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  <doi_batch_id>aspg-2-2789-1791417278</doi_batch_id>
  <timestamp>20261007235438</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Cybersecurity and Information Management</full_title>
    <abbrev_title>JCIM</abbrev_title>
    <issn media_type="print">2769-7851</issn>
    <issn media_type="electronic">2690-6775</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>13</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Computational genetic epidemiology: Leveraging HPC for large-scale AI models based on Cyber Security</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Vadali Pitchi</given_name>
      <surname>Raju</surname>
      <affiliations>
       <institution>
        <institution_name>Principal, Indur Institute of Engg. &amp; Tech, Siddipet, Bharat, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Tushar Kumar</given_name>
      <surname>Pandey</surname>
      <affiliations>
       <institution>
        <institution_name>Junior Engineer (Computer Science), Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Rajeev</given_name>
      <surname>Shrivastava</surname>
      <affiliations>
       <institution>
        <institution_name>Principal, Princeton Institute of Engineering &amp; Technology for Women Hyderabad, Telangana, India, 4Professor, CMR Engineering College, Hyderabad, (T. S.), India.</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Rajesh</given_name>
      <surname>Tiwari</surname>
      <affiliations>
       <institution>
        <institution_name>Professor, CMR Engineering College, Hyderabad, (T. S.), India.</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>S. Anjali</given_name>
      <surname>Devi</surname>
      <affiliations>
       <institution>
        <institution_name>Asst. Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, Bharat, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Neerugatti Varipallay</given_name>
      <surname>vishwanath</surname>
      <affiliations>
       <institution>
        <institution_name>Asst. Professor, Dept. of ECE, St. Martin's Engineering College, Secunderabad, Telangana, Bharat, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>To better understand disease susceptibility and prevention, computational genetic epidemiology is leading research. This paper introduces &quot;GenomeMinds,&quot; a breakthrough method for scaling large-scale AI models for disease risk prediction. HPC was used to develop the method. GenomeMinds is compared to six standard methods to demonstrate its benefits. GenomeMinds' incredible potential is shown by real-world performance assessments. These measures evaluate data processing speed, forecast accuracy, scalability, computer efficiency, privacy, and ethics. GenomeMinds benefits are shown via scatter plots, which visually compare data. According to the data, GenomeMinds may revolutionize computational genetic epidemiology by doing well across all criteria. GenomeMinds has faster data processing, better prediction accuracy, stronger scalability, higher computational efficiency, enhanced privacy and security, and a comprehensive ethical awareness.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>182</first_page>
     <last_page>190</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2789</item_number>
    </publisher_item>
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    <doi_data>
     <doi>10.54216/JCIM.130214</doi>
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