Advancements in mobile AI: A machine learning-driven approach to enhance user experience and functionality

Abstract

Artificial intelligence (AI) and mobile technology have synergized to achieve remarkable advancements across various sectors, transforming user interactions and significantly boosting mobile device performance. This study examines how integrating machine learning (ML) techniques into mobile applications enhances user satisfaction, productivity, and security. By deploying predictive models and real-time analysis directly on mobile devices, our approach reduces latency and personalizes experiences to better adapt to user behavior. Key findings reveal that the Hybrid CNN-LSTM model achieves superior accuracy (93.8%), precision (92.1%), and F1-score (91.5%) compared to standalone CNN or LSTM models, with a manageable latency of 140 ms, making it optimal for tasks requiring both image and sequential data processing. Additionally, applying optimization techniques like knowledge distillation reduces model size by 40% and latency by 25%, enhancing device efficiency without compromising performance. This study confirms that mobile-based AI equipped with advanced, autonomous decision-making capabilities enhances user-centric services and application responsiveness. Through experimental evaluations, this paper underscores the transformative impact of ML on mobile technology and proposes strategies to further integrate AI into the mobile ecosystem.