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001434788 001__ 1434788
001434788 003__ OCoLC
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001434788 019__ $$a1241442954
001434788 020__ $$a9783030708665$$q(electronic bk.)
001434788 020__ $$a3030708667$$q(electronic bk.)
001434788 020__ $$z3030708659
001434788 020__ $$z9783030708658
001434788 0247_ $$a10.1007/978-3-030-70866-5$$2doi
001434788 035__ $$aSP(OCoLC)1241448576
001434788 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dGW5XE$$dOCLCO$$dEBLCP$$dYDX$$dOCLCF$$dUKAHL$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCO$$dN$T$$dOCLCQ
001434788 049__ $$aISEA
001434788 050_4 $$aQ325.5
001434788 08204 $$a004.6$$223
001434788 1112_ $$aInternational Conference on Machine Learning for Networking$$n(3rd :$$d2020 :$$cOnline)
001434788 24510 $$aMachine learning for networking :$$bthird International Conference, MLN 2020, Paris, France, November 24-26, 2020, Revised Selected Papers /$$cÉric Renault, Selma Boumerdassi, Paul Mühlethaler (eds.).
001434788 2463_ $$aMLN 2020
001434788 260__ $$aCham :$$bSpringer,$$c2021.
001434788 300__ $$a1 online resource (386 pages)
001434788 336__ $$atext$$btxt$$2rdacontent
001434788 337__ $$acomputer$$bc$$2rdamedia
001434788 338__ $$aonline resource$$bcr$$2rdacarrier
001434788 4901_ $$aLecture Notes in Computer Science ;$$v12629
001434788 4901_ $$aLNCS sublibrary: SL 3, Information systems and applications, incl. internet/web, and HCI
001434788 500__ $$a"Due to the special health situation all around the world, all presentations at MLN 2020 were done remotely."
001434788 500__ $$aIncludes author index.
001434788 5050_ $$aBetter anomaly detection for access attacks using deep bidirectional LSTMs -- Using Machine Learning to Quantify the Robustness of Network Controllability -- Configuration faults detection in IP Virtual Private Networks based on machine learning -- Improving Android malware detection through dimensionality reduction techniques -- A Regret Minimization Approach to Frameless Irregular Repetition Slotted Aloha -- Mobility based Genetic algorithm for Heterogeneous wireless networks -- Geographical Information based Clustering Algorithm for Internet of Vehicles -- Active Probing for Improved Machine-Learned Recognition of Network Traffic -- A Dynamic Time Warping and Deep Neural Network Ensemble for Online Signature Verification -- Performance evaluation of some Machine Learning algorithms for Security Intrusion Detection -- Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection -- Learning resource allocation algorithms for cellular networks -- Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning -- Deep Learning-Aided Spatial Multiplexing with Index Modulation -- A Self-Gated Activation Function SINSIG Based on the Sine Trigonometric for Neural Network Models -- Spectral Analysis for Automatic Speech Recognition and Enhancement -- Road sign Identification with Convolutional Neural Network using TensorFlow -- A Semi-Automated Approach for Identification of Trends in Android Ransomware Literature -- Towards Machine Learning in Distributed Array DBMS: Networking Considerations -- Deep Learning Environment Perception and Self-Tracking for Autonomous and Connected Vehicles -- Remote Sensing Scene Classification Based on Effective Feature Learning by Deep Residual Networks -- Identifying Device Types for Anomaly Detection in IoT -- A novel heuristic optimization algorithm for solving the Delay-Constrained Least-Cost problem -- Terms Extraction from Clustered Web Search Results.
001434788 506__ $$aAccess limited to authorized users.
001434788 520__ $$aThis book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.
001434788 588__ $$aDescription based on print version record.
001434788 650_0 $$aMachine learning$$vCongresses.
001434788 650_0 $$aComputer networks$$vCongresses.
001434788 650_6 $$aApprentissage automatique$$vCongrès.
001434788 650_6 $$aRéseaux d'ordinateurs$$vCongrès.
001434788 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001434788 655_7 $$aConference papers and proceedings.$$2lcgft
001434788 655_7 $$aActes de congrès.$$2rvmgf
001434788 655_0 $$aElectronic books.
001434788 7001_ $$aRenault, Éric$$c(Computer scientist)
001434788 7001_ $$aBoumerdassi, Selma.
001434788 7001_ $$aMühlethaler, Paul.
001434788 77608 $$iPrint version:$$aRenault, Éric$$tMachine Learning for Networking : Third International Conference, MLN 2020, Paris, France, November 24-26, 2020, Revised Selected Papers$$dCham : Springer International Publishing AG,c2021$$z9783030708658
001434788 830_0 $$aLecture notes in computer science ;$$v12629.
001434788 830_0 $$aLNCS sublibrary.$$nSL 3,$$pInformation systems and applications, incl. Internet/Web, and HCI.
001434788 852__ $$bebk
001434788 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-70866-5$$zOnline Access$$91397441.1
001434788 909CO $$ooai:library.usi.edu:1434788$$pGLOBAL_SET
001434788 980__ $$aBIB
001434788 980__ $$aEBOOK
001434788 982__ $$aEbook
001434788 983__ $$aOnline
001434788 994__ $$a92$$bISE