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<records>
  <record>
    <language>eng</language>
    <publisher>Science and Education Publishing</publisher>
    <journalTitle>American Journal of Applied Mathematics and Statistics</journalTitle>
    <eissn>2328-7292</eissn>
    <publicationDate>2022-01-09</publicationDate>
    <volume>10</volume>
    <issue>1</issue>
    <startPage>1</startPage>
    <endPage>3</endPage>
    <doi>10.12691/ajams-10-1-1</doi>
    <publisherRecordId>AJAMS20221011</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">Application of Soft Sets to Assessment Processes</title>
    <authors>
      <author>
        <name>Michael Gr. Voskoglou</name>
        <email>mvoskoglou@gmail.com</email>
        <affiliationId>1</affiliationId>
      </author>
    </authors>
    <affiliationsList>
      <affiliationName affiliationId="1">Department of Mathematical Sciences, Graduate Technological Educational Institute of Western Greece, Patras, Greece</affiliationName>
    </affiliationsList>
    <abstract language="eng">From the time that Zadeh introduced the concept of fuzzy set in 1965 a lot of research has been carried out for generalizing and extending the corresponding theory on the purpose of tackling more effectively the existing in real life uncertainty. One such generalization is the concept of soft set aiming, among others, to overcome the existing difficulty of defining properly the membership function of a fuzzy set. A new model using soft sets is presented in this paper for assessing human-machine performance in a parametric manner and examples are given to illustrate its applicability in practice. Such kind of models are very useful when the assessment has qualitative rather than quantitative characteristics.</abstract>
    <fullTextUrl format="pdf">http://pubs.sciepub.com/ajams/10/1/1/ajams-10-1-1.pdf</fullTextUrl>
    <keywords language="eng">
      <keyword>fuzzy sets</keyword>
      <keyword>soft sets</keyword>
      <keyword>fuzzy assessment methods</keyword>
    </keywords>
  </record>
</records>