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Editorial: deep learning for 5G IoT systems

Detalles Bibliográficos
Autores principales: Cheng, Xiaochun, Zhang, Chengqi, Qian, Yi, Aloqaily, Moayad, Xiao, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8287284/
https://www.ncbi.nlm.nih.gov/pubmed/34306244
http://dx.doi.org/10.1007/s13042-021-01382-w
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author Cheng, Xiaochun
Zhang, Chengqi
Qian, Yi
Aloqaily, Moayad
Xiao, Yang
author_facet Cheng, Xiaochun
Zhang, Chengqi
Qian, Yi
Aloqaily, Moayad
Xiao, Yang
author_sort Cheng, Xiaochun
collection PubMed
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spelling pubmed-82872842021-07-19 Editorial: deep learning for 5G IoT systems Cheng, Xiaochun Zhang, Chengqi Qian, Yi Aloqaily, Moayad Xiao, Yang Int J Mach Learn Cybern Editorial Springer Berlin Heidelberg 2021-07-19 2021 /pmc/articles/PMC8287284/ /pubmed/34306244 http://dx.doi.org/10.1007/s13042-021-01382-w Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Editorial
Cheng, Xiaochun
Zhang, Chengqi
Qian, Yi
Aloqaily, Moayad
Xiao, Yang
Editorial: deep learning for 5G IoT systems
title Editorial: deep learning for 5G IoT systems
title_full Editorial: deep learning for 5G IoT systems
title_fullStr Editorial: deep learning for 5G IoT systems
title_full_unstemmed Editorial: deep learning for 5G IoT systems
title_short Editorial: deep learning for 5G IoT systems
title_sort editorial: deep learning for 5g iot systems
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8287284/
https://www.ncbi.nlm.nih.gov/pubmed/34306244
http://dx.doi.org/10.1007/s13042-021-01382-w
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