Enhancing Algorithmic Comprehension of Human Behavior in Daily Life: A Multi-Modal AI Framework

In the era of pervasive computing, algorithms increasingly interact with humans in daily contexts, from recommendation systems to smart assistants. This paper introduces a novel multi-modal framework designed to elevate algorithmic understanding of human behavior beyond superficial pattern recognition to a deeper, context-aware comprehension. By integrating computer vision, natural language processing (NLP), and sensor data fusion, our approach models human routines, emotions, and social dynamics in everyday scenarios such as commuting, shopping, and social interactions. We evaluate the framework on a dataset of ۵,۰۰۰ annotated daily life sequences, achieving a ۲۵% improvement in behavior prediction accuracy over baseline models. Implications for ethical AI deployment and future research directions are discussed. This work contributes to the field of human-AI interaction by bridging the gap between algorithmic efficiency and empathetic understanding.

کلیدواژه ها:

Human Behavior Modeling ، Multi-Modal AI ، Daily Life Algorithms ، Context-Aware Computing ، Ethical AI