Distance measure and its application to decision making, medical diagnosis, and pattern recognition problems under complex picture fuzzy sets.

The complex fuzzy sets, complex intuitionistic fuzzy sets, complex pythagorean fuzzy sets, and complex fermatean fuzzy sets cannot handle the neutrality grade of an object that plays an important role in medical diagnosis, network analysis, signal processing, human voting, and personnel selection problems. Therefore, in this article, we introduced the notion of complex picture fuzzy sets and investigated their basic properties. The complex picture fuzzy sets are effective and powerful tools for many ambiguous and uncertain problems. Distance measures play an essential role in establishing a useful link from the expression of imprecise and uncertain information to various real life applications, including machine learning, market predictions, decision-making processes, clustering, and pattern recognition. Therefore, in this study, we developed some measures of distances under complex picture fuzzy environments. Finally, we developed an algorithm using complex picture fuzzy environments and applied it to a practical application concerning decision making, medical diagnosis, and pattern recognition problems. [ABSTRACT FROM AUTHOR]