Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model

Virtual Reality (VR) is a technology that immerses users in simulated, computer-generated environment. It creates sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers the communication interaction between humans machines. Optimization plays crucial role enhance overall user system performance. This paper proposed an architecture Hidden Markov Model Grey Relational Analysis (GRA) integrated Salp Swarm Algorithm (SSA) for automated Interaction. The stated as model (HMM_ GRSS). HMM_GRSS estimates feature vector variables reality platform compute spaces. aims estimate within VR Models are used temporal behavior dynamics system, predictions understanding interactions. employed evaluate relationship relevance variables, aiding selection optimization. SSA helps optimize spaces by simulating collective salp swarms, improving efficiency effectiveness HMI system. process platform, improved Simulation analysis provides significant outcome estimation