An accurate assessment of the spatial and temporal radio frequency channel characteristics is essential for complex signal processing and cellular network optimization. Current research has employed numerous models to figure out how much signal propagation loss occurs along the propagation paths. However, there are issues in finding the right model for a particular terrain because these models are not universally applicable. By employing the lognormal function and the Maximum Likelihood model, a hybrid probabilistic statistical distribution model was evolved. Three LTE cell site locations in Port Harcourt, Nigeria, were used to create a hybrid model that describes the functional stochastic signal propagation loss in the area. The evaluated Maximum Likelihood model accurately estimates the relevant wireless channel properties based on observed field data. The minor square regression approach and the proposed hybrid parameter estimation methodology are compared. When it comes to estimating standard deviation errors as well as the root mean square errors, the ML‐based approach consistently outperforms the least square regression model. Finally, the proposed hybrid probabilistic statistical distribution model would be useful for mobile broadband network planning in related wireless propagation conditions.

The recent research is developing in a vast speed to develop the cloud orchestration system. In cloud system the remotely managed servers are storing, finding, removing, replacing and retrieving the various services in an adaptive optimized manner. The lot of services are provided by the vast number of providers in the market with the help of approximation theory by the rough set system (RST). RST finds in helping in getting the efficient cloud resources as a service to the users. The proposed OCRS (Optimized Cost Resource System) approach is being simulated and compared with the existing cloud simulator. The simulator gives the approximate results in many parameters of cloud services. In all aspects our algorithm is performing better.

Immersive learning using Virtual Reality (VR) has gained prominence for delivering experiential, engaging education. However, most VR learning environments lack real-time adaptability, personalization, and cognitive responsiveness. This study presents an Agentic AI-enabled VR framework that autonomously adjusts pedagogical content, interaction style, and challenge level based on learner behavior, emotions, and performance feedback. The proposed system integrates a reinforcement learning-based agent with a virtual reality module to form an intelligent tutor capable of independent decision-making. A neuro-symbolic model processes multi-modal feedback (gesture, speech, gaze, performance) to determine context-aware pedagogical strategies. The system employs a self-evolving curriculum logic that adapts in real time. Experiments are conducted using Unity3D integrated with Python-based RL agents and simulated student models. Results demonstrate a 35.7% improvement in learner retention and a 42.1% reduction in cognitive overload compared to traditional static VR systems. The agent successfully personalized 92.3% of scenarios without human intervention. Emotional adaptivity and dynamic pacing showed increased engagement and reduced frustration metrics among diverse learners. Agentic VR represents a paradigm shift in intelligent education systems, enabling autonomous, emotionally-aware, and responsive learning environments. The proposed framework outperforms conventional VR platforms by offering real-time adaptive learning without pre-scripted logic. Integrating reinforcement learning, neuro-symbolic reasoning, and affective feedback into a single VR space results in a novel contribution to adaptive educational technology. The reported metrics were obtained from an administrator-controlled user study of 10 participants (aged 18 - 25 years), all of whom participated in three VR-based learning experiences as described in a controlled evaluative protocol. This study provides a foundation for future work on autonomous agents in educational met verses, special education, and lifelong learning systems.

Real-time threat detection is crucial in domains like cybersecurity, surveillance, and public safety in general, where quick and effective detection of risks is critical. Current detection approaches suffer from limitations such as high latency, in-processing multi-modal data efficiently, and learning to adapt to evolvement of threats. Therefore, this paper proposes a new algorithm called Adaptive Multi-Modal Neural Network, or simply AMM-Net for a novel neural network algorithm for overcoming the above-listed limitations. AMM-Net incorporates the use of CNNs, RNNs, and transformers in fusing visual, sequential, and textual data streams. Its use of a dynamic attention-based fusion layer allows it to dynamically focus on the most relevant features across modalities for an efficient detection in a myriad and complex scenario. Techniques that aid in real-time deployment through the use of model pruning, quantization, and edge computing will further make it deployable to resource-constrained environments.