This proposal outlines a machine learning-based approach aimed at improving productivity in haulage operations within ...

Modern-day LLMs are "fiction machines," designed not to be truthful but to make sense. What can we expect from these machines, and what are their limitations?

Despite significant mathematical refinements, econometrics has shown the weaknesses of its logical underpinnings, primarily during economic turning points—financial crises, pandemics, and geopolitical ...

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output. Concept ...

Read more about AI and machine learning drive digital transformation across global mining operations on Devdiscourse ...

Utilities worldwide are turning to artificial intelligence (AI) and machine learning to stabilize networks, forecast consumption, detect faults, and optimize system performance in real-time. What was ...

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 ...

Google published a research paper on how to extract user intent from user interactions that can then be used for autonomous agents. The method they discovered uses on-device small models that do not ...

Abstract: Industrial noise classification plays a crucial role in equipment health monitoring and predictive maintenance, yet existing methods suffer from inadequate feature extraction, limited ...

Traditional interwell connectivity analysis methods for water-flooding reservoirs suffer from two major limitations: insufficient integration of seepage physics, leading to poor interpretability, and ...