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Full Description
Internet of Things (IoTs) are now being integrated at a large scale in fast-developing applications such as healthcare, transportation, education, finance, insurance and retail. The next generation of automated applications will command machines to do tasks better and more efficiently. Both industry and academic researchers are looking at transforming applications using machine learning and deep learning to build better models and by taking advantage of the decentralized nature of Blockchain. But the advent of these new technologies also brings very high expectations to industries, organisations and users. The decrease of computing costs, the improvement of data integrity in Blockchain, and the verification of transactions using Machine Learning are becoming essential goals.
This edited book covers the challenges, opportunities, innovations, new concepts and emerging trends related to the use of machine learning, Blockchain and Big Data analytics for IoTs. The book is aimed at a broad audience of ICTs, data science, machine learning and cybersecurity researchers interested in the integration of these disruptive technologies and their applications for IoTs.
Contents
Chapter 1: Introduction to machine learning, blockchain technologies, and Big Data analytics for IoTs: concepts, open issues, and critical challenges
Chapter 2: Image enhancement on low-light and dark images for object detection using Artificial Intelligence for field practitioners
Chapter 3: Cache memory architecture for the convergence of machine learning, Internet of Things (IoT), and blockchain technologies
Chapter 4: Machine learning algorithms for Big Data analytics including deep learning
Chapter 5: Machine learning-based blockchain technologies for data storage: challenges, and opportunities
Chapter 6: Clustering crowdsourced healthcare data from drones using Big Data analytics
Chapter 7: Authentication and authorization in cloud computing using blockchain
Chapter 8: Fundamentals of machine learning and blockchain technologies for applications in cybersecurity
Chapter 9: Real-world applications of generative adversarial networks and their role in blockchain technology
Chapter 10: Internet of Things (IoTs)-enabled security using artificial intelligence and blockchain technologies
Chapter 11: Blockchain network with artificial intelligence - DeFi affair management
Chapter 12: Vulnerabilities of smart contracts and solutions
Chapter 13: Data analytics for socio-economic factors affecting crime rates
Chapter 14: Deployment of automated teller machinery for e-polling
Chapter 15: Machine learning-based blockchain technology for protection and privacy against intrusion attacks in intelligent transportation systems
Chapter 16: Blockchain-enabled Internet of Things (IoTs) platforms for vehicle sensing and transportation monitoring
Chapter 17: Blockchain-enabled Internet of Things (IoTs) platforms for the healthcare sector
Chapter 18: An integrated dimensionality reduction model for classifying IoT-enabled smart healthcare genomic data
Chapter 19: Blockchain-based learning automated analytics platform in telemedicine
Chapter 20: A sensor-based architecture for telemedical and environmental air pollution monitoring system using 5G and blockchain
Chapter 21: Blockchain-enabled Internet of Things (IoT) platforms for financial services
Chapter 22: Blockchain and machine learning: an approach for predicting the commodity prices
Chapter 23: Knowledge extraction from abnormal stock returns: evidence from Indian stock market
Chapter 24: Impact of influence analysis of social media fake news - a machine learning perspective
Chapter 25: Application of machine learning techniques based on real-time images for site specific insect pest and disease management of crops
Chapter 26: A prioritized potential framework for combined computing technologies: IoT, Machine Learning, and blockchain
Chapter 27: Conclusion to this book
