o
lo
g
ies
to
en
h
an
ce
r
ea
l
-
tim
e
d
ietar
y
m
o
n
ito
r
i
n
g
an
d
ca
lo
r
ie
esti
m
atio
n
f
o
r
ch
il
d
r
en
.
T
h
e
f
in
d
in
g
s
af
f
ir
m
th
e
ac
h
iev
em
en
t
o
f
all
r
esear
ch
o
b
je
ctiv
es.
Firstl
y
,
in
ad
d
r
ess
in
g
th
e
n
ee
d
f
o
r
h
ig
h
er
p
r
ed
ictio
n
ac
c
u
r
ac
y
,
t
h
e
AE
KFFL
-
I
o
T
m
o
d
el
ac
h
iev
ed
th
e
l
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DATA AV
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Data
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RE
F
E
R
E
NC
E
S
[
1
]
W
o
r
l
d
H
e
a
l
t
h
O
r
g
a
n
i
z
a
t
i
o
n
,
“
O
b
e
si
t
y
a
n
d
o
v
e
r
w
e
i
g
h
t
,
”
W
o
r
l
d
H
e
a
l
t
h
O
r
g
a
n
i
z
a
t
i
o
n
.
A
c
c
e
sse
d
:
M
a
y
2
1
,
2
0
2
6
.
[
O
n
l
i
n
e
]
.
A
v
a
i
l
a
b
l
e
:
h
t
t
p
s:
/
/
w
w
w
.
w
h
o
.
i
n
t
/
n
e
w
s
-
r
o
o
m
/
f
a
c
t
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sh
e
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t
s/
d
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t
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l
/
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b
e
s
i
t
y
-
a
n
d
-
o
v
e
r
w
e
i
g
h
t
[
2
]
B
.
K
.
P
o
h
e
t
a
l
.
,
“
N
u
t
r
i
t
i
o
n
a
l
st
a
t
u
s
a
n
d
d
i
e
t
a
r
y
i
n
t
a
k
e
s
o
f
c
h
i
l
d
r
e
n
a
g
e
d
6
mo
n
t
h
s
t
o
1
2
y
e
a
r
s:
f
i
n
d
i
n
g
s
o
f
t
h
e
N
u
t
r
i
t
i
o
n
S
u
r
v
e
y
o
f
M
a
l
a
y
si
a
n
C
h
i
l
d
r
e
n
(
S
EA
N
U
TS
M
a
l
a
y
si
a
)
,
”
Bri
t
i
s
h
J
o
u
r
n
a
l
o
f
N
u
t
r
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.
1
1
0
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o
.
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U
P
P
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.
3
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p
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3
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:
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0
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1
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7
/
S
0
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7
1
1
4
5
1
3
0
0
2
0
9
2
.
[
3
]
W
.
S
.
Le
e
e
t
a
l
.
,
“
P
r
e
v
a
l
e
n
c
e
o
f
u
n
d
e
r
n
u
t
r
i
t
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o
n
a
n
d
a
ss
o
c
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a
t
e
d
f
a
c
t
o
r
s
i
n
y
o
u
n
g
c
h
i
l
d
r
e
n
i
n
M
a
l
a
y
s
i
a
:
a
n
a
t
i
o
n
w
i
d
e
s
u
r
v
e
y
,
”
Fro
n
t
i
e
rs
i
n
P
e
d
i
a
t
ri
c
s
,
v
o
l
.
1
0
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A
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2
0
2
2
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o
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:
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3
3
8
9
/
f
p
e
d
.
2
0
2
2
.
9
1
3
8
5
0
.
[
4
]
D
.
H
.
A
h
n
,
“
A
c
c
u
r
a
t
e
a
n
d
r
e
l
i
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b
l
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f
o
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t
r
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st
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ma
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se
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o
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n
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t
a
i
n
t
y
-
d
r
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v
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n
d
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e
p
l
e
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r
n
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n
g
mo
d
e
l
,
”
Ap
p
l
i
e
d
S
c
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e
n
c
e
s
(
S
w
i
t
zer
l
a
n
d
)
,
v
o
l
.
