Traditional risk measures often fall short in capturing the full spectrum of potential losses, and this research proposes a refined framework based on magnitude and propensity to provide a more nuanced assessment.
A new deep learning framework leverages both images and text from social media to accurately identify disaster-related content in the Bangla language.
A new deep learning framework combines the strengths of U-Net and LSTM networks, informed by the laws of physics, to accurately model how structures behave during earthquakes.
As artificial intelligence increasingly powers player risk detection in the gambling industry, a critical gap in standardized evaluation threatens effective harm reduction.
Researchers are leveraging the power of generative adversarial networks to create more accurate and realistic forecasts of how wildfires will spread.
New research demonstrates how accounting for the varying intervals in patient data can significantly improve the accuracy of clinical risk prediction.
A new framework leverages artificial intelligence to enhance the stability and optimization of power grids facing growing threats and disturbances.
New research shows that artificial intelligence models can effectively identify anomalies in power system data, offering a promising path toward more reliable and resilient energy infrastructure.
As artificial intelligence evolves beyond forecasting, researchers are exploring how it can actively shape desirable outcomes in policy and beyond.
A new framework leverages concepts from quantum mechanics to offer a more nuanced understanding of asset dependencies and systemic risk in financial markets.