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Artificial intelligence-based Internet of Things systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence-based Internet of Things systems/ edited by Souvik Pal, Debashis De, Rajkumar Buyya.
other author:	Pal, Souvik.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xvii, 509 p. :ill., digital ;24 cm.
[NT 15003449]:	Part - 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.
Contained By:	Springer Nature eBook
Subject:	Internet of things. -
Online resource:	https://doi.org/10.1007/978-3-030-87059-1
ISBN:	9783030870591

Artificial intelligence-based Internet of Things systems
Artificial intelligence-based Internet of Things systems[electronic resource] /edited by Souvik Pal, Debashis De, Rajkumar Buyya. - Cham :Springer International Publishing :2022. - xvii, 509 p. :ill., digital ;24 cm. - Internet of things,2199-1081. - Internet of things..

Part - 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.

The 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.

ISBN: 9783030870591

Standard No.: 10.1007/978-3-030-87059-1doiSubjects--Topical Terms:

2057703
Internet of things.

LC Class. No.: TK5105.8857 / .A77 2022

Dewey Class. No.: 004.678

Artificial intelligence-based Internet of Things systems
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505 0 $a Part - 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 $a The 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.
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