This new volume, Real-Life Applications of the Internet of Things: Challenges, Applications, and Advances, provides an overview of the Internet of Things along with its architectures, its vital technologies their uses in our daily life and other domains, and associated challenges. The book also covers some advanced topics where IoT is combined with other emerging technologies, such as blockchain and cloud, which will bring a revolution in the era of IoT. Topics in the volume cover the many powerful features and applications of IoT, such as for weather forecasting, in agriculture, in medical science, in surveillance systems, and much more. The first section of the book covers many of the issues and challenging arising from the Internet of Things. The section explores security challenges, such as attack detection and prevention systems as well as energy efficiency and resource management in IoT. Part II introduces various application areas of IoT, including the uses of smart technology in agricultural management, in healthcare diagnosis and monitoring, and in the financial industry. The third section of the book discusses the integration of IoT with blockchain, cloud, and big data and features some advanced topics. The variety of topics includes surveillance network technology, the shift from television to video streaming apps and the technology involved, using IoT-fog computing for smart healthcare, detection of anomalies in climate conditions, and even detection of illegal wood logging activity. The informative chapters in this volume will shed light on the varied challenges, issues, and advances in IoT technology and culture for scientists and researchers, faculty and students, and industry professionals.

This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation. The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.