2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 1 , PP: 277-292, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Kamalanathan Sundararajan 1 , Prasanna Santhanam 2 *
Doi: https://doi.org/10.54216/FPA.210120
Nowadays, virtual reality (VR) and immersive environments are research fields used in various educational and scientific areas. Immersive digital media desires new techniques for its immersive and interactive features it implies the model of new relationships and narratives with users. VR and technologies related to the virtuality sequence, like digital and immersive environments, are developing media. 3D environments generated with VR compatibility can be skilled from a stereoscopic and egocentric view that outperforms the immersion of the ‘classical’ screen-based view of 3D gamed virtual environments. Recent video games have complete, interactive scenes generated with innovative modeling and animation software and provided with hardware speeded-up graphics and physics. Their communication takes place with body-based sensing and commodity 3D motion controllers, like and in certain ways more progressive, than those discovered in conventional VEs do. Currently, artificial intelligence-based deep learning (DL) methods have been progressively applied to identify and assess user immersion levels in VR environments. In this paper, we present an Advanced Immersion Level Prediction Using Ensemble Classification Model and Metaheuristic Optimization Algorithm (ILPECM-MOA) in 3D Games Virtual Environments. This paper aims to develop a predictive model for assessing advanced immersion levels in 3D game virtual environments using behavioral and contextual data. At the primary stage, the data pre-processing stage uses Z-score normalization to transform input data into a beneficial pattern. Followed by, the presented ILPECM-MOA method designs ensemble models such as the temporal convolutional network (TCN) model, sparse denoising autoencoder (SDAE) method, and stacked long short-term memory (SLSTM) technique for the classification process. At last, the Hybrid ebola and Bald Eagle search optimization (HEBEO) approach fine-tunes the hyperparameter values of ensemble methods and results in the superior performance of classification. The effectiveness of the ILPECM-MOA model has been validated by the detailed studies utilizing the benchmark dataset. The mathematical outcome indicates that the ILPECM-MOA approach has improved performance and scalability in terms of various measures over the recent methods.
Immersion Level Prediction , 3D Games , Virtual Environments , Ensemble Classification Model , Metaheuristic Optimization Algorithm
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