IEEE Transactions on Intelligent Vehicles,
Год журнала:
2024,
Номер
unknown, С. 1 - 23
Опубликована: Янв. 1, 2024
Multiagent
Reinforcement
Learning
(MARL)
plays
a
pivotal
role
in
intelligent
vehicle
systems,
offering
solutions
for
complex
decision-making,
coordination,
and
adaptive
behavior
among
autonomous
agents.
This
review
aims
to
highlight
the
importance
of
fostering
trust
MARL
emphasize
significance
revolutionizing
systems.
First,
this
paper
summarizes
fundamental
methods
MARL.
Second,
it
identifies
limitations
safety,
robustness,
generalization,
ethical
constraints
outlines
corresponding
research
methods.
Then
we
summarize
their
applications
Considering
human
interaction
is
essential
practical
various
domains,
also
analyzes
challenges
associated
with
MARL's
human-machine
These
challenges,
when
overcome,
could
significantly
enhance
real-world
implementation
MARL-based
Mathematics,
Год журнала:
2023,
Номер
11(3), С. 707 - 707
Опубликована: Янв. 30, 2023
In
many
fields,
complicated
issues
can
now
be
solved
with
the
help
of
Artificial
Intelligence
(AI)
and
Machine
Learning
(ML).
One
more
modern
Metaheuristic
(MH)
algorithms
used
to
tackle
numerous
in
various
fields
is
Beluga
Whale
Optimization
(BWO)
method.
However,
BWO
has
a
lack
diversity,
which
could
lead
being
trapped
local
optimaand
premature
convergence.
This
study
presents
two
stages
for
enhancing
fundamental
algorithm.
The
initial
stage
BWO’s
Opposition-Based
(OBL),
also
known
as
OBWO,
helps
expedite
search
process
enhance
learning
methodology
choose
better
generation
candidate
solutions
BWO.
second
step,
referred
OBWOD,
combines
Dynamic
Candidate
Solution
(DCS)
OBWO
based
on
k-Nearest
Neighbor
(kNN)
classifier
boost
variety
improve
consistency
selected
solution
by
giving
potential
candidates
chance
solve
given
problem
high
fitness
value.
A
comparison
present
optimization
single-objective
bound-constraint
problems
was
conducted
evaluate
performance
OBWOD
algorithm
from
2022
IEEE
Congress
Evolutionary
Computation
(CEC’22)
benchmark
test
suite
range
dimension
sizes.
results
statistical
significance
confirmed
that
proposed
competitive
algorithms.
addition,
surpassed
seven
other
an
overall
classification
accuracy
85.17%
classifying
10
medical
datasets
different
sizes
according
evaluation
matrix.
IEEE Access,
Год журнала:
2023,
Номер
11, С. 30247 - 30272
Опубликована: Янв. 1, 2023
Over
the
past
few
years,
number
and
volume
of
data
sources
in
healthcare
databases
has
grown
exponentially.
Analyzing
these
voluminous
medical
is
both
opportunity
challenge
for
knowledge
discovery
health
informatics.
In
last
decade,
social
network
analysis
techniques
community
detection
algorithms
are
being
used
more
scientific
fields,
including
medicine.
While
have
been
widely
analysis,
a
comprehensive
review
its
applications
way
to
benefit
practitioners
informatics
still
overwhelmingly
missing.
This
paper
contributes
fill
this
gap
provide
up-to-date
literature
research.
Especially,
categorizations
existing
presented
discussed.
Moreover,
most
reviewed
categorized.
Finally,
publicly
available
datasets,
key
challenges,
gaps
field
studied
reviewed.
IEEE Access,
Год журнала:
2022,
Номер
10, С. 62613 - 62660
Опубликована: Янв. 1, 2022
The
origin
of
the
COVID-19
pandemic
has
given
overture
to
redirection,
as
well
innovation
many
digital
technologies.
Even
after
progression
vaccination
efforts
across
globe,
total
eradication
this
is
still
a
distant
future
due
evolution
new
variants.
To
proactively
deal
with
pandemic,
health
care
service
providers
and
caretaker
organizations
require
technologies,
alongside
improvements
in
existing
related
Internet
Things
(IoT),
Artificial
Intelligence
(AI),
Machine
Learning
terms
infrastructure,
efficiency,
privacy,
security.
This
paper
provides
an
overview
current
theoretical
application
prospects
IoT,
AI,
cloud
computing,
edge
deep
learning
techniques,
blockchain
social
networks,
robots,
machines,
security
techniques.
In
consideration
these
intersection
we
reviewed
technologies
within
broad
umbrella
AI-IoT
most
concise
classification
scheme.
review,
illustrated
that
technological
applications
innovations
have
impacted
field
healthcare.
essential
found
for
healthcare
were
fog
computing
learning,
blockchain.
Furthermore,
highlighted
several
aspects
their
impact
novel
methodology
using
techniques
from
image
processing,
machine
differential
system
modeling.
Informatics in Medicine Unlocked,
Год журнала:
2022,
Номер
30, С. 100941 - 100941
Опубликована: Янв. 1, 2022
Several
Artificial
Intelligence-based
models
have
been
developed
for
COVID-19
disease
diagnosis.
In
spite
of
the
promise
artificial
intelligence,
there
are
very
few
which
bridge
gap
between
traditional
human-centered
diagnosis
and
potential
future
machine-centered
Under
concept
human-computer
interaction
design,
this
study
proposes
a
new
explainable
intelligence
method
that
exploits
graph
analysis
feature
visualization
optimization
purpose
from
blood
test
samples.
model,
an
decision
forest
classifier
is
employed
to
classification
based
on
routinely
available
patient
data.
The
approach
enables
clinician
use
tree
guide
explainability
interpretability
prediction
model.
By
utilizing
novel
selection
phase,
proposed
model
will
not
only
improve
accuracy
but
decrease
execution
time
as
well.
IEEE Transactions on Industrial Informatics,
Год журнала:
2022,
Номер
19(3), С. 2814 - 2825
Опубликована: Март 22, 2022
Wind
power
forecasting
is
very
crucial
for
system
planning
and
scheduling.
Deep
neural
networks
(DNNs)
are
widely
used
in
applications
due
to
their
exceptional
performance.
However,
the
DNNs’
architectural
configuration
has
a
significant
impact
on
performance,
selection
of
proper
hyper-parameters
determines
success
or
failure
these
models.
Therefore,
one
challenging
issues
DNNs
how
assess
hyper-parameter
values
effectively.
Most
previous
researches
literature
have
tuned
manually,
which
weak
time-consuming
task.
Using
optimization/evolutionary
algorithms
an
effective
way
obtain
optimal
automatically.
In
this
article,
we
propose
novel
evolutionary
algorithm
that
based
grasshopper
optimization
(GOA)
improved
by
adding
two
operators,
opposition-based
learning
chaos
theory,
process.
Overall,
probabilistic
wind
model
named
GOA
deep
auto-regressive
(NGOA-DeepAr)
proposed
recurrent
network
optimized
its
hyper-parameters.
The
performance
NGOA-DeepAr
tested
different
datasets:
One
publicly
available
GEFCom-2014
dataset
other
Australian
Energy
Market
Operator
dataset.
prediction
interval
coverage
probability
pinball
loss
datasets
$[0.902,
0.320]$
notation="LaTeX">$[0.933,
1.4885]$
,
respectively.
According
experimental
findings,
our
much
faster
outperforms
benchmark
neuroevolutionary
