Interconnections, trend analysis and forecasting of water-air temperature with water level dynamics in Blue Moon Lake Valley: A statistical and machine learning approach
Journal of Environmental Management,
Год журнала:
2025,
Номер
379, С. 124829 - 124829
Опубликована: Март 8, 2025
Язык: Английский
Enhancing Drug Discovery with AI: Predictive Modeling of Pharmacokinetics Using Graph Neural Networks and Ensemble Learning
Intelligent Pharmacy,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 1, 2024
Язык: Английский
Hierarchical Power Output Prediction for Floating Photovoltaic Systems
Energy,
Год журнала:
2025,
Номер
unknown, С. 135883 - 135883
Опубликована: Март 1, 2025
Язык: Английский
A new feature extraction method for AI based classification of heart sounds: dual-frequency cepstral coefficients (DFCCs)
The European Physical Journal Special Topics,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 15, 2025
Язык: Английский
Short-term Air Conditioning Load Prediction Based on Improved Stacking Algorithm
Journal of Physics Conference Series,
Год журнала:
2025,
Номер
3001(1), С. 012010 - 012010
Опубликована: Апрель 1, 2025
Abstract
The
heating,
ventilation,
and
air-conditioning
systems
(HVAC)
account
for
over
40%
of
the
total
energy
consumption
in
buildings.
This
significant
proportion
highlights
substantial
potential
conservation
operational
optimization
HVAC
systems.
precise
rapid
prediction
short-term
load
system
is
crucial
achieving
optimized
scheduling.
Utilizing
robust
regression
capabilities
inherent
ensemble
algorithms
within
field
machine
learning,
this
study
has
developed
an
enhanced
three-tier
stacking
predictive
model.
accuracy
generalizability
model
were
evaluated
using
actual
building
datasets.
results
show
that
demonstrates
performance,
with
Mean
Absolute
Percentage
Error
(MAPE)
kept
below
7%
data
Coefficient
Variation
Root
Square
(CVRMSE)
maintained
9%.
Compared
traditional
models,
shows
improved
generalizability,
making
it
a
promising
choice
forecasting.
Язык: Английский
Application of machine learning models for predicting zinc oxide nanoparticle size
Measurement,
Год журнала:
2025,
Номер
unknown, С. 117785 - 117785
Опубликована: Май 1, 2025
Язык: Английский
Risk warning based on GA-improved stacking ensemble learning algorithm: a case study of alcohol
Annals of Operations Research,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 17, 2025
Язык: Английский
Exploring Hydrochemical Drivers of Drinking Water Quality in a Tropical River Basin Using Self-Organizing Maps and Explainable AI
Ajayakumar Appukuttan,
C. D. Aju,
Rajesh Reghunath
и другие.
Water Research,
Год журнала:
2025,
Номер
284, С. 123884 - 123884
Опубликована: Май 21, 2025
Язык: Английский
Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection
Land,
Год журнала:
2024,
Номер
13(11), С. 1878 - 1878
Опубликована: Ноя. 10, 2024
The
land
use
cover
(LULC)
map
is
extensively
employed
for
different
purposes.
Machine
learning
(ML)
algorithms
applied
in
remote
sensing
(RS)
data
have
been
proven
effective
image
classification,
object
detection,
and
semantic
segmentation.
Previous
studies
shown
that
random
forest
(RF)
support
vector
machine
(SVM)
consistently
achieve
high
accuracy
classification.
Considering
the
important
role
of
Portugal’s
Serra
da
Estrela
Natural
Park
(PNSE)
biodiversity
nature
conversation
at
an
international
scale,
availability
timely
on
PNSE
emergency
evaluation
periodic
assessment
crucial.
In
this
study,
application
RF
SVM
classifiers,
object-based
(OBIA)
pixel-based
(PBIA)
approaches,
with
Sentinel-2A
imagery
was
evaluated
using
Google
Earth
Engine
(GEE)
platform
classification
a
burnt
area
PNSE.
This
aimed
to
detect
change
closely
observe
vegetation
recovery
after
2022
wildfire.
combination
OBIA
achieved
highest
all
metrics.
At
same
time,
comparison
Normalized
Difference
Vegetation
Index
(NDVI)
Conjunctural
Land
Occupation
Map
(COSc)
2023
year
indicated
PBIA
resembled
maps
better.
Язык: Английский
AI-Enhanced Multi-Algorithm R Shiny App for Predictive Modeling and Analytics- A Case study of Alzheimer’s Disease Diagnostics (Preprint)
Опубликована: Дек. 18, 2024
BACKGROUND
Recent
studies
have
demonstrated
that
AI
can
surpass
medical
practitioners
in
diagnostic
accuracy,
underscoring
the
increasing
importance
of
AI-assisted
diagnosis
healthcare.
This
research
introduces
SMART-Pred
(Shiny
Multi-Algorithm
R
Tool
for
Predictive
Modeling),
an
innovative
AI-based
application
Alzheimer's
disease
(AD)
prediction
utilizing
handwriting
analysis
OBJECTIVE
Our
objective
is
to
develop
and
evaluate
a
non-invasive,
cost-effective,
efficient
tool
early
AD
detection,
addressing
need
accessible
accurate
screening
methods.
METHODS
methodology
employs
comprehensive
approach
AI-driven
prediction.
We
begin
with
Principal
Component
Analysis
dimensionality
reduction,
ensuring
processing
complex
data.
followed
by
training
evaluation
ten
diverse,
highly
optimized
models,
including
logistic
regression,
Naïve
Bayes,
random
forest,
AdaBoost,
Support
Vector
Machine,
neural
networks.
multi-model
allows
robust
comparison
different
machine
learning
techniques
To
rigorously
assess
model
performance,
we
utilize
range
metrics
sensitivity,
specificity,
F1-score,
ROC-AUC.
These
provide
holistic
view
each
model's
predictive
capabilities.
For
validation,
leveraged
DARWIN
dataset,
which
comprises
samples
from
174
participants
(89
patients
85
healthy
controls).
balanced
dataset
ensures
fair
our
models'
ability
distinguish
between
individuals
based
on
characteristics.
RESULTS
The
forest
strong
achieving
accuracy
88.68%
test
set
during
analysis.
Meanwhile,
AdaBoost
algorithm
exhibited
even
higher
reaching
92.00%
after
leveraging
models
identify
most
significant
variables
predicting
disease.
results
current
clinical
tools,
typically
achieve
around
81.00%
accuracy.
SMART-Pred's
performance
aligns
recent
advancements
prediction,
such
as
Cambridge
scientists'
82.00%
identifying
progression
within
three
years
using
cognitive
tests
MRI
scans.
Furthermore,
revealed
consistent
pattern
across
all
employed.
"air_time"
"paper_time"
consistently
stood
out
critical
predictors
(AD).
two
factors
were
repeatedly
identified
influential
assessing
probability
onset,
their
potential
detection
risk
assessment
CONCLUSIONS
Even
though
some
limitations
exist
SMART-Pred,
it
offers
several
advantages,
being
efficient,
customizable
datasets
diagnostics.
study
demonstrates
transformative
healthcare,
particularly
may
contribute
improved
patient
outcomes
through
intervention.
Clinical
validation
necessary
confirm
whether
key
this
are
sufficient
accurately
real-world
settings.
step
crucial
ensure
practical
applicability
reliability
these
findings
practice.
Язык: Английский