Design Analysis of Human-Computer Interaction and Information Communication in Artificial Intelligence Environments

Abstract

This study delves into Human-Computer Interaction (HCI), a multidisciplinary arena exploring the interface between humans and technology in designing computing systems. Drawing from interactional communication theories that posit communication as a reciprocal process involving message exchanges in specific sociocultural contexts, recent advancements in Artificial Intelligence (AI) have significantly advanced these interactions. However, inefficient interfaces remain a prominent challenge within HCI. To address this, the research introduces the Augmented Scalar Computation Algorithm (ASCA), aimed at enhancing HCI efficiency. The methodology encompasses collecting and preprocessing a dataset, employing Principal Component Analysis (PCA) for feature extraction, and utilizing a Genetic Algorithm (GA) in feature selection to refine ASCA. The efficacy of the proposed ASCA is rigorously assessed, demonstrating its superiority over conventional algorithms through comparative analysis.