Machine Learning for Networking
portes grátis
Machine Learning for Networking
8th International Conference, MLN 2025, Paris, France, December 2-4, 2025, Revised Selected Papers
Boumerdassi, Selma; Renault, Eric; Yellas, Nour El-Houda
Springer Nature Switzerland AG
04/2026
218
Mole
Inglês
9783032184931
Pré-lançamento - envio 15 a 20 dias após a sua edição
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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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Machine Learning Approaches;Machine Learning Algorithms;Artificial Intelligence;Pattern Recognition;Classification for Networks;Network Slicing Optimization;5G Networks;Performance Analysis;Experimental Evaluation;Security;Intelligent Cloud Support Communications;Resource Allocation;Wireless Networks
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Machine Learning Approaches;Machine Learning Algorithms;Artificial Intelligence;Pattern Recognition;Classification for Networks;Network Slicing Optimization;5G Networks;Performance Analysis;Experimental Evaluation;Security;Intelligent Cloud Support Communications;Resource Allocation;Wireless Networks
