  <?xml version="1.0"?>
<journal>
 <journal_metadata>
  <full_title>Journal of Intelligent Systems and Internet of Things</full_title>
  <abbrev_title>JISIoT</abbrev_title>
  <issn media_type="print">2690-6791</issn>
  <issn media_type="electronic">2769-786X</issn>
  <doi_data>
   <doi>10.54216/JISIoT</doi>
   <resource>https://www.americaspg.com/journals/show/2084</resource>
  </doi_data>
 </journal_metadata>
 <journal_issue>
  <publication_date media_type="print">
   <year>2019</year>
  </publication_date>
  <publication_date media_type="online">
   <year>2019</year>
  </publication_date>
 </journal_issue>
 <journal_article publication_type="full_text">
  <titles>
   <title>Athlete’s Performance Analysis Based on Improved Machine Learning Approach on Wearable Devices</title>
  </titles>
  <contributors>
   <organization sequence="first" contributor_role="author">Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq</organization>
   <person_name sequence="first" contributor_role="author">
    <given_name>Maryam Ghassan</given_name>
    <surname>Majeed</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Hawraa Ali</given_name>
    <surname>Sabah</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Computer Technologies Engineering, Al-Turath University College, Baghdad,Iraq</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Mustafa Nazar</given_name>
    <surname>Dawood</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Mohaned</given_name>
    <surname>Adile</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of Computer Technical Engineering, Technical Engineering College, Al-Ayen University, Thi- Qar, Iraq</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Noor Hanoon</given_name>
    <surname>Haroon</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of Computer Science and Engineering, University of Deusto, 48007 Bilbao, Spain</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Mariok</given_name>
    <surname>Jojoal</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of  Computer Engineering, University of Massachusetts Dartmouth, MA 02747Inst, USA</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Ahmed Mollah</given_name>
    <surname>Khan</surname>
   </person_name>
  </contributors>
  <jats:abstract xml:lang="en">
   <jats:p>Today, every nation strives for international recognition in a variety of sports. Governments invest in games and sports to raise the performance of their teams and athletes to get notoriety. Numerous people are involved in sports execution, including team management, coaches, and biomechanists who monitor athlete fitness and work to achieve remarkable results. Performance analysis is greatly aided by technological integration in sports management. The performance analysis of athletes is evaluated in this research using an upgraded machine learning approach on Improved Machine Learning approach on Wearable Devices (IMLA-WD). This design strategy utilizes wearable devices to collect health data, which is then fed into a machine-learning model to monitor athletes' progress. The athletes' performance is evaluated using standard machine learning methods, and the deep neural network monitors their health status. With a health prediction accuracy of 98.65%, the statistical findings of the proposed model demonstrate the highest performance compared to existing methodologies. </jats:p>
  </jats:abstract>
  <publication_date media_type="print">
   <year>2023</year>
  </publication_date>
  <publication_date media_type="online">
   <year>2023</year>
  </publication_date>
  <pages>
   <first_page>75</first_page>
   <last_page>91</last_page>
  </pages>
  <doi_data>
   <doi>10.54216/JISIoT.080108</doi>
   <resource>https://www.americaspg.com/articleinfo/18/show/2084</resource>
  </doi_data>
 </journal_article>
</journal>
