Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...

In machine learning, privacy risks often emerge from inference-based attacks. Model inversion techniques can reconstruct sensitive training data from model outputs. Membership inference attacks allow ...

In addition to improved performance from individual sensing technologies, including radar and light detection and ranging ...

Researchers have demonstrated, for the first time, that transfer learning can significantly enhance material Z-class identification in muon tomography, even in scenarios with limited or completely ...

Bangladeshi researcher Md Masum Billah is advancing the application of artificial intelligence in healthcare and digital security, focusing on practical solutions for real-world challenges, said a ...

Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. Using ...

AI-powered document processing automates data extraction, classification, and validation with 95-99% accuracyMarket projected ...