2022 International Wireless Communications and Mobile Computing (IWCMC),
Journal Year:
2023,
Volume and Issue:
unknown
Published: June 19, 2023
The
increasing
number
of
applications
and
devices
in
the
Sixth-generation
(6G)
networks
diversity
mobile
data,
architectures,
technologies
make
security
privacy
a
critical
concern.
Advanced
metaheuristics
algorithms
(MHAs)
have
recently
become
viable
solution
for
optimizing
wireless
networks,
combining
game
theory
convex
optimization,
several
other
advanced
models.
As
subfield
Artificial
Intelligence
(AI),
MHAs
are
inspired
by
concepts
from
Evolutionary
Algorithms
(EAs),
Trajectory-based
(TAs),
Swarm
(SI).
Recent
implementations
6G
effectively
solved
complex
problems.
This
study
examines
MHAs'
utilization
addressing
challenges
networks.
paper
provides
comprehensive
overview
their
use
solving
problems
6G.
current
limitations
literature
also
identified,
avenues
further
research
suggested.
reader
will
clear
image
needed
tools
securing
using
MHAs.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
12, P. 22991 - 23028
Published: Aug. 14, 2023
The
Grey
Wolf
Optimizer
(GWO)
has
emerged
as
one
of
the
most
captivating
swarm
intelligence
methods,
drawing
inspiration
from
hunting
behavior
wolf
packs.
GWO's
appeal
lies
in
its
remarkable
characteristics:
it
is
parameter-free,
derivative-free,
conceptually
simple,
user-friendly,
adaptable,
flexible,
and
robust.
Its
efficacy
been
demonstrated
across
a
wide
range
optimization
problems
diverse
domains,
including
engineering,
bioinformatics,
biomedical,
scheduling
planning,
business.
Given
substantial
growth
effectiveness
GWO,
essential
to
conduct
recent
review
provide
updated
insights.
This
delves
into
GWO-related
research
conducted
between
2019
2022,
encompassing
over
200
articles.
It
explores
GWO
terms
publications,
citations,
domains
that
leverage
potential.
thoroughly
examines
latest
versions
categorizing
them
based
on
their
contributions.
Additionally,
highlights
primary
applications
with
computer
science
engineering
emerging
dominant
domains.
A
critical
analysis
accomplishments
limitations
presented,
offering
valuable
Finally,
concludes
brief
summary
outlines
potential
future
developments
theory
applications.
Researchers
seeking
employ
problem-solving
tool
will
find
this
comprehensive
immensely
beneficial
advancing
endeavors.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 109477 - 109487
Published: Jan. 1, 2023
Pests
and
diseases
are
the
big
issues
in
paddy
production
they
make
farmers
to
lose
around
20%
of
rice
yield
world-wide.
Identification
leaves
at
early
stage
through
thermal
image
cameras
will
be
helpful
for
avoiding
such
losses.
The
objective
this
work
is
implement
a
Modified
Lemurs
Optimization
Algorithm
as
filter-based
feature
transformation
technique
enhancing
accuracy
detecting
various
machine
learning
techniques
by
processing
images
leaves.
original
altered
inspiration
Sine
Cosine
developing
proposed
Algorithm.
Five
namely
blast,
brown
leaf
spot,
folder,
hispa,
bacterial
blight
considered
work.
A
total
six
hundred
thirty-six
including
healthy
diseased
analysed.
Seven
statistical
features
seven
Box-Cox
transformed
extracted
from
each
four
K-Nearest
Neighbor
classifier,
Random
Forest
Linear
Discriminant
Analysis
Classifier,
Histogram
Gradient
Boosting
Classifier
tested.
All
these
classifiers
provide
balanced
less
than
65%
their
performance
improved
usage
transform
based
on
Optimization.
Especially,
90%
achieved
using
classifier.
Sustainability,
Journal Year:
2023,
Volume and Issue:
15(20), P. 15039 - 15039
Published: Oct. 19, 2023
In
modern
agriculture,
correctly
identifying
rice
leaf
diseases
is
crucial
for
maintaining
crop
health
and
promoting
sustainable
food
production.
This
study
presents
a
detailed
methodology
to
enhance
the
accuracy
of
disease
classification.
We
achieve
this
by
employing
Convolutional
Neural
Network
(CNN)
model
specifically
designed
images.
The
proposed
method
achieved
an
0.914
during
final
epoch,
demonstrating
highly
competitive
performance
compared
other
models,
with
low
loss
minimal
overfitting.
A
comparison
was
conducted
Transfer
Learning
Inception-v3
EfficientNet-B2
showed
superior
performance.
With
increasing
demand
precision
models
like
one
show
great
potential
in
accurately
detecting
managing
diseases,
ultimately
leading
improved
yields
ecological
sustainability.
