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Journal of Intelligent Systems and Internet of Things

Volume 9 , Issue 1 , PP: 08-23, 2023 | Cite this article as | XML | Html | PDF | Full Length Article

A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning

Mustafa Altaee 1 * , A. Jawad 2 , Mohammed Abdul Jalil 3 , Sanaa Al-Kikani 4 , Ahmed Oleiwi 5 , Hatıra Günerhan 6

  • 1 Department of medical instruments engineering techniques, Alfarahidi University, Baghdad, Iraq - (m.altaee@alfarahidiuc.edu.iq)
  • 2 Computer Communications Engineering Department, National University of science and technology , Thi Qar, Iraq - (a.jawad@nust.edu.iq)
  • 3 Department of Computer Engineering techniques, Alturath University college, Baghdad, Iraq; MEU Research Unit, Middle East University, Amman 11831, Jordan - (mohammedjalil24@gmail.com)
  • 4 Department of Physical Education and Sport Science, Al Mustaqbal University College, 51001 Hilla, Babylon, Iraq - (Dr.Sanaa@uomus.edu.iq)
  • 5 Biomedical Engineering, College of Engineering, University of Warith Al-Anbiyaa , Karbala, Iraq - (A.oleiwi@uowa.edu.iq)
  • 6 Department of Mathematics, Faculty of Education, Kafkas University, Kars, Turkey - (hatira.gunerhan@kafkas.edu.tr)

    • Doi: https://doi.org/10.54216/JISIoT.090101

    Received: January 17, 2023 Revised: April 02, 2023 Accepted: June 02, 2023
    Abstract

    To record and evaluate students’ physical education (PE) class participation, this study proposes using machine learning aided physical training framework (ML-PTF). Improve student achievement in PE with the help of the Multi-level Fusion System that employs machine learning strategies. The system integrates sensor data, video data, and contextual data to deliver a holistic and precise evaluation of student engagement. This study’s simulation analysis shows that the ML-PTF improves the reliability of evaluating universities’ PE programs. A important reference path and paradigm for advancing tertiary-level PE for graduates, the multi-level fusion system also provides an investigation of information technology and language education integration. The experimental findings demonstrate that the ML-PTF is superior to other approaches in terms of learning rate, f1-score, precision, and probability, as well as student engagement, involvement, and recognition accuracy.

    Keywords :

    Physical Education , Machine learning , A Multi-level Fusion System , Assessment model , Student Activity Prediction

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    Cite This Article As :
    Altaee, Mustafa. , Jawad, A.. , Abdul, Mohammed. , Al-Kikani, Sanaa. , Oleiwi, Ahmed. , , Hatıra. A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2023, pp. 08-23. DOI: https://doi.org/10.54216/JISIoT.090101
    Altaee, M. Jawad, A. Abdul, M. Al-Kikani, S. Oleiwi, A. , H. (2023). A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning. Journal of Intelligent Systems and Internet of Things, (), 08-23. DOI: https://doi.org/10.54216/JISIoT.090101
    Altaee, Mustafa. Jawad, A.. Abdul, Mohammed. Al-Kikani, Sanaa. Oleiwi, Ahmed. , Hatıra. A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning. Journal of Intelligent Systems and Internet of Things , no. (2023): 08-23. DOI: https://doi.org/10.54216/JISIoT.090101
    Altaee, M. , Jawad, A. , Abdul, M. , Al-Kikani, S. , Oleiwi, A. , , H. (2023) . A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning. Journal of Intelligent Systems and Internet of Things , () , 08-23 . DOI: https://doi.org/10.54216/JISIoT.090101
    Altaee M. , Jawad A. , Abdul M. , Al-Kikani S. , Oleiwi A. , H. [2023]. A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning. Journal of Intelligent Systems and Internet of Things. (): 08-23. DOI: https://doi.org/10.54216/JISIoT.090101
    Altaee, M. Jawad, A. Abdul, M. Al-Kikani, S. Oleiwi, A. , H. "A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 08-23, 2023. DOI: https://doi.org/10.54216/JISIoT.090101

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