Krishnamoorthy Ranganathan

The exponential growth of high-dimensional data across domains such as healthcare, finance, and industrial automation necessitates robust models capable of both accurate predictions and explainable decision-making. Traditional machine learning models, including Support Vector Machines (SVM), Random Forests, and ensemble techniques, often struggle to generalize effectively when feature spaces become large and noisy. Deep learning models such as CNNs, LSTMs, and Transformers, while powerful, typically operate as black boxes, providing limited interpretability and weak uncertainty quantification. To address these challenges, this study introduces the MetaXAI Adaptive Learning Model, a meta-learning-based explainable artificial intelligence framework designed for complex algorithmic decision-making in high-dimensional environments. MetaXAI integrates autoencoder-based dimensionality reduction, attention-driven embedding, and a Model-Agnostic Meta-Learning (MAML) engine with Gradient Episodic Memory (GEM) for task adaptation. Furthermore, it combines SHAP, LIME, and counterfactual explanation modules to enhance interpretability. Results demonstrate that MetaXAI achieves an accuracy of 98.91%, outperforming traditional models such as SVM (91.3%), XGBoost (93.8%), and Transformer (94.8%). It also exhibits superior explainability (fidelity 98.2%) and uncertainty calibration (Expected Calibration Error 0.9%). Federated deployment further ensures privacy preservation with minimal communication overhead. The findings suggest that MetaXAI bridges the gap between predictive performance and interpretability, making it suitable for critical domains where both accuracy and transparency are paramount.

The advancement of sixth‐generation (6G) wireless communication demands intelligent, adaptive solutions to meet the increasing requirements of ultra‐reliable low‐latency communication (URLLC), massive connectivity, and terahertz (THz) data rates. Intelligent Reflecting Metasurfaces (IRMs) have emerged as a revolutionary paradigm that reconfigures the wireless propagation environment by intelligently controlling the behavior of electromagnetic waves. However, traditional beamforming methods face significant challenges in dynamically adapting to complex, time‐varying wireless environments, especially in the presence of high user mobility and signal obstructions. These limitations hinder the efficiency of signal directionality and energy focusing, resulting in sub‐optimal communication performance. To overcome these issues, a novel framework, the Model‐Level Multi‐Scale Edge‐Guided Attention graph neural Network (M‐LMSedGAN), is introduced, enabling efficient learning from complex spatial relationships and heterogeneous signal interactions. Optimization of the reflective phase shifts is achieved using the Biruni Earth Radius Optimization (BERO) algorithm, which enhances the global search capability and convergence efficiency of the system. The proposed method enables real‐time and context‐aware beamforming adjustments, achieving superior signal quality and improved network performance. Experimental evaluations demonstrate a peak beamforming accuracy of 99.9%, indicating robust generalization across dynamic environments and diverse user distributions. This intelligent approach establishes a foundation for deploying energy‐efficient, high‐performance IRM‐assisted 6G communication infrastructures.

The evolution of 6G wireless networks demands highly efficient beamforming strategies to optimize spectral and energy efficiency in massive MIMO systems. This study introduces a Quantum-Driven Reinforcement Learning (QDRL) framework for Spectral Energy Optimization in Massive MIMO Hybrid Beamforming for 6G, leveraging Quantum Deep Q-Networks (Q-DQN), Quantum Policy Gradient (QPG), and Quantum Approximate Optimization Algorithm (QAOA). The framework integrates mruby-based lightweight scripting for efficient deployment in edge-AI environments, enhancing computational flexibility and resource efficiency. Performance evaluations demonstrate that the Hybrid Quantum Model achieves 11.21 bps/Hz spectral efficiency, 97% resource utilization efficiency, and reduces energy consumption to 0.50 Joules/bit, outperforming classical models. The Bit Error Rate (BER) is minimized to 0.0025, and the convergence time is 48.7 s, significantly improving computational efficiency. Comparative analysis with conventional Deep Reinforcement Learning (DRL) techniques shows that the proposed quantum-enhanced model provides a 32% improvement in energy efficiency and a 21% reduction in computational complexity. The integration of mruby enhances the adaptability of the system in low-power and embedded environments, making it a viable solution for real-time 6G hybrid beamforming. This research highlights the transformative potential of quantum-assisted AI frameworks for scalable, high-speed, and energy-efficient wireless communication.