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)
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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| Style | # |
|---|---|
| MLA | Mustafa Altaee, A. Jawad , Mohammed Abdul Jalil, Sanaa Al-Kikani, Ahmed Oleiwi, Hatıra Günerhan. "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. 9, No. 1, 2023 ,PP. 08-23 (Doi : https://doi.org/10.54216/JISIoT.090101) |
| APA | Mustafa Altaee, A. Jawad , Mohammed Abdul Jalil, Sanaa Al-Kikani, Ahmed Oleiwi, Hatıra Günerhan. (2023). A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning. Journal of Journal of Intelligent Systems and Internet of Things, 9 ( 1 ), 08-23 (Doi : https://doi.org/10.54216/JISIoT.090101) |
| Chicago | Mustafa Altaee, A. Jawad , Mohammed Abdul Jalil, Sanaa Al-Kikani, Ahmed Oleiwi, Hatıra Günerhan. "A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning." Journal of Journal of Intelligent Systems and Internet of Things, 9 no. 1 (2023): 08-23 (Doi : https://doi.org/10.54216/JISIoT.090101) |
| Harvard | Mustafa Altaee, A. Jawad , Mohammed Abdul Jalil, Sanaa Al-Kikani, Ahmed Oleiwi, Hatıra Günerhan. (2023). A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning. Journal of Journal of Intelligent Systems and Internet of Things, 9 ( 1 ), 08-23 (Doi : https://doi.org/10.54216/JISIoT.090101) |
| Vancouver | Mustafa Altaee, A. Jawad , Mohammed Abdul Jalil, Sanaa Al-Kikani, Ahmed Oleiwi, Hatıra Günerhan. A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning. Journal of Journal of Intelligent Systems and Internet of Things, (2023); 9 ( 1 ): 08-23 (Doi : https://doi.org/10.54216/JISIoT.090101) |
| IEEE | Mustafa Altaee, A. Jawad, Mohammed Abdul Jalil, Sanaa Al-Kikani, Ahmed Oleiwi, Hatıra Günerhan, A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 9 , No. 1 , (2023) : 08-23 (Doi : https://doi.org/10.54216/JISIoT.090101) |