Applied Mathematics and Nonlinear Sciences,
Journal Year:
2024,
Volume and Issue:
9(1)
Published: Jan. 1, 2024
Abstract
This
paper
discusses
the
development
of
school
soccer
with
help
artificial
intelligence.
Propose
a
machine
learning-based
action
feature
extraction
method
for
students
in
soccer.
Obtain
images
playing
and
identify
actions
based
on
threshold
recognition
algorithm.
The
Harris
3D
operator
is
used
to
establish
potential
function
sequence,
AdaBoost
algorithm
filter
data
soccer,
which
as
training
sample
realize
To
extract
effective
values
improve
accuracy
algorithm,
model
SVM
was
constructed.
feasibility
DTW
scoring
field
has
been
verified.
strongest
denoising
ability,
its
rate
maintained
between
80%
90%
features
large
amplitude
higher,
suitable
this
study.
Physics of Fluids,
Journal Year:
2025,
Volume and Issue:
37(4)
Published: April 1, 2025
This
paper
examines
the
effects
of
an
inclined
heated
fin
on
fluid
flow
and
heat
transfer
within
a
lid-driven
cavity
in
presence
non-Newtonian
bioconvection.
With
help
computational
dynamics
(CFD)
artificial
intelligence
(AI)
analysis,
complex
interplay
between
bioconvection
motile
microorganisms
thermal
performance
with
is
investigated.
The
CFD
simulations
examine
deep
insights
into
velocity
temperature
fields
highlight
impact
overall
system.
AI
models
are
used
to
predict
optimize
characteristics
based
various
controlling
parameters.
obtained
results
demonstrate
that
incorporating
can
improve
control
efficiency.
A
total
twelve
datasets
created
for
this
study
were
analyzed
using
Categorical
Boosting
(CATBoost)
regression
algorithm.
target
data
accurately
predicted
over
96%
test
each
dataset.
Despite
dataset
containing
170
000
points,
algorithm
demonstrated
rapid
performance.
indicate
CATBoost
achieved
successful
outcomes
data.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(9), P. 4090 - 4090
Published: May 1, 2025
Given
their
dominant
role
in
energy
expenditure
within
China’s
Hot
Summer
and
Warm
Winter
(HSWW)
zone,
high-fidelity
performance
prediction
multi-objective
optimization
framework
during
the
early
design
phase
are
critical
for
achieving
sustainable
efficiency.
This
study
presents
an
innovative
approach
integrating
machine
learning
(ML)
algorithms
genetic
to
predict
optimize
of
high-rise
office
buildings
HSWW
zone.
By
Rhino/Grasshopper
parametric
modeling,
Ladybug
Tools
simulation,
Python
programming,
this
developed
a
building
model
validated
five
advanced
mature
predicting
use
intensity
(EUI)
useful
daylight
illuminance
(UDI)
based
on
architectural
form
parameters
under
climatic
conditions.
The
results
demonstrate
that
CatBoost
algorithm
outperforms
other
models
with
R2
0.94
CVRMSE
1.57%.
Pareto
optimal
solutions
identify
substantial
shading
dimensions,
southeast
orientations,
high
aspect
ratios,
appropriate
spatial
depths,
reduced
window
areas
as
determinants
optimizing
EUI
UDI
research
fills
gap
existing
literature
by
systematically
investigating
application
ML
complex
relationships
between
metrics
design.
proposed
data-driven
provides
architects
engineers
scientific
decision-making
tool
early-stage
design,
offering
methodological
guidance
similar
regions.
