Interval Neutrosophic Cubic Bézier Curve Approximation Model for Complex Data

Abstract

Complex data is defined as data that has the qualities of huge data, a lack of data information, and uncertainty. This paper discussed constructing the interval neutrosophic cubic Bézier curve (INCBC) approximation model for complex data. To construct the interval neutrosophic data point (INDP) based on the definition of interval neutrosophic set (INS), interval neutrosophic relation (INR) and interval neutrosophic point (INP). Next is the introduction of an interval neutrosophic control point (INCP) that blends with the theory of interval neutrosophic set and the Bernstein basis function. Later, the interval neutrosophic cubic Bézier curve (INCBC) model is visualizing with a four-by-four control points relation approximates the curves for truth, false, and indeterminacy membership. At the end of this paper will demonstrate the algorithm for the creation of the interval neutrosophic cubic Bézier curve (INCBC). The scientific value of this work is the acceptance of complex uncertainty data. As a result, due to the fact it combines fuzzy geometric modelling, this approach has the potential to make a significant contribution to complex uncertainty modelling by using this spline which is Bézier curve model.