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Applica
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h
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s
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m
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s
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ti
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s
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ss
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n
t
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d
u
str
ies
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ll
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m
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c
h
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e
s
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terp
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t
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n
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m
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k
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l
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c
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s
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a
s
m
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to
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led
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m
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ra
.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
Ar
tific
ial
n
eu
r
al
n
etwo
r
k
C
o
n
v
o
lu
tio
n
n
eu
r
al
n
etwo
r
k
I
n
ter
n
et
o
f
th
in
g
s
Ma
ch
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e
lear
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s
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p
e
n
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c
c
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ss
a
rticle
u
n
d
e
r th
e
CC B
Y
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SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Du
o
n
g
Hu
u
Ai
Dep
ar
tm
en
t o
f
E
lectr
o
n
ics E
n
g
in
ee
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in
g
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lty
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p
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te
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g
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ee
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n
d
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lectr
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h
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Un
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ity
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Dan
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g
-
Vietn
am
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Ko
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Un
i
v
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ity
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I
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f
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an
d
C
o
m
m
u
n
icatio
n
T
ec
h
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o
lo
g
y
Dan
an
g
,
Vietn
am
E
m
ail:
d
h
ai
@
v
k
u
.
u
d
n
.
v
n
1.
I
NT
RO
D
UCT
I
O
N
Ar
tific
ial
in
tellig
en
ce
,
s
o
m
eti
m
es
ca
lled
AI
,
is
in
tellig
e
n
ce
d
em
o
n
s
tr
ated
b
y
m
ac
h
in
es,
as o
p
p
o
s
ed
to
n
atu
r
al
h
u
m
an
in
tellig
en
ce
.
U
s
u
ally
,
th
e
ter
m
AI
is
o
f
te
n
u
s
ed
to
d
escr
ib
e
co
m
p
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ter
s
ca
p
a
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le
o
f
ca
p
tu
r
in
g
th
e
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itiv
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f
u
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ctio
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s
th
at
h
u
m
an
s
n
o
r
m
ally
ass
o
ciate
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e
m
in
d
,
s
u
ch
as
"lea
r
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g
"
an
d
"p
r
o
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lem
s
o
lv
in
g
".
As
m
ac
h
in
es
b
ec
o
m
e
in
cr
ea
s
in
g
ly
ca
p
ab
le,
task
s
d
ee
m
ed
n
ec
ess
ar
y
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o
r
"in
tellig
en
ce
"
ar
e
o
f
ten
d
r
o
p
p
ed
f
r
o
m
th
e
d
ef
i
n
itio
n
o
f
AI
,
a
p
h
en
o
m
en
o
n
k
n
o
wn
as
th
e
AI
ef
f
ec
t.
A
m
ax
im
in
T
esler
'
s
T
h
eo
r
em
s
tates
th
at
"AI
is
a
n
y
t
h
in
g
th
at
h
as
n
o
t b
ee
n
d
o
n
e"
.
Fo
r
ex
a
m
p
le,
o
p
tical
ch
ar
ac
ter
r
ec
o
g
n
iti
o
n
,
o
f
ten
ex
clu
d
ed
f
r
o
m
wh
at
is
co
n
s
id
er
ed
AI
,
h
as
b
ec
o
m
e
a
co
n
v
en
tio
n
al
tech
n
o
lo
g
y
.
Mo
d
e
r
n
m
ac
h
in
e
ca
p
ab
ilit
ies
co
m
m
o
n
ly
class
if
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as
AI
in
clu
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e
s
u
cc
ess
f
u
lly
u
n
d
er
s
tan
d
in
g
h
u
m
an
s
p
e
ec
h
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co
m
p
etin
g
at
th
e
h
ig
h
est
lev
el
in
a
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tr
ateg
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g
am
e
(
s
u
ch
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ch
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)
,
au
to
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o
m
o
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s
v
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h
icles,
r
o
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ti
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f
o
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atio
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tellig
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n
co
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t
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n
etwo
r
k
s
,
an
d
m
ilit
ar
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s
im
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lat
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s
[
1
]
–
[
6
]
.
T
h
e
in
ter
n
et
o
f
t
h
in
g
s
(
I
o
T
)
o
f
f
er
s
s
ev
er
al
ap
p
licatio
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s
in
in
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ir
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p
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tio
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en
h
an
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s
af
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,
s
p
ee
d
,
an
d
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f
ec
tiv
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f
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.
T
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tech
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cr
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,
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d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
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tell
I
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N:
2252
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8
9
3
8
A
p
p
lica
tio
n
s
o
f
a
r
tifi
cia
l in
tell
ig
en
ce
in
in
d
o
o
r
fir
e
p
r
ev
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tio
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fig
h
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ty
[
7
]
–
[
1
1
]
.
