Editorial Topical Collection: “Explainable and Augmented Machine Learning for Biosignals and Biomedical Images”
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Ieracitano, Cosimo
Mahmud, Mufti
Doborjeh, Maryam
Lay-Ekuakille, Aimé
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MDPI AG
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 [...].
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46 Information and Computing Sciences, 4602 Artificial Intelligence, 4611 Machine Learning, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Data Science, Bioengineering, 0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, Analytical Chemistry, 3103 Ecology, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware, 4104 Environmental management, 4606 Distributed computing and systems software
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Ieracitano, 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
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© 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/).
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Except where otherwise noted, this item's license is described as © 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/).