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, ...

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 ...

This study presents a transfer learning–based method for predicting train-induced environmental vibration. The method applies ...

A team from the Faculty of Medicine and Health Sciences and the Institute of Neurosciences at the University of Barcelona ...

20+ Machine Learning Methods in Groundbreaking Periodic Table From MIT, Google, Microsoft Your email has been sent A new “periodic table for machine learning” is reshaping how researchers explore AI, ...

A new technical paper titled “Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate” was published by researchers at KTH Royal Institute of Technology and ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

A new research paper shows the approach performs significantly better than the random-walk forecasting method.

Learn how to find and make the best use of valuable insights buried in your company’s databases.

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

The aspect of a job interview will have turned into an evaluation backed with data, behavior analysis, and competency models by the year 2026.