- Neu
Boumerdassi / Yellas / Renault Machine Learning for Networking
Erscheinungsjahr 2026
ISBN: 978-3-032-18494-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
8th International Conference, MLN 2025, Paris, France, December 2–4, 2025, Revised Selected Papers
E-Book, Englisch, 219 Seiten
Reihe: Computer Science (R0)
ISBN: 978-3-032-18494-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book constitutes the refereed proceedings of the 8th International Conference on Machine Learning for Networking, MLN 2025, held in Paris, France, during December 2–4, 2025.
The 14 full papers presented in this book were carefully reviewed and selected from 30 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 services.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Slotted Reinforcement Learning-based Radio Resource Allocation in Sliced 5G Networks.- Bone Fracture Recognition using Robust Deep Learning Techniques.- Machine Learning-Based Region Segmentation for Enhanced Wi-Fi Fingerprinting in Indoor Localization.- Enhanced DiNATrAX for Multi-Protocol Anomaly Detection.- Ensemble Neuro-Symbolic AI and Logic Tensor Networks for Detecting Fraud on the Ethereum Blockchain.- Generative Adversarial Network Framework for Synthetic Rainfall Generation and Climate Resilience Planning.- Intelligent Aggregation of Single-Sensor Classifiers for Enhanced Structural Health Monitoring Networks.- Enhancing The Assessment of the Quality of Explanations for AI-based Network IDS.- An Availability Management Framework for Microservices based Safety-critical CIoT Systems.- Dataflow for Predicting Stone Degradation in Built Heritage up to 2100.- Balancing Accuracy and Energy: An Empirical Study of Optimal Subset Size Selection.- Multi-Objective IoT Service Placement in Cloud-Fog-Edge Environments Using Deep Reinforcement Learning.- Predicting Intents: LSTM-Based Modeling.- Multi-Objective Deep RLL Based RAT Selection for V2X Communication.
