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  <doi_batch_id>aspg-39-3026-1792223087</doi_batch_id>
  <timestamp>20261017074447</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Neutrosophic and Information Fusion</full_title>
    <abbrev_title>NIF</abbrev_title>
    <issn media_type="electronic">2836-7863</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>3</volume>
    </journal_volume>
    <issue>1</issue>
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   <journal_article publication_type="full_text">
    <titles>
     <title>On The Analysis of Some Deep Learning Algorithms for Object Detection and Applications</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Sandy Montajab</given_name>
      <surname>Hazzouri</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Informatics Engineering, Albaath University, Syria</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The traditional methods of discovering objects no longer meet the requirements of the times as a result of their reliance on non-dynamic methods and their slow performance in light of the world’s dependence on a huge amount of multimedia and social media. The rapid development of deep learning provides more powerful tools capable of manipulating high-level and complex semantic features of objects. Several techniques have been developed to detect objects using deep learning algorithms. This research presents a comparative analysis of the most famous deep learning techniques for object detection, explaining their mechanisms and use cases and providing an experimental evaluation of their performance.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>12</first_page>
     <last_page>19</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3026</item_number>
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     <doi>10.54216/NIF.030103</doi>
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