國立虎尾科技大學 |

Machine Learning for Networking = Third International Conference, MLN 2020, Paris, France, November 24–26, 2020, Revised Selected Papers /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning for Networking/ edited by Éric Renault, Selma Boumerdassi, Paul Mühlethaler.
Reminder of title:	Third International Conference, MLN 2020, Paris, France, November 24–26, 2020, Revised Selected Papers /
other author:	Renault, Éric.
Description:	XIII, 375 p. 165 illus., 148 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-030-70866-5
ISBN:	9783030708665

Machine Learning for Networking = Third International Conference, MLN 2020, Paris, France, November 24–26, 2020, Revised Selected Papers /
Machine Learning for NetworkingThird International Conference, MLN 2020, Paris, France, November 24–26, 2020, Revised Selected Papers /[electronic resource] :edited by Éric Renault, Selma Boumerdassi, Paul Mühlethaler. - 1st ed. 2021. - XIII, 375 p. 165 illus., 148 illus. in color.online resource. - Information Systems and Applications, incl. Internet/Web, and HCI ;12629. - Information Systems and Applications, incl. Internet/Web, and HCI ;9149.

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. .

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.

ISBN: 9783030708665

Standard No.: 10.1007/978-3-030-70866-5doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Machine Learning for Networking = Third International Conference, MLN 2020, Paris, France, November 24–26, 2020, Revised Selected Papers /
LDR:04600nam a22004215i 4500 001 1046512
003 DE-He213
005 20210826183702.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030708665 $9 978-3-030-70866-5
024 7 $a 10.1007/978-3-030-70866-5 $2 doi
035 $a 978-3-030-70866-5
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
245 1 0 $a Machine Learning for Networking $h [electronic resource] : $b Third International Conference, MLN 2020, Paris, France, November 24–26, 2020, Revised Selected Papers / $c edited by Éric Renault, Selma Boumerdassi, Paul Mühlethaler.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XIII, 375 p. 165 illus., 148 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Information Systems and Applications, incl. Internet/Web, and HCI ; $v 12629
505 0 $a 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. .
520 $a 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.
650 0 $a Data mining. $3 528622
650 0 $a Computers. $3 565115
650 0 $a Machine learning. $3 561253
650 0 $a Computer communication systems. $3 1115394
650 0 $a Architecture, Computer. $3 1255731
650 0 $a Special purpose computers. $3 1204562
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Computing Milieux. $3 669921
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Computer Communication Networks. $3 669310
650 2 4 $a Computer System Implementation. $3 669803
650 2 4 $a Special Purpose and Application-Based Systems. $3 669833
700 1 $a Renault, Éric. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1264766
700 1 $a Boumerdassi, Selma. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1069412
700 1 $a Mühlethaler, Paul. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1298571
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030708658
776 0 8 $i Printed edition: $z 9783030708672
830 0 $a Information Systems and Applications, incl. Internet/Web, and HCI ; $v 9149 $3 1253558
856 4 0 $u https://doi.org/10.1007/978-3-030-70866-5
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)