Medicine is rapidly evolving from statistical, evidence-based approaches to predictive, genotype-directed care, driven by ...

Scientists aiming to advance cancer diagnostics have developed a machine learning tool that is able to identify metabolism-related molecular profile differences between patients with colorectal cancer ...

Copper is one of the most critical minerals for the global transition to low-carbon energy. However, as copper mining activities expand worldwide, they often result in significant environmental ...

Cardiovascular disease is the leading cause of death worldwide. Hypertension, as one of the predisposing factors of cardiovascular disease, is an important reason for the high incidence of ...

Coastal and nearshore zones face growing pressure from storms, flooding, erosion, and sea-level rise, which threaten civil infrastructure such as ports, ...

Explore how machine learning enhances energy monitoring in smart homes with CEO Mike Phillips from Sense. This interview ...

An interdisciplinary team of researchers has developed a machine learning framework that uses limited water quality samples to predict which inorganic pollutants are likely to be present in a ...

Please provide your email address to receive an email when new articles are posted on . Machine learning can use patient-reported outcomes to identify low disease activity in rheumatoid arthritis.

Machine learning has transformed tool condition monitoring by enabling non-invasive, data-driven assessment of cutting tool health. Modern systems deploy a range of sensors—vibration, cutting force, ...

If you liked this story, share it with other people. Scientists have combined passive acoustic monitoring, machine-learning tools and aerial surveys to estimate the population of North Atlantic right ...

Using smartwatch and phone app data, machine‑learning models identified each participant’s primary lifestyle triggers of low mood, enabling coaches to create individualized mood improvement plans.