Adaptive dense bidirectional-based recurrent neural network with exponential soft ratio loss function for analyzing the virtual reality experiences using AI-based deep features
Virtual reality (VR) systems are highly employed in applications such as immersive training, virtual education, interactive gaming, and healthcare simulations, where precise user experience validation and interaction are crucial. Nevertheless, validating VR experiences remains complex due to the intricate temporal dynamics of user interactions and the high-dimensional sensory data involved. To address these issues, this work proposes an artificial intelligence (AI)-based model for analyzing VR experiences, focusing on estimating user engagement in virtual environments. Initially, essential data is collected from the available online sources. Further, the collected data is subjected to a Sparse Multi-Head Attention-based Residual Autoencoder (SMA-RAE) for extracting the deep features. The extracted features are given as input to the Adaptive Dense Bidirectional Recurrent Neural Network with Exponential Soft Ratio Loss Function (ADBi-RNN-ESRLF) for analyzing the VR experience. Here, a novel algorithm named Renewed Foraging Process of Addax Optimization (RFPAO) is introduced for optimizing the parameters of the Dense Bidirectional Recurrent NeuralNetwork (DBi-RNN) model. Moreover, the result validations are performed, showing that the suggested model outperforms existing networks.