We propose the PGDPNet, the first end-to-end deep learning model for explicit geometry diagram parsing. And we construct a large-scale dataset PGDP5K, containing dense and fine-grained annotations of ...

Embedding a deep-learning model in the known structure of cellular systems yields DCell, ... A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 ...

Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-driven (e.g., machine learning, ML) approaches, while each suffers from either deficient physics or small data ...

In this project, we have developed a basic CNN model which is used for "Automatic Modulation Classification" using constellation diagrams. Also we have experimented and compared the results obtained ...

Study in Npj Digital Medicine evaluates COMPOSER, a deep learning model for early sepsis prediction, showing its effectiveness in improving patient care and reducing in-hospital mortality rates.

The deep learning model outperformed the other models in this measure (0.196 vs 0.135-0.166, selecting the top 1%). In contrast, deep learning did not improve the negative predictive value.

This is what people often call the “art” of deep learning: choosing which attributes to consider or ignore can significantly influence your model’s prediction accuracy.

Deep learning model to predict adverse drug-drug interactions. ScienceDaily . Retrieved June 2, 2025 from www.sciencedaily.com / releases / 2022 / 05 / 220504092943.htm ...

Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-driven (e.g., machine learning, ML) approaches, while each suffers from either deficient physics or small data ...