More information: Eitan Fass et al, Machine learning models based on hyperspectral imaging for pre-harvest tomato fruit quality monitoring, Computers and Electronics in Agriculture (2024). DOI: 10 ...

Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

A crucial part of the machine learning lifecycle is managing data drift to ensure the model remains effective and continues to provide business value. Data is an ever-changing landscape, after all.

Strategies to reduce data bias in machine learning. Chances are that you’re familiar with the concept of bias. It is widespread, turning up in discussions about scientific discoveries, politics ...

Methane (CH4) pyrolysis, a reaction that produces hydrogen without emitting carbon dioxide, often utilizes molten media ...

Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends ...

It can be relatively cheap to gather a lot of bio-signal data. To teach a machine-learning algorithm to find a relationship between bio-signals and health outcomes, however, you need to teach the ...

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.