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Recent advances on effective and efficient deep learning-based solutions
This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131985/ https://www.ncbi.nlm.nih.gov/pubmed/35637932 http://dx.doi.org/10.1007/s00521-022-07344-9 |
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author | Martín, Alejandro Camacho, David |
author_facet | Martín, Alejandro Camacho, David |
author_sort | Martín, Alejandro |
collection | PubMed |
description | This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions. |
format | Online Article Text |
id | pubmed-9131985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-91319852022-05-26 Recent advances on effective and efficient deep learning-based solutions Martín, Alejandro Camacho, David Neural Comput Appl Editorial This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions. Springer London 2022-05-25 2022 /pmc/articles/PMC9131985/ /pubmed/35637932 http://dx.doi.org/10.1007/s00521-022-07344-9 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 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 Martín, Alejandro Camacho, David Recent advances on effective and efficient deep learning-based solutions |
title | Recent advances on effective and efficient deep learning-based solutions |
title_full | Recent advances on effective and efficient deep learning-based solutions |
title_fullStr | Recent advances on effective and efficient deep learning-based solutions |
title_full_unstemmed | Recent advances on effective and efficient deep learning-based solutions |
title_short | Recent advances on effective and efficient deep learning-based solutions |
title_sort | recent advances on effective and efficient deep learning-based solutions |
topic | Editorial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131985/ https://www.ncbi.nlm.nih.gov/pubmed/35637932 http://dx.doi.org/10.1007/s00521-022-07344-9 |
work_keys_str_mv | AT martinalejandro recentadvancesoneffectiveandefficientdeeplearningbasedsolutions AT camachodavid recentadvancesoneffectiveandefficientdeeplearningbasedsolutions |