Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Andrea Campagner"'
Autor:
Federico Cabitza, Andrea Campagner, Chiara Natali, Enea Parimbelli, Luca Ronzio, Matteo Cameli
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 1, Pp 269-286 (2023)
The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI sy
Externí odkaz:
https://doaj.org/article/a3f66c0e4be74ae99c3c935b94866b42
Autor:
Andrea Campagner, Frida Milella, Stefania Guida, Susan Bernareggi, Giuseppe Banfi, Federico Cabitza
Publikováno v:
Diagnostics, Vol 13, Iss 6, p 1189 (2023)
Total hip (THA) and total knee (TKA) arthroplasty procedures have steadily increased over the past few decades, and their use is expected to grow further, mainly due to an increasing number of elderly patients. Cost-containment strategies, supporting
Externí odkaz:
https://doaj.org/article/3133133d4ac74faf8dbfc06bdebed985
Autor:
Nuno Bento, Joana Rebelo, Marília Barandas, André V. Carreiro, Andrea Campagner, Federico Cabitza, Hugo Gamboa
Publikováno v:
Sensors, Vol 22, Iss 19, p 7324 (2022)
Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the
Externí odkaz:
https://doaj.org/article/8feb243e58774b089adfe924c0796b42
Publikováno v:
Journal of Intelligence, Vol 9, Iss 2, p 17 (2021)
Medical errors have a huge impact on clinical practice in terms of economic and human costs. As a result, technology-based solutions, such as those grounded in artificial intelligence (AI) or collective intelligence (CI), have attracted increasing in
Externí odkaz:
https://doaj.org/article/d7b3ad719d2e432e971a0fa08daa30e5
Autor:
Federico Cabitza, Andrea Campagner, Domenico Albano, Alberto Aliprandi, Alberto Bruno, Vito Chianca, Angelo Corazza, Francesco Di Pietto, Angelo Gambino, Salvatore Gitto, Carmelo Messina, Davide Orlandi, Luigi Pedone, Marcello Zappia, Luca Maria Sconfienza
Publikováno v:
Applied Sciences, Vol 10, Iss 11, p 4014 (2020)
In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metri
Externí odkaz:
https://doaj.org/article/215bf220cd494d83b64352bab2fc310e
Autor:
Domenico Albano, Andrea Campagner, Alberto Aliprandi, Carmelo Messina, Luca Maria Sconfienza, Marcello Zappia, Francesco Di Pietto, Angelo Gambino, Alberto Bruno, Vito Chianca, Federico Cabitza, Davide Orlandi, Luigi Pedone, Salvatore Gitto, Angelo Corazza
Publikováno v:
Applied Sciences, Vol 10, Iss 4014, p 4014 (2020)
Applied Sciences
Volume 10
Issue 11
Applied Sciences
Volume 10
Issue 11
In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metri