Ieracitano, CosimoMahmud, MuftiDoborjeh, MaryamLay-Ekuakille, Aimé2025-02-192025-02-192023-12-09Ieracitano, C., Mahmud, M., Doborjeh, M., & Lay-Ekuakille, A. (2023). Editorial Topical Collection: “Explainable and Augmented Machine Learning for Biosignals and Biomedical Images”. Sensors, 23(24), 9722. https://doi.org/10.3390/s232497221424-82201424-8220http://hdl.handle.net/10292/18722Machine 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 [...].Electronic© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).https://creativecommons.org/licenses/by/4.0/46 Information and Computing Sciences4602 Artificial Intelligence4611 Machine LearningNetworking and Information Technology R&D (NITRD)Machine Learning and Artificial IntelligenceData ScienceBioengineering0301 Analytical Chemistry0502 Environmental Science and Management0602 Ecology0805 Distributed Computing0906 Electrical and Electronic EngineeringAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwareAlgorithmsArtificial IntelligenceComputer SystemsMachine LearningModels, StatisticalArtificial IntelligenceMachine LearningAlgorithmsComputer SystemsModels, StatisticalModels, StatisticalAlgorithmsArtificial IntelligenceComputer SystemsMachine LearningArtificial IntelligenceMachine LearningAlgorithmsComputer SystemsModels, StatisticalOther Form of Assessable OutputOpenAccess10.3390/s23249722