To
address
the
challenge
of
inaccuracies
in
human
assessment
movement
accuracy,
author
suggests
incorporating
artificial
intelligence
technology
into
sports
training.
Leveraging
advancements
computer
vision,
this
method
involves
athletes
intentionally
making
incorrect
movements
during
training
sessions.
By
integrating
intelligent
recognition
capabilities,
particularly
identifying
erroneous
actions,
approach
aims
to
enhance
overall
accuracy
recognizing
and
rectifying
flawed
So
first
introduced
vision
technology,
which
can
perform
digital
analysis
on
captured
images
has
strong
application
performance.
Then,
feature
extraction
athlete
is
analyzed,
Bayesian
algorithms
are
used
identify
movements,
resulting
a
three-dimensional
visual
detection
model.
Finally,
experimental
research
was
conducted
3D
The
results
show
that
proposed
by
all
greater
than
90%,
while
traditional
methods
only
70%
-76%.
While
ensuring
it
ensure
high
accuracy.
significantly
improved
compared
conventional
methods,
confirms
its
feasibility
behaviors.
Brain Sciences,
Journal Year:
2025,
Volume and Issue:
15(2), P. 168 - 168
Published: Feb. 8, 2025
Background:
In
motor
imagery
brain-computer
interface
(MI-BCI)
research,
electroencephalogram
(EEG)
signals
are
complex
and
nonlinear.
This
complexity
nonlinearity
render
signal
processing
classification
challenging
when
employing
traditional
linear
methods.
Information
entropy,
with
its
intrinsic
nonlinear
characteristics,
effectively
captures
the
dynamic
behavior
of
EEG
signals,
thereby
addressing
limitations
methods
in
capturing
features.
However,
multitude
entropy
types
leads
to
unclear
application
scenarios,
a
lack
systematic
descriptions.
Methods:
study
conducted
review
63
high-quality
research
articles
focused
on
MI-BCI,
published
between
2019
2023.
It
summarizes
names,
functions,
scopes
13
commonly
used
measures.
Results:
The
findings
indicate
that
sample
(16.3%),
Shannon
(13%),
fuzzy
(12%),
permutation
(9.8%),
approximate
(7.6%)
most
frequently
utilized
features
MI-BCI.
majority
studies
employ
single
feature
(79.7%),
dual
(9.4%)
triple
(4.7%)
being
prevalent
combinations
multiple
applications.
incorporation
can
significantly
enhance
pattern
accuracy
(by
8-10%).
Most
(67%)
utilize
public
datasets
for
verification,
while
minority
design
conduct
experiments
(28%),
only
5%
combine
both
Conclusions:
Future
should
delve
into
effects
various
specific
problems
clarify
their
scenarios.
As
methodologies
continue
evolve
advance,
poised
play
significant
role
wide
array
fields
contexts.
In
order
to
improve
the
traditional
computer
English
pronunciation
quality
assessment
method,
this
paper
uses
spoken
of
university
students
in
China
as
research
object,
and
examines
various
measures
such
intonation
accuracy,
speaking
rate,
rhythm,
intonation.
Intonation
is
measured
by
Mel-frequency
cepstral
characteristic
expression,
speech
rate
according
time,
rhythm
evaluated
short-term
power
pair
variation
index,
base
frequency.
Based
on
evaluation
indicators
quality,
article
develops
an
model
based
DBN.
Experiments
were
conducted
performance
model,
test
results
showed
that
recognition
DBN
developed
was
96.65%,
which
better
than
other
models.
addition,
consistency
experiments
between
machine
manual
evaluation,
total
value
87.5%,
environmental
similarity
level
100%,
Pearson
correlation
coefficient
0.722,
indicating
evaluation.
Traditional
online
learning
systems
have
long
system
response
times
and
low
accuracy
in
predicting
learners'
behavior,
results,
or
personalized
needs.
In
order
to
optimize
the
article
on
this
issue,
a
STEAM
for
artificial
intelligence
is
constructed.
This
research
adopts
combination
of
technology
data
analysis
methods
construction.
First,
learner's
personal
information,
behavior
results
other
are
collected,
effective
carried
out.
