Machine Learning for Networking : Third International Conference, MLN 2020, Paris, France, November 24-26, 2020, Revised Selected Papers / edited by Éric Renault, Selma Boumerdassi, Paul Mühlethaler.

Format:

Book

Contributor:

Renault, Eric, Editor.

Boumerdassi, Selma, Editor.

Mühlethaler, Paul, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI ; SL 3, 12629

Information Systems and Applications, incl. Internet/Web, and HCI ; 12629

Language:

English

Subjects (All):

Data mining.

Computers.

Machine learning.

Computer networks.

Computer systems.

Computers, Special purpose.

Data Mining and Knowledge Discovery.

Computing Milieux.

Machine Learning.

Computer Communication Networks.

Computer System Implementation.

Special Purpose and Application-Based Systems.

Local Subjects:

Data Mining and Knowledge Discovery.

Computing Milieux.

Machine Learning.

Computer Communication Networks.

Computer System Implementation.

Special Purpose and Application-Based Systems.

Physical Description:

1 online resource (XIII, 375 pages) : 165 illustrations, 148 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

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. .

Other Format:

Printed edition:

ISBN:

978-3-030-70866-5

9783030708665

Access Restriction:

Restricted for use by site license.