Machine Learning for Networking
Description
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.
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Content
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.
