Quantitative of Badminton Practice Based on Wireless Sensor Network

With the continuous deepening of my country's education reform and the continuous development of informatization education methods, the informatization teaching environment and conditions of various universities have also been greatly improved, and information technology is changing the learning methods of college students at an alarming speed. This is the advantage of the blended learning model. In the badminton specialization of physical education, strengthening the professional practice ability training of college badminton students is an important reform direction for the current talent training goal of our country. In the teaching mode, the traditional classroom teaching mode and online learning mode coexist and each has their own advantages. Based on the investigation and analysis of the current situation of badminton students' teaching ability, this paper proposes methods such as literature method, expert interview method, questionnaire survey method, derivative Delphi method, etc., and designs and constructs a future-oriented badminton student teaching ability evaluation system. In-depth study of the blended learning model and provide a certain theoretical and practical basis. The experimental results of this article show that by selecting two periods of indicators and calculating the weights, it can be seen that in the large-scale badminton teaching ability evaluation system, the badminton teaching organization and management ability (26%), badminton skills and tactics are satisfactory, and the execution ability is satisfactory. (35%) is relatively large, and the sum of the two exceeds 60%. The blended learning model can give full play to the advantages of traditional teaching, combined with online learning to promote the development of students' professional badminton practice ability.

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Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

Liu

2022

Computational Intelligence and Neuroscience

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In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized.

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Quantitative of Badminton Practice Based on Wireless Sensor Network

Cited by 3 publications

References 27 publications

Designing Badminton Training System Using Improved Apriori Association Rule Algorithm

Designing Badminton Training System Using Improved Apriori Association Rule Algorithm

A Study on the Construction of the Evaluation System of the Teaching Ability of Students using Pattern Recognition for Studying Majoring in Badminton in the Mixed Learning Model of Physical Education Majors and Self-Learning System

Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

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