Abstract: Deep learning models are increasingly used for making predictions based on clinical time series data, but model generalization remains a challenge. Continual learning approaches, which ...

Many engineering challenges come down to the same headache—too many knobs to turn and too few chances to test them. Whether tuning a power grid or designing a safer vehicle, each evaluation can be ...

The development of next-generation metallic materials is entering a transformative era driven by data-driven methodologies. Traditional trial-and-error ...

Validate learning, predict and design next-gen C-N coupling catalyst material.

Abstract: Ensuring safe and human-like decision-making is a critical component of autonomous vehicle decision systems. However, conventional approaches often simplify the action space into discrete ...

BAILA 3.0 gives advisors an AI investment strategist, freeing them to focus on client relationships and growing their practice ...

A new study introduces a global probabilistic forecasting model that predicts when and where ionospheric disturbances—measured by the Rate of total electron content (TEC) Index (ROTI)—are likely to ...

New deep-learning framework reconstructs hourly PM2.5 chemical composition using air-quality and meteorological data ...

Many real-world applications for complex industrial engineering or design problems can be modelled as optimisation problems. These problems often have features such as multi-modality (multiple optimal ...

ChemXploreML is a user-friendly desktop application specifically designed to bring the power of machine learning to chemistry research. It streamlines the entire machine learning pipeline for ...