Neutrosophic Bézier Curve Model for Uncertainty Problem Using Approximation Approach

Abstract

The problem of gathering data with uncertainty is difficult to address since certain values are eliminated owing to noise. Thus, the fundamental gap revealed is that fuzzy and intuitionistic fuzzy sets cannot deal with indeterminacy problems as compared to neutrosophic sets. This research demonstrates how to use a neutrosophic set to approximate the Bézier curve. The neutrosophic set and its qualities are used to identify the neutrosophic control point relation in the first stage. The control point and the Bernstein basis function are then combined to form a neutrosophic Bézier. The curve is then depicted using an approximation method involving truth membership, false membership, and indeterminacy membership curves. A numerical example and an algorithm for obtaining the neutrosophic Bézier curve are provided at the end of this work. As a result, this research can help data analysts acquire data without wasting any uncertain information data. Besides, this study can make a significant contribution to the scope of computational mathematics and modeling.