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In order to solve the problems of poor physical fitness of college students and low efficiency of college sport venues' management, an intelligent sports management system based on deep learning technology is designed by using information technology and human-computer interaction under artificial intelligence. Based on the Browser/Server (B/S) stru...
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... In the traditional physical education teaching process, many teachers use the way of explanation and demonstration to teach [6]. However, students lack the active thinking process in this learning process, always imitate and copy the teacher's movements, and mechanically accept passively, which, to a certain extent, hinders the dispersion of students' free thinking and restricts their creativity [7][8][9][10]. For such a teaching method, students are prone to rejection and resentment, which is not conducive to the teacher's teaching. ...
This paper innovatively applies smart wearable devices to college sports flipped classrooms, using the photoelectric volumetric method to measure blood oxygen pulse and monitor heart rate during exercise, and realizing the calculation and measurement of exercise status by detecting the discrimination between peak exercise and normal exercise threshold. The sports-flipped classroom will improve the quality of physical education by using intelligent sports equipment to enable students to learn independently and teachers to guide them at the right time. The results show that there is a significant difference in the cognition of college students’ sports behavior in the duration of using smart wearable devices (P=0.044), and the frequency and time of using smart wearable devices will have a very significant impact on sports intensity (P<0.01). There were significant differences (P < 0.01) in height, 50-meter run, seated forward bend, and 800/1000 meters, and no significant differences in weight (P = 0.824), blood oxygen level, heart rate, and gait after smart device sports flipped classroom instruction. There were significant differences (P<0.05) in “novelty, negativity, attention, exploratory, enjoyment, and positivity” in the experimental group after the flipped classroom teaching of sports with intelligent devices, and the negativity showed a significant decreasing trend.
... Wang and Park [22] have presented DII sports training system for college SMHE. Here, considers user information such as age, BMI, and physical health state. ...
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.
... For instance, the National Aquatics Center utilized artificial intelligence technology to transform its overhead structure, enabling the swimming pool area to double as a curling venue during the Winter Olympics [58]. This innovative approach not only saved on construction costs but also enhanced the overall intelligence of services, catering to diverse functions within the same service scenario [59,60]. ...
This paper leverages Citespace and VOSviewer software to perform a comprehensive bibliometric analysis on a corpus of 384 references related to smart sports venues, spanning from 1998 to 2022. The analysis encompasses various facets, including author network analysis, institutional network analysis, temporal mapping, keyword clustering, and co-citation network analysis. Moreover, this paper constructs a smart stadiums strategic assessment model (SSSAM) to compensate for confusion and aimlessness by genetic algorithms (GA). Our findings indicate an exponential growth in publications on smart sports venues year over year. Arizona State University emerges as the institution with the highest number of collaborative publications, Energy and Buildings becomes the publication with the most documents. While, Wang X stands out as the scholar with the most substantial contribution to the field. In scrutinizing the betweenness centrality indicators, a paradigm shift in research hotspots becomes evident-from intelligent software to the domains of the Internet of Things (IoT), intelligent services, and artificial intelligence (AI). The SSSAM model based on artificial neural networks (ANN) and GA algorithms also reached similar conclusions through a case study of the International University Sports Federation (FISU), building Information Modeling (BIM), cloud computing and artificial intelligence Internet of Things (AIoT) are expected to develop in the future. Three key themes developed over time. Finally, a comprehensive knowledge system with common references and future hot spots is proposed.
... They facilitate course management, allowing instructors to create and organize course materials, assignments, and assessments efficiently. Moreover, TMS often incorporate features for online learning, enabling institutions to offer flexible and accessible education options such as hybrid or fully online courses [7]. Communication tools within TMS foster interaction among students and instructors, facilitating discussions, collaboration on projects, and feedback exchange [8]. ...
