The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
TWCN Tech News

Best Machine Learning Models to know

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...
Electricity Forum

Incipient Fault Detection Using AI Classification of Waveform Analysis

Incipient fault detection using AI classification represents a fundamental advancement in distribution system reliability engineering. By continuously analyzing waveform behavior and classifying ...
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 ...

Research Ignited Students Publish Academic Papers and Build AI Models in Expanded PhD-Led Program

High school students gain PhD-led mentorship, publish original research, and build real-world AI models through ...
Frontiers

Brain-Computer Interfaces (BCIs) for daily activities: Innovations in EEG signal analysis and machine learning approaches

Brain-Computer Interfaces (BCIs) are emerging as transformative tools that enable direct communication between the human brain and external devices. With recent advancements in Electroencephalography ...
International Monetary Fund

GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...