Machine learning is a mechanism where computers learn patterns from data instead of humans writing instructions step by step.

AI, machine learning, and deep learning. These are all common terms, but many people find themselves at a loss when asked to ...

Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains ...

Researchers in India have developed ASHA, a federated learning algorithm that weights each client's updates by a dynamic ...

One of the most stubborn problems in modern population genetics is not finding evidence of adaptation in the genome, but ...

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

Chemists routinely optimize reactions to maximize the yield of their desired products, but understanding why those reactions ...

An AI approach developed by researchers from the University of Sheffield and AstraZeneca, could make it easier to design proteins needed for new treatments. Inverse protein folding is a critical ...

Cytological tests are a common method of screening for cancer cells in stained cell samples. Using a microscope, pathologists examine cells collected from bodily fluids, looking for telltale signs of ...

Steatotic liver disease (SLD), formerly named fatty liver disease, has a prevalence estimated at 30–38% in adults. Detection of SLD is important, since prompt initiation of treatment can stop disease ...

This presentation explores how machine learning can be used to model storm surge hazards at continental and global scales. Participants will learn why broadscale storm surge information is important ...

How RFID and machine learning stop tool theft on construction sites, cutting $1 billion in annual losses through digital perimeters and predictive AI.