Teaching management systems (TMS) are comprehensive platforms designed to streamline various aspects of educational administration, instruction, and communication within academic institutions. These systems typically offer features such as course scheduling, grade management, attendance tracking, and communication tools for instructors, students, and administrators. TMS also facilitates content delivery, assessment creation, and student progress monitoring, providing a centralized hub for all aspects of teaching and learning. With the integration of modern technologies like cloud computing and mobile applications, TMS enhance accessibility, efficiency, and collaboration among stakeholders in education. This paper explores the integration of Frequent Pattern Decision Support Systems (FP-DSS) into Teaching Management Systems (TMS) and its impact on teaching and learning practices in higher education. Through a comprehensive experimental analysis, we investigate the effectiveness of FP-DSS in improving learning outcomes, personalization, and student engagement across various teaching methodologies. The findings reveal significant improvements in learning outcomes, with average exam scores increasing by up to 12% when FP-DSS is incorporated into innovative teaching methodologies. Additionally, we observed enhanced personalization of instruction, with a rating of 9 out of 10 for the effectiveness of FP-DSS in tailoring learning experiences to individual student needs. Furthermore, student engagement showed notable improvements across all experiments, with students actively participating in the learning process and demonstrating higher levels of motivation and interest.
... In the assessment, students' achievement is emphasized, while students' subjectivity in teaching activities and the improvement of comprehensive quality are neglected (T. Wang & Park, 2021). Simply taking scores as the assessment standard of students' learning level cannot be the basis for evaluating students' of all levels, and it will gradually hurt students' learning motivation and reduce their interest in learning. ...
As the cradle of cultivating talents, universities are facing great opportunities and challenges in their education. Among them, IPE (ideological and political education), as an important foundation for the future growth of university students, is of great significance. This paper discusses the relationship between IPE and psychological fitness education in university teaching. This paper expounds the necessity and feasibility of playing the role of psychological fitness education in IPECU (ideological and political education in colleges and universities). Based on this, this paper gives the strategy of infiltrating psychological fitness education into IPE. This paper combines NN (neural network) method to construct an assessment model of IPE quality. In this paper, MATLAB is used for simulation and comparative analysis. The final experiment shows that the RMSE of this algorithm is 0.512, MAE is 1.089, and the accuracy of the algorithm is 0.958.
... Among ML methods, Artificial Neural Networks (ANNs) (Wang and Park 2021) are arguably most often used approach to challenge of predicting sports results. So, for the sake of this review, we concentrate on research that use ANNs. ...
Recent decades have seen rapid progress in machine learning, paralleling advances in quantum computing. It's reasonable to ponder whether standard machine learning techniques may be enhanced by using the existing noisy intermediate-scale quantum technologies. In sports analysis, deep learning on smart data generated by photonic integrated circuits (PICs) may complement a machine learning model developed on a conventional computer. Here, the PIC integrated soft sensor is used to assess a player's health data for stamina and blood circulation. This information was then extracted and classified using an Unet graph neural network that relied on kernel vector perceptron learning. The results of the experiments are analysed with respect to the following metrics: prediction accuracy, precision, MSE, AUC, and F-1 score. We discovered that the main organisational barrier is a lack of organisational motivation in applying the new technique, whereas the biggest local obstacles are a lack of resources for design and initiative. Proposed technique attained prediction accuracy 95%, precision 81%, F-1 score 61%, MSE 51%, AUC 59%.
... Currently, there are many deficiencies in college mental health education, including inadequate regular education, a lack of psychological counseling resources, limited promotional methods, and a lack of effective communication platforms. To address these challenges, there is an urgent need to establish a comprehensive mental health education platform that provides continuous and systematic psychological support to students through online education, interactive communication, and professional counseling, promoting their psychological well-being and personal growth [2]. ...
... The results of this research will provide scientifically feasible solutions to address mental health issues among college students, offering them personalized psychological support and assistance. Additionally, it will provide new insights and methods for the application of artificial intelligence in the field of mental health (Wang T & Park J., 2021). ...
