Optimized virtual reality design through user immersion level detection with novel feature fusion and explainable artificial intelligence

• DOI: 10.7717/peerj-cs.2150
• Publication Date: 2024-07-19T08:10:41Z
• Authors: Ali Raza; Amjad Rehman; Rukhshanda Sehar; Faten S. Alamri; Sarah Alotaibi; Bayan Al Ghofaily; Tanzila Saba
• Keywords: Immersion; Benchmark (surveying); Hyperparameter

Virtual reality (VR) and immersive technology have emerged as powerful tools with numerous applications. VR creates a computer-generated simulation that immerses users in virtual environment, providing highly realistic interactive experience. This finds applications various fields, including gaming, healthcare, education, architecture, training simulations. Understanding user immersion levels is crucial challenging for optimizing the design of Immersion refers to extent which feel absorbed engrossed environment. research primarily aims detect using an efficient machine-learning model. We utilized benchmark dataset based on experiences environments conduct our experiments. Advanced deep machine learning approaches are applied comparison. proposed novel technique called Polynomial Random Forest (PRF) feature generation mechanisms. The PRF approach extracts polynomial class prediction probability features generate new set. Extensive experiments show random forest outperformed state-of-the-art approaches, achieving high level detection rate 98%, technique. hyperparameter optimization cross-validation validate performance scores. Additionally, we explainable artificial intelligence (XAI) interpret reasoning behind decisions made by model VR. Our has potential revolutionize VR, enhancing process.