Secondly,
application
recommendation
algorithms
intelligent
models
recommend
content,
projects
activities
suitable
individual
needs
based
their
interests,
abilities
history.
Through
experimental
tests,
mean
square
error
paper
maintained
at
0.01-0.05,
can
improve
user
experience,
effect
performance.
Under
the
background
of
knowledge
economy
and
globalization,
entrepreneurship
education
in
colleges
universities
has
become
an
important
force
to
promote
economic
development
social
progress.
Optimizing
allocation
educational
resources
stimulating
students'
innovative
spirit
practical
ability
are
key
directions
current
higher
reform.
This
paper
will
discuss
how
cultivate
talents
meet
needs
future
society
through
optimizing
resource
universities.
In
worldwide,
the
wheat
is
a
significant
crop
which
generates
main
source
of
food
for
numerous
peoples.
Through,
development
productions
vulnerable
through
various
diseases
such
as
bacterial,
viral
and
fungal
infections.
These
type
disease
can
cause
important
damage
in
crops
leads
to
diminish
yield
production
grain
quality.
This
paper
proposed
Three-Dimensional
Convolutional
Neural
Network
(3D-CNN)
rust
classification
that
learns
recognize
pattern
structure
using
convolution
filter
layers.
The
CGIAR
dataset
used
contains
1486
images
it
pre-processed
by
gaussian
reduces
noise
smoothens
image.
Then,
Discrete
Wavelet
Transform
(DWT)
feature
extraction
works
discrete
timespan
outputs
low
computational
cost.
3D-CNN
disease.
performance
estimated
accuracy,
recall,
f1score
precision.
attains
accuracy
99.83%,
recall
98.89%,
98.81
%
precision
98.79
%
when
compared
existing
techniques
like
GNet+FERSPNET-50
Few-shot
learning
based
EfficientNet.
Asian Journal of Control,
Journal Year:
2023,
Volume and Issue:
25(5), P. 3305 - 3317
Published: May 9, 2023
Abstract
This
paper
proposed
a
hybrid
feature
extraction
algorithm
based
on
local
mean
decomposition
(LMD),
which
has
better
solved
the
existing
problems
of
low
classification
performance
and
adaptability
limitation.
LMD
is
employed
to
decompose
electroencephalogram
(EEG)
signal
into
multiple
components,
then,
features
instantaneous
energy,
fuzzy
entropy,
mathematical
morphological
are
extracted
specific
optimal
combination
selected
by
analysis
variance
(ANOVA).
Finally,
result
output
linear
discriminant
(LDA)
classifier.
The
results
show
that
maximum
accuracy
subjects
in
Data
Set
III
BCI‐II
method
this
92.14%,
mutual
information
value
0.8.
number
novel
used
small,
complexity
reduced.
It
can
adaptively
select
effective
according
individual
differences
good
robustness.
Network
public
opinion
has
the
characteristics
of
suddenness,
easy
dissemination,
and
uncontrollability.
With
help
new
generation
information
technology,
ability
to
effectively
accurately
improve
network
governance
can
be
improved.
The
Dirichlet
distribution
is
a
multivariate
beta
distribution,
there
conjugate
relationship
between
polynomial
distributions.
This
article
based
on
basic
ideas
LDA
model,
constructs
model
topology
structure,
studies
core
algorithm
composed
Gibbs
sampling
TF-IDF
feature
weight
algorithm,
conducts
experimental
design
result
analysis.
At
same
time,
functional
structure
analysis
system
was
designed,
providing
guidance
for
software
development.
research
results
are
used
construction
online
culture,
strengthen
opinion,
enhance
respond
decision-making
level.
With
the
continuous
development
of
science
and
technology,
field
education
is
constantly
undergoing
reform
innovation.
Among
them,
wisdom
teaching,
as
a
new
teaching
mode,
has
gradually
become
hot
topic
in
education.
Wisdom
refers
to
optimal
allocation
educational
resources
improvement
quality
effect
by
means
modern
information
technology.
In
English
increasingly
widely
used.
This
paper
will
discuss
evaluation
order
provide
some
reference
for
educators.
