2787 papers
This Design Science Research study presents the development of RAIA, a governance-bound AI artifact designed to support national regulatory authorities in evidence-linked medicines reliance reviews by integrating secure data handling, traceable evidence synthesis, and human-supervised decision-making within a reusable architectural framework.
This paper proposes a novel network anomaly detection framework that combines multi-period balanced attribute subgraphs with a Diffusion-HAN network to effectively address data imbalance and classification uncertainty, achieving significant improvements in unknown attack detection and F1-score compared to state-of-the-art methods.
This paper presents a modular, service-oriented system that integrates computer vision, deep learning, and 3D avatar rendering to enable effective, near real-time bidirectional communication between deaf and hearing individuals using Portuguese Sign Language.
This paper proposes GhostV2-YOLO, a hardware-aware lightweight UAV detection network that integrates DFC-Light attention, a full-link C2f_GhostV2 module, and strategic Coordinate Attention to achieve high accuracy and real-time inference on resource-constrained edge NPUs while demonstrating strong cross-dataset generalization.
This paper proposes an evidence-based methodology using Evidence Theory to automatically identify key stakeholders by aggregating subjective salience recommendations, effectively managing conflicts to reach a consensus that optimizes requirements prioritization.
This study addresses the lack of domain-specific guidance for human-AI collaboration in manufacturing by synthesizing literature and expert interviews to propose a refined socio-technical design framework comprising eight key dimensions that align with Human-Centred AI and Industry 5.0 principles to promote worker well-being, resilience, and effective teamwork.
This paper proposes CFCO, a social-welfare-maximizing collaborative coalition formation method that unifies local, D2D, and MEC computing modes to optimize task offloading in vehicular edge computing, demonstrating superior performance in delay, fairness, and efficiency compared to existing baselines on real-world trajectory data.
This paper proposes a hybrid machine learning framework that integrates Variational Mode Decomposition for signal decomposition, Recursive Feature Elimination for feature selection, SMOTE for class balancing, and a CatBoost–SVM classifier to achieve a highly accurate (98.5%) air quality classification system using CPCB data.
This paper introduces Galois-Theoretic Quantum Nash Learning (GT-QNL), a framework proving that classical optimizers fail to find Quantum Nash Equilibria in non-solvable algebraic landscapes due to the Abel-Ruffini theorem, while a novel quantum braiding algorithm overcomes this obstruction by physically realizing Galois group actions to guarantee convergence.
This paper presents a comprehensive, simulation-based study that generates a labeled dataset of five representative RPL routing-layer attacks across diverse topology and mobility scenarios using Contiki-NG, extracting over 50 features to establish a reproducible benchmark for developing and evaluating topology-aware machine learning detection systems in low-power IoT networks.