Medical
image
analysis
is
an
important
branch
in
the
field
of
medicine,
which
mainly
uses
processing
and
techniques
to
interpret
diagnose
medical
data.
data
helps
doctors
effectively
observe
patients'
body
structures,
tissues
lesions.
has
been
research
area
field,
it
for
disease
diagnosis,
treatment
planning,
condition
monitoring.
In
recent
years,
rapid
development
deep
learning
computer
vision
technologies
contributed
greatly
automation,
multimodal
fusion,
real-time
application,
accuracy
improvement
analysis.
addition,
given
rise
some
new
areas
analysis,
such
as
Generative
Adversarial
Networks
(GANs)
synthetic
images,
self-supervised
unsupervised
feature
learning,
neural
network
interpretability.
this
paper,
we
will
introduce
optimisation
methods
images
are
effective
improving
accuracy,
efficiency
reliability
Applied Soft Computing,
Год журнала:
2024,
Номер
157, С. 111504 - 111504
Опубликована: Март 22, 2024
Ambient
air
pollution
is
a
pervasive
global
issue
that
poses
significant
health
risks.
Among
pollutants,
ozone
(O3)
responsible
for
an
estimated
1
to
1.2
million
premature
deaths
yearly.
Furthermore,
O3
adversely
affects
climate
warming,
crop
productivity,
and
more.
Its
formation
occurs
when
nitrogen
oxides
volatile
organic
compounds
react
with
short-wavelength
solar
radiation.
Consequently,
urban
areas
high
traffic
volume
elevated
temperatures
are
particularly
prone
levels,
which
pose
risk
their
inhabitants.
In
response
this
problem,
many
countries
have
developed
web
mobile
applications
provide
real-time
information
using
sensor
data.
However,
while
these
offer
valuable
insight
into
current
predicting
future
pollutant
behavior
crucial
effective
planning
mitigation
strategies.
Therefore,
our
main
objectives
develop
accurate
efficient
prediction
models
identify
the
key
factors
influence
levels.
We
adopt
time
series
forecasting
approach
address
objectives,
allows
us
analyze
predict
behavior.
Additionally,
we
tackle
feature
selection
problem
most
relevant
features
periods
contribute
accuracy
by
introducing
novel
method
called
Time
Selection
Layer
in
Deep
Learning
models,
significantly
improves
model
performance,
reduces
complexity,
enhances
interpretability.
Our
study
focuses
on
data
collected
from
five
representative
Seville,
Cordova,
Jaen
provinces
Spain,
multiple
sensors
capture
comprehensive
compare
performance
of
three
models:
Lasso,
Decision
Tree,
without
incorporating
Layer.
results
demonstrate
including
effectiveness
interpretability
achieving
average
improvement
9%
across
all
monitored
areas.
International Journal of Molecular Sciences,
Год журнала:
2024,
Номер
25(7), С. 4082 - 4082
Опубликована: Апрель 6, 2024
Glioblastoma
(GBM)
is
a
fatal
brain
tumor
with
limited
treatment
options.
O6-methylguanine-DNA-methyltransferase
(MGMT)
promoter
methylation
status
the
central
molecular
biomarker
linked
to
both
response
temozolomide,
standard
chemotherapy
drug
employed
for
GBM,
and
patient
survival.
However,
MGMT
captured
on
tissue
which,
given
difficulty
in
acquisition,
limits
use
of
this
feature
monitoring.
protein
expression
levels
may
offer
additional
insights
into
mechanistic
understanding
but,
currently,
they
correlate
poorly
methylation.
The
acquiring
testing
drives
need
non-invasive
methods
predict
status.
Feature
selection
aims
identify
most
informative
features
build
accurate
interpretable
prediction
models.
This
study
explores
new
application
combined
(i.e.,
LASSO
mRMR)
rank-based
weighting
method
ProFWise)
non-invasively
link
serum
patients
GBM.
Our
provides
promising
results,
reducing
dimensionality
(by
more
than
95%)
when
two
large-scale
proteomic
datasets
(7k
SomaScan®
panel
CPTAC)
all
our
analyses.
computational
results
indicate
that
proposed
approach
14
shared
biomarkers
be
helpful
diagnostic,
prognostic,
and/or
predictive
operations
GBM-related
processes,
further
validation.
