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  <doi_batch_id>aspg-21-3910-1791472160</doi_batch_id>
  <timestamp>20261008150920</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>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>26</volume>
    </journal_volume>
    <issue>4</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Neutrosophic EWMA and DEWMA Control Chart on Exponential and Transformed Exponential Distributions</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Ishah Maria</given_name>
      <surname>Mathew</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Mathematics, Amrita School of Physical Sciences, Coimbatore, Amrita Vishwa Vidyapeetham, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>O. S.</given_name>
      <surname>Deepa</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Mathematics, Amrita School of Physical Sciences, Coimbatore, Amrita Vishwa Vidyapeetham, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The sophisticated statistical methods known as Bayesian EWMA and DEWMA control charts are intended to track process performance and identify changes in data over time. They improve the capacity to monitor minute changes in the process by combining conventional smoothing methods with Bayesian inference. By integrating the idea of neutrosophic approaches into Bayesian EWMA and DEWMA models, the suggested approach seeks to address and get beyond this restriction. In this study, neutrosophic approaches are utilized to provide the manufacturing process with two tolerance limits instead of a set value for upper and lower control limits, particularly when all observations are uncertain, imprecise, or fuzzy. By combining the Exponential, Inverse Rayleigh, and Weibull distributions, five symmetric loss functions are examined while taking uniform prior into account. Additionally, for mean, variance, and control limits of the proposed work have been derived. Simulation studies were conducted and compared with previous work as well as all projected works. This study significantly advances the subject of control chart technique, especially when it comes to managing hard, vast, and complicated information.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>184</first_page>
     <last_page>203</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3910</item_number>
    </publisher_item>
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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.260418</doi>
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