Applied Sciences,
Journal Year:
2024,
Volume and Issue:
15(1), P. 241 - 241
Published: Dec. 30, 2024
This
study
proposes
a
two-stage
methodology
for
predicting
wind
energy
production
using
time,
environmental,
technical,
and
locational
variables.
In
the
first
stage,
machine
learning
algorithms,
including
random
forest
(RF),
gradient
boosting
(GB),
k-nearest
neighbors
(kNNs),
linear
regression
(LR),
decision
trees
(Tree),
were
employed
to
estimate
output.
Among
these,
RF
exhibited
best
performance
with
lowest
error
metrics
(MSE:
0.003,
RMSE:
0.053)
highest
R2
value
(0.988).
second
analysis
of
variance
(ANOVA)
was
conducted
evaluate
statistical
relationships
between
independent
variables
predicted
dependent
variable,
identifying
speed
(p
<
0.001)
rotor
as
most
influential
factors.
Furthermore,
GB
models
produced
predictions
closely
aligned
actual
data,
achieving
values
88.83%
89.30%
in
ANOVA
validation
phase.
Integrating
highlighted
robustness
methodology.
These
findings
demonstrate
integrating
verification
methods.
PeerJ Computer Science,
Journal Year:
2025,
Volume and Issue:
11, P. e2682 - e2682
Published: Feb. 25, 2025
As
the
world
grapples
with
pandemics
and
increasing
stress
levels
among
individuals,
heart
failure
(HF)
has
emerged
as
a
prominent
cause
of
mortality
on
global
scale.
The
most
effective
approach
to
improving
chances
individuals'
survival
is
diagnose
this
condition
at
an
early
stage.
Researchers
widely
utilize
supervised
feature
selection
techniques
alongside
conventional
standalone
machine
learning
(ML)
algorithms
achieve
goal.
However,
these
approaches
may
not
consistently
demonstrate
robust
performance
when
applied
data
that
they
have
encountered
before,
struggle
discern
intricate
patterns
within
data.
Hence,
we
present
Multi-objective
Stacked
Enable
Hybrid
Model
(MO-SEHM),
aims
find
out
best
subsets
numerous
different
sets,
considering
multiple
objectives.
(SEHM)
plays
role
classifier
integrates
multi-objective
method,
Non-dominated
Sorting
Genetic
Algorithm
II
(NSGA-II).
We
employed
HF
dataset
from
Faisalabad
Institute
Cardiology
(FIOC)
evaluated
six
ML
models,
including
SEHM
without
NSGA-II
for
experimental
purposes.
Pareto
front
(PF)
demonstrates
our
introduced
MO-SEHM
surpasses
other
obtaining
94.87%
accuracy
nine
relevant
features.
Finally,
Local
Interpretable
Model-agnostic
Explanations
(LIME)
explain
reasons
individual
outcomes,
which
makes
model
transparent
patients
stakeholders.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 138813 - 138826
Published: Jan. 1, 2023
In
today’s
world,
services
are
improved
and
advanced
in
every
field
of
life.
Especially
the
health
sector,
information
technology
(IT)
plays
a
vigorous
role
electronic
(e-health).
To
achieve
benefits
from
e-health,
its
cloud-based
implementation
is
necessary.
With
this
environment’s
multiple
benefits,
privacy
security
loopholes
exist.
As
number
users
grows,
Electronic
Healthcare
System’s
(EHS)
response
time
becomes
slower.
This
study
presented
trust
mechanism
for
access
control
(AC)
known
as
role-based
(RBAC)
to
address
issue.
method
observes
user’s
behavior
assigns
roles
based
on
it.
The
AC
module
has
been
implemented
using
SQL
Server,
an
administrator
develops
controls
with
EHS
modules.
validate
value,
A
.net-based
framework
introduced.
e-health
proposed
research
ensures
that
can
protect
their
data
intruders
other
threats.
Cybernetics & Systems,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 51
Published: Feb. 17, 2025
Heart
disease
remains
a
major
global
cause
of
mortality,
underscoring
the
need
for
advancements
in
early
detection
and
prognosis
to
enhance
patient
recovery.
This
study
proposes
an
innovative
framework
integrating
deep
learning
(DL)
models
optimal
resource
allocation
strategies
improve
heart
prognosis.
The
begins
with
rigorous
preprocessing
Internet
Things
(IoT)
captured
Electrocardiogram
(ECG)
data,
employing
min–max
normalization,
advanced
median
filtering
techniques
noise
reduction
baseline
wander
correction.
Statistical
features
are
extracted
from
preprocessed
while
such
Improved
Empirical
Mode
Decomposition
(EMD),
RR
interval,
R
peak,
PR
interval
derived
ECG
signals.
These
then
augmented
using
technique
dataset
diversity
model
robustness.
Furthermore,
introduces
hybrid
combining
Deep
Residual
Network
(DRN)
Bidirectional
Gated
Recurrent
Unit
severity
classification
detection,
leveraging
features.
Optimal
is
facilitated
by
Walrus
Optimization
Algorithm
(WaOA),
optimizing
ventilator,
Intensive
Care
(ICU)
bed,
medical
staff,
medication
based
on
predicted
severity.
Evaluation
real-world
datasets
demonstrates
superior
diagnostic
accuracy
utilization
efficiency,
highlighting
transformative
potential
IoT
AI-driven
approaches
cardiovascular
healthcare.
BMC Psychiatry,
Journal Year:
2025,
Volume and Issue:
25(1)
Published: March 18, 2025
Accurately
diagnosing
Anxiety-Depression
Comorbidity
Syndrome
in
Gastroenterology
Inpatients
(ADCS-GI)
shows
significant
challenges
as
traditional
diagnostic
methods
fail
to
meet
expectations
due
patient
hesitance
and
non-psychiatric
healthcare
professionals'
limitations.
Therefore,
the
need
for
objective
diagnostics
highlights
potential
of
machine
learning
identifying
treating
ADCS-GI.
A
total
1186
ADCS
patients
were
recruited
this
study.
We
conducted
extensive
studies
dataset,
including
data
quantification,
equilibrium,
correlation
analysis.
Eight
models,
Gaussian
Naive
Bayes
(NB),
Support
Vector
Classifier
(SVC),
K-Neighbors
Classifier,
RandomForest,
XGB,
CatBoost,
Cascade
Forest,
Decision
Tree,
utilized
compare
prediction
efficacy,
with
an
effort
minimize
dependency
on
subjective
questionnaires.
Among
eight
algorithms,
Tree
K-nearest
neighbors
models
demonstrated
accuracy
exceeding
81%
a
sensitivity
same
range
detecting
patients.
Notably,
when
moderate
severe
cases,
achieved
above
88%
90%.
Furthermore,
trained
without
reliance
questionnaires
showed
promising
performance,
indicating
feasibility
developing
questionnaire-free
early
detection
applications.
Machine
algorithms
can
be
used
identify
among
gastroenterology
This
help
facilitate
intervention
psychological
disorders
patients'
care.