C
o
m
b
i
n
in
g
I
o
T
s
y
s
tem
s
with
AI
g
r
ea
tly
en
h
an
ce
s
t
h
eir
ca
p
ab
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p
r
o
v
id
in
g
m
o
r
e
i
n
tellig
en
t,
ad
ap
tiv
e,
an
d
ef
f
ic
ien
t
s
o
lu
tio
n
s
f
o
r
a
wid
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r
an
g
e
o
f
ap
p
licatio
n
s
,
in
clu
d
in
g
f
ir
e
p
r
e
v
en
tio
n
an
d
f
ir
ef
ig
h
tin
g
.
AI
en
ab
les
I
o
T
s
y
s
tem
s
to
an
aly
ze
d
ata,
r
ec
o
g
n
ize
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atter
n
s
,
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d
m
ak
e
d
ec
is
io
n
s
au
to
n
o
m
o
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s
ly
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ich
ca
n
tr
a
n
s
f
o
r
m
h
o
w
in
d
o
o
r
f
ir
e
s
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m
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ag
e
d
.
AI
ca
n
b
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class
if
ied
in
to
th
r
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d
if
f
er
e
n
t
ty
p
es
o
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s
y
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tem
s
:
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tic,
h
u
m
an
-
in
s
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ir
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d
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d
AI
.
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tical
AI
h
as
o
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ly
ch
ar
ac
ter
is
tics
th
at
m
atch
co
g
n
itiv
e
in
tellig
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;
cr
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te
a
co
g
n
itiv
e
r
ep
r
esen
tatio
n
o
f
th
e
wo
r
ld
an
d
u
s
e
lear
n
in
g
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ased
o
n
p
ast
ex
p
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s
to
in
f
o
r
m
f
u
tu
r
e
d
ec
is
io
n
s
.
Hu
m
an
-
in
s
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ir
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AI
h
as
elem
en
ts
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m
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g
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n
d
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o
tio
n
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tellig
en
ce
;
u
n
d
er
s
tan
d
h
u
m
an
em
o
tio
n
s
,
b
ey
o
n
d
co
g
n
itiv
e
f
ac
to
r
s
,
an
d
co
n
s
id
er
th
em
i
n
d
ec
is
io
n
m
ak
in
g
.
Per
s
o
n
i
f
ied
AI
s
h
o
ws
ch
ar
ac
ter
is
tics
o
f
all
k
in
d
s
o
f
co
m
p
eten
cies
,
ca
p
ab
le
o
f
s
elf
-
awa
r
e
n
e
s
s
an
d
s
elf
-
awa
r
en
ess
in
in
ter
ac
tio
n
s
[
1
2
]
–
[
1
7
]
.
Alth
o
u
g
h
s
cien
tis
ts
n
ee
d
to
i
n
co
r
p
o
r
ate
lar
g
e
am
o
u
n
ts
o
f
d
ata
in
to
A
I
m
ac
h
in
es
f
o
r
au
t
h
en
tic
an
d
ac
cu
r
ate
r
esu
lts
,
th
e
m
ain
p
u
r
p
o
s
e
o
f
d
esig
n
in
g
AI
m
ac
h
in
e
s
f
o
r
f
ir
ef
ig
h
tin
g
is
to
p
r
ed
ict
f
ir
e
o
u
tb
r
ea
k
s
u
s
in
g
h
o
w
to
ap
p
ly
all
ca
lcu
latio
n
s
o
n
av
ailab
le
d
ata.
AI
p
o
wer
e
d
s
o
f
twar
e
is
b
ein
g
d
ep
lo
y
ed
b
y
s
cien
tis
ts
in
th
e
s
p
ac
e
an
d
g
r
o
u
n
d
to
ac
cu
r
ately
m
ap
wild
f
ir
e
h
az
a
r
d
s
to
t
h
e
s
u
r
r
o
u
n
d
in
g
s
wh
e
n
wild
f
ir
es
b
r
ea
k
o
u
t.
E
v
en
s
o
,
th
e
tech
n
o
lo
g
y
is
in
its
ea
r
ly
s
tag
es
an
d
it
ta
k
es
tim
e
to
u
n
d
er
s
tan
d
th
e
co
m
p
lex
ity
o
f
th
e
f
ir
e
[
1
8
]
–
[
2
2
]
.
