Machine learning and deep learning methods for wireless network applications
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1687-1499
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Machine learning and deep learning methods for wireless network applications ; volume:2022 ; number:1 ; day:29 ; month:11 ; year:2022 ; pages:1-4 ; date:12.2022
EURASIP journal on wireless communications and networking / European Association for Speech, Signal and Image Processing ; 2022, Heft 1 (29.11.2022), 1-4, 12.2022
- Urheber
-
Chen, Abel C. H.
Jia, Wen-Kang
Hwang, Feng-Jang
Liu, Genggeng
Song, Fangying
Pu, Lianrong
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s13638-022-02196-2
- URN
-
urn:nbn:de:101:1-2023020921370573660208
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 11:01 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Chen, Abel C. H.
- Jia, Wen-Kang
- Hwang, Feng-Jang
- Liu, Genggeng
- Song, Fangying
- Pu, Lianrong
- SpringerLink (Online service)
Ähnliche Objekte (12)
Machine learning and deep learning
Machine learning and deep learning
Machine learning and deep learning
Machine Learning Based Beam Selection for Maximizing Wireless Network Capacity
Optimizing 802.11 wireless communications with machine learning
Machine learning and deep learning applications in microbiome research
Deep phenotyping using machine learning approaches
Machine understanding and deep learning representation
Deep Learning your Broadband Network @HOME
Machine Learning for Biomedical Applications: From Crowdsourcing to Deep Learning
Diabetes detection based on machine learning and deep learning approaches
Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network
Machine learning and deep learning
Machine learning and deep learning
Machine learning and deep learning
Machine Learning Based Beam Selection for Maximizing Wireless Network Capacity
Optimizing 802.11 wireless communications with machine learning
Machine learning and deep learning applications in microbiome research
Deep phenotyping using machine learning approaches
Machine understanding and deep learning representation
Deep Learning your Broadband Network @HOME
Machine Learning for Biomedical Applications: From Crowdsourcing to Deep Learning
Diabetes detection based on machine learning and deep learning approaches
Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network
Machine learning and deep learning
Machine learning and deep learning
Machine learning and deep learning
Machine Learning Based Beam Selection for Maximizing Wireless Network Capacity
Optimizing 802.11 wireless communications with machine learning
Machine learning and deep learning applications in microbiome research
Deep phenotyping using machine learning approaches
Machine understanding and deep learning representation
Deep Learning your Broadband Network @HOME
Machine Learning for Biomedical Applications: From Crowdsourcing to Deep Learning
Diabetes detection based on machine learning and deep learning approaches