Search Results for author:
Found 10 papers, 1 papers with code
Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches
This study addresses the prediction of geomagnetic disturbances by exploiting machine learning techniques.
Greedy feature selection: Classifier-dependent feature selection via greedy methods
The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection.
AI-FLARES: Artificial Intelligence for the Analysis of Solar Flares Data
AI-FLARES (Artificial Intelligence for the Analysis of Solar Flares Data) is a research project funded by the Agenzia Spaziale Italiana and by the Istituto Nazionale di Astrofisica within the framework of the ``Attivit\`a di Studio per la Comunit\`a Scientifica Nazionale Sole, Sistema Solare ed Esopianeti'' program.
A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics
In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks.
Physics-driven machine learning for the prediction of coronal mass ejections' travel times
Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma and magnetic field from the solar corona into the heliosphere.
Operational solar flare forecasting via video-based deep learning
Operational flare forecasting aims at providing predictions that can be used to make decisions, typically at a daily scale, about the space weather impacts of flare occurrence.
Prediction of severe thunderstorm events with ensemble deep learning and radar data
The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms.
Score-oriented loss (SOL) functions
Loss functions engineering and the assessment of forecasting performances are two crucial and intertwined aspects of supervised machine learning.
Bad and good errors: value-weighted skill scores in deep ensemble learning
In this paper we propose a novel approach to realize forecast verification.
Desaturating EUV observations of solar flaring storms
Image saturation has been an issue for several instruments in solar astronomy, mainly at EUV wavelengths.
