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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>IJIRCSTJournal</PublisherName>
      <JournalTitle>International Journal of Innovative Research in Computer Science and Technology</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>Volume 11 Issue 3</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Computer Science &amp; Engineering</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>May - June 2023</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2023</Year>
        <Month>09</Month>
        <Day>07</Day>
      </PubDate>
      <ArticleType>Computer Sciences</ArticleType>
      <ArticleTitle>Employing Semi-Supervised and Supervised Learning to Discover False Online Ratings</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>94</FirstPage>
      <LastPage>95</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Giribabu Sadineni</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
                      <FirstName>Janardhan Reddy D</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>Ch. Meghana Sri</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>K.Deepthi</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>M.Kaveri</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>J.Aiswarya</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
           
        </Author>
      </AuthorList>
      <DOI>https://doi.org/10.55524/ijircst.2023.11.3.18</DOI>
      <Abstract>Today&amp;#39;s modern industry and trade, internet evaluations matter a lot. Buying web items is often influenced by the opinions of other customers. Because of this, unscrupulous folks or organisations attempt to rig customer evaluations to their personal advantage. Using a lodging rating database, this research examines the performance of semi-supervised (SSVD) and supervised (SVD) word extraction methods for detecting false ratings.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Online Products, Fake Reviews, Identifica-tion, Classification, E-Commerce</Keywords>
      <URLs>
        <Abstract>https://ijircst.org/abstract.php?article_id=1171</Abstract>
      </URLs>      
    </Journal>
  </Article>
</ArticleSet>