SAED: Self-adaptive Error Detection Automation for Leveraging Computational Efficiency of HCI Systems

Abstract

The human–computer interaction (HCI) system is one of the state-of-art consolidations of intellectual psychology and computer automation technology in the recent era. An organized HCI focuses on designing intelligent and competent processing systems for interaction through human–reciprocation. Processing efficiency and computational precision of intelligent systems unambiguously rely on user interfaces. The improper access to user interfaces results in error-prone responses and hence the computation increases in account of better accuracy. In this manuscript, the self-adaptive error detection (SAED) automation method is introduced for leveraging the computation precision of HCI systems. SAED harmonizes a bi-linear learning process based on Bayes classification subsided by a cumulative distribution function (CDF). CDF defines the maximum error unleashing probability of the inputs to recognize certain computations. Bayes classification regresses the decisive computation as a reference to classify inputs by detaining errors to provide more relevant suggestions as a part of automation. SAED results in non-trivial input processing to enhance the choice of HCI integrated into intelligent computing systems. This method focuses on improving the prediction of inputs by reducing error, computation time, and memory overloading. The errors and memory overloading due to improper and partially classified inputs and convergence are thwarted using this method. The specular conduct of SAED is gauged using experimental modeling and error detection rate, predicted inputs, and distribution function metrics are analyzed. The proposed method is found to achieve 13.61% less completion time, 5.63% less error, and 19.29% less memory consumption, respectively.