This paper presents a machine learning-based system for analyzing the mental health of college students. The system utilizes data mining techniques to analyze and process psychological data, enabling personalized mental health assessment and guidance. Firstly, the paper introduces the basic concepts and steps of data mining, as well as the system architecture of the data warehouse. Then, it discusses the methods of incorporating clustering, anomaly mining algorithms, and association rules into the analysis of psychological data in college students. Next, the paper provides a detailed description of the overall structure and workflow of the machine learning-based system for analyzing the mental health of college students. The mental health assessment model utilizes evaluation criteria determination and weight assignment methods. Finally, the accuracy and effectiveness of the system are validated through performance testing. This system provides college students with scientifically feasible mental health assessment and guidance, which has significant practical implications for addressing mental health issues among college students.
... The importance of practitioners in creating health treatments and education for mental health is equal to that for physical health . In an endeavor to strengthen the mental health of adolescents, improving mental health through a sports lifestyle refers to instances of self-stability and behavioral traits (Wang and Park 2021) Adolescents' personal resilience to avoid falling into negative things can be increased by the effects of physical education on emotional intelligence and adolescent mental health (Rocliffe et al., 2023). Other findings showed that the particular factors of teenage personal resilience were school involvement and good parenting (Kothari et al., 2021). ...
Adolescents are the nation's future leaders, so their immediate environment, including their families, schools, and the government, must promote a healthy environment for them both physically and mentally. This is necessary because improving an adolescent's psychological condition is one of the most crucial investments for raising a good generation, but more and more young people are developing psychological disorders. This study employed a pseudo-experiment method of research. The findings of the study in the treatment group, as shown in the "Paired Samples Test" output table above, indicate that the t count negative value is -45,020. This difference between the average Pre-Test and the average Post-Test is what causes the t count negative value to be negative. A negative t-count may turn positive in this situation. As a result, the computed t-value is 45,020. While in the control group, it is evident from the "Paired Samples Test" output table above that the negative value t count of -7,760 t counts is due to the average Pre-Test value being lower than the average Post-Test value. A negative t-count may turn positive in this situation. Consequently, the computed t value is now -7.760. Despite the fact that both groups have an impact on pre- and post-test results, the outcomes can be seen if there is a sizable difference between the treatment and control groups. This demonstrates that the treatment group receiving mental education achieves greater results than the group not receiving such treatment. Teenagers' pre-test and post-test personal endurance results for each group show this. Keywords: emotional intelligence, mental health education, youth resilience, physical education
... The importance of practitioners in creating health treatments and education for mental health is equal to that for physical health . In an endeavor to strengthen the mental health of adolescents, improving mental health through a sports lifestyle refers to instances of self-stability and behavioral traits (Wang and Park 2021) Adolescents' personal resilience to avoid falling into negative things can be increased by the effects of physical education on emotional intelligence and adolescent mental health (Rocliffe et al., 2023). Other findings showed that the particular factors of teenage personal resilience were school involvement and good parenting (Kothari et al., 2021). ...
Adolescents are the nation's future leaders, so their immediate environment, including their families, schools, and the government, must promote a healthy environment for them both physically and mentally. This is necessary because improving an adolescent's psychological condition is one of the most crucial investments for raising a good generation, but more and more young people are developing psychological disorders. This study employed a pseudo-experiment method of research. The findings of the study in the treatment group, as shown in the "Paired Samples Test" output table above, indicate that the t count negative value is - 45,020. This difference between the average Pre-Test and the average Post-Test is what causes the t count negative value to be negative. A negative t-count may turn positive in this situation. As a result, the computed t-value is 45,020. While in the control group, it is evident from the "Paired Samples Test" output table above that the negative value t count of -7,760 t counts is due to the average Pre- Test value being lower than the average Post-Test value. A negative t-count may turn positive in this situation. Consequently, the computed t value is now -7.760. Despite the fact that both groups have an impact on pre- and post-test results, the outcomes can be seen if there is a sizable difference between the treatment and control groups. This demonstrates that the treatment group receiving mental education achieves greater results than the group not receiving such treatment. Teenagers' pre-test and post-test personal endurance results for each group show this
