Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...

Abstract: This research investigates the application of Random Forest algorithms to enhance disease prediction within healthcare analytics. Using large healthcare datasets, the research compares ...

CERES program updates include operational satellite instruments, algorithm advancements, machine learning applications, and ongoing missions measuring Earth’s energy budget and climate system changes.

A research paper by scientists from Beihang University proposed a machine learning (ML)-driven cerebral blood flow (CBF) prediction model, featuring multimodal imaging data integration and an ...

Abstract: By evaluating intricate datasets to maximize plant growth, boost yields, and advance sustainability, smart agriculture—powered by Random Forest machine learning—is transforming botany.

Researchers developed and validated a machine-learning algorithm for predicting nutritional risk in patients with nasopharyngeal carcinoma.

The review reports that blockchain-enhanced federated learning systems typically achieve slightly lower raw accuracy than ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

A Hebrew University study suggests AI tools could help growers better manage water use by predicting healthy plant behavior and flagging early signs of stress.

A new Israeli study suggests that machine-learning models may soon give growers a far more precise way to predict how much water their crops use each day, while also laying the groundwork for earlier ...