Journal of Management Analytics,
Journal Year:
2019,
Volume and Issue:
6(1), P. 1 - 29
Published: Jan. 2, 2019
Artificial
intelligence
(AI)
is
one
of
the
core
drivers
industrial
development
and
a
critical
factor
in
promoting
integration
emerging
technologies,
such
as
graphic
processing
unit,
Internet
Things,
cloud
computing,
blockchain,
new
generation
big
data
Industry
4.0.
In
this
paper,
we
construct
an
extensive
survey
over
period
1961–2018
AI
deep
learning.
The
research
provides
valuable
reference
for
researchers
practitioners
through
multi-angle
systematic
analysis
AI,
from
underlying
mechanisms
to
practical
applications,
fundamental
algorithms
achievements,
current
status
future
trends.
Although
there
exist
many
issues
toward
it
undoubtful
that
has
become
innovative
revolutionary
assistant
wide
range
applications
fields.
IEEE Access,
Journal Year:
2017,
Volume and Issue:
5, P. 7776 - 7797
Published: Jan. 1, 2017
The
Big
Data
revolution
promises
to
transform
how
we
live,
work,
and
think
by
enabling
process
optimization,
empowering
insight
discovery
improving
decision
making.
realization
of
this
grand
potential
relies
on
the
ability
extract
value
from
such
massive
data
through
analytics;
machine
learning
is
at
its
core
because
learn
provide
driven
insights,
decisions,
predictions.
However,
traditional
approaches
were
developed
in
a
different
era,
thus
are
based
upon
multiple
assumptions,
as
set
fitting
entirely
into
memory,
what
unfortunately
no
longer
holds
true
new
context.
These
broken
together
with
characteristics,
creating
obstacles
for
techniques.
Consequently,
paper
compiles,
summarizes,
organizes
challenges
Data.
In
contrast
other
research
that
discusses
challenges,
work
highlights
cause–effect
relationship
organizing
according
Vs
or
dimensions
instigated
issue:
volume,
velocity,
variety,
veracity.
Moreover,
emerging
techniques
discussed
terms
they
capable
handling
various
ultimate
objective
helping
practitioners
select
appropriate
solutions
their
use
cases.
Finally,
matrix
relating
presented.
Through
process,
provides
perspective
domain,
identifies
gaps
opportunities,
strong
foundation
encouragement
further
field
EURASIP Journal on Advances in Signal Processing,
Journal Year:
2016,
Volume and Issue:
2016(1)
Published: May 28, 2016
There
is
no
doubt
that
big
data
are
now
rapidly
expanding
in
all
science
and
engineering
domains.
While
the
potential
of
these
massive
undoubtedly
significant,
fully
making
sense
them
requires
new
ways
thinking
novel
learning
techniques
to
address
various
challenges.
In
this
paper,
we
present
a
literature
survey
latest
advances
researches
on
machine
for
processing.
First,
review
highlight
some
promising
methods
recent
studies,
such
as
representation
learning,
deep
distributed
parallel
transfer
active
kernel-based
learning.
Next,
focus
analysis
discussions
about
challenges
possible
solutions
data.
Following
that,
investigate
close
connections
with
signal
processing
Finally,
outline
several
open
issues
research
trends.
Journal of Applied Remote Sensing,
Journal Year:
2017,
Volume and Issue:
11(04), P. 1 - 1
Published: Sept. 23, 2017
In
recent
years,
deep
learning
(DL),
a
re-branding
of
neural
networks
(NNs),
has
risen
to
the
top
in
numerous
areas,
namely
computer
vision
(CV),
speech
recognition,
natural
language
processing,
etc.
Whereas
remote
sensing
(RS)
possesses
number
unique
challenges,
primarily
related
sensors
and
applications,
inevitably
RS
draws
from
many
same
theories
as
CV;
e.g.,
statistics,
fusion,
machine
learning,
name
few.
This
means
that
community
should
be
aware
of,
if
not
at
leading
edge
advancements
like
DL.
Herein,
we
provide
most
comprehensive
survey
state-of-the-art
DL
research.
We
also
review
new
developments
field
can
used
for
RS.
Namely,
focus
on
theories,
tools
challenges
community.
Specifically,
unsolved
opportunities
it
relates
(i)
inadequate
data
sets,
(ii)
human-understandable
solutions
modelling
physical
phenomena,
(iii)
Big
Data,
(iv)
non-traditional
heterogeneous
sources,
(v)
architectures
algorithms
spectral,
spatial
temporal
data,
(vi)
transfer
(vii)
an
improved
theoretical
understanding
systems,
(viii)
high
barriers
entry,
(ix)
training
optimizing
IEEE Access,
Journal Year:
2014,
Volume and Issue:
2, P. 1660 - 1679
Published: Jan. 1, 2014
The
Internet
of
Things
(IoT)
is
a
dynamic
global
information
network
consisting
Internet-connected
objects,
such
as
RFIDs,
sensors,
and
actuators,
well
other
instruments
smart
appliances
that
are
becoming
an
integral
component
the
Internet.
Over
last
few
years,
we
have
seen
plethora
IoT
solutions
making
their
way
into
industry
marketplace.
Context-aware
communication
computing
has
played
critical
role
throughout
years
ubiquitous
expected
to
play
significant
in
paradigm
well.
In
this
article,
examine
variety
popular
innovative
terms
context-aware
technology
perspectives.
More
importantly,
evaluate
these
using
framework
built
around
well-known
theories.
This
survey
intended
serve
guideline
conceptual
for
contextaware
product
development
research
paradigm.
It
also
provides
systematic
exploration
existing
products
marketplace
highlights
number
potentially
directions
trends.
