Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries [by Team-IRA]

Machine learning (ML) and the internet of things (IoT) are the top technologies used by businesses to increase efficiency, productivity, and competitiveness in this fast-paced digital era transformation. ML is the key tool for fast processing and decision making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. IoT technology has proven efficient in solving many real-world problems, and ML algorithms combined with IoT means the fusion of product and intelligence to achieve better automation, efficiency, productivity, and connectivity. The Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries highlights the importance of ML for IoT's success and diverse ML-powered IoT applications. This book addresses the problems and challenges in energy, industry, and healthcare and solutions proposed for ML-enabled IoT and new algorithms in ML. It further addresses their accuracy for existing real-time applications. Covering topics such as agriculture, pattern recognition, and smart applications, this premier reference source is an essential resource for engineers, scientists, educators, students, researchers, and academicians. Title Page Copyright Page Book Series List of Contributors Table of Contents Detailed Table of Contents Preface Chapter 1: Challenges in Various Applications Using IoT Chapter 2: Pattern Recognition by IoT Systems of Machine Learning Chapter 3: Institutional Pressures on the Oil and Gas Industry Chapter 4: Generative Adversarial Networks Chapter 5: Machine Learning-Enabled Internet of Things Solution for Smart Agriculture Operations Chapter 6: An Investigative Study on Internet of Things in Healthcare Chapter 7: Machine Learning-Based Threat Identification Systems Chapter 8: Future Outlier Detection Algorithm for Smarter Industry Application Using ML and AI Chapter 9: Edge Computing Chapter 10: The Role of Wireless Body Area Networks in Smart Healthcare System in the Context of Big Data and AI Chapter 11: Significance of Fog Computing to Machine Learning-Enabled IoT for Smart Applications Across Industries Chapter 12: New Cloud Computing-Based Strategy for Coordinating Multi-Robot Systems Chapter 13: Impact of UAVs in Agriculture Chapter 14: A Survey on Diagnosis of Hazardous Gas Emission Using AI Techniques Chapter 15: IoVST Chapter 16: Smart Cities Chapter 17: IoT and Machine Learning on Smart Home-Based Data and a Perspective on Fog Computing Implementation Chapter 18: Activity Recognition and IoT-Based Analysis Using Time Series and CNN Chapter 19: A Comprehensive Review of IoT Reliability and Its Measures Chapter 20: Sustainable IoT for Smart Environmental Control Chapter 21: Evolutionized Industry With the Internet of Things Chapter 22: Integration of WSN and IoT Chapter 23: The Current Generation 5G and Evolution of 6G to 7G Technologies Compilation of References About the Contributors Index