Frontiers

Generative Models of Neural Activity: Using AI to Simulate, Interpret, and Predict Brain Dynamics

The advent of high-density recording technologies, such as Neuropixels and large-scale calcium imaging, has provided an unprecedented look into the ...
Journal of Medical Internet Research

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study

Model performance was evaluated using accuracy, balanced accuracy, Brier score, detection prevalence, F1-score, Jaccard index, κ coefficient, Matthews correlation coefficient, negative predictive ...
IEEE

Development of Generic Static Equivalent Models for Active Distribution Networks

Abstract: The increasing integration of converter-interfaced renewable energy sources (CI-RES) in distribution networks (DNs), combined with the gradual decommissioning of synchronous generators (SGs) ...
IEEE

State-of-Art, Development, and Challenges of Model-Free Predictive Control on Motor Drives

Abstract: Model-free predictive control (MFPC) is an essentially robust strategy in motor driving systems, garnering significant attention and research. However, the existing literature lacks a ...
Journal of Medical Internet Research

Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study

In addition, the model was developed into a web-based application using the Streamlit framework to facilitate its clinical application. Results: A total of 46,097 patients with sepsis from multiple ...