2667 papers
This paper proposes SOFE, an SDN-based task offloading technique for Fog-IoEV networks that utilizes a binary linear programming model to optimize task assignment to fog nodes or parked vehicles based on load, mobility, energy, and deadlines, thereby reducing response time and extending electric vehicle lifespans compared to existing methods.
This paper proposes DSA-MERC, a reliable multimodal conversational emotion recognition model that utilizes a dual-stage attention mechanism combining Bi-Directional MultiAttention and an audio-guided leader-follower network with multi-scale optimization, alongside confidence and transfer learning strategies, to effectively integrate multimodal features and mitigate label noise for improved accuracy and robustness.
This paper proposes PerC-SimAM-BGRU, a novel MetaFormer-based framework integrating position-aware circular convolution and a bi-directional gated recurrent unit to achieve state-of-the-art myocardial infarction detection with 99.4% accuracy while balancing performance and computational complexity.
This study integrates standard bibliometric analysis with Latent Dirichlet Allocation (LDA) topic modelling on a curated corpus of 2,631 healthcare AI documents from 2017 to 2025 to overcome keyword limitations, revealing a maturation trajectory from deployment-ready applications to governance-dependent frontiers like large language models and explainable AI.
This paper presents a simulation-based command-selection layer for quadruped robots that uses rendered egocentric views and candidate commands to predict short-horizon body risks, significantly improving safety and command agreement while maintaining forward progress without relying on terrain maps or lookahead rollouts.
The paper introduces CRysTal, a neuron-level post-hoc explanation framework that maps AI model activations to standardized clinical terminology, demonstrating superior alignment with radiology reports, enhanced diagnostic reliability, and improved efficiency for physicians compared to existing explanation methods across multiple medical imaging tasks.
This paper proposes an Explainable and Trustworthy AI framework utilizing intuitionistic fuzzy sets to establish a transparent, trust-weighted voting consensus mechanism that enhances message verification efficiency, privacy protection, and security in dynamic intelligent transportation systems.
This paper presents an interpretable multimodal machine learning framework that combines CBC and HPLC biomarkers to achieve highly accurate (99.89%) and clinically consistent multiclass classification of thalassemia using a stacking ensemble model, with SHAP analysis confirming the top predictive features align with standard diagnostic criteria.
This study evaluates a bilingual retrieval-augmented generation system built on ANVISA regulatory documents, demonstrating its high accuracy in locating, citing, and summarizing Portuguese and English regulatory requirements for drug development while effectively handling unanswerable queries after calibration.
This paper proposes a CLCM model, integrating Conv1D-LSTM, CNN, and a multi-head attention mechanism, to effectively assess security risks in virtual reality learning systems within the Unmanned Swarms Internet of Things by accurately analyzing heterogeneous data modalities and overcoming limitations of existing models.