AI-Driven context classification in mobile computing: methodologies and technologies for enhanced user experience
Hichem Chaalal, Mohammed Amin Chemrak, Fouad Khatemi · STUDIES IN ENGINEERING AND EXACT SCIENCES · 2024
This research explores the incorporation of sophisticated artificial intelligence (AI) methodologies, with a specific focus on deep learning approaches like Convolutional Neural Networks (CNNs), to improve context-aware computing in mobile settings. The proliferation of mobile devices in daily life has led to the generation of substantial amounts of contextual information, facilitated by integrated sensors like accelerometers, GPS, and cameras. The capacity to effectively categorize and react to this data in real time is essential for enhancing user experiences that are both personalized and responsive. Traditional methods of context classification often fall short in handling the complexities of dynamic and heterogeneous mobile data. This research explores how AI, specifically CNNs, can address these limitations by analyzing multi-source sensor inputs and providing robust, real-time context classification. The study further examines the implementation of AI-driven context-aware systems across various applications, including healthcare and transportation, demonstrating their potential to improve user experience and safety significantly. it brings to light the difficulties associated with ensuring data privacy and security, along with the imperative for effective data processing solutions in mobile settings that face resource constraints. By focusing on the development and application of AI methodologies for context classification, this research offers insights into overcoming the challenges associated with adaptive mobile computing systems, ultimately contributing to the advancement of more intelligent and efficient context-aware technologies.