Zobrazeno 1 - 10
of 73
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
Publikováno v:
Big Data & Society, Vol 9 (2022)
Emotion recognition, and in particular acial emotion recognition (FER), is among the most controversial applications of machine learning, not least because of its ethical implications for human subjects. In this article, we address the controversial
Externí odkaz:
https://doaj.org/article/ca8e646484a441b99c181427e1fcc9a2
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
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-21 (2020)
Abstract Background We focus on the importance of interpreting the quality of the labeling used as the input of predictive models to understand the reliability of their output in support of human decision-making, especially in critical domains, such
Externí odkaz:
https://doaj.org/article/a3c7b490f6a141ec98e8ac3a86122b34
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 20, Iss S5, Pp 1-14 (2020)
Abstract Background Despite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how
Externí odkaz:
https://doaj.org/article/abedf1f7072542f7ab6fab4ee9d13f13
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
Publikováno v:
Information Fusion. 90:241-252
In this work we present a large-scale comparison of 21 learning and aggregation methods proposed in the ensemble learning, social choice theory (SCT), information fusion and uncertainty management (IF-UM) and collective intelligence (CI) fields, base
Publikováno v:
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.
In this article, we propose a conceptual and methodological framework for measuring the impact of the introduction of AI systems in decision settings, based on the concept of technological dominance, i.e. the influence that an AI system can exert on