Hacène / Selma / Éric Machine Learning for Networking
Erscheinungsjahr 2025
ISBN: 978-3-032-00552-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
7th International Conference, MLN 2024, Reims, France, November 27–29, 2024, Revised Selected Papers
E-Book, Englisch, 209 Seiten
Reihe: Computer Science (R0)
ISBN: 978-3-032-00552-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning for Networking, MLN 2024, held in Reims, France, during November 27–29, 2024.
The 14 full papers presented in this book were carefully reviewed and selected from 25 submissions. The International Conference on Machine Learning for Networking (MLN) aims at providing a top forum for researchers and practitioners to present and discuss new trends in machine learning, deep learning, pattern recognition and optimization for network architectures and service.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
.- Learning per-flow SD-WAN load-balancing policies.
.- Survey on Federated Learning in Smart Healthcare.
.- Complex Communication Networks Management with Distributed AI:Challenges and Open Issues.
.- A Framework for Global Trust and Reputation Management in 6G Networks.
.- DRL Framework for Minimizing Beam Switching Time and Maintaining QoS in 6G-V2X Base Stations.
.- Reducing BLE energy loss in busy 2.4GHz band.
.- Leveraging SHAP to advance the Robustness of Large Language Models.
.- Keyword-Driven Email Classification: Leveraging Machine Learning Techniques.
.- Predicting Intents: ARMA-Based Modeling.
.- Design and Evaluation of a Lightweight SDN Controller for Integrated Road and Rail Networks.
.- PiPS: An effective strategy and approach for Privacy in Public Surveillance.
.- A comprehensive review of deep learning approaches for tomato leaf diseases detection and classification in smart agriculture.
.- A review on advancement in PEM Fuel cell Diagnosis based on Machine
learning techniques.
.- GPS Spoofing Attack against UAVs: a timeseries dataset case study.
