In the life sciences and pharmaceutical sector, cost forecasting has long been treated as a backward-looking exercise, anchored in historical averages and stati ...

This proposal outlines a machine learning-based approach aimed at improving productivity in haulage operations within ...

A new study explores how artificial intelligence models can support clinical decision-making for sepsis management. Their research, titled “Responsible AI for Sepsis Prediction: Bridging the Gap ...

This release is good for developers building long-context applications, real-time reasoning agents, or those seeking to reduce GPU costs in high-volume production environments.

Researchers developed a scalable framework that predicts insulin resistance using wearable-device signals, routine blood biomarkers, and demographic data, with stronger performance when these data ...

The techniques that have served marketers for over fifty years are evolving rapidly, driven by artificial intelligence, increasing market volatility and a fundamental shift in what we expect ...

Abstract: Power-hardware-in-the-loop (PHIL) is a form of real-time simulation that allows a real power device to interact with a simulated power system. In PHIL simulation, the power equipment under ...

When experimental results don't match scientists' predictions, it's usually assumed that the predictions were wrong. But new research into materials that pull carbon dioxide directly from the air ...

Welcome! This is a work in progress. We want to create a practical guide to developing quality predictive models from tabular data. We'll publish materials here as we create them and welcome community ...