Machine Learning for Networking

Nyhet

8th International Conference, MLN 2025, Paris, France, December 2–4, 2025, Revised Selected Papers

Häftad, Engelska, 2026

AvSelma Boumerdassi,Nour El-Houda Yellas,Éric Renault

1 329 kr

Kommande

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.

Produktinformation

Utgivningsdatum2026-04-13
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor218
FörlagSpringer Nature Switzerland AG
ISBN9783032184931

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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.