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<p>In this paper, we utilize cloud computing to measure the best features in the Rhythmic Gymnastics dataset and compare them to obtain the most relevant feature information. By measuring the degree of uncertainty of random variables and calculating the course recommendation of target students relative to those in the subgroups, the clustering results of Rhythmic Gymnastics students were integrated with the target students’ performance information to derive the probability of Rhythmic Gymnastics courses appearing in the subgroups. Weight control, physical fitness, and good shape were significantly altered, and 95.66% of the Rhythmic Gymnastics students who took the course felt more flexible. Integration of online videos from cloud computing with the Rhythmic Gymnastics course makes full use of various media forms to provide more learning opportunities for students.</p>
</abstract>

In this paper, we utilize cloud computing to measure the best features in the Rhythmic Gymnastics dataset and compare them to obtain the most relevant feature information. By measuring the degree of uncertainty of random variables and calculating the course recommendation of target students relative to those in the subgroups, the clustering results of Rhythmic Gymnastics students were integrated with the target students’ performance information to derive the probability of Rhythmic Gymnastics courses appearing in the subgroups. Weight control, physical fitness, and good shape were significantly altered, and 95.66% of the Rhythmic Gymnastics students who took the course felt more flexible. Integration of online videos from cloud computing with the Rhythmic Gymnastics course makes full use of various media forms to provide more learning opportunities for students.

Cloud Computing Based Online Video Rhythmic Gymnastics Course Management

Department of Public Physical and Art Education

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Cloud Computing Based Online Video Rhythmic Gymnastics Course Management"}

In this paper, we utilize cloud computing to measure the best features in the Rhythmic Gymnastics dataset and compare them to obtain the most relevant feature...

Cloud Computing Based Online Video Rhythmic Gymnastics Course Management

Online veröffentlicht: 13. Nov. 2023

Seitenbereich: -

Eingereicht: 04. Dez. 2022

Akzeptiert: 24. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01118

Schlüsselwörter
Speech recognition, Multimedia technology, Wavelet noise reduction, Wiener filtering, Speech enhancement

© 2023 Yingying Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Cloud Computing Based Online Video Rhythmic Gymnastics Course Management

Online veröffentlicht: 13. Nov. 2023

Seitenbereich: -

Eingereicht: 04. Dez. 2022

Akzeptiert: 24. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01118

SchlüsselwörterSpeech recognition, Multimedia technology, Wavelet noise reduction, Wiener filtering, Speech enhancement

© 2023 Yingying Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Speech recognition, Multimedia technology, Wavelet noise reduction, Wiener filtering, Speech enhancement