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    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Rea Press</journal-id>
      <journal-id journal-id-type="publisher-id">null</journal-id>
      <journal-title>Rea Press</journal-title><issn pub-type="ppub">3009-4496</issn><issn pub-type="epub">3009-4496</issn><publisher>
      	<publisher-name>Rea Press</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">https://doi.org/10.22105/kfyyze86</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group><subject>Perplexity, AI, ChatGPT, Entropy, Natural language processing, Increasing decreasing test‎, Threshold‎</subject></subj-group>
      </article-categories>
      <title-group>
        <article-title>Ismail’s Threshold Theory to Master Perplexity AI</article-title><subtitle>Ismail’s Threshold Theory to Master Perplexity AI</subtitle></title-group>
      <contrib-group><contrib contrib-type="author">
	<name name-style="western">
	<surname> A Mageed</surname>
		<given-names>Ismail</given-names>
	</name>
	<aff>AIMMA, IEEE, IAENG, School of Computer Science, AI, and Electronics, Faculty of engineering and Digital, Technologies, ‎University of Bradford, United Kingdom.</aff>
	</contrib></contrib-group>		
      <pub-date pub-type="ppub">
        <month>05</month>
        <year>2024</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>01</day>
        <month>05</month>
        <year>2024</year>
      </pub-date>
      <volume>1</volume>
      <issue>2</issue>
      <permissions>
        <copyright-statement>© 2024 Rea Press</copyright-statement>
        <copyright-year>2024</copyright-year>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.5/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p></license>
      </permissions>
      <related-article related-article-type="companion" vol="2" page="e235" id="RA1" ext-link-type="pmc">
			<article-title>Ismail’s Threshold Theory to Master Perplexity AI</article-title>
      </related-article>
	  <abstract abstract-type="toc">
		<p>
			Undoubtedly,  there is a potential impact of  Large Language Models (LLMs)  like ChatGPT on revolutionizing information retrieval and knowledge discovery, particularly in the context of the vast amount of electronic material available. On another strong note, the current work offers a first time ever mathematical approach to accurately fine- tune perplexity AI, which sets a giant step ahead towards accuracy of the next generations AI. Having started this world- leading discovery, the upper and lower bounds of perplexity AI, namely   , respectively, are obtained for the first time.
		</p>
		</abstract>
    </article-meta>
  </front>
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