Intelligent Internet of Things for Healthcare and Industry

This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of machine learning-based data analytics of IoT infrastructures. It focuses on the emerging trends, strategies, and applications of IoT in healthcare and industry data analytics, helping to design and develop intelligent medical and industry solutions.

Format: Paperback / softback
Length: 384 pages
Publication date: 14 February 2023
Publisher: Springer Nature Switzerland AG

This book is a valuable resource for researchers, practitioners, and students interested in exploring the design and investigation of machine learning-based data analytics of IoT infrastructures. It promotes and facilitates exchanges of research knowledge and findings across different disciplines, enabling a comprehensive understanding of the emerging trends, strategies, and applications of IoT in healthcare and industry data analytics.

The data analytics discussed in this book are crucial for addressing the technical challenges and issues that arise in healthcare and industry as they strive to realize the full potential of IoT. By leveraging the power of data analytics and machine learning, this book helps to design and develop intelligent medical and industry solutions that are driven by data insights.

At the end of each chapter, readers are provided with brainstorming summaries, discussions, exercises, and solutions to reinforce their understanding of the topics covered. This book serves as a comprehensive guide for researchers, practitioners, and students who want to stay ahead of the curve in the rapidly evolving field of IoT data analytics.

Introduction:
The Internet of Things (IoT) has revolutionized the way we interact with the world, enabling the collection and analysis of vast amounts of data from various devices and systems. This has led to the emergence of machine learning-based data analytics as a powerful tool for extracting valuable insights and making informed decisions.

Objective:
The primary objective of this book is to promote and facilitate exchanges of research knowledge and findings across different disciplines on the design and investigation of machine learning-based data analytics of IoT infrastructures. By bringing together experts from various fields, we aim to provide a comprehensive understanding of the emerging trends, strategies, and applications of IoT in healthcare and industry data analytics.

Chapter 1:
In this chapter, we will provide an overview of the IoT and its significance in healthcare and industry data analytics. We will discuss the challenges and opportunities that arise from the massive amounts of data generated by IoT devices and the need for efficient and effective data analytics solutions.

Chapter 2:
In this chapter, we will explore the emerging trends in IoT data analytics, including big data analytics, artificial intelligence, and machine learning. We will discuss the advantages and limitations of each trend and how they can be applied to healthcare and industry data analytics.

Chapter 3:
In this chapter, we will delve into the strategies and applications of IoT in healthcare data analytics. We will discuss the use of IoT devices for remote monitoring, patient diagnosis, and personalized treatment. We will also explore the challenges and opportunities associated with healthcare data privacy and security.

Chapter 4:
In this chapter, we will examine the strategies and applications of IoT in industry data analytics. We will discuss the use of IoT devices for supply chain management, asset tracking, and predictive maintenance. We will also explore the challenges and opportunities associated with industry data privacy and security.

Chapter 5:
In this chapter, we will discuss the challenges and opportunities associated with the design and investigation of machine learning-based data analytics of IoT infrastructures. We will explore the different methodologies and techniques used in machine learning, including supervised learning, unsupervised learning, and reinforcement learning.

Chapter 6:
In this chapter, we will showcase some real-world examples of machine learning-based data analytics of IoT infrastructures. We will discuss the use of machine learning in healthcare and industry to improve patient outcomes, reduce costs, and increase efficiency.

Conclusion:
In conclusion, this book is a valuable resource for researchers, practitioners, and students interested in exploring the design and investigation of machine learning-based data analytics of IoT infrastructures. It provides a comprehensive understanding of the emerging trends, strategies, and applications of IoT in healthcare and industry data analytics, and serves as a guide for developing intelligent medical and industry solutions that are driven by data insights.

Weight: 611g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030814755
Edition number: 1st ed. 2022