Evaluation Index of School Sports Resources Based on Artificial Intelligence and Edge Computing

Hao, Long; Zhou, Limin

doi:10.1155/2022/9925930

Cited by 3 publications

(4 citation statements)

References 25 publications

(21 reference statements)

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“…erefore, the time complexity of line 11 is O(n 2 1 ). Similar to line 11, the time complexity of line 12 is O(n 2 2 ), and the line 13 is O(n 2 3 ). e time complexity of line 14 is O((…”

Section: Dynamic Optimization Processmentioning

confidence: 99%

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A Composite Service Provisioning Mechanism in Edge Computing

Zhang

Zhao

Wang

et al. 2022

Mobile Information Systems

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Users can invoke various composite services in Edge Computing (EC) with the development of Service Computing. Generally, users cannot invoke the optimal composite service due to the migration of component service or failure in the edge device. In this study, a Composite Service Provisioning mechanism in EC (CSP-EC) is proposed. It starts when the location of component service changes and terminates when the optimal composite service is obtained. CSP-EC employs dynamic optimization process to meet the real-time requirements. It first caches m optimized intermediate solutions from the first m users, which can be reused by other users to get the final optimal solution. Moreover, a multipopulation Estimation of Distribution Algorithm is used to reduce the probability of falling into a local optimum, and the roulette mechanism is used to accelerate the optimization process. Extensive simulations are conducted based on the real-world dataset. The results show that the proposed mechanism achieves higher quality, better stability, and shorter execution time compared with other schemes.

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“…erefore, the time complexity of line 11 is O(n 2 1 ). Similar to line 11, the time complexity of line 12 is O(n 2 2 ), and the line 13 is O(n 2 3 ). e time complexity of line 14 is O((…”

Section: Dynamic Optimization Processmentioning

confidence: 99%

“…EC provides IT services and cloud computing capabilities at the edge mobile network, within the radio access network and in close proximity to mobile subscribers. It reduces latency, ensures highly e cient network operation and service delivery, and o ers an impressed user experience [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Composite Service Provisioning Mechanism in Edge Computing

Zhang

Zhao

Wang

et al. 2022

Mobile Information Systems

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“…The extensive search space and complex spatial patterns of optimization problems further complicate the modeling of vast and intricate systems. In this context, the fuzzy cognitive map (FCM), as an intelligent computing tool, stands out for its advantages in abstractness, flexibility, and fuzzy reasoning, making it an ideal choice for system modeling and reasoning when compared to standard techniques like expert systems and neural networks 7 . In recent years, researchers have explored the application of AI techniques, such as neural networks and Bayesian networks, in education.…”

Section: Introductionmentioning

confidence: 99%

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

Song

2024

Sci Rep

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This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.

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“…Literature [23] acquired student sports data in the wisdom mode based on semiconductors, and this method can cache the wisdom sports data with colossal content, realize the digital transformation of college sports, and make the intrinsic factors of wisdom teaching effectively articulated. Literature [24] constructed an innovative sports teaching resource platform based on artificial intelligence and edge computing, which establishes a college sports information management platform and mobile Internet terminals through Internet technology, optimizes college sports resources and venue information management services, and contributes to the intelligent allocation of school sports resources. Literature [25] pointed out that the set of informationization, intelligence, and integration of college sports teaching venues is the main direction of future development.…”

Section: Introductionmentioning

confidence: 99%

Research on the Innovation of Smart Teaching Mode of University Physical Education Driven by Digital Technology

Cao,

Cui

2024

Applied Mathematics and Nonlinear Sciences

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Physical education teaching mode is the link between physical education teaching theory and physical education teaching practice, which has a direct impact on the quality and teaching effect of physical education teaching. In this paper, the recognition algorithm is applied to physical education, and a graph-convolutional neural network is constructed. In order to solve the problem that a graph convolutional neural network can only deal with non-motion graphs, using the Masked Attention mechanism, assigning different weights to neighboring nodes, extracting node features and features between neighboring nodes, thus the graph attention neural network, and introducing the YOLO model to predict the single and multiple postures and correct the students’ technical movements. Through the comparative experimental study in this paper, it was found that the p-values of the four dimensions of students’ attention to athletics, adverse interest in athletics learning, positive interest in athletics learning, and daily athletics exercise were all 0.000<0.05. In the two dimensions of positive interest in athletic learning and daily athletic exercise, the mean values of the students in the experimental group reached 29.11 and 32.89, respectively. In terms of comparing the results of the students in the two groups, the 50-meter Run p=0.021<0.05, Vertical Long Jump p=0.002<0.05, Seated Body Flexion and Sit-ups for girls p=0.000<0.05. Therefore, the data-intelligent physical education teaching model constructed in this paper can increase the degree of students’ interest in physical education, improve students’ participation in physical education classrooms, and optimize students’ physical education achievement and performance.

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Evaluation Index of School Sports Resources Based on Artificial Intelligence and Edge Computing

Cited by 3 publications

References 25 publications

A Composite Service Provisioning Mechanism in Edge Computing

A Composite Service Provisioning Mechanism in Edge Computing

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

Research on the Innovation of Smart Teaching Mode of University Physical Education Driven by Digital Technology

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