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 ...
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 ...
Devdiscourse

AI outperforms traditional models in bankruptcy forecasting

Bankruptcy prediction has traditionally relied on statistical approaches such as Altman’s Z-score, which use financial ratios ...
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), ...
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 ...
Scientific Research Publishing

Machine Learning for Financial Risk Management: Modeling Time-Varying Factor Sensitivities Using Factor Variational Autoencoders ()

Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management.
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 ...