Design of Immersive VR Tourism Analysis Model Based on Fuzzy Logic Algorithm

Abstract

Immersive virtual reality (VR) is a technology that transports users into fully immersive digital environments, often through the use of specialized headsets and sensory equipment. A three-dimensional environment, immersive VR offers users an unparalleled sense of presence and interaction, enabling them to explore and interact with virtual worlds as if they were physically present. This paper develops an effective immersive Virtual Reality (VR) model for tourism. The proposed model uses the fuzzy-based logic rules for tourism in VR for the classification with the Backpropagation Feedforward Neural Network (BFNN). Through the developed model efficacy of BFNN-based algorithms in accurately classifying diverse virtual environments and detecting edges with precision. The analysis of the results stated that the through BFNN model MSE and PSNR value is achieved with the value of 0.007 and 28.9 respectively. With the developed model significant classification is achieved with the BFNN model value for the exploration, cultural heritage, adventure, Urban exploration, and Relaxation.  Additionally, comparative analyses demonstrate the superiority of BFNNs over alternative classification models, underscoring their effectiveness in accurately categorizing immersive tourism experiences. These findings stated that the advancement of immersive VR technology also offers practical insights for optimizing computational algorithms in immersive tourism applications. The potential of BFNNs redefine the landscape of immersive VR tourism, delivering captivating and personalized virtual experiences that elevate user engagement and satisfaction to unprecedented levels.