01195nam0 22003011i 450 UON0032874720231205104211.62197-334-0057-220090804d1990 |0itac50 barumRO|||| 1||||BomarzoManuel Mujica Laineztraducere si note de Romeo Magherescuprefata de Dan GrigorescuBucurestiEditura Univers1990XXIII, 615 p.20 cm.001UON001755862001 Piragua. Novela210 Buenos AiresEditorial Sudamericana.135ROBucureştiUONL000071Ar863Letteratura argentina. Narrativa21MUJICA LAINEZManuelUONV119019174925GRIGORESCUDanUONV126744MAGHERESCURomeoUONV187264Editions UniversUONV275546650ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00328747SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI FONDO ONCIULESCU A 0479 SI EO 42769 7 0479 Bomarzo525748UNIOR04103nam 22006375 450 991088110090332120260605211324.09783031600272(electronic bk.)10.1007/978-3-031-60027-2(MiAaPQ)EBC31608985(Au-PeEL)EBL31608985(CKB)34118620400041(DE-He213)978-3-031-60027-2(EXLCZ)993411862040004120240820d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierActivity Recognition and Prediction for Smart IoT Environments /edited by Michele Ianni, Antonella Guzzo, Raffaele Gravina, Hassan Ghasemzadeh, Zhelong Wang1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Springer,2024.1 online resource (188 pages)Internet of Things, Technology, Communications and Computing,2199-1081Print version: Ianni, Michele Activity Recognition and Prediction for Smart IoT Environments Cham : Springer,c2024 9783031600265 Includes bibliographical references and index.Introduction -- Methodology for human activity recognition based on wearable sensor networks -- Efficient Sensing and Classification for Extended Battery Life -- Multi-user activity monitoring based on contactless sensing -- An efficient approach exploiting Ensemble Learning for Human Activity Recognition -- Activity Recognition Using 2-D LiDAR based on Improved MobileNet -- Habit mining through process-mining techniques. Survey and research challenges -- The role of ML in Activity Recognition in the Industry 4.0 -- IoT Based HAR patterns using Sensors based Approach in smart environment and enabled assistive technologies -- Trace2AR: a novel embedding for the detection of complex activity recognition -- Situation Aware Wearable Systems for Human Activity Recognition -- Conclusion.This book provides the latest developments in activity recognition and prediction, with particular focus on the Internet of Things. The book covers advanced research and state of the art of activity prediction and its practical application in different IoT related contexts, ranging from industrial to scientific, from business to daily living, from education to government and so on. New algorithms, architectures, and methodologies are proposed, as well as solutions to existing challenges with a focus on security, privacy, and safety. The book is relevant to researchers, academics, professionals and students. Provides a comprehensive review of the field of activity recognition; Covers an array of topics and applications illustrating the use of activity recognition in IoT related scenarios; Explains how to extract value from application logs and use the data to classify activities and predict actions. .Internet of Things, Technology, Communications and Computing,2199-1081Cooperating objects (Computer systems)TelecommunicationUser interfaces (Computer systems)Human-computer interactionBiometric identificationCyber-Physical SystemsCommunications Engineering, NetworksUser Interfaces and Human Computer InteractionBiometricsCooperating objects (Computer systems)Telecommunication.User interfaces (Computer systems)Human-computer interaction.Biometric identification.Cyber-Physical Systems.Communications Engineering, Networks.User Interfaces and Human Computer Interaction.Biometrics.613.04244Ianni MicheleMiAaPQMiAaPQMiAaPQ9910881100903321Activity Recognition and Prediction for Smart IoT Environments4207506UNINA