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 ...
Demand for lithium is skyrocketing as factories across the world churn out electric vehicles and the massive batteries that make wind turbines and solar panels reliable sources of energy.
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 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.
You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters ...
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