Shaking up economic early warning systems with an artificial jellyfish algorithm

A study in the International Journal of Critical Infrastructures discusses a financial early warning system based on an ...
Technology Networks

Biomarkers Identified for MASLD Using Genetics and Machine Learning

Researchers have identified multiple causal biomarkers for metabolic dysfunction-associated steatotic liver disease (MASLD), ...
Devdiscourse

New challenges in securing billions of IoT devices in the AI era

Traditional machine learning methods like Support Vector Machines, Random Forest, and gradient boosting have shown strong ...
Neurology Advisor

Machine Learning Tool Predicts MS Relapse Risk After DMT Discontinuation

A machine learning–based tool accurately predicted risk for recurrent inflammatory activity after DMT discontinuation in MS, highlighting its potential to guide personalized treatment decisions.
Visual Studio Magazine

Random Forest Regression and Bagging Regression Using C#

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random forest regression technique (and a variant called bagging regression), where the goal is to ...
Healio

MRI-based clinical prediction model identified risk factors for patellar instability

Please provide your email address to receive an email when new articles are posted on . The Insall-Salvati ratio, tibial tubercle-trochlear groove distance and trochlear depth had the greatest ...
BMJ

Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...