The uncertainty and vagueness of datasets associated with real-world problems render them complex and unpredictable.Problems such as pattern recognition and image processing require advanced mathematical tools to capture the maximum uncertainty, and conventional measures are typically inadequate in achieving this goal.Associating information measures with a fuzzy environment can resolve the issue of vagueness and precisely quantify data.In this study, a novel picture fuzzy divergence measure is proposed.The proposed study discusses information measures for picture fuzzy sets and illustrates important properties of the proposed measures.The effectiveness of the proposed measure over existing measures is demonstrated in the image-processing and pattern-recognition problems discussed.Comparative analysis with existing information measures highlights the constructive performance of the proposed measure.This study contributes to the advancement of fuzzy divergence theory and opens new avenues for uncertainty modeling in various domains.In general, this study contributes to the understanding of the significant role of fuzzy environments in managing imprecise and vague patterns.
Sharma et al. (Tue,) studied this question.