This special issue explores how emerging machine learning (ML) and artificial intelligence (AI) algorithms can help computer networks become smarter. The final goal is to disseminate cutting-edge research findings and computer network advances on innovative data-driven methodologies and technologies to grow innovation in ML-empowered communication networks. This particular issue aims to present advances on cross-cutting edge machine learning solutions tailored to the computer communication networking area, focusing on algorithmic aspects. The objective is to present which ML methodologies are the most effective and promising ones in the networking context so that they can inspire other researchers and practitioners in the research area of computer networks.

Machine learning empowered computer networks / Cerquitelli, Tania; Meo, Michela; Curado, Marilia; Skorin-Kapov, Lea; Tsiropoulou, Eirini-Eleni. - In: COMPUTER NETWORKS. - ISSN 1389-1286. - STAMPA. - 230:(2023), p. 109807. [10.1016/j.comnet.2023.109807]

Machine learning empowered computer networks

Cerquitelli, Tania;Meo, Michela;
2023

Abstract

This special issue explores how emerging machine learning (ML) and artificial intelligence (AI) algorithms can help computer networks become smarter. The final goal is to disseminate cutting-edge research findings and computer network advances on innovative data-driven methodologies and technologies to grow innovation in ML-empowered communication networks. This particular issue aims to present advances on cross-cutting edge machine learning solutions tailored to the computer communication networking area, focusing on algorithmic aspects. The objective is to present which ML methodologies are the most effective and promising ones in the networking context so that they can inspire other researchers and practitioners in the research area of computer networks.
2023
Machine learning empowered computer networks / Cerquitelli, Tania; Meo, Michela; Curado, Marilia; Skorin-Kapov, Lea; Tsiropoulou, Eirini-Eleni. - In: COMPUTER NETWORKS. - ISSN 1389-1286. - STAMPA. - 230:(2023), p. 109807. [10.1016/j.comnet.2023.109807]
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S1389128623002529-main.pdf

accesso riservato

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 975.29 kB
Formato Adobe PDF
975.29 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2981992