Название: Machine Learning and IoT Applications for Health Informatics
Автор: Pijush Samui, Sanjiban Sekhar Roy, Wengang Zhang, Y H Taguchi
Издательство: CRC Press
Год: 2025
Страниц: 251
Язык: английский
Формат: pdf (true), epub
Размер: 33.6 MB

This book brings together leading experts from around the world to explore the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) in healthcare. It provides a platform for studying a future where healthcare becomes more precise, personalized, and accessible for all. The book covers recent advancements that will shape the future of healthcare and how artificial intelligence is revolutionizing disease detection, from analyzing chest X-rays for pneumonia to solving the secrets of our genes. It investigates the transformative potential of smart devices, real-time analysis of heart data, and personalized treatment plan creation. It shows how ML and IoT work and presents real-world examples of how they are leading to earlier and more accurate diagnoses and personalized treatments. Therefore, this edited book will be an invaluable resource for researchers, healthcare professionals, data scientists, or simply someone passionate about the future of healthcare. Readers will discover the exciting possibilities that lie ahead at the crossroads of ML, IoT, and health informatics. This book brings together leading experts from around the world to explore the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) in healthcare. It provides a platform for studying a future where healthcare becomes more precise, personalized, and accessible for all.

The convergence of Machine Learning (ML) and the Internet of Things (IoT) forms a significant area of interest in today’s technological world. The primary goal of integrating these two fields is to process the large streams of data from IoT devices and extract more meaningful and useful information. Primarily, Machine Learning provides the ability to learn from large and complex datasets, which is vital for analyzing raw data (e.g., sensor data) collected by IoT devices and deriving beneficial insights. Machine Learning models can use these data to identify patterns, detect abnormal behaviors, and predict future trends.

The continuous data generation by IoT devices necessitates their immediate processing and analysis. Machine Learning aids in this process by efficiently processing data, thereby enhancing the performance, security, and efficiency of IoT systems. This is particularly critical for real-time applications. Overall, the amalgamation of Machine Learning and IoT aims to create a more connected and efficient technological environment by enabling smarter use of data. This integration allows for maximizing the use of data collected by IoT devices, with Machine Learning algorithms effectively processing this data to offer new and valuable insights. This synergy significantly contributes to the technological advancements of the future, enabling smarter and more autonomous functioning of devices both in our homes and industrial applications. In the literature, there are various Artificial Intelligence techniques and IoT applications.








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