Machine learning

A new framework aligns data from multiple sensors across time, boosting the accuracy of machine failure predictions.

A new AI-driven computational pipeline paired with rigorous lab-based testing accelerates the discovery of novel molecules for efficient organic solar cells.

A new machine learning tool could help predict cancer treatment outcomes for more effective and targeted treatments.

Departing from costly simulations and trial-and-error methods, researchers harness machine learning and automation to efficiently discover new recipes for green hydrogen catalysts.

A new machine learning model identifies key dementia features from complex clinical datasets, potentially making diagnoses faster and less subjective.

A new training strategy enables robots to efficiently collaborate and learn in dynamic environments, improving their performance in real-world tasks.

Machine-learning-based predictions intelligently analyse complex sensor data to enhance equipment maintenance and reliability.

A*STAR researchers develop machine learning models that quickly and accurately simulate chemical reactions involving complex liquid interactions.

Advanced photonic processors boost machine learning efficiency by handling vast data streams in parallel, with potential benefits for enhanced patient monitoring and cloud computing.

Researchers find that pre-training AI models on a global scale boosts their performance in machine learning tasks, particularly in scenarios with limited training data.

A robust machine learning approach to predict and optimise fuel consumption in ships is critical to practically enhance maritime environmental sustainability.

The innovative integration of robotics and machine learning accelerates the development of efficient, sustainable hydrogen catalysts.