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  <doi_batch_id>aspg-18-1847-1792025597</doi_batch_id>
  <timestamp>20261015005317</timestamp>
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   <depositor_name>American Scientific Publishing Group</depositor_name>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Intelligent Systems and Internet of Things</full_title>
    <abbrev_title>JISIoT</abbrev_title>
    <issn media_type="print">2769-786X</issn>
    <issn media_type="electronic">2690-6791</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <journal_volume>
     <volume>9</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mustafa</given_name>
      <surname>Altaee</surname>
      <affiliations>
       <institution>
        <institution_name>Department of medical instruments engineering techniques, Alfarahidi University, Baghdad, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>A.</given_name>
      <surname>Jawad</surname>
      <affiliations>
       <institution>
        <institution_name>Computer Communications Engineering Department, National University of science and technology , Thi Qar, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Mohammed Abdul</given_name>
      <surname>Jalil</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Engineering techniques, Alturath University college, Baghdad, Iraq; MEU Research Unit, Middle East University, Amman 11831, Jordan</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Sanaa</given_name>
      <surname>Al-Kikani</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Physical Education and Sport Science, Al Mustaqbal University College, 51001 Hilla, Babylon, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Ahmed</given_name>
      <surname>Oleiwi</surname>
      <affiliations>
       <institution>
        <institution_name>Biomedical Engineering, College of Engineering, University of Warith Al-Anbiyaa , Karbala, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Hatıra</given_name>
      <surname>Günerhan</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Mathematics, Faculty of Education, Kafkas University, Kars, Turkey</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>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.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <pages>
     <first_page>08</first_page>
     <last_page>23</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">1847</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
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     <doi>10.54216/JISIoT.090101</doi>
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