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 ...

Researchers propose a self-distillation fix for ‘catastrophic forgetting’ in LLMs

LLMs tend to lose prior skills when fine-tuned for new tasks. A new self-distillation approach aims to reduce regression and ...
EurekAlert!

Journal of Environmental Sciences study reveals how artificial intelligence can transform PM2.5 monitoring

Finely dispersed particulate matter with a diameter of ≤2.5 μm (PM2.5) poses a significant health- and climate-risk, yet ...
North Penn Now

Machine Learning Using Python: A Complete Learning Path With Practical Projects

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...
IEEE

Tabular Data Classification and Regression: XGBoost or Deep Learning With Retrieval-Augmented Generation

Abstract: Tabular data is the most prevalent form of structured data, necessitating robust models for classification and regression tasks. Traditional models like eXtreme Gradient Boosting (XGBoost) ...
CIO

Predicting the future is easy — deciding what to do is the hard part

AI agents help businesses stop guessing — linking predictions to actions so teams can move from “what might happen” to ...
IEEE

Design and Analysis of a Hybrid Excitation Transverse Flux Linear Machine Used for Linear Railway Traction

Abstract: Due to their simple structure and high efficiency, various types of linear machines are employed in direct-drive applications such as linear railway traction. The transverse flux linear ...