Adam M. Root argues businesses must anchor ML in customer problems, not technology. He details a strategy using ...
Picture a single forecasting mistake triggering a cascade of negative consequences, such as surplus inventory, strained supplier relationships and disappointed customers. In today's world, accurate ...
AI methods are increasingly being used to improve grid reliability. Physics-informed neural networks are highlighted as a ...
A range of national meteorological services across Europe and ECMWF have launched Anemoi, a framework for creating machine learning (ML) weather forecasting systems. Named after the Greek gods of the ...
Accurately predicting solar irradiance and wind flow patterns is requisite for renewable energy forecasting—but precision alone simply isn't enough. The data must be actionable, fast, and seamlessly ...
In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...
Researchers in Slovakia have demonstrated a machine-learning framework that predicts PV inverter output and detects anomalies using only electrical and temporal data, achieving 100% accuracy in ...
The system generates daily forecasts of cargo tonnage, ULDs, and piece counts by flight, truck, customer, transport mode, and ...
In this paper we investigate oil market volatility prediction using a comprehensive data set of 205 variables spanning macroeconomic, financial, energy-related and sentiment indicators. We employ ...
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