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Guest Editorial: Neutrosophic decision making and applications in knowledge management
Author(s) -
Garg Harish
Publication year - 2020
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2020.0096
Subject(s) - sort , indeterminacy (philosophy) , computer science , set (abstract data type) , fuzzy set , cognition , management science , domain (mathematical analysis) , fuzzy logic , knowledge management , artificial intelligence , mathematics , epistemology , engineering , psychology , information retrieval , mathematical analysis , philosophy , neuroscience , programming language
• Artificial Intelligence and KM • Neutrosophic Decision Making for KM • Enterprise Knowledge Computing and Evaluation • Neutrosophic Decision Models for Knowledge Sharing • Neutrosophic Economic Decisions with Knowledge-Based Systems • Neutrosophic Decision Models for Learning • Group Decision Making and Applications in KM • Neutrosophic Decision Models for Knowledge Applications • Neutrosophic Decision Models for Knowledge Creation • Neutrosophic Knowledge Management Systems • Multiple Criteria Decision Making in KM • Multiple Criteria Decision Making in Information Technology • Neutrosophic Graph theory for KM Due to the complexity of the current socio-economic environment, decision making is one of the most notable ventures, whose mission is to decide the best alternative under the numerous known or unknown criteria. In cognition of things, people may not possess a precise or sufficient level of knowledge of the problem domain and hence they usually have face uncertainties in their preferences over the objects. This will make the performance of the cognitive in terms of three-ways model namely acceptation, rejection, indeterminacy, which is neutrosophic set, an extension of the fuzzy set theory. In recent years, human beings inescapably are met with numerous decision-making problems, which affect multiple fields; however, few of them fall within the criteria of Knowledge Management (KM).

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