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008			220111s2022 sz | s |||| 0|eng d
020			_a9783030870591
024	7		_a10.1007/978-3-030-87059-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.8857 _b2022 EB
245	0	0	_aArtificial Intelligence-based Internet of Things Systems _cedited by Souvik Pal, Debashis De, Rajkumar Buyya
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (XVII, 509 páginas) _b167 ilustraciones, 119 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aInternet of Things Technology Communications and Computing _x2199-1081
505	0		_aPart - I. Architecture, Systems, and Services -- Chapter1. Artificial Intelligence-based Internet of Things for Industry 5.0 -- Chapter2. IoT Ecosystem: Functioning Framework, Hierarchy of Knowledge and Intelligence -- Chapter3. Artificial Neural Networks and Support Vector Machine for IoT -- Chapter4. The Role of Machine Learning Techniques in Internet of Things Based Cloud Applications -- Chapter5. Deep Learning Frameworks for Internet of Things -- Chapter6. Fog-Cloud enabled Internet of Things using Extended Classifier System (XCS) -- Chapter7. Convolutional Neural Network (CNN) - Based Signature Verification via Cloud-enabled Raspberry Pi System -- Chapter8. Machine to Machine (M2M), Radio Frequency Identification (RFID), Software-defined Networking (SDN): Facilitators of Internet of Things -- Chapter9. Architecture, Generative Model, Deep Reinforcement Learning for IoT Applications: Deep Learning Perspective -- Chapter10. Enabling Inference and Training of Deep Learning Models for AI Applications on IoT Edge Devices -- Chapter11. Non-volatile Memory based Internet of Things: A survey -- Chapter12. Integration of AI and IoT approaches for evaluating cyber Security risk on smart city -- Chapter13. Cognitive Internet of Things: Challenges and Solutions -- Part - II. Applications -- Chapter14. An AI Approach to Rebalance Bike Sharing Systems with Adaptive User Incentive -- Chapter15. IoT-driven Bayesian Learning: A Case Study of Reducing Road Accidents of Commercial Vehicles on Highways -- Chapter16. On the Integration of AI and IoT Systems: A Case Study of Airport Smart Parking -- Chapter17. Vision-based End-to-End Deep Learning for Autonomous Driving in Next-Generation IoT Systems -- Chapter18. A Study on the Application of Bayesian Learning and Decision Trees IoT-enabled system in Post-harvest Storage.
520			_aThe book discusses the evolution of future generation technologies through Internet of Things (IoT) in the scope of Artificial Intelligence (AI). The main focus of this volume is to bring all the related technologies in a single platform, so that undergraduate and postgraduate students, researchers, academicians, and industry people can easily understand the AI algorithms, machine learning algorithms, and learning analytics in IoT-enabled technologies. This book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable AI-enabled IoT ecosystem and to implement cyber-physical pervasive infrastructure solutions. This book brings together some of the top IoT-enabled AI experts throughout the world who contribute their knowledge regarding different IoT-based technology aspects. Addresses the complete functional framework workflow in AI-enabled IoT ecosystem; Presents intelligent object identification and object discovery through the IoT ecosystem and its implications to the real world; Explores security and privacy issues and trustworthy machine learning related to data-intensive technologies in AI-based IoT ecosystems.
988			_aSpringer_Computer_2022
650		7	_2embne _9483083 _aInternet de los objetos
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aPal, Souvik _eeditor literario
700	1		_aDe, Debashis _eeditor literario
700	1		_aBuyya, Rajkumar _eeditor literario
773	0		_tSpringer Nature eBook
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-87059-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2022 _dz _eu _zSI