Fu
r
th
er
m
o
r
e
,
it
h
as
b
ee
n
an
al
y
ze
d
th
at
m
ac
h
in
e
lea
r
n
in
g
m
eth
o
d
s
s
u
ch
as
s
p
ec
tr
al
clu
s
ter
in
g
an
d
m
an
if
o
l
d
lear
n
in
g
ar
e
b
ein
g
u
s
ed
to
d
is
tin
g
u
is
h
s
m
o
k
e
ty
p
es
h
elp
in
g
m
an
ag
er
s
g
ain
im
p
o
r
tan
t
in
f
o
r
m
atio
n
to
r
e
d
u
ce
in
d
o
o
r
f
ir
e
ca
u
s
ed
b
y
f
ir
es.
R
ec
en
tly
,
a
d
ev
elo
p
m
en
t
p
la
n
f
o
r
in
tellig
en
t
f
ir
e
ex
tin
g
u
is
h
in
g
s
y
s
tem
s
h
as
b
ee
n
lau
n
ch
ed
t
o
p
r
e
v
en
t f
ir
e
s
p
r
ea
d
,
p
r
o
tectio
n
an
d
s
er
v
ices o
cc
u
r
r
in
g
i
n
an
e
m
er
g
en
c
y
s
itu
atio
n
[
2
3
]
–
[
2
7
]
.
I
n
th
is
s
tu
d
y
,
we
th
eo
r
etica
lly
an
aly
ze
th
e
a
p
p
licatio
n
s
o
f
AI
in
in
d
o
o
r
f
ir
e
p
r
ev
e
n
tio
n
an
d
f
ig
h
tin
g
,
th
e
s
tu
d
y
is
o
r
g
a
n
ized
as
f
o
llo
ws.
AI
in
f
ir
e
p
r
o
tectio
n
is
p
r
esen
t
in
s
ec
tio
n
2
.
Sectio
n
3
p
r
esen
ts
th
e
s
y
s
tem
an
aly
s
is
an
d
d
esig
n
.
T
h
e
n
u
m
er
ical
r
esu
lts
an
d
d
is
cu
s
s
io
n
s
ar
e
p
r
esen
ts
in
s
ec
tio
n
4
.
T
h
e
s
tu
d
y
is
in
cl
u
d
ed
in
s
ec
tio
n
5
.
2.
ARTI
F
I
CI
AL
I
NT
E
L
L
I
G
E
NCE I
N
F
I
RE
P
RO
T
E
C
T
I
O
N
2
.
1
.
Co
nv
o
lutio
na
l
neura
l net
wo
rk
T
h
e
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
et
wo
r
k
s
(
C
NN)
ar
e
s
h
o
w
in
F
ig
u
r
e
1
,
C
NN
ar
e
a
class
o
f
d
ee
p
lear
n
in
g
m
o
d
els
p
r
im
ar
ily
u
s
ed
f
o
r
i
m
ag
e
p
r
o
ce
s
s
in
g
,
co
m
p
u
ter
v
is
io
n
,
a
n
d
p
atter
n
r
ec
o
g
n
iti
o
n
task
s
.
T
h
ey
ar
e
in
s
p
ir
ed
b
y
th
e
v
is
u
al
co
r
te
x
o
f
th
e
h
u
m
a
n
b
r
ain
a
n
d
a
r
e
p
ar
ticu
lar
ly
ef
f
ec
ti
v
e
in
h
an
d
li
n
g
s
p
atial
d
ata
[
2
]
.
C
NNs
ar
e
r
ev
o
lu
tio
n
izin
g
in
d
u
s
tr
ies
b
y
p
r
o
v
id
in
g
ef
f
icien
t
v
is
u
al
r
ec
o
g
n
itio
n
ca
p
a
b
ilit
ies.
Fro
m
h
ea
lth
ca
r
e
to
s
elf
-
d
r
iv
in
g
ca
r
s
,
th
eir
im
p
ac
t
is
v
ast an
d
co
n
tin
u
o
u
s
ly
g
r
o
wi
n
g
.
I
t is a
n
eu
r
al
n
etwo
r
k
ar
c
h
itectu
r
e
th
at
is
well
s
u
ited
f
o
r
p
r
o
b
lem
s
wh
er
e
th
e
d
ata
is
im
ag
es o
r
v
id
e
o
.
Fig
u
r
e
1
.
C
o
n
v
o
lu
tio
n
al
n
eu
r
a
l n
etwo
r
k
T
h
e
co
n
v
o
lu
tio
n
lay
e
r
is
th
e
co
r
e
b
u
ild
in
g
b
lo
c
k
o
f
a
C
NN.
I
t
is
r
esp
o
n
s
ib
le
f
o
r
d
etec
t
in
g
f
ea
tu
r
es
s
u
ch
as
ed
g
es,
tex
tu
r
es,
s
h
a
p
es,
an
d
p
atter
n
s
in
im
ag
es.
