2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 13 , Issue 1 , PP: 162-174, | Cite this article as | XML | Html | PDF | Full Length Article
Maryam Ghassan Majeed 1 * , Waleed Hameed 2 , Noor Hanoon Haroon 3 , Sahar R. Abdul Kadeem 4 , Hayder Mahmood Salman 5 , Seifedine Kadry 6
Doi: https://doi.org/10.54216/FPA.130113
Resource Management in Physical Education (RMPE) is the term used to describe the management of the curriculum, materials, and human resources needed for Physical Education (PE). Due to increased sports and physical activity participation, student performance in PE classes across all schools and universities has decreased. According to the analysis, it is hard for the available PE educators and managers to establish a relationship between all the resources. This study uses a robotic system with 5G capability for RMPE. The Big Data Analytics-based Artificial Neural Network method (BDA-ANNA) handles all PE resources in this computerized system. The BDA-ANNA can efficiently increase RMPE work quality and efficiency, enabling managers to obtain and save appropriate information accurately and quickly. With assistance from the robotic system, the material stock may be maintained. With the aid of BDA-ANNA, the mechanical system can keep the material stored. Automated systems with 5G capabilities can provide PE instructors with complete remote-control access with a 2-millisecond latency. These two clauses mandate that the RMPE supervise athletic events and physical activity. The suggested 5 G-enabled robotic systems for RMPE can manage all the resources effectively and efficiently with a low error rate. The advanced system and BDA-ANNA were put through a simulation exercise, demonstrating their independence in classifying and managing resources while reducing processing time. The experimental result improves a prediction ratio of 95.5 %, a learning ratio of 90.5%, an error rate of 92.3%, an Efficiency ratio of 96.6%, an Accuracy ratio of 92.5%, and performance ratio of 96.7%, a Movement Detection ratio of 90.7% compared to other methods.
Physical Education , Big Data Analytics , Resources , 5G , and robotic system.
[1] P. K. Mishra, S. Pandey and S. K. Biswash, "Efficient resource management by exploiting D2D communication for 5G Networks", IEEE Access, vol. 4, pp. 9910-9922, 2017
[2] M. Ghogho, A. Swami, Physical layer secrecy of MIMO communications in the presence of a Poisson random field of eavesdroppers, in: Proceedings of the IEEE ICC Workshop on Physical Layer Security, Kyoto, Japan, 2011, pp. 1–5.
[3] Yang, X., Nazir, S., Khan, H. U., Shafiq, M., & Mukhtar, N. (2021). Parallel computing for efficient and intelligent industrial Internet of Health Things: an overview. Complexity, 2021.
[4] Umer, A., Hassan, S. A., Pervaiz, H., Ni, Q., Musavian, L., & Ahmed, S. H. (2018, May). Secrecy outage analysis for massive MIMO-enabled multi-tier 5G hybrid HetNets. In 2018 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1-6). IEEE.
[5] Sutrala, A. K., Obaidat, M. S., Saha, S., Das, A. K., Alazab, M., & Park, Y. (2021). Authenticated Key Agreement Scheme With User Anonymity and Untraceability for 5G-Enabled Softwarized Industrial Cyber-Physical Systems. IEEE Transactions on Intelligent Transportation Systems.
[6] Liu, J., Wan, J., Jia, D., Zeng, B., Li, D., Hsu, C. H., & Chen, H. (2017). High-efficiency urban traffic management in context-aware computing and 5G communication. IEEE Communications Magazine, 55(1), 34-40.
[7] Garg, S., Kaur, K., Kaddoum, G., Ahmed, S. H., & Jayakody, D. N. K. (2019). SDN-based secure and privacy-preserving scheme for vehicular networks: A 5G perspective. IEEE Transactions on Vehicular Technology, 68(9), 8421-8434.
[8] Jacob, S., Menon, V. G., Joseph, S., Vinoj, P. G., Jolfaei, A., Lukose, J., & Raja, G. (2020). A novel spectrum sharing scheme using dynamic long short-term memory with CP-OFDMA in 5G networks. IEEE Transactions on Cognitive Communications and Networking, 6(3), 926-934.
