Human Computer Interaction in Saas Medium Using Non-Linear Radial Deep Belief Networks

Abstract

In recent years, Human-Computer Interaction (HCI) within Software-as-a-Service (SaaS) platforms has become increasingly complex, driven by the need for adaptive and intelligent user interfaces. Traditional models often fall short in capturing the non-linearities and complex patterns inherent in user interactions.The problem addressed in this research is the inadequacy of linear models and conventional deep learning methods in accurately predicting and adapting to the dynamic nature of user interactions within SaaS platforms. This limitation results in suboptimal user experiences, reducing overall engagement and efficiency.The methodology involved implementing a non-linear RDBN model, which was trained on a large dataset of user interaction logs from various SaaS applications. The RDBN was configured with multiple layers, each employing radial basis functions to capture the intricate non-linear relationships between input features. The model's performance was evaluated against traditional deep belief networks and other machine learning models.The results demonstrate a significant improvement in predictive accuracy and user experience optimization. The RDBN model achieved an accuracy of 92.7% in predicting user actions, compared to 85.3% for traditional deep belief networks and 78.6% for conventional linear models. Additionally, user engagement metrics improved by 15% on average, indicating a more intuitive and responsive interface.