Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
See all related articles

Related Experiment Video

Updated: Sep 24, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.2K

Research on the Effectiveness of Probabilistic Stochastic Convolution Neural Network Algorithm in Physical Education

Wei Cui1

  • 1Physical Education Department, Shanghai University of Finance and Economics, Shanghai 200433, China.

Computational Intelligence and Neuroscience
|May 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a position adaptive probabilistic neural network for physical education (PE) teaching evaluation. The new method balances accuracy and speed, offering a practical tool for efficient PE teaching quality assessment.

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.7K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Related Experiment Videos

Last Updated: Sep 24, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.2K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.7K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Computer Science

Background:

  • Current physical education (PE) teaching evaluation models using probabilistic neural networks prioritize accuracy over speed.
  • This imbalance hinders efficient and practical PE teaching estimation.
  • Optimization contradictions in traditional methods prevent accurate node coordinate identification.

Purpose of the Study:

  • To address the accuracy-speed trade-off in PE teaching evaluation.
  • To propose a novel position adaptive probabilistic neural network regression method.
  • To enhance the efficiency and practicality of PE teaching quality assessment.

Main Methods:

  • Developed a position adaptive Softmax model by introducing learnable parameters.
  • Integrated the adaptive Softmax model with probabilistic neural network regression.
  • Implemented a simplified training strategy to reduce computational costs.
  • Utilized MATLAB for simulation and verification.

Main Results:

  • The proposed position adaptive method resolves optimization contradictions in traditional models.
  • The model achieves accurate node coordinate identification.
  • MATLAB simulations confirmed the feasibility and accuracy of the approach.
  • Experimental data validated the effectiveness for PE teaching quality evaluation.

Conclusions:

  • The position adaptive probabilistic neural network offers a feasible solution for PE teaching evaluation.
  • The method meets accuracy requirements while improving efficiency.
  • This provides a convenient and practical tool for assessing PE teaching quality.