927 papers
MAP-RAG is a novel multi-view adaptive framework that enhances clinical diagnostic accuracy and efficiency by integrating structured electronic medical record decomposition with hybrid retrieval and dynamic confidence routing, demonstrating superior performance across multiple medical departments compared to baseline models.
To address the translational gap in medical segmentation foundation models caused by overoptimistic benchmarks and the accuracy-fairness trade-off, this paper introduces OrganSegBench, a rigorous multi-dimensional evaluation framework that demonstrates how principled model synergy through ensemble strategies outperforms monolithic models in achieving safe, equitable, and clinically trustworthy AI.
This paper presents a hybrid Retrieval Augmented Generation framework that integrates BM25 and BioBERT-based semantic search with Reciprocal Rank Fusion to enhance explainable, personalized clinical decision support and early disease risk prediction, achieving superior retrieval performance on a dataset of 182,000 medical records compared to traditional baselines.
This study proposes an explainable Hybrid Classical-Quantum Support Vector Machine model that leverages multicountry data to achieve superior maternal mental health risk prediction performance (99.86% accuracy) while identifying key clinical predictors through SHAP analysis.
This paper demonstrates that Hubble/eBPF network telemetry, when combined with carefully constructed ground-truth labels, can effectively detect runtime attacks and misconfigurations in Kubernetes environments, revealing that destination-side identity attributes are the strongest indicators of threats despite a trade-off between the high precision of rule-based detectors and the higher recall of Random Forest classifiers.
This paper introduces ST-HAE, an unsupervised spatio-temporal hybrid autoencoder that achieves high-precision anomaly detection in heterogeneous IoT networks under realistic class imbalance by training exclusively on benign traffic and utilizing bidirectional temporal encoding to significantly reduce false alarm rates and variance compared to existing methods.
This paper proposes a multimodal Transformer-based method that integrates video, audio, and attitude data to robustly assess preschoolers' attention levels in classrooms, demonstrating superior performance and stability across various focus states compared to baseline models.
This paper addresses the challenge of detecting AI-generated product images in cross-border e-commerce by introducing the CBEC-AIGC-Bench dataset and proposing MFF-EComDet, a multi-feature fusion network that combines spatial semantic and frequency artifact branches with a cross-attention mechanism to effectively distinguish generation artifacts from promotional text interference.
FluxGraph is an unsupervised framework for time series anomaly detection that synergizes dynamic graph attention with flux memory and bidirectional feature flow to effectively identify subtle masquerade anomalies and gradual baseline drifts in industrial and automotive systems, outperforming existing methods through a novel multi-residual fusion approach.
This paper proposes a multi-layer framework that enhances the factual reliability of large language models by decomposing responses into atomic claims, grounding them with retrieval, and verifying them through a small language model and neuro-symbolic reasoning, achieving significant performance gains on HotpotQA and SciFact datasets while maintaining interpretability and computational efficiency.