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  <doi_batch_id>aspg-21-3483-1791472126</doi_batch_id>
  <timestamp>20261008150846</timestamp>
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   <depositor_name>American Scientific Publishing Group</depositor_name>
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  <journal>
   <journal_metadata language="en">
    <full_title>International Journal of Neutrosophic Science</full_title>
    <abbrev_title>IJNS</abbrev_title>
    <issn media_type="print">2692-6148</issn>
    <issn media_type="electronic">2690-6805</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>25</volume>
    </journal_volume>
    <issue>4</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Optimize Decision-Making in the Industrial Sector under Uncertainty: A Neutrosophic Inverse Exponential Distribution Approach</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mansour F.</given_name>
      <surname>Yassen</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The most widely used distribution for risk management data for modeling longevity is the one-parameter inverse exponential distribution. Among alternative models, we suggest the neutrosophic inverse exponential (NIE) model, which generalizes the extended inverse exponential distributions and the classical structure. For the suggested model, we derive explicit formulations for the quantile functions, median, mode, cumulative distribution function, and probability density function. Data generating process of the proposed model under neutrosophic environment is discussed. To estimate the model parameters, we use the maximum likelihood approach. Using the proposed model, we run the simulation setup for randomly generated data. A genuine data set is also used to support the proposed model applicability.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>295</first_page>
     <last_page>304</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3483</item_number>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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    <doi_data>
     <doi>10.54216/IJNS.250425</doi>
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