Research Square (Research Square),
Год журнала:
2023,
Номер
unknown
Опубликована: Окт. 17, 2023
Abstract
Multi-threshold
image
segmentation
is
a
simple
and
effective
approach.
Image
techniques
are
significant
in
the
fields
of
pattern
recognition
computer
vision.
However,
as
number
thresholds
increases,
temporal
complexity
selecting
best
threshold
increases
exponentially.
A
meta-heuristic
optimization
approach
called
differential
evolution
(DE)
algorithm
was
utilized
to
address
problem.
This
paper
proposes
an
enhanced
DE
with
adaptive
control
parameters
(IJADE)
for
multi-threshold
segmentation.
In
this
study,
optimizes
five
distinct
eight
standard
test
images
using
maximum
between-class
variance
(OTSU)
technique
objective
function.
Comparison
analysis
IJADE
other
benchmark
algorithms
demonstrated
viability
efficiency
proposed
method.
The
quantitative
findings
demonstrate
that
peak
signal-to-noise
ratio
structural
similarity
index
measure
results
under
various
can
be
significantly
improved
by
compared
existing
methods.
Peak
ratios
fabric
crane
were
22.197
23.1786,
respectively,
at
5,
both
placing
top.
With
superior
performance
digital
segmentation,
proven
more
effective.
Intelligent Decision Technologies,
Год журнала:
2024,
Номер
unknown, С. 1 - 18
Опубликована: Авг. 30, 2024
Inspired
by
the
fundamentals
of
biological
evolution,
bio-inspired
algorithms
are
becoming
increasingly
popular
for
developing
robust
optimization
techniques.
These
metaheuristic
algorithms,
unlike
gradient
descent
methods,
computationally
more
efficient
and
excel
in
handling
higher
order
multi-dimensional
non-linear.
OBJECTIVES:
To
understand
hybrid
Bio-inspired
domain
Medical
Imaging
its
challenges
feature
selection
METHOD:
The
primary
research
was
conducted
using
three
major
indexing
database
Scopus,
Web
Science
Google
Scholar.
RESULT:
included
198
articles,
after
removing
103
duplicates,
95
articles
remained
as
per
criteria.
Finally
41
were
selected
study.
CONCLUSION:
We
recommend
that
further
area
based
field
diagnostic
imaging
clustering.
Additionally,
there
is
a
need
to
investigate
use
Deep
Learning
models
integrating
include
strengths
each
enhances
overall
model.
iScience,
Год журнала:
2023,
Номер
26(10), С. 107896 - 107896
Опубликована: Сен. 14, 2023
An
improved
whale
optimization
algorithm
(SWEWOA)
is
presented
for
global
issues.
Firstly,
the
sine
mapping
initialization
strategy
(SS)
used
to
generate
population.
Secondly,
escape
energy
(EE)
introduced
balance
exploration
and
exploitation
of
WOA.
Finally,
wormhole
search
(WS)
strengthens
capacity
exploitation.
The
hybrid
design
effectively
reinforces
capability
SWEWOA.
To
prove
effectiveness
design,
SWEWOA
performed
in
two
test
sets,
CEC
2017
2022,
respectively.
advantage
demonstrated
26
superior
comparison
algorithms.
Then
a
new
feature
selection
method
called
BSWEWOA-KELM
developed
based
on
binary
kernel
extreme
learning
machine
(KELM).
verify
its
performance,
8
high-performance
algorithms
are
selected
experimentally
studied
16
public
datasets
different
difficulty.
results
demonstrate
that
performs
excellently
selecting
most
valuable
features
classification
problems.
Biotechnology and Bioengineering,
Год журнала:
2023,
Номер
121(3), С. 823 - 834
Опубликована: Дек. 27, 2023
Abstract
This
review
covers
currently
available
cardiac
implantable
electronic
devices
(CIEDs)
as
well
updated
progress
in
real‐time
monitoring
techniques
for
CIEDs.
A
variety
of
and
wearable
that
can
diagnose
monitor
patients
with
cardiovascular
diseases
are
summarized,
various
working
mechanisms
principles
Telehealth
mHealth
discussed.
In
addition,
future
research
directions
presented
based
on
the
rapidly
evolving
landscape
including
Artificial
Intelligence
(AI).
Journal of Computational Design and Engineering,
Год журнала:
2023,
Номер
10(6), С. 2094 - 2121
Опубликована: Окт. 19, 2023
Abstract
We
present
a
bee
foraging
behavior-driven
mutational
salp
swarm
algorithm
(BMSSA)
based
on
an
improved
strategy
and
unscented
mutation
strategy.
The
is
leveraged
in
the
follower
location
update
phase
to
break
fixed
range
search
of
algorithm,
while
optimal
solution
employed
enhance
quality
solution.
Extensive
experimental
results
public
CEC
2014
benchmark
functions
validate
that
proposed
BMSSA
performs
better
than
nine
well-known
metaheuristic
methods
seven
state-of-the-art
algorithms.
binary
(bBMSSA)
further
for
feature
selection
by
using
as
support
vector
machine
classifier.
Experimental
comparisons
12
UCI
datasets
demonstrate
superiority
bBMSSA.
Finally,
we
collected
dataset
return-intentions
overseas
Chinese
after
coronavirus
disease
(COVID-19)
through
anonymous
online
questionnaire
performed
case
study
setting
up
bBMSSA-based
optimization
model.
outcomes
manifest
model
exhibits
conspicuous
prowess,
attaining
accuracy
exceeding
93%.
shows
development
prospects,
family
job
place
residence,
seeking
opportunities
China,
possible
time
return
China
are
critical
factors
influencing
willingness
COVID-19.
