Book Image

Hands-On Deep Learning for IoT

By : Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim
Book Image

Hands-On Deep Learning for IoT

By: Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim

Overview of this book

Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
4
Section 2: Hands-On Deep Learning Application Development for IoT
10
Section 3: Advanced Aspects and Analytics in IoT

IoT in healthcare

Globally, health and medical care services are facing tremendous challenges owing to, for example, soaring costs, an ageing population, an increase in the prevalence of chronic and/or multiple conditions, and a shortage of skilled healthcare professionals. In addition, traditional care services relying on average and/or qualitative data and a one-size-fits-all prescribing approach do not work well. In this context, the use of IoT in healthcare services addresses many of these challenges by offering the following features:

  • Seamless integration with various existing technologies
  • Support of big data processing and analysis
  • Personalized services
  • Remote and real-time monitoring-based connected healthcare services
  • Quantitative data, which offer more effective services than qualitative data
  • Interactive and real-time interaction between healthcare professionals and...