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  <doi_batch_id>aspg-34-4482-1791482494</doi_batch_id>
  <timestamp>20261008180134</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
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  <journal>
   <journal_metadata language="en">
    <full_title>Prospects for Applied Mathematics and Data Analysis</full_title>
    <abbrev_title>PAMDA</abbrev_title>
    <issn media_type="electronic">2836-4449</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <journal_volume>
     <volume>6</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Generating Neutrosophic Random Variables Based on Generalized Gamma Distribution</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Khalifa</given_name>
      <surname>AlShaqsi</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Mathematical and Physical Sciences, College of Arts and Sciences, University of Nizwa, Oman</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>In practice, we encounter many systems that cannot be studied directly, either due to high costs or because some of these systems are not directly detectable. Therefore, we resort to simulation, which involves applying the study to systems similar to real-world systems and then projecting the results if they are suitable for the real system. The simulation process requires a thorough understanding of probability distributions and the methods used to transform random numbers following a regular distribution on [0,1] into random variables that follow it. This allows us to maximize the benefits of the simulation process and obtain more accurate results for all emerging conditions. The generalized gamma distribution is a family of three parameters characterized by high flexibility. It includes several important distributions as special cases, including the gamma, Weibull, exponential, and Rayleigh distributions, making it exceptionally valuable in engineering and reliability analysis. In previous research, we presented a neutrosophic view of the process of generating random numbers and some techniques used to generate random variables. In this research, we present a neutrosophic study for generating neutrosophic random variables following the generalized gamma distribution, a distribution widely used in engineering applications. The neutrosophic approach takes into account the uncertainty and indeterminacy of the parameters, resulting in random intervals for the variables rather than specific values, and thus provides more accurate simulation results that adapt to all the conditions that the system in operation may encounter.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <pages>
     <first_page>23</first_page>
     <last_page>28</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">4482</item_number>
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     <doi>10.54216/PAMDA.060104</doi>
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