Machine Learning for Networking (eBook, PDF)

7th International Conference, MLN 2024, Reims, France, November 27-29, 2024, Revised Selected Papers
Redaktion: Hacène, Fouchal; Éric, Renault; Selma, Boumerdassi

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 209
• Erscheinungstermin: 12. Oktober 2025
• Englisch
• ISBN-13: 9783032005526
• Artikelnr.: 75653872

Inhaltsangabe

.- 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.