Recent advances in froth flotation optimisation have increasingly leaned on machine learning methodologies to improve process control and enhance mineral recovery. By integrating data‐driven ...

Innovative machine learning techniques are rapidly transforming particle accelerator physics by integrating advanced data analytics with established accelerator models. This integration has led to ...

Explore how AI in high-throughput screening improves drug discovery through advanced data analysis, hit identification and ...

Machine learning has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the ...

At the beginning of the 21st century, the National Academy of Engineering published a report identifying the 20 greatest engineering achievements of the 20th century. 1,2 These engineering ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integral to database management, driving new levels of automation and intelligence in how data systems are administered. Modern ...

A new study by Justin Grandinetti of the University of North Carolina at Charlotte challenges one of the most dominant narratives in artificial intelligence: that modern AI systems are inherently ...

The study, titled “Teach AI What It Doesn’t Know,” published in AI Magazine, presents a detailed research agenda by Sean Du of Nanyang Technological University, focused on building reliable machine ...

Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper publishing April 22 in the Cell Press ...

Head and neck cancers represent a biologically heterogeneous group of malignancies requiring accurate diagnosis, staging, and risk stratification for ...

Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper appearing in Patterns. Though large language models ...