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• W3046024303 startingPage "2797" @default.
• W3046024303 abstract "An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks." @default.
• W3046024303 created "2020-08-03" @default.
• W3046024303 creator A5001270995 @default.
• W3046024303 creator A5009981956 @default.
• W3046024303 creator A5064149356 @default.
• W3046024303 date "2020-10-07" @default.
• W3046024303 modified "2023-09-26" @default.
• W3046024303 title "Certain models of granular computing based on rough fuzzy approximations" @default.
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• W3046024303 doi "https://doi.org/10.3233/jifs-191165" @default.
• W3046024303 hasPublicationYear "2020" @default.
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