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 ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

Review re-maps multi-view learning into four supervised scenarios and three granular sub-tiers, delivering the first unified blueprint for researchers to navigate classification, clustering, ...

Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

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, ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

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 ...

In 1854, London was struck by a devastating cholera outbreak. Public authorities responded using the best tools and scientific understanding available at the time.Disease was widely believed to spread ...

The built environment faces increasing pressures from climate change, resource limitations, and rapid urbanisation.

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...