Getting retrieval-augmented generation right requires a deep understanding of embedding models, similarity metrics, chunking, ...

Researchers developed multiple architectures, including U-Net 2D and 3D CNNs, as well as a Vision Transformer (ViT), to ...

The study introduces an AI-driven approach to concrete mix design, optimizing for strength and sustainability while reducing ...

Understand how CT metrics match FVC in predicting ALS survival, offering a spirometry alternative for patients with bulbar ...

Google's AlphaEvolve is the epitome of a best-practice AI agent orchestration. It offers a lesson in production-grade agent engineering. Discover its architecture & essential takeaways for your ...

The core problem tackled by this research is the opaque nature of many high-performing AI models used in fraud detection.

A new deep learning model shows promise in detecting and segmenting ... compared with physician-delineated volumes. Performance metrics included sensitivity, specificity, false positive rate ...

For recognition, deep learning techniques, particularly convolutional neural networks (CNNs ... Based on Equation 16, we can see that the Precision metric focuses on the model’s positive predictions ...

Therefore, researchers must understand the metrics used to evaluate ML models which can influence the critical ... the regulatory impacts of single nucleotide polymorphisms (SNPs). For example, the ...

Objectives To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data. Design A single-centre, retrospective cohort ...