Renault / Boumerdassi / Mühlethaler Machine Learning for Networking

Third International Conference, MLN 2020, Paris, France, November 24–26, 2020, Revised Selected Papers
1. Auflage 2021
ISBN: 978-3-030-70866-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 375 Seiten, eBook

Reihe: Information Systems and Applications, incl. Internet/Web, and HCI

This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.

Zielgruppe

Research

Autoren/Hrsg.

Renault, Éric

Boumerdassi, Selma

Mühlethaler, Paul

Inhaltsverzeichnis

Better anomaly detection for access attacks using deep bidirectional LSTMs.- Using Machine Learning to Quantify the Robustness of Network Controllability.- Configuration faults detection in IP Virtual Private Networks based on machine learning.- Improving Android malware detection through dimensionality reduction techniques.- A Regret Minimization Approach to Frameless Irregular Repetition Slotted Aloha.- Mobility based Genetic algorithm for Heterogeneous wireless networks.- Geographical Information based Clustering Algorithm for Internet of Vehicles.- Active Probing for Improved Machine-Learned Recognition of Network Traffic.- A Dynamic Time Warping and Deep Neural Network Ensemble for Online Signature Verification.- Performance evaluation of some Machine Learning algorithms for Security Intrusion Detection.- Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection.- Learning resource allocation algorithms for cellular networks.- Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning.- Deep Learning-Aided Spatial Multiplexing with Index Modulation.- A Self-Gated Activation Function SINSIG Based on the Sine Trigonometric for Neural Network Models.- Spectral Analysis for Automatic Speech Recognition and Enhancement.- Road sign Identification with Convolutional Neural Network using TensorFlow.- A Semi-Automated Approach for Identification of Trends in Android Ransomware Literature.- Towards Machine Learning in Distributed Array DBMS: Networking Considerations.- Deep Learning Environment Perception and Self-Tracking for Autonomous and Connected Vehicles.- Remote Sensing Scene Classification Based on Effective Feature Learning by Deep Residual Networks.- Identifying Device Types for Anomaly Detection in IoT.- A novel heuristic optimization algorithm for solving the Delay-Constrained Least-Cost problem.- Terms Extraction from Clustered Web Search Results.