An Implementation of Intuitionistic Fuzzy Soft Sets Similarity Measure centered on Speech Emotion Recognition Distance

Abstract

Accurately representing and analyzing large data sets is becoming more and more important in domains like as economics, pattern identification, medical diagnosis, and stock market analysis. As digital technology has spread, digitalized patterns and images have proliferated. These patterns are multidimensional, containing properties that are both physical and non-physical. A strong fuzzy-based model is required in order to represent and interpret this data efficiently. The Complex Vague Soft Set (CVSS) model is presented in this study. It is intended to concisely represent the multi-dimensional information included in digital photographs. In order to tackle pattern identification problems in digital images, this defines information measures for CVSSs, mainly concentrating on distance and similarity measurements. The study examines the connections between this similarity measure and the associated distance and provides an axiomatic definition of a distance-based similarity measure for CVSSs. Within the scope of CVSS, these linkages are both proposed and validated. This approachs usefulness is illustrated by applying it to a pattern recognition task, in which digital photographs are examined using multi-dimensional data, such as physical characteristics and extra metadata like timestamps and locations. This study demonstrates how well the CVSS model handles complicated data, making it a potent tool for raising the accuracy of pattern recognition in a range of applications.