Researchers developed a machine learning model using CatBoost to predict disturbances in drone formations with an R² of 83.3%, significantly improving from the previous baseline of 54%. The SHAP ...

Data further showed that a machine learning model outperformed other metrics in terms of diagnostic accuracy (75%) compared with MMSE (55%), clinician analysis (49.5%) and modified TICS (45%).

More information: Nina Horat et al, Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning, Advances in Atmospheric Sciences (2024 ...

Density functional theory is a widely used computer-based quantum mechanical method for calculating properties of atoms, molecules, and materials.

Cemil Emre Yavas, Research Lead at Georgia Southern University Georgia Southern University researchers develop a machine learning model with 97.97% accuracy in earthquake forecasting. STATESBORO ...

Machine learning boosts accuracy of solar power forecasts. Institute of Atmospheric Physics, Chinese Academy of Sciences. Journal Advances in Atmospheric Sciences DOI 10.1007/s00376-024-4219-2.