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Quality Evaluation of College Students' Sports Work Based on Intellectual or Intuitive Fuzzy Information in Language
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Numerous factors influence college students' athletic behaviour and psychological qualities such as sports learning interest, autonomy support in sports play important roles in forming their participation in sports activities. The study used acceptable research methodologies to analyse effect of sports learning interest, autonomy support in sports on college students' sports behaviour, specifically their physical activity levels. In this research work, Quality Evaluation of College Students' Sports Work Based on Intellectual or Intuitive Fuzzy Information in Language (QECSSW-IGNN-QCTO) is proposed. The input data are collected from College student data from Sichuan University. Then, the input data are pre-processed using Adaptive-Noise Augmented Kalman Filter (ANAKF) for finding missing data and cleaning the duplicate data. Then the pre-processed data are given to Iso-Geometric Neural Network (IGNN) for evaluating the quality of college students sports work (sports exercise grade). In general, IGNN doesn’t express some adoption of optimization approaches for determining optimal parameters to evaluating the quality of college students’ sports work. Hence QCTO is proposed to optimize IGNN classifier which precisely evaluates the quality of college student’s sports work. The proposed QECSSW-IGNN-QCTO method is implemented in Python, and it assessed with several performance metrics like, Accuracy, Cross validation scores, Recall, F1 score, and ROC. The results show QECSSW-IGNN-QCTO attains 23.4%, 28.3%, and 22.6% higher Accuracy, 25.9%, 17.6%, and 29.4% lower Cross validation scores, 24.6%, 27.5%, and 18.7% higher Recall are analysed with existing methods such as, prediction method of college students’ sports behaviour depend on machine learning method (PMC-SSB-MLM), Designing and implementing an innovative sports training system for college students' mental health education (DII-STSC-SMHE), The effect of sports science students' online learning attitudes on their readiness to learn online in emerging coronavirus pandemic (ESS-SOLA-ECP) methods respectively.
Title: Quality Evaluation of College Students' Sports Work Based on Intellectual or Intuitive Fuzzy Information in Language
Description:
Numerous factors influence college students' athletic behaviour and psychological qualities such as sports learning interest, autonomy support in sports play important roles in forming their participation in sports activities.
The study used acceptable research methodologies to analyse effect of sports learning interest, autonomy support in sports on college students' sports behaviour, specifically their physical activity levels.
In this research work, Quality Evaluation of College Students' Sports Work Based on Intellectual or Intuitive Fuzzy Information in Language (QECSSW-IGNN-QCTO) is proposed.
The input data are collected from College student data from Sichuan University.
Then, the input data are pre-processed using Adaptive-Noise Augmented Kalman Filter (ANAKF) for finding missing data and cleaning the duplicate data.
Then the pre-processed data are given to Iso-Geometric Neural Network (IGNN) for evaluating the quality of college students sports work (sports exercise grade).
In general, IGNN doesn’t express some adoption of optimization approaches for determining optimal parameters to evaluating the quality of college students’ sports work.
Hence QCTO is proposed to optimize IGNN classifier which precisely evaluates the quality of college student’s sports work.
The proposed QECSSW-IGNN-QCTO method is implemented in Python, and it assessed with several performance metrics like, Accuracy, Cross validation scores, Recall, F1 score, and ROC.
The results show QECSSW-IGNN-QCTO attains 23.
4%, 28.
3%, and 22.
6% higher Accuracy, 25.
9%, 17.
6%, and 29.
4% lower Cross validation scores, 24.
6%, 27.
5%, and 18.
7% higher Recall are analysed with existing methods such as, prediction method of college students’ sports behaviour depend on machine learning method (PMC-SSB-MLM), Designing and implementing an innovative sports training system for college students' mental health education (DII-STSC-SMHE), The effect of sports science students' online learning attitudes on their readiness to learn online in emerging coronavirus pandemic (ESS-SOLA-ECP) methods respectively.
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