A
co
n
v
o
lu
tio
n
o
p
e
r
atio
n
is
p
er
f
o
r
m
ed
b
y
s
lid
in
g
a
s
m
all
f
ilter
(
k
er
n
el)
o
v
er
an
in
p
u
t
im
ag
e
o
r
f
ea
t
u
r
e
m
a
p
.
At
ea
ch
p
o
s
itio
n
,
th
e
d
o
t
p
r
o
d
u
ct
o
f
th
e
f
ilter
an
d
th
e
co
r
r
esp
o
n
d
in
g
r
eg
io
n
o
f
th
e
i
n
p
u
t
is
co
m
p
u
ted
an
d
s
u
m
m
e
d
to
p
r
o
d
u
ce
a
s
in
g
le
o
u
tp
u
t
v
alu
e.
I
n
th
is
lay
e
r
th
er
e
ar
e
4
m
ain
o
b
jects: in
p
u
t
m
atr
ix
,
r
ec
e
p
tiv
e
f
ield
,
f
ilter
s
,
an
d
f
ea
tu
r
e
m
ap
,
th
a
t is
s
h
o
w
n
in
F
ig
u
r
e
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
4
,
Au
g
u
s
t
20
25
:
2
6
4
6
-
2
6
5
4
2648
Fig
u
r
e
2
.
Featu
r
e
m
ap
Fil
ter
s
h
elp
ex
tr
ac
t
s
p
ec
if
ic
f
e
atu
r
es
f
r
o
m
im
ag
es,
th
e
r
ec
ep
t
iv
e
f
ield
d
eter
m
in
es h
o
w
m
u
c
h
co
n
tex
t
a
n
eu
r
o
n
ca
p
tu
r
es
. D
ee
p
n
etwo
r
k
s
with
lar
g
er
r
ec
ep
tiv
e
f
ield
s
im
p
r
o
v
e
o
b
ject
d
etec
tio
n
an
d
class
if
icatio
n
.
I
n
p
u
t
m
atr
ix
;
th
e
im
ag
e
o
r
f
ea
tu
r
e
m
ap
b
ein
g
p
r
o
ce
s
s
ed
,
f
ilter
(
k
er
n
el
)
;
a
s
m
all
m
atr
ix
u
s
ed
to
ex
tr
ac
t
f
ea
tu
r
es,
r
ec
ep
tiv
e
f
ield
;
th
e
lo
ca
l
r
eg
i
o
n
o
f
th
e
in
p
u
t
th
at
th
e
f
ilter
in
ter
ac
ts
with
,
f
ea
tu
r
e
m
ap
;
th
e
o
u
tp
u
t
m
atr
ix
co
n
tain
in
g
e
x
tr
ac
ted
f
ea
t
u
r
es.
3.
SYST
E
M
ANA
L
YS
I
S AN
D
DE
S
I
G
N
3
.
1
.
Sy
s
t
e
m
des
ig
n
T
h
e
b
u
ilt
s
y
s
tem
co
n
s
is
ts
o
f
two
m
ain
p
ar
ts
:
h
ar
d
war
e
d
ev
ice
p
air
in
g
an
d
s
y
s
tem
d
ep
lo
y
m
en
t
s
o
f
twar
e.
First
ab
o
u
t
th
e
h
ar
d
war
e
s
y
s
tem
,
th
e
h
ar
d
war
e
is
d
iv
id
ed
in
t
o
two
m
ain
p
ar
ts
,
t
h
e
f
ir
s
t
is
th
e
s
en
s
o
r
s
th
at
co
llect
in
f
o
r
m
atio
n
a
b
o
u
t
th
e
e
n
v
ir
o
n
m
en
t
an
d
th
e
s
ec
o
n
d
is
th
e
s
er
v
er
t
h
at
h
a
n
d
les
task
s
s
u
ch
as
d
etec
tin
g
f
ir
e,
g
iv
in
g
war
n
in
g
s
,
an
d
n
o
tific
atio
n
.
T
h
e
co
n
n
ec
t
io
n
m
o
d
e
l o
f
th
e
s
y
s
tem
is
s
h
o
wn
in
F
ig
u
r
e
3
.
Fig
u
r
e
3
.
Ov
e
r
v
iew
m
o
d
el
Data
is
co
llec
ted
th
r
o
u
g
h
s
en
s
o
r
n
o
d
es
s
en
t
to
th
e
g
atew
ay
b
y
lo
r
a
wav
es,
th
e
d
ata
is
ag
g
r
eg
ated
an
d
s
en
t
to
th
e
web
s
er
v
er
.
