Recent advances on effective and efficient deep learning-based solutions

Martín García, Alejandro ORCID: https://orcid.org/0000-0002-0800-7632 and Camacho Fernández, David ORCID: https://orcid.org/0000-0002-5051-3475 (2022). Recent advances on effective and efficient deep learning-based solutions. "Neural Computing and Applications", v. 34 (n. 13); pp. 10205-10210. ISSN 1433-3058. https://doi.org/10.1007/s00521-022-07344-9.

Descripción

Título: Recent advances on effective and efficient deep learning-based solutions
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Neural Computing and Applications
Fecha: 25 Mayo 2022
ISSN: 1433-3058
Volumen: 34
Número: 13
Materias:
ODS:
Palabras Clave Informales: Affordable and clean energy
Escuela: E.T.S.I. de Sistemas Informáticos (UPM)
Departamento: Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Proyectos asociados

Tipo
Código
Acrónimo
Responsable
Título
Gobierno de España
PID2020-117263GB-100
FightDIS
Sin especificar
Sin especificar
Gobierno de España
PLEC2021-007681
XAI-Disinfodemics
Sin especificar
Sin especificar

Más información

ID de Registro: 88875
Identificador DC: https://oa.upm.es/88875/
Identificador OAI: oai:oa.upm.es:88875
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/9932155
Identificador DOI: 10.1007/s00521-022-07344-9
URL Oficial: https://link.springer.com/article/10.1007/s00521-0...
Depositado por: iMarina Portal Científico
Depositado el: 05 May 2025 16:09
Ultima Modificación: 05 May 2025 16:09