Novel a-divergence measures on picture fuzzy sets and interval-valued picture fuzzy sets with diverse applications

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dc.authoridLiu, Zhe/0000-0002-8580-9655
dc.authoridZhu, Sijia/0009-0006-3694-6473
dc.contributor.authorZhu, Sijia
dc.contributor.authorLiu, Zhe
dc.contributor.authorUlutagay, Gozde
dc.contributor.authorDeveci, Muhammet
dc.contributor.authorPamucar, Dragan
dc.date.accessioned2024-08-31T07:50:03Z
dc.date.available2024-08-31T07:50:03Z
dc.date.issued2024
dc.departmentEge Üniversitesien_US
dc.description.abstractCurrently, many studies have developed distance or divergence measures between intuitionistic fuzzy sets (IFSs) and interval-valued fuzzy sets (IvFSs). As a generalization of IFSs, picture fuzzy sets (PFSs) provide a more nuanced representation of uncertain and ambiguous information. Interval-valued picture fuzzy sets (IvPFSs) combine the concepts of IvIFSs and PFSs, providing a highly effective means of representing and processing uncertain, ambiguous and incomplete information. How to better measure the differences between PFSs and IvPFSs is still an open issue. This paper proposes some novel a-divergence measures for PFSs and IvPFSs, respectively. We demonstrate the basic properties of the proposed divergence measures, including non- negativity, non-degeneracy and symmetry. Besides, we analyze some special cases of the proposed divergence measures that degenerate into or are related to several well-known divergences. Then, we construct some numerical examples to demonstrate the effectiveness of the proposed measures concerning existing measures. Finally, the proposed a-divergence measures are applied to pattern recognition, multi-attribute decision-making (MADM) and clustering, demonstrating that these measures possess a high confidence level and can produce trustworthy results, especially in comparable situations.en_US
dc.identifier.doi10.1016/j.engappai.2024.109041
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85199938979en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2024.109041
dc.identifier.urihttps://hdl.handle.net/11454/105095
dc.identifier.volume136en_US
dc.identifier.wosWOS:001287677000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240831_Uen_US
dc.subjectPicture Fuzzy Setsen_US
dc.subjectInterval-Valued Picture Fuzzy Setsen_US
dc.subjectAlpha-Divergenceen_US
dc.subjectPattern Recognitionen_US
dc.subjectMulti-Attribute Decision-Makingen_US
dc.subjectClusteringen_US
dc.titleNovel a-divergence measures on picture fuzzy sets and interval-valued picture fuzzy sets with diverse applicationsen_US
dc.typeArticleen_US

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