833 papers
This systematic review of 25 studies reveals that despite the critical importance of user-centered design, machine learning-based clinical decision support systems for emergency department triaging frequently lack comprehensive interface features, usability testing, and interdisciplinary collaboration, thereby hindering their real-world adoption and effectiveness.
This paper proposes the REGAL-Driven Hierarchical Graph Reinforcement Network (HGRN), a novel framework that integrates Graph Neural Networks, Residual Energy Gradient Attention, Deep Reinforcement Learning, and an Energy-Adaptive Partridge Optimization Algorithm to achieve stable, energy-efficient, and load-balanced communication in large-scale Wireless Sensor Networks, demonstrating superior performance in network lifetime, energy conservation, and packet delivery compared to existing clustering approaches.
This paper formalizes and empirically quantifies the blast radius of triple-level poisoning attacks in Knowledge Graph RAG systems, demonstrating that hub poisoning causes the most severe propagation of corruption and proposing a graph-aware defense based on degree-weighted trust scoring to mitigate these risks.
This paper proposes a hybrid LightGBM-MLP intrusion detection system enhanced through adversarial training with the Fast Gradient Sign Method (FGSM) on the UNSW-NB15 dataset, successfully improving robustness against evasion attacks by maintaining 77.80% accuracy under strong perturbations while incurring only a minimal 6.66% reduction in clean data performance.
This paper proposes an adaptive framework for volumetric density field sequences that utilizes a novel structural descriptor for optimal keyframe selection via dynamic programming and a particle-based interpolation scheme to achieve high-fidelity reconstruction with minimal artifacts.
This paper proposes a lightweight, real-time detector for unmanned surface vehicles that enhances weak water-surface target detection by integrating frequency-guided feature routing and scale-normalized adaptive localization supervision to address challenges like low contrast and small object size while maintaining strict latency and model-size constraints.
This paper introduces a closed-loop Auto Research framework that uses language-model agents to edit molecular representations, model code, and external evidence, successfully discovering and certifying generalizable improvements across multiple molecular property benchmarks while distinguishing true transferable gains from non-transferable selection variance and distribution shifts through rigorous held-out testing.
This paper proposes an explainable AI framework that combines transfer learning, Vision Transformers, and ensemble methods to achieve superior breast cancer prediction accuracy (96.82%) on the BreakHis dataset while utilizing techniques like Grad-CAM and LIME to enhance model transparency and interpretability.
The paper introduces R-Super, a novel framework that leverages over 100,000 radiology reports to train tumor segmentation models without requiring scarce manual masks, thereby enabling large-scale multi-cancer detection that significantly outperforms both existing AI models and human radiologists.
This paper introduces SonicPlay, an Unreal Engine-based framework that operationalizes audio-based accessibility principles into reusable tools, demonstrating through dual studies with blind players and professional developers that it effectively enables independent spatial navigation while seamlessly integrating into standard game development workflows.