All Title Author
Keywords Abstract

Publish in OALib Journal
ISSN: 2333-9721
APC: Only $99

ViewsDownloads

Relative Articles

More...

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

DOI: 10.4236/jilsa.2024.162005, PP. 53-79

Keywords: Artificial Intelligence, Machine Learning, Spark Apache, Big Data, SAIM

Full-Text   Cite this paper   Add to My Lib

Abstract:

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

References

[1]  Berisha, B., Mëziu, E. and Shabani, I. (2022) Big Data Analytics in Cloud Computing: An Overview. Journal of Cloud Computing, 11, Article No. 24.
https://doi.org/10.1186/s13677-022-00301-w
[2]  Jadhav, A., Rasool, A. and Gyanchandani, M. (2023) Quantum Machine Learning: Scope for Real-World Problems. Procedia Computer Science, 218, 2612-2625.
https://doi.org/10.1016/j.procs.2023.01.235
[3]  Saratchandra, M. and Shrestha, A. (2022) The Role of Cloud Computing in Knowledge Management for Small and Medium Enterprises: A Systematic Literature Review. Journal of Knowledge Management, 26, 2668-2698.
https://doi.org/10.1108/JKM-06-2021-0421
[4]  Al-Jumaili, A.H.A., Muniyandi, R.C., Hasan, M.K., Paw, J.K.S. and Singh, M.J. (2023) Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations. Sensors, 23, Article No. 2952.
https://doi.org/10.3390/s23062952
[5]  Li, Y. and Hei, X. (2022) Performance Optimization of Computing Task Scheduling Based on the Hadoop Big Data Platform. Neural Computing and Applications.
https://doi.org/10.1007/s00521-022-08114-3
[6]  Deshmukh, S.S. (2023) Progress in Machine Learning Techniques for Stock Market Movement Forecast. Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022), Vol. 105, 69.
https://doi.org/10.2991/978-94-6463-136-4_9
[7]  Mentis, A.F.A., Lee, D. and Roussos, P. (2023) Applications of Artificial Intelligence—Machine Learning for Detection of Stress: A Critical Overview. Molecular Psychiatry.
https://doi.org/10.1038/s41380-023-02047-6
[8]  Ding, Z., Wang, H., Sun, Y. and Qin, H. (2022) Adaptive Prescribed Performance Second-Order Sliding Mode Tracking Control of Autonomous Underwater Vehicle Using Neural Network-Based Disturbance Observer. Ocean Engineering, 260, Article ID: 111939.
https://doi.org/10.1016/j.oceaneng.2022.111939
[9]  Martin, P.E., Siler, W.L. and Hoffman, D. (1990) Electromyographic Analysis of Bow String Release in Highly Skilled Archers. Journal of Sports Sciences, 8, 215-221.
https://doi.org/10.1080/02640419008732147
[10]  Dzwil, M. (2023) Predicting Rookie Season Performance Based on National Football League (NFL) Scouting Combine Movement Analysis. Doctoral Dissertation, Worcester Polytechnic Institute, Worcester.
[11]  Nguyen, N.H., An Nguyen, D.T., Ma, B.K. and Hu, J. (2022) The Application of Machine Learning and Deep Learning in Sport: Predicting NBA Players’ Performance and Popularity. Journal of Information and Telecommunication, 6, 217-235.
https://doi.org/10.1080/24751839.2021.1977066
[12]  Yao, P. (2021) Real-Time Analysis of Basketball Sports Data Based on Deep Learning. Complexity, 2021, Article ID: 9142697.
https://doi.org/10.1155/2021/9142697
[13]  Zhu, H., Zhang, P., Wang, L., Zhang, X. and Jiao, L. (2019) A Multiscale Object Detection Approach for Remote Sensing Images Based on MSE-DenseNet and the Dynamic Anchor Assignment. Remote Sensing Letters, 10, 959-967.
https://doi.org/10.1080/2150704X.2019.1633486
[14]  Shan, C., Brea, V.M. and Velipasalar, S. (2020) Special Issue on Smart Cameras for Real-Time Image and Video Processing. Journal of Real-Time Image Processing, 17, 1755-1756.
https://doi.org/10.1007/s11554-020-01006-6
[15]  Liu, G., Luo, Y., Schulte, O. and Kharrat, T. (2020) Deep Soccer Analytics: Learning an Action-Value Function for Evaluating Soccer Players. Data Mining and Knowledge Discovery, 34, 1531-1559.
https://doi.org/10.1007/s10618-020-00705-9
[16]  Rangasamy, K., As’ari, M.A., Rahmad, N.A., Ghazali, N.F. and Ismail, S. (2020) Deep Learning in Sport Video Analysis: A Review. Telkomnika (Telecommunication Computing Electronics and Control), 18, 1926-1933.
https://doi.org/10.12928/telkomnika.v18i4.14730
[17]  Rana, M. and Mittal, V. (2020) Wearable Sensors for Real-Time Kinematics Analysis in Sports: A Review. IEEE Sensors Journal, 21, 1187-1207.
https://doi.org/10.1109/JSEN.2020.3019016
[18]  Van Rossem, S., Tavernier, W., Colle, D., Pickavet, M. and Demeester, P. (2019) Profile-Based Resource Allocation for Virtualized Network Functions. IEEE Transactions on Network and Service Management, 16, 1374-1388.
https://doi.org/10.1109/TNSM.2019.2943779
[19]  Van Rossem, S., Tavernier, W., Colle, D., Pickavet, M. and Demeester, P. (2020) Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions. Journal of Network and Systems Management, 28, 1482-1521.
https://doi.org/10.1007/s10922-020-09547-8
[20]  Schneider, S., Satheeschandran, N.P., Peuster, M. and Karl, H. (2020) Machine Learning for Dynamic Resource Allocation in Network Function Virtualization. Proceedings of the 2020 6th IEEE Conference on Network Softwarization (NetSoft), Ghent, 29 June-3 July 2020, 122-130.
https://doi.org/10.1109/NetSoft48620.2020.9165348

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413