Machine Learning for Networking : First International Conference, MLN 2018, Paris, France, November 27–29, 2018, Revised Selected Papers / / edited by Éric Renault, Paul Mühlethaler, Selma Boumerdassi

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
• Edizione: 1st ed. 2019.
• Descrizione fisica: 1 online resource (XIII, 388 p. 208 illus., 156 illus. in color.)
• Disciplina: 006.31
• Soggetto topico: Data mining; Artificial intelligence; Computer communication systems; Special purpose computers; Application software; Data Mining and Knowledge Discovery; Artificial Intelligence; Computer Communication Networks; Special Purpose and Application-Based Systems; Information Systems Applications (incl. Internet)
• Persona (resp. second.): RenaultÉric; MühlethalerPaul; BoumerdassiSelma
• Nota di contenuto: Learning Concave-Convex Profiles of Data Transport Over Dedicated Connections -- Towards Analyzing C-ITS Security Data -- Towards a Statistical Approach for User Classification in Twitter -- RILNET: A Reinforcement Learning Based Load Balancing Approach for Datacenter Networks -- Building a Wide-Area File Transfer Performance Predictor: An Empirical Study -- Advanced Hybrid Technique in Detecting Cloud Web Application's Attacks -- Machine-Learned Classifiers for Protocol Selection on a Shared Network -- Common Structures in Resource Management as Driver for Reinforcement Learning: a Survey and Research Tracks -- Inverse Kinematics Using Arduino and Unity for People with Motor Skill Limitations -- Delmu: A Deep Learning Approach to Maximizing the Utility of Virtualised Millimetre-Wave Backhauls -- Malware Detection System Based on an In-depth Analysis of the Portable Executable Headers -- DNS Traffic Forecasting Using Deep Neural Networks -- Energy-Based Connected Dominating Set for Data Aggregation for Intelligent Wireless Sensor Networks -- Touchless Recognition of Hand Gesture Digits and English Characters Using Convolutional Neural Networks -- LSTM Recurrent Neural Network for Anomaly Detection in Cellular Mobile Networks -- Towards a Better Compromise Between Shallow and Deep CNN for Binary Classification Problems of Unstructured Data -- Reinforcement Learning Based Routing Protocols Analysis for Mobile Ad-Hoc Networks -- Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting -- The Comment of BBS: How Investor Sentiment Affects a Share Market of China -- A Hybrid Neural Network Approach for Lung Cancer Classification with Gene Expression Dataset and Prior Biological Knowledge -- Plant Leaf Disease Detection and Classification Using Particle Swarm Optimization -- A Game Theory Approach for Intrusion Prevention Systems -- WSN Heterogeneous Architecture Platform for IoT -- An IoT Framework for Detecting Movement Within Indoor Environments -- A Hybrid Architecture for Cooperative UAV and USV Swarm Vehicles -- Detecting Suspicious Transactions in Smart Living Spaces -- Intelligent ERP Based Multi Agent Systems and Cloud Computing.
• Sommario/riassunto: This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: 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, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.
• Titolo autorizzato: Machine learning for networking
• ISBN: 3-030-19945-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910337845903321
• Lo trovi qui: Univ. Federico II
• Serie: Information Systems and Applications, incl. Internet/Web, and HCI ; ; 11407