Her
e
th
e
d
ata
is
p
r
o
ce
s
s
ed
to
g
iv
e
th
e
p
r
o
b
a
b
ilit
y
o
f
a
f
ir
e
o
cc
u
r
r
in
g
.
T
h
e
s
en
s
o
r
n
o
d
es
ar
e
eq
u
ip
p
ed
with
tem
p
er
atu
r
e
an
d
h
u
m
id
ity
s
en
s
o
r
s
,
alo
n
g
with
t
h
e
AT
m
e
g
a3
2
8
ce
n
tr
al
m
icr
o
co
n
tr
o
ller
r
u
n
n
in
g
o
n
th
e
Ar
d
u
i
n
o
B
o
o
tlo
ad
er
p
latf
o
r
m
.
Her
e
d
ata
is
c
o
llected
an
d
s
en
t
to
th
e
g
atew
ay
b
y
lo
r
a
wav
es.
Gate
way
L
o
r
a
is
a
p
lace
to
ag
g
r
eg
ate
d
ata
f
r
o
m
s
en
s
o
r
n
o
d
es.
Sen
d
to
s
er
v
er
with
h
ttp
p
r
o
to
c
o
l.
R
aw
d
ata
co
llected
f
r
o
m
s
en
s
o
r
s
is
s
to
r
ed
in
th
e
clo
u
d
,
wh
e
r
e
th
e
d
a
ta
will
b
e
lab
eled
f
o
r
AI
ca
lcu
latio
n
s
.
R
elev
an
t
f
u
n
ctio
n
a
n
d
s
ce
n
ar
i
o
in
f
o
r
m
a
tio
n
in
o
u
r
an
aly
s
is
is
p
r
o
v
i
d
e
d
in
T
ab
le
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
p
p
lica
tio
n
s
o
f
a
r
tifi
cia
l in
tell
ig
en
ce
in
in
d
o
o
r
fir
e
p
r
ev
en
tio
n
a
n
d
fig
h
tin
g
(
Du
o
n
g
Hu
u
A
i
)
2649
T
ab
le
1
.
Sy
s
tem
o
p
er
atio
n
s
ce
n
ar
io
F
u
n
c
t
i
o
n
S
c
e
n
a
r
i
o
I
n
f
o
r
ma
t
i
o
n
F
i
r
e
f
o
r
e
c
a
s
t
i
n
g
f
u
n
c
t
i
o
n
Th
e
f
u
n
c
t
i
o
n
w
o
r
k
s
b
a
s
e
d
o
n
t
h
e
t
e
m
p
e
r
a
t
u
r
e
c
h
a
n
g
e
s
o
f
t
h
e
e
n
v
i
r
o
n
m
e
n
t
:
h
u
m
i
d
i
t
y
,
a
n
d
t
e
m
p
e
r
a
t
u
r
e
.
t
o
g
i
v
e
p
r
e
d
i
c
t
i
o
n
r
e
su
l
t
s.
M
e
ss
a
g
e
s
e
n
d
i
n
g
f
u
n
c
t
i
o
n
W
h
e
n
t
h
e
r
e
i
s
a
p
r
e
d
i
c
t
i
o
n
r
e
s
u
l
t
,
i
f
t
h
e
p
r
e
d
i
c
t
i
o
n
r
e
s
u
l
t
i
s
>
6
0
%
,
t
h
e
sy
s
t
e
m
w
i
l
l
s
e
n
d
a
mes
sa
g
e
t
o
t
h
e
p
r
o
c
e
ss
i
n
g
c
e
n
t
e
r
a
n
d
t
h
e
a
c
c
o
u
n
t
s
h
a
v
e
b
e
e
n
se
t
u
p
b
e
f
o
r
e
t
h
e
r
e
i
s
a
si
g
n
o
f
f
i
r
e
.
A
l
a
r
m fu
n
c
t
i
o
n
W
h
e
n
a
f
i
r
e
o
c
c
u
r
s
w
i
t
h
i
n
t
h
e
o
p
e
r
a
t
i
n
g
r
a
n
g
e
o
f
t
h
e
d
e
v
i
c
e
,
t
h
e
s
y
s
t
e
m
w
i
l
l
se
n
d
a
n
a
l
a
r
m
s
i
g
n
a
l
t
o
t
h
e
p
r
o
c
e
ss
i
n
g
c
e
n
t
e
r
a
n
d
t
o
s
o
u
n
d
a
n
a
l
a
r
m w
i
t
h
a
h
o
r
n
o
r
s
p
e
a
k
e
r
.
