Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces. At the ...

Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...

Hundreds of thousands of children in China have been separated from their parents. A Yale SOM study finds that a machine-learning approach could cut years off family reunification efforts by matching ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Data is fundamental to hydrological modeling and water resource management; however, it remains a major challenge in many ...

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

No audio available for this content. High-precision GNSS applications, such as real-time displacement monitoring and vehicle navigation, rely heavily on resolving carrier-phase ambiguities. However, ...

A scientist in Sweden has developed a new hybrid local features-based method using thermographs to identify faulty solar panels. A researcher from Sweden’s Jönköping University has proposed a machine ...

Musculoskeletal (MSK) conditions drive a large share of global pain, disability, and lost productivity. Rehabilitation can be effective, but outcomes vary ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...