2982 papers
This paper proposes the Moment-guided Progressive Geometric Reasoning (MPGR) framework, which addresses robust 6D pose estimation under severe occlusion by reformulating the task as a distribution-aware inference problem that utilizes high-order statistical modeling to repair fragmented geometric structures and enhance feature consistency.
This paper proposes a graph neural network-based framework that models team sports as dynamic heterogeneous graphs to predict offensive efficiency and defensive formations while optimizing tactical strategies through graph reinforcement learning and counterfactual reasoning, demonstrating superior performance over existing baselines on UEFA Champions League and NBA datasets.
This paper evaluates YOLOv11 and EfficientNetV2M models on the imbalanced HAM10000 dataset for skin tumor classification, demonstrating that YOLOv11 achieves superior recall (86.5%) for high-sensitivity screening while EfficientNetV2M combined with an SVM classifier offers higher specificity (96.8%) for precise triage.
This paper proposes a comprehensive hybrid quantum-classical framework comprising five specialized models (QAFEN-CNN, VQTM-LSTM, QGCL-Net, QVAE-DD, and QMRL-XAI) that collectively enhance financial fraud detection accuracy, adaptability to dynamic ecosystems, and regulatory explainability through advanced quantum feature entanglement, graph construction, and meta-reinforcement learning techniques.
This survey synthesizes emerging research on autonomous vulnerability remediation across diverse computing environments, organizing the literature into six key streams and a formal closed-loop task model to highlight critical themes, operational safety mechanisms, and significant research gaps in deploying agentic AI and reinforcement learning for secure system repair.
This paper establishes that the per-task energy and water demands of AI inference are orders of magnitude lower than those of the human labor it augments or replaces, even when accounting for the full lifecycle resource footprint of human work.
This study introduces and validates a four-layer machine learning framework that significantly reduces audit detection risk and processing time compared to traditional methods by leveraging unsupervised and supervised models on 3.3 million real-world transactions from a Mongolian energy utility, while also uncovering a novel "aggregation masking effect" in Benford's Law anomalies.
This study proposes an explainable AI framework that integrates association rule mining with tri-dimensional sensory profiling to analyze Thai restaurant transaction data, thereby uncovering culturally grounded dish pairing patterns and enabling the generation of interpretable, authentic meal recommendations.
This research proposes a lightweight neural network approach for phishing URL detection using the PhiUSIIL dataset, demonstrating that optimizing hyperparameters and applying data scaling significantly enhances classification accuracy while maintaining low computational complexity.
This study proposes an auditable generative workflow for Miao and Dong timber heritage scenes that integrates morphological priors, photogrammetric conditioning, and provenance logging to ensure reviewable semantic and structural evidence without claiming survey-grade reconstruction.