Machine learning (ML) and deep learning (DL) as two well-known methods of artificial intelligence (AI) have emerged as powerful tools in extracting insights and patterns from vast amounts of data. In ...

For a project in Bangladesh, Prof. Mushfiq Mobarak and his team used machine-learning models applied to mobile phone records ...

The project for which Hulsebos received the grant is called DataLibra, which runs from 2024 to 2029. Over those five years, ...

An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...

We present a knowledge‐guided machine learning framework for operational hydrologic forecasting at the catchment scale. Our approach, a Factorized Hierarchical Neural Network (FHNN), has two main ...

This video explores why Windows 11 has become one of the most controversial operating systems in Microsoft’s history. Once seen as a reliable personal tool, Windows now pushes ads, cloud services, ...

This Research Topic is Volume III of a series. The previous volumes can be found here: Volume I and Volume II. Machine learning has recently made impressive advances in applications ranging from ...

Abstract: The increasing penetration of inverter-based distributed generation (DG) into power grids improves access to electricity and provides a significant possibility for decarbonization. However, ...

Abstract: This research investigates the transformative role of machine learning (ML) in automating knowledge extraction (AKE) from unstructured text data, a critical challenge in the era of big data.

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, ...