833 papers
This paper proposes a lightweight two-stage framework utilizing YOLO11s-seg for instance-level candidate generation and YOLOv8n-cls for hard-negative and occlusion-aware classification, achieving high harvesting decision accuracy and zero false harvest rates in complex oil palm plantation environments.
This paper proposes a neuro-symbolic fuzzy rule-based framework that integrates deep learning-based dermoscopic attribute estimation with automatically extracted clinical rules to improve melanoma classification accuracy and interpretability compared to image-only deep learning approaches.
This study demonstrates that analyzing the structural characteristics of a paper's reference list at the time of publication—specifically self-citation rates, the spread of reference years, and the recency of cited literature—can significantly improve the early detection of future retraction risks before any post-publication evidence emerges.
This paper proposes an Explainable TabNet Framework that leverages attention-based deep learning and SHAP values to accurately, robustly, and fairly predict remote employee performance using a large-scale dataset, outperforming traditional baseline models while ensuring transparent and equitable human resource decision-making.
This study demonstrates that advanced Large Language Models, specifically BioClinicalBERT and BioGPT, achieve high accuracy (98% F1 score and accuracy) in automatically identifying causative drugs for adverse events from social media and drug review datasets, offering a promising real-time solution for drug safety monitoring.
This paper proposes a Distributed Consent Based Sociocracy 3.0 governance framework that integrates S3 principles with ROS 2 architecture to enhance ethical, transparent, and scalable coordination in autonomous multi-robot systems, demonstrating significant improvements in conflict reduction, debuggability, and consent success rates through evaluation on the DROID 100 dataset.
The paper proposes InvDec, a hybrid multivariate time series forecasting architecture that achieves principled separation between temporal encoding and variate-level decoding through an inverted decoder and delayed variate embeddings, demonstrating significant performance gains on high-dimensional datasets by effectively balancing temporal patterns and cross-variate dependencies.
This paper proposes YOLO-LOLG Mamba, a novel steel surface defect detection framework that integrates LODConv, LDAM, and MsGroupMamba modules to enhance cross-scale feature fusion and global context modeling, achieving superior accuracy and generalization on industrial datasets compared to YOLOv8n.
This study demonstrates that a Convolutional Neural Network-based approach, specifically utilizing AlexNet with transfer learning, can effectively and accurately distinguish authentic saffron stigmas from counterfeit materials, offering a promising non-destructive solution for quality control.
This paper introduces NEDOQwen, a Turkish-centric 824M-parameter language model, to demonstrate a low-cost, auditable workflow for diagnosing internal-external evaluation mismatches and achieving targeted benchmark improvements through fine-tuning and repair, while cautioning that such gains do not equate to broad competence.