F
i
r
e
f
i
g
h
t
i
n
g
f
u
n
c
t
i
o
n
W
h
e
n
t
h
e
a
l
a
r
m
i
s
w
i
t
h
i
n
3
0
se
c
o
n
d
s
w
i
t
h
o
u
t
a
n
y
h
u
m
a
n
c
o
mm
a
n
d
,
t
h
e
s
y
st
e
m
w
i
l
l
a
u
t
o
m
a
t
i
c
a
l
l
y
e
x
t
i
n
g
u
i
sh
t
h
e
f
i
r
e
w
i
t
h
t
h
e
n
o
z
z
l
e
.
Fig
u
r
e
4
s
h
o
ws
an
o
v
er
v
iew
o
f
th
e
o
p
e
r
atin
g
p
r
o
ce
s
s
o
f
th
e
s
y
s
tem
.
T
h
e
f
ir
e
alar
m
r
at
in
g
s
er
v
er
s
o
f
twar
e
in
clu
d
es
th
e
f
o
llo
wi
n
g
m
ain
m
o
d
u
les.
Vid
eo
an
a
ly
s
is
:
th
e
m
o
d
u
le
is
r
esp
o
n
s
ib
le
f
o
r
ex
tr
ac
tin
g
ev
en
ts
f
r
o
m
v
id
e
o
s
tr
ea
m
s
s
e
n
t
to
th
e
p
r
o
ce
s
s
in
g
ce
n
ter
.
T
h
is
i
s
an
im
p
o
r
tan
t
p
r
o
ce
s
s
f
lo
w
o
f
th
e
s
y
s
tem
b
ec
au
s
e
it
h
as
to
d
ea
l
with
a
l
ar
g
e
am
o
u
n
t
o
f
in
f
o
r
m
atio
n
,
with
h
ig
h
r
eliab
ilit
y
.
Pro
p
er
s
em
an
tic
an
aly
s
is
will
r
ed
u
ce
f
alse
alar
m
s
.
E
n
v
ir
o
n
m
en
tal
s
en
s
o
r
:
th
e
m
o
d
u
le
h
a
s
th
e
r
o
le
o
f
s
to
r
in
g
an
d
d
is
p
lay
in
g
in
f
o
r
m
ati
o
n
f
r
o
m
tr
a
d
itio
n
al
f
i
r
e
alar
m
s
en
s
o
r
n
o
d
es.
T
h
is
f
l
o
w
o
f
in
f
o
r
m
atio
n
n
o
t
o
n
ly
h
el
p
s
u
s
to
d
ec
id
e
o
n
f
ir
e
war
n
in
g
s
,
b
u
t a
ls
o
h
elp
s
in
f
o
r
ec
asti
n
g
ar
ea
s
o
f
h
ig
h
f
ir
e
r
is
k
.
Fig
u
r
e
4
.
Flo
wch
ar
t
o
f
b
u
ild
in
g
th
e
f
ir
e
i
d
en
tific
atio
n
s
y
s
tem
A
n
a
l
y
s
is
a
n
d
d
e
c
is
i
o
n
:
t
h
is
i
s
w
h
e
r
e
t
h
e
a
n
a
l
y
s
is
e
v
a
l
u
at
e
s
t
h
e
w
a
r
n
i
n
g
i
n
f
o
r
m
a
t
i
o
n
f
o
r
t
h
e
w
h
o
l
e
b
u
i
l
d
i
n
g
.
F
r
o
m
a
n
o
m
a
l
ie
s
o
n
s
e
n
s
o
r
n
o
d
es
a
n
d
c
a
m
e
r
as
in
t
h
e
b
u
i
l
d
i
n
g
,
c
o
m
b
i
n
e
d
w
i
th
t
h
e
e
x
p
e
r
i
e
n
c
e
o
f
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
s
t
o
g
i
v
e
a
p
p
r
o
p
r
i
a
t
e
w
a
r
n
i
n
g
le
v
e
l
s
.
W
a
r
n
i
n
g
s
y
s
t
e
m
:
t
h
e
ta
s
k
o
f
t
h
e
w
a
r
n
i
n
g
s
y
s
te
m
i
s
t
h
a
t
a
f
t
e
r
r
ec
e
i
v
i
n
g
th
e
r
e
s
u
l
ts
o
f
e
n
v
i
r
o
n
m
e
n
t
a
l
a
n
a
l
y
s
is
f
r
o
m
t
h
e
c
o
l
l
ec
t
e
d
d
a
t
a
,
i
t
w
il
l
is
s
u
e
a
n
al
a
r
m
d
e
p
e
n
d
i
n
g
o
n
t
h
e
r
e
s
u
l
t
s
r
ec
e
i
v
e
d
.
T
h
e
a
l
e
r
t
s
y
s
t
e
m
c
a
n
s
e
n
d
m
e
s
s
a
g
es
t
o
za
l
o
a
c
c
o
u
n
ts
i
n
t
h
e
i
n
s
t
al
l
e
d
li
s
t
.
