Investing Novel Interaction Techniques Using DeepNets To Improve User Engagement and Usability In Human-Computer Interfaces

Human-computer interfaces (HCIs) play a critical role in facilitating user engagement and usability in various domains. Traditional interaction techniques have been widely used, but they often have limitations in terms of user experience and overall effectiveness. In recent years, deep neural networks (DeepNets) have emerged as a powerful tool for modeling complex patterns and relationships in data. This paper presents a novel framework that leverages deep neural networks to develop interaction techniques that enhance user engagement and usability in HCIs. The proposed framework enables real-time adaptation of the interface based on user behavior and preferences, resulting in a more personalized and context-aware user experience. To evaluate the effectiveness of our proposed framework, we conducted a user study comparing the novel interaction techniques with traditional approaches. The results demonstrate that the DeepNet-based interaction techniques significantly improve user engagement and usability metrics, including task completion time, error rate, and user satisfaction. Moreover, qualitative feedback from participants indicates a higher level of immersion, enjoyment, and ease of use with the DeepNet-based techniques. The experimental results demonstrate the effectiveness of the framework and highlight the potential for further advancements in HCI design using deep learning techniques.