The advent of a wide variety of affordable communication devices and cameras has enabled IoT systems to provide effective solutions for a wide range of civil and military applications. One of the potential applications is a surveillance system in which several cameras collaborate to monitor a specific area. However, existing surveillance systems are often based on traditional camera distribution and come with additional communication costs and redundancy in the detection range. Thus, we propose a smart and efficient camera distribution system based on machine learning using two Reinforcement Learning (RL) methods: Q-Learning and neural networks. Our proposed approach initially uses a geometric distributed network clustering algorithm that optimizes camera placement based on the camera Field of View (FoV). Then, to improve the camera distribution system, we integrate it with an RL technique, the role of which is to dynamically adjust the previous/existing setup to maximize target coverage while minimizing the number of cameras. The reinforcement agent modifies system parameters—such as the overlap distance between adjacent cameras, the camera FoV, and the number of deployed cameras—based on changing traffic distribution and conditions in the surveilled area. Simulation results confirm that the proposed camera distribution algorithm outperforms the existing methods when comparing the required number of cameras, network coverage percentage, and traffic coverage.

Intracranial Hemorrhage (ICH) remains a critical life-threatening condition where timely and accurate diagnosis using non-contrast Computed Tomography (CT) scans is vital to reduce mortality and long-term disability. Deep learning methods have shown strong potential for automated hemorrhage detection, yet most existing approaches lack confidence quantification and clinical interpretability, which limits their adoption in high-stakes care. This study presents X-HEM, an explainable hemorrhage ensemble model for reliable detection of Intracranial Hemorrhage (ICH) on non-contrast head CT scans. The aim is to improve diagnostic accuracy, interpretability, and confidence for real-time clinical decision support. X-HEM integrates three convolutional backbones (VGG16, ResNet50, DenseNet121) through soft voting. Bayesian uncertainty is estimated using Monte Carlo Dropout, while Grad-CAM++ and SHAP provide spatial and global interpretability. Training and validation were conducted on the RSNA ICH dataset, with external testing on CQ500. The model achieved AUCs of 0.96 (RSNA) and 0.94 (CQ500), demonstrated well-calibrated confidence (low Brier/ECE), and provided explanations that aligned with radiologist-marked regions. The integration of ensemble learning, Bayesian uncertainty, and dual explainability enables X-HEM to deliver confidence-aware, interpretable ICH predictions suitable for clinical use.

The prediction accuracy of RNA’s tertiary structure remains a core challenge in the field of computational biology. Existing models frequently encounter significant challenges due to the complexities of diverse topologies and the intricate nature of long-range interactions. We introduce RNAFoldDiff, a generative framework that integrates a sequence-aware graph transformer with a geometric diffusion process for end-to-end RNA 3D structure prediction. RNA sequences and secondary structures are converted into graph representations that capture backbone connectivity and base pair topology. The transformer models local motifs and global dependencies, while the diffusion module iteratively denoises coordinates into physically consistent conformations. The model was pretrained on more than 15,000 structural motifs from the RNA 3D Hub and fine-tuned on complete RNAs from the RNA-Puzzles dataset. In benchmarking tests, RNAFold-Diff achieved an average root mean square deviation (RMSD) of 2.64 Å, a Global Distance Test (GDT) score of 68.7%, and a base pair accuracy of 89.5%, reducing RMSD by nearly 30% and improving GDT by 9 points compared to RoseTTAFoldNA. The framework also outperformed FARFAR2, SimRNA, and RNAformer. Ablation experiments confirmed the contributions of diffusion refinement, edge-aware graph encoding, and motif-level pretraining, while qualitative analyses showed biologically plausible folds including helices, junctions, and multiloops. By combining topology-aware graph learning with generative diffusion, RNAFoldDiff advances RNA tertiary structure modeling and provides a practical tool for RNA design, ribozyme analysis, and structure-guided drug discovery.

Chest X-ray (CXR) imaging remains a widely used diagnostic modality for the detection of thoracic diseases, including pneumonia, tuberculosis, pleural effusion, lung cancer and cardiomegaly. Despite the extensive use of CXR examinations in clinical practice, the development of robust multi-label artificial intelligence (AI) systems remains constrained by limitations in dataset availability, annotation consistency and reproducible labeling methodologies. Existing public datasets frequently rely on heterogeneous labeling protocols, limited pathology coverage or annotations that inadequately account for linguistic negation and uncertainty within radiology reports. In this paper, we introduce MultiCaRe-Thorax, a curated thoracic benchmark dataset derived from the publicly available MultiCaRe repository, with a reproducible negation-aware natural language processing (NLP) framework for automated generation of multi-label thoracic disease annotations. Unlike existing CXR benchmarks that are distributed in their pre-curated form, MultiCaRe-Thorax provides a fully reproducible pipeline from raw case reports to curated dataset, establishing a new resource that is both scalable and transparent. Our pipeline performs thoracic case identification, image–report matching, duplicate removal, data cleaning, disease-specific label extraction and quality-control verification to produce a curated cohort comprising 5252 chest radiographs from 3415 unique patients. Sixteen clinically relevant thoracic pathologies were automatically annotated using a dictionary- and pattern-based NLP framework incorporating explicit negation detection and n-gram mining, achieving 95% accuracy against 500 manually reviewed reports (Cohen’s $\kappa =0.89$). Sixteen clinically relevant thoracic pathologies were automatically annotated using a dictionary- and pattern-based NLP framework incorporating explicit negation detection and n-gram mining, achieving 95% agreement against 500 manually reviewed reports. Five ImageNet-pretrained convolutional neural network (CNN) architectures (ResNet50, DenseNet121, EfficientNetB0, EfficientNetB7 and InceptionV3) with two hybrid ensemble configurations were evaluated under a patient-level data partitioning protocol. Among individual architectures, DenseNet121 attains the best AUC on tuberculosis (0.757) and pleural effusion (0.767) with roughly one-third the parameters of ResNet50, while ResNet50 attains the higher mean AUC across all sixteen pathologies (0.688 vs. 0.682) and on interstitial lung disease (0.769) and lung mass/cancer (0.777); we report this as an accuracy–efficiency trade-off rather than a single best architecture. The DenseNet121-InceptionV3 ensemble achieves the highest reported AUC of 0.821 for lung mass/cancer detection. We report 95% confidence intervals for the evaluated ensemble and remaining backbones and note substantial estimation uncertainty for the rarest classes (e.g., congenital heart disease, $n=8$ test cases). Grad-CAM visualizations were also employed to provide clinically interpretable explanations of model predictions.