833 papers
This paper introduces GRAD-Net, a unified pathology-guided deep learning framework that integrates lesion-aware attention mechanisms and multi-scale feature aggregation to achieve state-of-the-art accuracy and clinical interpretability in automated diabetic retinopathy grading.
This paper introduces CRC-LS-MOCBO, a sequential multi-objective causal Bayesian optimization framework that ensures safe interventions under uncertain causal structures by integrating candidate-graph uncertainty, stability-weighted priors, and conformal risk calibration to minimize constraint violations while maximizing objective gains.
This paper presents a low-cost, real-time earthquake early warning system that utilizes IoT hardware (ESP32, GPS, GSM) and embedded machine learning models trained on historical seismic data to detect patterns and instantly alert users via SMS, even in areas with limited connectivity.
This paper introduces HybridPII, a tunable hybrid ensemble model that combines weighted regex and multi-model NER to achieve superior recall for automated PII detection, specifically addressing the critical need to minimize false negatives in compliance-critical privacy applications.
This paper introduces Weak-Topological Neural Operators (WTNO), a framework that enhances neural operator selection for partial differential equations by coupling state predictors with geometric, topological, and weak-measure certificates to ensure robust physical predictions in scenarios involving interfaces, topological changes, and rare events.
This study provides empirical evidence that iterative language model self-refinement consistently converges toward stable semantic fixed points across diverse architectures and objectives, suggesting an underlying energy landscape where self-improvement acts as an implicit optimization process.
This study evaluates the educational usability of WEKA and Orange for beginner data mining learners through task-based comparisons and student feedback, concluding that Orange is more effective due to its intuitive visual workflow and user-friendly interface.
This paper introduces MLCompete, a scalable, multilingual web platform designed to evaluate machine-learning olympiads through an asynchronous, metric-agnostic pipeline and a secure, two-tier execution environment, demonstrating its robustness and reliability through successful deployment in Romania's National AI Olympiad and international participation.
This paper proposes a Dual-Guidance framework for semi-supervised semantic segmentation that combines an Uncertainty-Aware Dynamic Pseudo-Label Refinement module and a Class-Prototype Contrastive Guidance module to jointly refine predictions and regularize feature representations, achieving state-of-the-art performance on PASCAL VOC 2012 and Cityscapes benchmarks.
This systematic review of 108 studies (2010–2026) analyzes how AI reshapes marketing measurement by proposing a "Measure–Validate–Protect" framework that balances the enhanced predictive power of deep learning and neural media mix models against the causal limitations and accuracy costs imposed by privacy-preserving techniques.