Fermatean fuzzy sets and its extensions: a systematic literature review

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Artificial Intelligence Review, vol.57, no.6, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 57 Issue: 6
  • Publication Date: 2024
  • Doi Number: 10.1007/s10462-024-10761-y
  • Journal Name: Artificial Intelligence Review
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Educational research abstracts (ERA), Index Islamicus, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA), Metadex, Psycinfo, zbMATH, Civil Engineering Abstracts
  • Keywords: Aggregation Operators, Decision-making, Fermatean fuzzy set, Fuzzy sets, Literature review, SPAR-4-SLR
  • Galatasaray University Affiliated: Yes


The Fermatean Fuzzy Set (FFS) theory emerges as a crucial and prevalent tool in addressing uncertainty across diverse domains. Despite its recognized utility in managing ambiguous information, recent research lacks a comprehensive analysis of key FFS areas, applications, research gaps, and outcomes. This study, conducted through the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol, delves into an exploration of the FFS literature, reviewing 135 relevant articles. The documents are meticulously analyzed based on their integrated methodologies, Aggregation Operators (AOs), linguistic sets, and extensions. Additionally, a thematic analysis, facilitated by the Bibliometrix tool, is presented to provide nuanced insights into future research directions and crucial areas within the literature. The study unveils valuable findings, including the integration of linguistic variables with interval-valued FFS, fostering robust environments for dynamic decision-making—a mere glimpse of the potential directions for future research. The gaps and future directions section further articulates recommendations, offering a structured foundation for researchers to enhance their understanding of FFS and chart future studies confidently.