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

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aut.event.place, Switzerland
aut.relation.issue24
aut.relation.startpage9722
aut.relation.volume23
dc.contributor.authorIeracitano, Cosimo
dc.contributor.authorMahmud, Mufti
dc.contributor.authorDoborjeh, Maryam
dc.contributor.authorLay-Ekuakille, Aimé
dc.date.accessioned2025-02-19T22:35:36Z
dc.date.available2025-02-19T22:35:36Z
dc.date.issued2023-12-09
dc.description.abstractMachine 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 [...].
dc.format.mediumElectronic
dc.identifier.citationIeracitano, 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/s23249722
dc.identifier.doi10.3390/s23249722
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10292/18722
dc.languageeng
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/1424-8220/23/24/9722
dc.rights© 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/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subject4611 Machine Learning
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectData Science
dc.subjectBioengineering
dc.subject0301 Analytical Chemistry
dc.subject0502 Environmental Science and Management
dc.subject0602 Ecology
dc.subject0805 Distributed Computing
dc.subject0906 Electrical and Electronic Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.subject.meshAlgorithms
dc.subject.meshArtificial Intelligence
dc.subject.meshComputer Systems
dc.subject.meshMachine Learning
dc.subject.meshModels, Statistical
dc.subject.meshArtificial Intelligence
dc.subject.meshMachine Learning
dc.subject.meshAlgorithms
dc.subject.meshComputer Systems
dc.subject.meshModels, Statistical
dc.subject.meshModels, Statistical
dc.subject.meshAlgorithms
dc.subject.meshArtificial Intelligence
dc.subject.meshComputer Systems
dc.subject.meshMachine Learning
dc.subject.meshArtificial Intelligence
dc.subject.meshMachine Learning
dc.subject.meshAlgorithms
dc.subject.meshComputer Systems
dc.subject.meshModels, Statistical
dc.titleEditorial Topical Collection: “Explainable and Augmented Machine Learning for Biosignals and Biomedical Images”
dc.typeOther Form of Assessable Output
pubs.elements-id533320

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