[9] Ali, M.H., Al-Azzawi, W.K., Jaber, M., Abd, S.K., Alkhayyat, A., and Rasool, Z.I., 2022. Improving coal mine safety with internet of things (IoT) based Dynamic Sensor Information Control System. Physics and Chemistry of the Earth, 128.
[10] Zhang, C., Xu, C., Sharif, K., & Zhu, L. (2021). Privacy-preserving contact tracing in 5G-integrated and blockchain-based medical applications. Computer Standards & Interfaces, 77, 103520
[11] Duan B-Y (2020) Evolution and innovation of antenna systems for beyond 5G and 6G. Front Info Technol Electron Eng 21(1):1–3
[12] Kurdi, S.Z., Ali, M.H., Jaber, M.M., Saba, T., Rehman, A., and Damaševičius, R., 2023. Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks. Journal of Personalized Medicine, 13(2).
[13] Jin, N., Zhang, X., Hou, Z., Sanz-Prieto, I., & Mohammed, B. S. (2021). IoT BASED PSYCHOLOGICAL AND PHYSICAL STRESS EVALUATION IN SPORTSMEN USING HEART RATE VARIABILITY. Aggression and Violent Behavior, 101587.
[14] Ali, F., El-Sappagh, S., Islam, S. R., Ali, A., Attique, M., Imran, M., & Kwak, K. S. (2021). An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generation Computer Systems, 114, 23-43.
[15] Attaran, M. (2021). The impact of 5G on the evolution of intelligent automation and industry digitization. Journal of Ambient Intelligence and Humanized Computing, 1-17.
[16] Tang, Y., Dananjayan, S., Hou, C., Guo, Q., Luo, S., & He, Y. (2021). A survey on the 5G network and its impact on agriculture: Challenges and opportunities. Computers and Electronics in Agriculture, 180, 105895.
[17] Kumar, K., Kumar, N., Kumar, A., Mohammed, M.A., Al-Waisy, A.S., Jaber, M.M., Pandey, N.K., Shah, R., Saini, G., Eid, F., Eid, F., and Al-Andoli, M.N., 2022. Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models. Computational Intelligence and Neuroscience, 2022.
[18] Jin, Z., Liu, L., Gong, D., & Li, L. (2021). Target Recognition of Industrial Robots Using Machine Vision in 5G Environment. Frontiers in Neurorobotics, 15, 15.
[19] Du, J., & Nan, Z. (2021). Research on the intelligent model of progress in physical education training based on motion sensor. Microprocessors and Microsystems, 82, 103903.
[20] Feng, L. (2021). Application and development prospects of 5 G communication technology in aerobics sports. Microprocessors and Microsystems, 82, 103945.
[21] Çetinkaya, A., & Baykan, Ö. K. (2020). Prediction of middle school students' programming talent using artificial neural networks. Engineering Science and Technology, an International Journal, 23(6), 1301-1307.
[22] Simon, J., & Prabakaran, N. Optimal wavelets with Binary Phase Shift Keying modulator utilized simulation analysis of 4G/5G OFDM systems. International Journal of Communication Systems, e4775.
[23] Sophokleous, A. Christodoulou, P., Doitsidis, L., & Chatzichristofis, S. A. (2021). Computer Vision Meets Educational Robotics. Electronics, 10(6), 730.
[24] Fiaidhi, J., & Mohammed, S. (2021). Virtual care for cyber–physical systems (VH_CPS): NODE-RED, community of practice and thick data analytics ecosystem. Computer Communications, 170, 84-94.
[25] Zeebaree, I. M., & Kareem, O. S. (2024). Face Mask Detection Using Haar Cascades Classifier To Reduce The Risk Of Coved-19. International Journal of Mathematics, Statistics, and Computer Science, 2, 19–27. https://doi.org/10.59543/ijmscs.v2i.7845
[26] Ghanbari Ghoushchi, N. ., Ahmadzadeh, K., & Jafarzadeh Ghoushchi, S. (2023). A New Extended Approach to Reduce Admission Time in Hospital Operating Rooms Based on the FMEA Method in an Uncertain Environment. Journal of Soft Computing and Decision Analytics, 1(1), 80-101. https://doi.org/10.31181/jscda11202310