3
.
2
.
T
ra
ini
ng
m
o
del
Af
ter
tr
ain
in
g
th
e
d
ataset,
we
p
r
o
ce
ed
to
u
s
e
th
e
c
r
o
s
s
-
v
alid
atio
n
tech
n
iq
u
e
to
esti
m
ate
th
e
ac
cu
r
ac
y
o
r
er
r
o
r
o
f
th
e
alg
o
r
ith
m
,
th
e
p
u
r
p
o
s
e
o
f
th
e
tech
n
iq
u
e
is
t
o
d
iv
id
e
th
e
in
itial
d
ata
s
et
in
t
o
th
e
tr
ain
in
g
d
ata
u
s
ed
to
tr
ain
th
e
m
o
d
el
an
d
an
in
d
ep
en
d
en
t
d
ataset
is
u
s
e
d
f
o
r
ev
alu
atio
n
.
T
h
e
m
o
s
t
c
o
m
m
o
n
m
eth
o
d
is
K
-
f
o
ld
,
w
h
er
e
th
e
in
itial
d
ata
s
et
is
d
iv
id
ed
in
to
e
q
u
ally
s
ized
s
u
b
s
ets,
ca
lled
“f
o
ld
s
”.
T
h
e
K
v
al
u
e
is
th
e
n
u
m
b
er
o
f
d
ata
s
ets to
b
e
s
p
lit.
T
h
is
m
eth
o
d
is
r
ep
ea
ted
m
an
y
tim
es
u
n
til
th
er
e
ar
e
K
n
u
m
b
er
o
f
d
if
f
er
en
t
m
o
d
els,
o
n
e
o
f
th
e
k
s
ets
is
u
s
ed
as
th
e
test
s
et
an
d
th
e
o
th
er
s
ets
ar
e
r
ea
s
s
em
b
led
i
n
to
th
e
tr
ain
i
n
g
s
et.
T
h
e
esti
m
ate
o
f
ac
cu
r
ac
y
o
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
4
,
Au
g
u
s
t
20
25
:
2
6
4
6
-
2
6
5
4
2650
er
r
o
r
is
av
er
ag
ed
o
v
er
k
-
test
s
to
ev
alu
ate
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
m
o
d
el.
T
h
e
tr
ain
in
g
m
o
d
e
l
o
n
T
en
s
o
r
f
lo
w
is
s
h
o
wn
in
F
ig
u
r
e
5
.
Fig
u
r
e
5
.
T
r
ain
in
g
m
o
d
el
o
n
T
en
s
o
r
f
lo
w
T
h
e
tr
ain
in
g
f
lo
wch
ar
t
o
f
th
e
y
o
u
o
n
ly
lo
o
k
o
n
ce
(
YOL
O
)
-
b
ased
f
ir
e
r
ec
o
g
n
itio
n
m
o
d
el
i
s
s
h
o
wn
in
th
e
F
ig
u
r
e
6
.
First,
we
co
n
v
er
t
th
e
d
ataset
lab
els
in
to
a
u
s
ab
l
e
lab
el
f
ile
f
o
r
YOL
O.
YOL
O
r
eq
u
ir
es
a
.
tx
t
f
ile
f
o
r
ea
ch
.
Fu
r
th
e
r
m
o
r
e
,
YOL
O
r
eq
u
ir
es
s
ev
er
al
f
iles
to
s
ta
r
t
tr
ain
in
g
.
T
h
e
v
al
u
e
o
f
th
e
f
ilter
s
in
th
e
YOL
O
co
n
f
ig
u
r
atio
n
f
ile
(
.
c
f
g
f
ile)
f
o
r
th
e
s
ec
o
n
d
f
i
n
al
lay
er
is
n
o
t
ar
b
itra
r
y
a
n
d
d
ep
en
d
s
o
n
t
h
e
to
tal
n
u
m
b
e
r
o
f
lay
er
s
.
T
h
e
n
u
m
b
er
o
f
f
ilter
s
ca
n
b
e
p
r
o
v
id
e
d
b
y
:
f
ilter
s
=5
*
(
2
+
n
u
m
b
er
_
o
f
_
class
es).
Fig
u
r
e
6
.
Flo
wch
ar
t
o
f
tr
ai
n
in
g
m
o
d
el
al
g
o
r
ith
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
p
p
lica
tio
n
s
o
f
a
r
tifi
cia
l in
tell
ig
en
ce
in
in
d
o
o
r
fir
e
p
r
ev
en
tio
n
a
n
d
fig
h
tin
g
(
Du
o
n
g
Hu
u
A
i
)
2651
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
B
ased
o
n
th
e
in
d
icato
r
s
o
f
th
e
co
n
f
u
s
io
n
m
atr
i
x
f
o
r
th
e
cl
ass
if
icatio
n
m
o
d
el
to
b
e
ev
alu
ated
an
d
ad
ju
s
ted
ef
f
ec
tiv
ely
.
