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Table of Contents
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.
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.