1
4
,
n
o
.
1
8
,
p
.
8
5
7
5
,
S
e
p
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
a
p
p
1
4
1
8
8
5
7
5
.
[
5
]
A
.
D
o
u
s
t
m
o
h
a
m
m
a
d
i
a
n
,
N
.
O
m
i
d
v
a
r
,
N
.
K
e
s
h
a
v
a
r
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-
M
o
h
a
m
m
a
d
i
,
H
.
E
i
n
i
-
Z
i
n
a
b
,
M
.
A
m
i
n
i
,
a
n
d
M
.
A
b
d
o
l
l
a
h
i
,
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T
h
e
a
s
s
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c
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o
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a
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m
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d
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F
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d
N
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L
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(
F
N
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w
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t
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v
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1
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1
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y
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a
r
s
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:
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AUTH
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RS
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r
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lt
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rsiti
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l
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e
lak
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h
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p
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rt
ise
in
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ftwa
re
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n
g
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e
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g
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m
o
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il
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p
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li
c
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ti
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v
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m
e
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ig
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o
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ta
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in
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n
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rm
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ti
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Tec
h
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l
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g
y
fr
o
m
UTe
M
(
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).
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re
se
a
rc
h
in
tere
sts
fo
c
u
s
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m
a
c
h
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lea
rn
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o
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re
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ictio
n
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b
a
se
d
i
n
telli
g
e
n
t
sy
ste
m
s,
h
a
lal
fo
o
d
c
las
sifica
ti
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n
u
sin
g
se
m
a
n
ti
c
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n
d
le
x
ica
l
m
a
p
p
in
g
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se
c
u
ri
ty
re
q
u
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e
n
ts
e
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g
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ri
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g
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c
li
m
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te
risk
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o
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ll
in
g
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n
d
m
o
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o
m
p
u
ti
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g
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p
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li
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ti
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h
a
s
a
c
ti
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ly
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te
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m
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s
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ti
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rn
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n
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,
fu
z
z
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sy
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m
s,
d
isa
ste
r
risk
re
d
u
c
ti
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n
,
a
n
d
AI
-
d
r
iv
e
n
d
e
c
isio
n
s
u
p
p
o
rt
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m
s.
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No
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rre
z
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m
h
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s
p
u
b
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e
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x
ten
si
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imp
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r
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o
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re
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ro
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g
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n
d
i
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b
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rti
c
u
larly
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th
e
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re
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s
o
f
in
telli
g
e
n
t
sy
ste
m
s,
d
isa
ste
r
in
fo
rm
a
ti
c
s,
so
ftwa
re
re
q
u
irem
e
n
ts
e
n
g
in
e
e
r
in
g
,
a
n
d
a
p
p
l
ied
m
a
c
h
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e
lea
rn
in
g
.
H
is
wo
rk
in
te
g
ra
tes
th
e
o
re
ti
c
a
l
re
se
a
rc
h
with
re
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l
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w
o
rld
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n
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u
strial
a
n
d
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c
ieta
l
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p
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li
c
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ti
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p
a
rti
c
u
larly
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d
isa
ste
r
m
a
n
a
g
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m
e
n
t,
h
e
a
lt
h
c
a
re
,
e
d
u
c
a
ti
o
n
,
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n
d
h
a
lal
in
d
u
str
y
tec
h
n
o
l
o
g
ies
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
o
o
rre
z
a
m
@u
tem
.
e
d
u
.
m
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
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&
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o
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m
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f
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a
n
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g
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m
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t
o
f
re
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ts.
S
h
e
c
a
n
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c
o
n
tac
ted
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t
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m
a
il
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a
ss
il
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u
tem
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d
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m
y
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h
d
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r
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lec
tu
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r
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t
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ik
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l
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a
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e
lak
a
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a
lay
sia
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o
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ra
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M
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s we
ll
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s p
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h
e
c
a
n
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e
c
o
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tac
ted
a
t
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m
a
il
:
fa
iru
z
n
u
rr@
u
it
m
.
e
d
u
.
m
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.