Survey on Education for Cyber-Physical Systems

Marwedel, Peter; Mitra, Tulika; Grimheden, Martin; Andrade, Hugo A.

doi:10.1109/mdat.2020.3009613

Cited by 19 publications

(7 citation statements)

References 22 publications

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“…Some scholars believed that students' classification and achievement integration can improve students' potential and realize personalized teaching [ 6 ]. Some scholars believed that the integration of teaching theory and practice and the division of grades can improve the effect of classroom teaching [ 7 ]. Some scholars also believed that the intelligent score management scale, score grade, and potential development were the indicators for the evaluation of tennis teaching effect [ 8 ], which can realize the comprehensive analysis of students and tennis teaching.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Decision Method for Smart Teaching Effect Based on Cloud Computing and Deep Learning

Jiang

Yu-wei

2022

Computational Intelligence and Neuroscience

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In order to improve the effect of intelligent teaching and give full play to the role of intelligent technology in modern physical education, in this paper, cloud computing and deep learning methods are used to comprehensively evaluate the teaching effect of colleges and universities, and calculate the evaluation effect and accuracy. Cloud computing and deep learning algorithm combine the teaching evaluation scale, teaching content, and characteristics to formulate teaching plans for different students and realize targeted teaching evaluation. The results show that the teaching evaluation method proposed in this paper can improve students’ learning interest by about 30%, enhance learning initiative by about 20%, and the matching rate between the actual teaching effect and the expected requirements is 98%. Therefore, cloud computing and deep learning model can improve the accuracy of teaching effect evaluation in colleges and universities, provide support for the formulation of teaching evaluation schemes, and promote the development of intelligent teaching in colleges and universities.

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Section: Literature Reviewmentioning

confidence: 99%

An Optimized Decision Method for Smart Teaching Effect Based on Cloud Computing and Deep Learning

Jiang

Yu-wei

2022

Computational Intelligence and Neuroscience

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“…Health is divided into subhealth, health, and various diseases. However, due to the individual differences, basic diseases, and their own health quality, the health judgment scheme cannot effectively meet the needs of the public [ 1 ]. The survey results show that by 2020, the number of patients with health abnormalities in China will reach 2232/100000, and by 2030, this data will increase by 30%.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

Cheng

Liu

2022

Computational Intelligence and Neuroscience

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The rapid development of social economy not only increases people’s living pressure but also reduces people’s health. Looking for a healthy development prediction model has become a domestic concern. Based on the analysis of the influencing factors of health development, this paper looks for a model to predict the development of public health, so as to improve the accuracy of health development prediction. In this paper, the linear sequential extreme learning machine algorithm can be used to evaluate the health status of a large number of data, analyze the differences of each evaluation index, and construct the analysis model of health status. Therefore, this paper introduces rough set theory into linear sequential extreme learning machine algorithm. Rough set can analyze the double analysis of evaluation scheme, predict the health development of different individuals, and improve the evaluation accuracy of mass health evaluation. The simulation results show that the improved line sequential extreme learning machine algorithm can accurately analyze the mass health and meet the needs of different individuals’ health evaluation.

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“…is paper analyzes the influence of physical education industry teaching on overall fitness effect [11], social function, and local economy. e collected sample data are normalized to reduce the impact of data attributes on the results [12].…”

Section: Discussionmentioning

confidence: 99%

An Evaluation Model for the Teaching Reform of the Physical Education Industry

Yu-wei

Jiang

2021

Discrete Dynamics in Nature and Society

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In order to promote the development of sports industry teaching and accurately evaluate the teaching reform of sports industry, this paper constructs an evaluation model based on the teaching reform of sports industry. In addition, the k-means method is used to classify the teaching effect and simplify the data collection process, so as to improve the accuracy of teaching reform evaluation. The sample data come from the data released by the sports department and the government from 2017 to 2020, as well as the actual survey. The 26 evaluation indexes were determined by expert survey, questionnaire interview, and relevant domestic literature. In addition, the Euclidean distance in K-means method is used to calculate the weight of each index, and the results of the evaluation model are analyzed. The results show that the evaluation accuracy of the sports industry teaching reform model proposed in this paper is 98.4% and the error is 1.3%. The evaluation result is better than the previous ant colony model and is suitable for the evaluation of sports teaching reform.

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Survey on Education for Cyber-Physical Systems

Cited by 19 publications

References 22 publications

An Optimized Decision Method for Smart Teaching Effect Based on Cloud Computing and Deep Learning

An Optimized Decision Method for Smart Teaching Effect Based on Cloud Computing and Deep Learning

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

An Evaluation Model for the Teaching Reform of the Physical Education Industry

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