31 citations · 87 across the 11 of their papers we have counts for
12 papers
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review
Giovanni Ciatto, Federico Sabbatini, Andrea Agiollo +2
In this paper we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities, namely, symbolic knowledge extraction (SKE) and i…
Evaluating Machine Learning Models against Clinical Protocols for Enhanced Interpretability and Continuity of Care
Christel Sirocchi, Muhammad Suffian, Federico Sabbatini +2
In clinical practice, decision-making relies heavily on established protocols, often formalised as rules. Concurrently, Machine Learning (ML) models, trained on clinical data, aspi…
Characterization of hydrogenated amorphous silicon sensors on polyimide flexible substrate
M. Menichelli, L. Antognini, S. Aziz +50
Hydrogenated amorphous silicon (a-Si:H) is a material having an intrinsically high radiation hardness that can be deposited on flexible substrates like Polyimide. For these propert…
Particle monitoring capability of the Solar Orbiter Metis coronagraph through the increasing phase of solar cycle 25
Catia Grimani, Vincenzo Andretta, Ester Antonucci +26
Context. Galactic cosmic rays (GCRs) and solar particles with energies greater than tens of MeV penetrate spacecraft and instruments hosted aboard space missions. The Solar Orbiter…
Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA
Federico Sabbatini, Catia Grimani
In this work we study the potentialities of machine learning models in reconstructing the solar wind speed observations gathered in the first Lagrangian point by the ACE satellite…
Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper
Federico Sabbatini, Roberta Calegari
As opaque decision systems are being increasingly adopted in almost any application field, issues about their lack of transparency and human readability are a concrete concern for…