Prediction of Volleyball Competition Using Machine Learning and Edge Intelligence

被引:7
|
作者
Liu, Qiang [1 ]
Liu, Qiannan [1 ]
机构
[1] Woosuk Univ, Grad Sch, Wonju, South Korea
关键词
USER;
D O I
10.1155/2021/5595833
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Data analysis and machine learning are the backbones of the current era. Human society has entered machine learning and data science that increases the data capacity. It has been widely acknowledged that not only does the number of information increase exponentially, but also the way of human information management and processing is completed to be changed from manual to computer, mainly depending on the transformation of information technology including a computer, network, and communication. This paper is aimed at a solution to the lag of the methods and means of volleyball technique prediction in China. Through field visits, it is found that the way of analysis and research of techniques and tactics in Chinese volleyball practice is relatively backward, which to a certain extent affected the rapid development of Chinese volleyball. Therefore, it is a necessary and urgent task to realize the reform of the methods and means of volleyball technical and tactical analysis in China. The data analysis and prediction are based on the machine learning and data mining algorithm applied to volleyball in this paper is an inevitable trend. The proposed model is applied to the data produced at the edges of the systems and thoroughly analyzed. The Apriori algorithm of the machine learning algorithm is utilized to process the data and provide a prediction about the strategies of a volleyball match. The Apriori algorithm of machine learning is also optimized to perform better data analysis. The effectiveness of the proposed model is also highlighted.
引用
收藏
页数:8
相关论文
共 50 条
  • [11] Machine learning based soft sensor model for BOD estimation using intelligence at edge
    Pattnaik, Bhawani Shankar
    Pattanayak, Arunima Sambhuta
    Udgata, Siba Kumar
    Panda, Ajit Kumar
    COMPLEX & INTELLIGENT SYSTEMS, 2021, 7 (02) : 961 - 976
  • [12] Artificial intelligence and machine learning for protein toxicity prediction using proteomics data
    Vishnoi, Shubham
    Matre, Himani
    Garg, Prabha
    Pandey, Shubham Kumar
    CHEMICAL BIOLOGY & DRUG DESIGN, 2020, 96 (03) : 902 - 920
  • [13] Machine Intelligence at the Edge
    Henkel, Jorg
    IEEE DESIGN & TEST, 2021, 38 (04) : 4 - 4
  • [14] Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)
    Zaidi, Syed Ali Raza
    Hayajneh, Ali M.
    Hafeez, Maryam
    Ahmed, Q. Z.
    IEEE ACCESS, 2022, 10 : 100867 - 100877
  • [15] Emphasizing privacy and security of edge intelligence with machine learning for healthcare
    Rajendran, Sukumar
    Mathivanan, Sandeep Kumar
    Jayagopal, Prabhu
    Purushothaman Janaki, Kumar
    Manickam Bernard, Benjula Anbu Malar
    Pandy, Suganya
    Sorakaya Somanathan, Manivannan
    INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING AND CYBERNETICS, 2022, 15 (01) : 92 - 109
  • [16] An IoT based System with Edge Intelligence for Rice Leaf Disease Detection using Machine Learning
    Rumy, S. M. Shahidur Harun
    Hossain, Md Ishan Arefin
    Jahan, Fmji
    Tanvin, Tanjina
    2021 IEEE INTERNATIONAL IOT, ELECTRONICS AND MECHATRONICS CONFERENCE (IEMTRONICS), 2021, : 441 - 446
  • [17] Artificial intelligence, machine learning, and deep learning for clinical outcome prediction
    Pettit, Rowland W.
    Fullem, Robert
    Cheng, Chao
    Amos, Christopher I.
    EMERGING TOPICS IN LIFE SCIENCES, 2021, 5 (06) : 729 - 745
  • [18] Risk Prediction of Diabetic Foot Amputation Using Machine Learning and Explainable Artificial Intelligence
    Oei, Chien Wei
    Chan, Yam Meng
    Zhang, Xiaojin
    Leo, Kee Hao
    Yong, Enming
    Chong, Rhan Chaen
    Hong, Qiantai
    Zhang, Li
    Pan, Ying
    Tan, Glenn Wei Leong
    Mak, Malcolm Han Wen
    JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, 2024,
  • [19] Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study
    da Silva Junior, Antonio Carlos
    da Silva, Michele Jorge
    Cruz, Cosme Damiao
    Sant'Anna, Isabela de Castro
    Silva, Gabi Nunes
    Nascimento, Moyses
    Azevedo, Camila Ferreira
    PLOS ONE, 2021, 16 (11):
  • [20] Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning
    Muneer, Rizwan
    Hashmet, Muhammad Rehan
    Pourafshary, Peyman
    Shakeel, Mariam
    NANOMATERIALS, 2023, 13 (07)