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Found 19 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.
Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images
The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia, i. e. a systemic loss of skeletal muscle mass and function that may be correlated to multifactorial etiological aspects.
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.
Visibility Interpolation in Solar Hard X-ray Imaging: Application to RHESSI and STIX
Space telescopes for solar hard X-ray imaging provide observations made of sampled Fourier components of the incoming photon flux.
Machine learning as a flaring storm warning machine: Was a warning machine for the September 2017 solar flaring storm possible?
Machine learning is nowadays the methodology of choice for flare forecasting and supervised techniques, in both their traditional and deep versions, are becoming the most frequently used ones for prediction in this area of space weather.
Gain and Loss of Function mutations in biological chemical reaction networks: a mathematical model with application to colorectal cancer cells
This paper studies a system of Ordinary Differential Equations modeling a chemical reaction network and derives from it a simulation tool mimicking Loss of Function and Gain of Function mutations found in cancer cells.
MEM_GE: a new maximum entropy method for image reconstruction from solar X-ray visibilities
Although widely used in image deconvolution, this method has been formulated in radio astronomy for the analysis of observations in the spatial frequency domain, and an Interactive Data Language (IDL) code has been implemented for image reconstruction from solar X-ray Fourier data.
Image Deconvolution
Image Reconstruction
+1
Solar and Stellar Astrophysics
Instrumentation and Methods for Astrophysics
49N45, 94A08
Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma
Multiple Myeloma (MM) is a blood cancer implying bone marrow involvement, renal damages and osteolytic lesions.
Automated Definition of Skeletal Disease Burden in Metastatic Prostate Carcinoma: a 3D analysis of SPECT/CT images
SVol was compared with the estimation of a commercial software.
Desaturating EUV observations of solar flaring storms
Image saturation has been an issue for several instruments in solar astronomy, mainly at EUV wavelengths.
A hybrid supervised/unsupervised machine learning approach to solar flare prediction
We introduce a hybrid approach to solar flare prediction, whereby a supervised regularization method is used to realize feature importance and an unsupervised clustering method is used to realize the binary flare/no-flare decision.
DESAT: an SSW tool for SDO/AIA image de-saturation
Saturation affects a significant rate of images recorded by the Atmospheric Imaging Assembly on the Solar Dynamics Observatory.
