LEADER 01580cam0-2200541---450 001 990005513420403321 005 20171107085036.0 035 $a000551342 035 $aFED01000551342 035 $a(Aleph)000551342FED01 035 $a000551342 100 $a19990604d1966----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $aaf--ac--001yy 200 1 $aUrbanistica delle città greche$fAntonio Giuliano 210 $aMilano$cCasa editrice Il Saggiatore$d1966 215 $a286 p.$cill.$d21 cm 225 1 $aUomo e mito$v49 320 $aBibliografia: p. 235-238 610 0 $aUrbanistica$aGrecia antica 676 $a711 676 $a307.760938 676 $a711.0938 700 1$aGiuliano,$bAntonio$f<1930- >$023685 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005513420403321 952 $a307.76 GIU 1$bBIBL. 40127$fFLFBC 952 $a711 GIU 2$bIst.Fil.Cl.s.i.$fFLFBC 952 $aURB.LE B 594$b7612$fFARBC 952 $aURB.LE B 590$b8104A MANCA$fFARBC 952 $aURB.LE B 587$b8575A$fFARBC 952 $aURB.LE B 591$b8104B$fFARBC 952 $aURB.LE B 592$b8104C$fFARBC 952 $aURB.LE B 593$b8104D$fFARBC 952 $aURB.LE B 589$b8575C$fFARBC 952 $aURB.LE B 588$b8575B MANCA$fFARBC 952 $aI(FN) E 273$b2931$fDCATA 952 $a04.003$b4903$fDARST 952 $aRGT 640$b2875$fDARPU 959 $aDARPU 959 $aFLFBC 959 $aFARBC 959 $aDCATA 959 $aDARST 996 $aUrbanistica delle città greche$9279428 997 $aUNINA LEADER 03631nam 22006015 450 001 9910734899303321 005 20251116192559.0 010 $a3-031-36183-0 024 7 $a10.1007/978-3-031-36183-8 035 $a(MiAaPQ)EBC30621296 035 $a(Au-PeEL)EBL30621296 035 $a(DE-He213)978-3-031-36183-8 035 $a(PPN)272249866 035 $a(CKB)27498429800041 035 $a(EXLCZ)9927498429800041 100 $a20230706d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Networking $e5th International Conference, MLN 2022, Paris, France, November 28?30, 2022, Revised Selected Papers /$fedited by Éric Renault, Paul Mühlethaler 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (190 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13767 311 08$aPrint version: Renault, Éric Machine Learning for Networking Cham : Springer International Publishing AG,c2023 9783031361821 327 $aComparison of AI-based algorithms for low energy communication -- Development of an Intent-Based Network incorporating Machine Learning for service Assurance of E-commerce Online Stores -- Cyber-attack proactive defense using multivariate time series and machine learning with Fuzzy Inference-based Decision System -- iPerfOPS: a Tool for Machine Learning-Based Optimization through Protocol Selection -- GRAPHSEC -- Advancing the Application of AI/ML to Network Security through Graph Neural Networks -- Low Complexity Adaptive ML Approaches for End-to-End Latency Prediction -- TDMA-based MAC protocols designed or optimized using Artificial Intelligence for safety data dissemination in Vehicular ad-hoc network: A Survey -- A Machine Learning Based Approach to Detect Stealthy Cobalt Strike C\&C Activities from Encrypted Network Traffic -- Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents -- Deep Learning Based Camera Switching for Sports Broadcasting -- Phisherman: Phishing Link Scanner -- Leader-Assisted Client Selection for Federated Learning in Iot via the Cooperation of Nearby Devices. . 330 $aThis book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning for Networking, MLN 2022, held in Paris, France, November 28?30, 2022. The 12 full papers presented in this book were carefully reviewed and selected from 27 submissions. The papers present novel ideas, results, experiences and work-in-process on all aspects of Machine Learning and Networking. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13767 606 $aData mining 606 $aComputer networks 606 $aApplication software 606 $aData Mining and Knowledge Discovery 606 $aComputer Communication Networks 606 $aComputer and Information Systems Applications 615 0$aData mining. 615 0$aComputer networks. 615 0$aApplication software. 615 14$aData Mining and Knowledge Discovery. 615 24$aComputer Communication Networks. 615 24$aComputer and Information Systems Applications. 676 $a006.312 676 $a006.31 700 $aRenault$b Eric$00 701 $aMu?hlethaler$b Paul$00 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734899303321 996 $aMachine Learning for Networking$93404234 997 $aUNINA