In the current article, we establish a distinct version of the operators defined by Berwal et al., which is the Kantorovich type modification of \(\alpha \) -Bernstein operators to approximate Lebesgue’s integrable functions. We define a modified operator preserving linear functions and analyze its characteristics. Furthermore, we extend the practical utility of these operators to signal processing applications, where we evaluate their performance on real-world signals including electrocardiogram data (ECG), financial time series, and audio waveforms. The numerical experiments demonstrate the operators’ effectiveness in approximating signals with varying smoothness characteristics while preserving essential features. Additionally, we construct the bivariate of blending type operators by Berwal et al. We analyze both its convergence and error of approximation properties by using the conventional tools of approximation theory. Finally, we demonstrate our results by presenting examples that highlight graphical visuals using MATLAB.
