Recent innovations in machine learning for skin cancer lesion analysis and classification: A comprehensive analysis of computer‐aided diagnosis
Precision Medical Sciences,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 17, 2025
Abstract
The
global
primary
health
concern
of
skin
cancer
emphasizes
the
need
for
quick
and
accurate
diagnosis
to
improve
patient
outcomes.
Although,
it
might
be
challenging
evaluate
possible
risk
a
spot
merely
by
looking
at
feeling
it.
This
review
article
offers
thorough
overview
current
breakthroughs
in
machine
learning
(ML)
computer‐aided
diagnostics
(CAD)
aim
analysis
classification
lesions
over
past
6
years.
paper
carefully
reviews
whole
diagnostic
process:
data
preparation,
lesion
segmentation,
feature
extraction,
selection,
final
classification.
Analyzed
are
many
publicly
accessible
datasets
creative
ideas
including
deep
(DL)
ML
integrated
with
computer
vision,
together
their
impact
on
increasing
accuracy.
Given
variety
complexity
lesions,
even
enormous
progress,
there
still
major
obstacles.
rigorously
assesses
methods,
notes
areas
great
challenge,
provides
recommendations
direct
next
research
targeted
improving
early
detection
strategies
CAD
systems.
Язык: Английский
Using the TSA-LSTM two-stage model to predict cancer incidence and mortality
PLoS ONE,
Год журнала:
2025,
Номер
20(2), С. e0317148 - e0317148
Опубликована: Фев. 20, 2025
Cancer,
the
second-leading
cause
of
mortality,
kills
16%
people
worldwide.
Unhealthy
lifestyles,
smoking,
alcohol
abuse,
obesity,
and
a
lack
exercise
have
been
linked
to
cancer
incidence
mortality.
However,
it
is
hard.
Cancer
lifestyle
correlation
analysis
mortality
prediction
in
next
several
years
are
used
guide
people's
healthy
lives
target
medical
financial
resources.
Two
key
research
areas
this
paper
Data
preprocessing
sample
expansion
design
Using
experimental
comparison,
study
chooses
best
cubic
spline
interpolation
technology
on
original
data
from
32
entry
points
420
converts
annual
into
monthly
solve
problem
insufficient
prediction.
Factor
possible
because
sources
indicate
changing
factors.
TSA-LSTM
Two-stage
attention
popular
tool
with
advanced
visualization
functions,
Tableau,
simplifies
paper's
study.
Tableau's
testing
findings
cannot
analyze
predict
time
series
data.
LSTM
utilized
by
optimization
model.
By
commencing
input
feature
attention,
model
technique
guarantees
that
encoder
converges
subset
sequence
features
during
output
features.
As
result,
model's
natural
learning
trend
quality
enhanced.
The
second
step,
performance
maintains
We
can
choose
network
improve
forecasts
based
real-time
performance.
Validating
source
factor
using
Most
cancers
overlapping
risk
factors,
excessive
drinking,
exercise,
obesity
breast,
colorectal,
colon
cancer.
A
poor
directly
promotes
lung,
laryngeal,
oral
cancers,
according
visual
tests.
expected
climb
18-21%
between
2020
2025,
2021.
Long-term
projection
accuracy
98.96
percent,
smoking
may
be
main
causes.
Язык: Английский
Automatic modulation classification scheme for next-generation cellular networks using optimized adaptive multi-scale dual attention network
G. Dinesh,
W. Deva Priya,
C P Shirley
и другие.
Peer-to-Peer Networking and Applications,
Год журнала:
2025,
Номер
18(3)
Опубликована: Апрель 5, 2025
Язык: Английский
OPTIONS Attack Detection in WSN using Optimized Multitask Multi-Attention Residual Shrinkage Convolutional Neural Network
Knowledge-Based Systems,
Год журнала:
2024,
Номер
300, С. 112227 - 112227
Опубликована: Июль 8, 2024
Язык: Английский
Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids
E3S Web of Conferences,
Год журнала:
2024,
Номер
511, С. 01032 - 01032
Опубликована: Янв. 1, 2024
This
study
investigates
the
optimization
of
size
a
solar-wind
hybrid
microgrid
using
Particle
Swarm
Optimization
(PSO)
to
improve
energy
production
efficiency,
economic
feasibility,
and
overall
sustainability.
By
past
solar
wind
resource
data,
load
demand
profiles,
system
component
specifications,
PSO
algorithm
effectively
maximized
capabilities
panels
turbines.
The
findings
indicate
significant
rise
in
daily
production,
with
15%
enhancement
panel
capability
12%
boost
turbine
capability.
increased
plays
crucial
role
dealing
natural
irregularity
renewable
resources,
hence
enhancing
resilience
self-reliance
microgrid.
calculations
demonstrate
improvements
feasibility
designs.