First,
in
cr
ea
s
e
th
e
r
ate
o
f
t
r
u
e
p
o
s
itiv
e
(
TP
)
,
tr
u
e
n
eg
ativ
e
(
TN
)
,
an
d
d
ec
r
ea
s
e
f
alse
p
o
s
itiv
e
(
FP
)
,
f
alse
n
eg
ativ
e
(
FN
)
to
in
cr
ea
s
e
ac
cu
r
ac
y
r
ate
an
d
r
ed
u
ce
er
r
o
r
r
ate
[
1
4
]
.
T
h
e
co
n
f
u
s
io
n
m
atr
ix
in
d
icato
r
is
s
h
o
wn
in
F
ig
u
r
e
7
.
Acc
u
r
ac
y
=
+
+
+
+
Pre
cisi
o
n
=
+
R
ec
all
=
+
W
h
er
e:
T
P:
n
u
m
b
er
o
f
co
r
r
e
ct
p
r
ed
ictio
n
s
,
T
N:
i
n
d
ir
ec
tly
co
r
r
ec
tly
p
r
ed
icted
s
alar
y
,
FP
(
ty
p
e
1
er
r
o
r
)
:
n
u
m
b
er
o
f
f
alse p
r
ed
ictio
n
s
,
a
n
d
FN (
ty
p
e
2
er
r
o
r
)
:
n
u
m
b
e
r
o
f
in
d
ir
ec
tly
f
alse p
r
ed
ictio
n
s
.
Fig
u
r
e
7
.
C
o
n
f
u
s
io
n
m
atr
i
x
in
d
icato
r
Fig
u
r
e
8
,
we
ca
n
s
ee
h
e
r
e
th
at
p
r
ec
is
io
n
r
et
u
r
n
s
a
f
air
ly
h
ig
h
r
esu
lt
>0
.
9
an
d
r
ec
a
ll
is
also
r
elativ
ely
>0
.
9
,
we
ca
n
s
ee
th
at
th
e
m
o
d
el
h
e
r
e
will
n
o
t
f
all
in
to
two
ca
s
es:
h
ig
h
r
ec
all
lo
w
p
r
ec
is
io
n
o
r
lo
w
p
r
ec
is
io
n
r
ec
al
l.
H
ig
h
,
b
u
t
at
h
ig
h
p
r
ec
is
io
n
th
r
esh
o
ld
an
d
h
ig
h
r
ec
all
r
etu
r
n
r
elativ
e
r
esu
lt
s
b
u
t
r
etu
r
n
r
esu
lts
ac
cu
r
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9
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rc
h
in
t
e
re
sts
in
c
lu
d
e
d
a
tab
a
se
,
a
rti
ficia
l
in
telli
g
e
n
c
e
,
I
o
T,
a
n
d
o
p
ti
c
a
l
wire
les
s
c
o
m
m
u
n
ica
ti
o
n
s.
He
can
be
c
o
n
tac
ted
at
e
m
a
il
:
lk
t
y
@v
k
u
.
u
d
n
.
v
n
.
Vie
t
Tr
u
o
n
g
Le
re
c
e
iv
e
d
h
is
M
a
ste
r
o
f
S
c
ien
c
e
i
n
I
n
fo
r
m
a
ti
c
s
fro
m
Hu
e
Un
iv
e
rsity
,
Vie
tn
a
m
i
n
2
0
0
5
.
Cu
rre
n
tl
y
,
h
e
is
a
lec
tu
re
r
a
t
T
h
e
Un
i
v
e
rsity
o
f
Da
n
a
n
g
-
Vie
tn
a
m
-
Ko
re
a
Un
iv
e
rsity
o
f
In
fo
rm
a
ti
o
n
a
n
d
Co
m
m
u
n
ica
ti
o
n
Tec
h
n
o
l
o
g
y
,
Da
n
a
n
g
Cit
y
,
Vie
tn
a
m
.
His res
e
a
r
c
h
in
tere
st
s in
c
lu
d
e
d
a
tab
a
se
,
d
a
ta wa
re
h
o
u
se
,
d
a
ta m
in
in
g
,
sy
ste
m
a
n
a
ly
si
s
a
n
d
d
e
si
g
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
lv
tr
u
o
n
g
@v
k
u
.
u
d
n
.
v
n
.
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