基于智力或语言直觉模糊信息的大学生体育作品质量评价

IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Yinchun Tang
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引用次数: 0

摘要

影响大学生体育行为的因素有很多,而体育学习兴趣、体育自主支持等心理品质对大学生参与体育活动起着重要作用。本研究采用可接受的研究方法,分析体育学习兴趣、体育自主支持对大学生体育行为,特别是体育活动水平的影响。在这项研究工作中,提出了基于智力或语言直觉模糊信息的大学生体育工作质量评价(QECSSW-IGNN-QCTO)。输入数据来自四川大学的学生数据。然后,使用自适应噪声增强卡尔曼滤波器(ANAKF)对输入数据进行预处理,以查找缺失数据并清理重复数据。然后,将预处理后的数据交给等几何神经网络(IGNN),用于评价大学生体育锻炼的质量(体育锻炼等级)。一般来说,等几何神经网络并不能通过一些优化方法来确定评价大学生体育锻炼质量的最佳参数。因此,提出了 QCTO 来优化 IGNN 分类器,从而精确评价大学生体育锻炼的质量。所提出的 QECSSW-IGNN-QCTO 方法是用 Python 实现的,并用几个性能指标进行了评估,如准确率、交叉验证得分、召回率、F1 分数和 ROC。结果显示,QECSSW-IGN-QCTO 的准确率分别提高了 23.4%、28.3% 和 22.6%,交叉验证得分分别降低了 25.9%、17.6% 和 29.4%,召回率分别提高了 24.6%、27.5% 和 18.7%。7%,分别与现有的大学生体育行为预测方法(PMC-SSB-MLM)、大学生心理健康教育创新体育训练系统的设计与实施(DII-STSC-SMHE)、体育科学专业学生在线学习态度对新兴冠状病毒流行中在线学习准备度的影响(ESS-SOLA-ECP)等方法进行分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quality Evaluation of College Students' Sports Work Based on Intellectual or Intuitive Fuzzy Information in Language
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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来源期刊
Journal of Electrical Systems
Journal of Electrical Systems ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
1.10
自引率
25.00%
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0
审稿时长
10 weeks
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