Pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets

Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures.