Editorial Topical Collection: "Explainable and Augmented Machine Learning for Biosignals and Biomedical Images"

Cosimo Ieracitano; Mufti Mahmud; Maryam Doborjeh; Aimé Lay-Ekuakille

doi:10.3390/s23249722

Editorial Topical Collection: "Explainable and Augmented Machine Learning for Biosignals and Biomedical Images"

Sensors (Basel). 2023 Dec 9;23(24):9722. doi: 10.3390/s23249722.

Authors

Cosimo Ieracitano¹, Mufti Mahmud^{2

3

4}, Maryam Doborjeh⁵, Aimé Lay-Ekuakille⁶

Affiliations

¹ DICEAM Department, University Mediterranea of Reggio Calabria, Via Zehender, Feo di Vito, 89122 Reggio Calabria, Italy.
² Department of Computer Science, Nottingham Trent University, Nottingham NG11 8NS, UK.
³ Computing and Informatics Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK.
⁴ Medical Technologies Innovation Facility, Nottingham Trent University, Nottingham NG11 8NS, UK.
⁵ Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand.
⁶ Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy.

Abstract

Machine learning (ML) is a well-known subfield of artificial intelligence (AI) that aims at developing algorithms and statistical models able to empower computer systems to automatically adapt to a specific task through experience or learning from data [...].

Publication types

Editorial

MeSH terms

Algorithms
Artificial Intelligence*
Computer Systems
Machine Learning*
Models, Statistical

Grants and funding

This work was supported in part by Programma Operativo Nazionale (PON) “Ricerca e Innovazione” 2014–2020 CCI2014IT16M2OP005 (CUP C35F21001220009 code: I05).