Until recently, AI's role in research felt like having a useful assistant. It could summarize a paper, clean up a dataset or ...

Unlike traditional systems that produce a single output, ML-driven tax planning generates a set of ranked strategies.

Penn researchers have developed a smarter AI method for solving notoriously difficult inverse equations, which help ...

Abstract: Spaceborne GNSS reflectometry (GNSS-R) missions have been successfully launched in recent years, such as Technology Demonstration Satellite (TDS-1) in 2014, Cyclone Global Navigation ...

During SAS Innovate 2026 in Dallas, principal quantum systems architect Bill Wisotsky tells ARTHUR GOLDSTUCK what will take ...

Lessons learned from developing an inferential model for predicting food insecurity yield essential insights and actionable ...

Also essentially still embryonic, quantum AI is an approach involving running machine learning algorithms on existing quantum hardware to run tasks in hours (instead of weeks) and/or work on rendering ...

You're probably a little tired of reading or hearing about AI, right? Well, if that's the case, then you're in the right place because here, we're going to talk about machine learning (ML). Yes, it's ...

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...

Accurate crop yield prediction is vital for ensuring global food security, particularly amid growing environmental challenges such as climate change. Although deep learning (DL) methods have shown ...

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...

School of Economics, The University of Nottingham-Ningbo, Ningbo, China. The study focuses on identifying and distinguishing whether there are differences between those students receiving special ...