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  <doi_batch_id>aspg-18-4065-1792025499</doi_batch_id>
  <timestamp>20261015005139</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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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>2026</year>
    </publication_date>
    <journal_volume>
     <volume>18</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>DNA Sequence Identification via Biologically Guided Feature Engineering and Hybrid ML–LSTM Networks</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Marwa Mawfaq Mohamedsheet</given_name>
      <surname>Al-Hatab</surname>
      <affiliations>
       <institution>
        <institution_name>Technical Engineering College, Northern Technical University, Mosul, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Maysaloon Abed</given_name>
      <surname>Qasim</surname>
      <affiliations>
       <institution>
        <institution_name>Technical Engineering College for Computer and Artificial Intelligence, Northern Technical University, Mosul, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Sinan S. Mohammed</given_name>
      <surname>Sheet</surname>
      <affiliations>
       <institution>
        <institution_name>Technical Engineering College, Northern Technical University, Mosul, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The promoter is the part of DNA, which is responsible of initiating RNA polymerase transcription of a gene. The location of this part of DNA is upstream the transcription start site. According to researches, the genetic promotors contribute majorly in many human diseases such as cancer, diabetes and Huntington’s disease. Therefore, promotor detection corresponds as a very crucial task. In this study, a hypered detection system, which integrates biologically developed feature extraction with traditional machine learning (ML) algorithms in addition to use Long Short-Term Memory (LSTM) network as a deep learning approach, has been proposed. The dataset used includes 106 nucleotide sequences. Results obtained from the study show that the perfect performance across all metrics (accuracy, sensitivity, specificity, precision, and F1-score) has been achieved when Naive Bayes was used as a classifier, which reached 100% and AUC=1. The confusion matrix analyses and ROC curve confirm that LSTM model achieved 100% training accuracy and 84.38% test accuracy. The architecture and performance of the proposed model make it applicable in IoT-based intelligent genomic and healthcare systems, which enabling real-time and remote promoter detection.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <pages>
     <first_page>315</first_page>
     <last_page>326</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">4065</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.180222</doi>
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