A Schematic View of Plithogenic Cubic Sets in Pattern Recognition using Jaccard Similarity Measure

Abstract

Plithogenic cubic sets extend the cubic set framework into new mathematical territory that enables superior handling of decision-making and pattern recognition when dealing with uncertainty. Through Plithogenic logic researchers could develop advanced methods to represent complex patterns by supporting inconsistent and self-contradictory or combined features. This research paper explores the schematic explanation of Plithogenic cubic sets and their use in pattern recognition applications. The strong nature of pattern recognition emerge from Plithogenic logic's dimensionality flexibility when combined with a two-member structure in cubic sets while resolving imprecision and vagueness and the complexity of information aspects. Through theoretical foundations and illustrative examples the author presented their capability to employ Plithogenic cubic sets successfully in pattern recognition and classification applications. The schematic method delivers a complete grasp of Plithogenic cubic sets as an enabling instrument for pattern recognition research.