The integration of High-Altitude Platform Stations (HAPS) with Reconfigurable Intelligent Surfaces (RIS) represents a critical advancement for next-generation wireless networks, offering unprecedented opportunities for ubiquitous connectivity. However, existing research reveals significant gaps in dynamic resource allocation, joint optimization, and equitable service provisioning under varying channel conditions, limiting practical deployment of these technologies. This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning (FAIR-DQL) framework for joint resource management and phase configuration in HAPS-RIS systems. Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control, priority-based user scheduling, and dynamic learning mechanisms. The FAIR-DQL approach utilizes advanced reinforcement learning with experience replay and fairness-aware reward functions to balance competing objectives while adapting to dynamic environments. Key findings demonstrate substantial improvements: 9.15 dB SINR gain, 12.5 bps/Hz capacity, 78% power efficiency, and 0.82 fairness index. The framework achieves rapid 40-episode convergence with consistent delay performance. These contributions establish new benchmarks for fairness-aware resource allocation in aerial communications, enabling practical HAPS-RIS deployments in rural connectivity, emergency communications, and urban networks.

Lung cancer accounts for the highest number of cancer deaths globally, underscoring the urgent need for early and precise detection to enhance patient outcomes. While deep learning has made remarkable strides in analyzing medical images, current approaches face a fundamental challenge. They cannot adequately capture detailed local patterns and broader contextual relationships within lung Computed tomography (CT) scans. To address this limitation, we introduce AMVT-NMN (adaptive multi-scale vision transformer with neuromorphic memory networks), which combines three complementary mechanisms. The dynamic adaptive kernel networks component intelligently adjusts receptive field sizes based on input characteristics, enabling flexible feature capture across multiple scales. The neuromorphic contextual memory attention module draws inspiration from how human memory systems process information, maintaining a dynamic record of diagnostically relevant patterns to inform current predictions. The hierarchical cross-scale fusion mechanism with learnable weights synthesizes information from different resolution levels through adaptive weighting. Testing on the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTHNCCD) dataset demonstrates strong performance: 97.9% accuracy, 96.5% sensitivity, 98.7% specificity, and 99.2% Area under the Curve-Receiver Operating Characteristic (AUC-ROC). These results surpass existing methods such as CNN-GD, which achieved 97.2% accuracy. Notably, the high specificity translates to fewer false alarms, potentially reducing unnecessary biopsies and follow-up imaging outcomes that matter considerably in clinical practice. Result of AMVT-NMN generalization to the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), Lung nodule analysis (LUNA16), and Non-Small Cell Lung Cancer (NSCLC)-Radiomics datasets showed AUCs of 96.5%, 92.8%, and 97.2%, respectively. Ablation experiments confirm that each architectural element of AMVT-NMN contributes meaningfully to overall performance. Five-fold cross-validation yielded consistent results (97.71 ± 0.57%), indicating reliable performance across different patient subsets. The memory-augmented design shows particular promise for handling diagnostically ambiguous cases. It is focused on pattern recognition and computational intelligence, which is useful for coping with uncertain information in intelligent diagnosis systems, meeting the growing trend for trusted artificial intelligence (AI) in decision-making.