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  <doi_batch_id>aspg-28-2546-1793997846</doi_batch_id>
  <timestamp>20261106204406</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Artificial Intelligence and Metaheuristics</full_title>
    <abbrev_title>JAIM</abbrev_title>
    <issn media_type="electronic">2833-5597</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>7</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Advances and Challenges in Feature Selection Methods: A Comprehensive Review</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mohamed Ziad</given_name>
      <surname>Ali</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura 35111, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Abdulrahman</given_name>
      <surname>Abdullah</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura 35111, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Ahmed Mohamed</given_name>
      <surname>Zaki</surname>
      <affiliations>
       <institution>
        <institution_name>Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Faris H.</given_name>
      <surname>Rizk</surname>
      <affiliations>
       <institution>
        <institution_name>Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Marwa M.</given_name>
      <surname>Eid</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Elsayed M.</given_name>
      <surname>El-Kenway</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>This review examines the role of feature selection in data analytics and its growing use in data-intensive fields, including cybersecurity, healthcare, and finance. It surveys foundational concepts and current applications, with particular attention to reducing dimensionality and improving the interpretation of complex relationships in healthcare data. The review covers optimization-based methods, swarm intelligence, and feature-selection approaches for heart-disease diagnosis, intrusion detection, and other classification tasks. It also discusses hybrid Ant Colony Optimization and Grey Wolf Optimizer methods, alongside the Improved Salp Swarm Algorithm (ISSA). The reviewed studies illustrate that feature selection is more than a preprocessing step: it is an important component of machine learning and data science that can improve model efficiency, interpretability, and classification performance. Challenges and open research directions are summarized to support future work.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>39</first_page>
     <last_page>44</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2546</item_number>
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     <doi>10.54216/JAIM.070105</doi>
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