Using machine learning techniques to identify major determinants of electricity usage in residential buildings of Pakistan
Journal of Building Engineering,
Год журнала:
2025,
Номер
100, С. 111800 - 111800
Опубликована: Янв. 7, 2025
Язык: Английский
Medical image enhancement using war strategy optimization algorithm
Biomedical Signal Processing and Control,
Год журнала:
2025,
Номер
106, С. 107740 - 107740
Опубликована: Фев. 19, 2025
Язык: Английский
Off-grid multi-region energy system design based on energy load demand estimation using hybrid nature-inspired optimization algorithms
Energy Conversion and Management,
Год журнала:
2024,
Номер
315, С. 118766 - 118766
Опубликована: Июль 14, 2024
Язык: Английский
Optimized deep neural network architectures for energy consumption and PV production forecasting
Energy Strategy Reviews,
Год журнала:
2025,
Номер
59, С. 101704 - 101704
Опубликована: Апрель 8, 2025
Язык: Английский
War strategy optimization-based methods for pattern synthesis of antenna arrays and optimization of microstrip patch antenna
Journal of Computational Electronics,
Год журнала:
2024,
Номер
23(5), С. 1125 - 1134
Опубликована: Авг. 8, 2024
Язык: Английский
A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration
World Electric Vehicle Journal,
Год журнала:
2024,
Номер
15(11), С. 493 - 493
Опубликована: Окт. 29, 2024
This
paper
presents
a
novel
grid-to-vehicle
modeling
framework
that
leverages
probabilistic
methods
and
neural
networks
to
accurately
forecast
electric
vehicle
(EV)
charging
demand
overall
energy
consumption.
The
proposed
methodology,
tailored
the
specific
context
of
Medellin,
Colombia,
provides
valuable
insights
for
optimizing
infrastructure
grid
operations.
Based
on
collected
local
data,
mathematical
models
are
developed
coded
reflect
characteristics
EV
charging.
Through
rigorous
analysis
criteria,
indices,
relationships,
most
suitable
model
city
is
selected.
By
combining
with
networks,
this
study
offers
comprehensive
approach
predicting
future
as
penetration
increases.
effectively
captures
behavior
various
types,
while
network
forecasts
demand.
findings
can
inform
decision-making
regarding
planning,
investment
strategies,
policy
development
support
sustainable
integration
vehicles
into
power
grid.
Язык: Английский
VoltaVistaMan: Energy Dynamics Intelligent Predictive Analysis Utilizing Bayesian Hyper-Tuned Neural Networks – A Case Study on Switzerland's National Electricity Demand
Опубликована: Июнь 17, 2024
Язык: Английский
Research on the innovation of “four-work synergy” service education system for social work majors in higher vocational colleges and universities under the background of Internet+
Applied Mathematics and Nonlinear Sciences,
Год журнала:
2024,
Номер
9(1)
Опубликована: Янв. 1, 2024
Abstract
This
paper
puts
forward
an
innovative
approach
to
the
service
education
system
of
higher
vocational
colleges
and
universities
constructs
a
“four-work
synergy”
from
four
aspects:
integration,
construction,
revitalization,
innovation.
Using
war
strategy
optimization
algorithm,
is
optimized
adjusted.
An
artificial
neural
network-based
quality
assessment
model
for
“four-industry
was
established,
applied
in
practice
measure
research
subjects.
The
students
were
satisfied
with
their
impression
school,
value
school
services,
importance
work,
all
which
above
three
points.
six
dimensions
educational
services
object
P
high
statistics,
including
highest
mean
score
3.6496
assurance,
except
tangible
empathy,
other
similar,
distribution
range
between
3.3-3.5.
A
pre-and
post-test
experiment
designed
examine
effect
on
improvement
students’
professional
skills.
During
post-tests,
it
revealed
that
average
skills
increased
by
6.0587
points,
while
overall
passing
rate
17.83%.
Through
independent
sample
t-test,
further
found
t=-4.679,
sig=0.000,
there
significant
difference
data,
shows
“four-worker
cooperative”
proposed
this
has
positive
effect.
Язык: Английский
Efficient Prediction of Judicial Case Decisions Based on State Space Modeling
International Journal of Computational Intelligence Systems,
Год журнала:
2024,
Номер
17(1)
Опубликована: Ноя. 12, 2024
With
the
rapid
advancement
of
information
technology
and
artificial
intelligence,
digitization
legal
texts
has
caused
a
swift
increase
in
volume
materials.
Judges
now
face
increased
professional
demands,
larger
loads,
more
complex
case
structures,
which
heightens
their
workload
demands.
To
enhance
quality
efficiency
judicial
work
drive
modernization
system,
application
intelligent
prediction
models
become
essential.
This
paper
presents
MambaEffNet
model,
integrates
multiple
modules
such
as
Convolutional
Neural
Networks
(CNN)
Multilayer
Perceptrons
(MLP).
The
core
convolutional
structure
is
improved
using
state
space
multi-directional
feature
fusion
designed
to
performance
sequence
extraction.
Generative
Adversarial
(GAN)
are
employed
for
data
augmentation,
address
issue
missing
features
predictions.
EfficientNetV2
architecture
used
optimize
kernel
size
expansion
ratio
input
output
channels.
Experimental
results
demonstrate
that
model
achieves
accuracy
92.05%
on
Nigerian
Supreme
Court
judgment
dataset
performs
excellently
other
datasets,
significantly
improving
efficiency.
Specifically,
increases
criminal
civil
judgments
by
9.53%
11.57%,
respectively.
Additionally,
excels
handling
long
data,
effectively
capturing
key
providing
comprehensive
decision
support.
Язык: Английский