Levelized
Cost
Energy
(LCOE)
undergoes
10%
decrease,
suggesting
more
economically
efficient
generation.
Moreover,
payback
time
for
original
expenditure
is
reduced
by
15%,
indicating
faster
returns
on
investment.
highlight
practical
advantages
size,
line
goal
creating
sustainable
solutions
while
minimizing
costs.
improved
performance
shown
thorough
comparison
other
approaches,
such
as
Genetic
Algorithms
(GA)
Simulated
Annealing
(SA).
superior
convergence
rate
PSO,
together
solution
quality
relative
GA
SA,
underscores
efficiency
efficacy
traversing
complex
space
associated
size.
PSO’s
comparative
advantage
makes
it
an
effective
tool
tackling
intricacies
integrating
energy,
highlighting
its
potential
extensive
use
design
optimization.
sensitivity
evaluations
that
optimized
are
resilient
even
when
important
parameters
vary,
thereby
stability
dependability
approach.
In
addition
technical
factors,
evaluates
environmental
consequences
social
aspects
optimum
land
has
seen
enhancement,
demonstrating
application
area
infrastructure.
addition,
there
7%
improvement
community
approval,
which
demonstrates
algorithm’s
ability
handle
promote
comprehensive
socially
acceptable
approach
projects.
Язык: Английский
Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids
E3S Web of Conferences,
Год журнала:
2024,
Номер
537, С. 03011 - 03011
Опубликована: Янв. 1, 2024
This
study
investigates
the
optimization
of
size
a
solar
wind
hybrid
microgrid
using
Particle
Swarm
Optimization
(PSO)
to
improve
energy
production
efficiency,
economic
feasibility,
and
overall
sustainability.
By
past
resource
data,
load
demand
profiles,
system
component
specifications,
PSO
algorithm
effectively
maximized
capabilities
panels
turbines.
The
findings
indicate
significant
rise
in
daily
production,
with
15%
enhancement
panel
capability
12%
boost
turbine
capability.
increased
plays
crucial
role
dealing
natural
irregularity
renewable
resources,
hence
enhancing
resilience
self-reliance
microgrid.
calculations
demonstrate
improvements
feasibility
designs.
Levelized
Cost
Energy
(LCOE)
undergoes
10%
decrease,
suggesting
more
economically
efficient
generation.
Moreover,
payback
time
for
original
expenditure
is
reduced
by
15%,
indicating
faster
returns
on
investment.
highlight
practical
advantages
size,
line
goal
creating
sustainable
solutions
while
minimizing
costs.
improved
performance
shown
thorough
comparison
other
approaches,
such
as
Genetic
Algorithms
(GA)
Simulated
Annealing
(SA).
superior
convergence
rate
PSO,
together
solution
quality
relative
GA
SA,
underscores
efficiency
efficacy
traversing
complex
space
associated
size.
PSO's
comparative
advantage
makes
it
an
effective
tool
tackling
intricacies
integrating
energy,
highlighting
its
potential
extensive
use
design
optimization.
sensitivity
evaluations
that
optimized
are
resilient
even
when
important
parameters
vary,
thereby
stability
dependability
approach.
In
addition
technical
factors,
evaluates
environmental
consequences
social
aspects
optimum
land
has
seen
enhancement,
demonstrating
application
area
infrastructure.
addition,
there
7%
improvement
community
approval,
which
demonstrates
algorithm's
ability
handle
promote
comprehensive
socially
acceptable
approach
projects.
Язык: Английский
Optimization of Wind Farm Layout using Genetic Algorithms
E3S Web of Conferences,
Год журнала:
2024,
Номер
581, С. 01024 - 01024
Опубликована: Янв. 1, 2024
In
order
to
increase
the
economic
feasibility,
sustainability,
and
efficiency
of
energy
production,
this
research
proposes
an
improved
optimization
framework
for
hybrid
wind-solar
systems
that
use
augmented
Genetic
Algorithm
(GA).
Wind
turbine
size
photovoltaic
(PV)
panel
orientation
were
optimized
using
historical
data
on
wind
solar
resources,
system
load
profiles,
component
specifications.
There
was
18%
in
a
14%
improvement
efficiency,
16%
output
because
GA's
outstanding
performance.
An
reduction
payback
time
12%
Levelized
Cost
Energy
(LCOE)
achieved.
Results
from
evaluation
project's
social
environmental
consequences
showed
community
acceptability
increased
by
9
percentage
points
land-use
12
points.
A
sensitivity
study
verified
could
withstand
several
scenarios.
The
results
demonstrate
promise
GA-based
improving
renewable
systems.
Язык: Английский