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Hands-On Deep Learning for IoT

You're reading from  Hands-On Deep Learning for IoT

Product type Book
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Pages 308 pages
Edition 1st Edition
Languages
Authors (2):
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Profile icon Dr. Mohammad Abdur Razzaque
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
View More author details
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
2. The End-to-End Life Cycle of the IoT 3. Deep Learning Architectures for IoT 4. Section 2: Hands-On Deep Learning Application Development for IoT
5. Image Recognition in IoT 6. Audio/Speech/Voice Recognition in IoT 7. Indoor Localization in IoT 8. Physiological and Psychological State Detection in IoT 9. IoT Security 10. Section 3: Advanced Aspects and Analytics in IoT
11. Predictive Maintenance for IoT 12. Deep Learning in Healthcare IoT 13. What's Next - Wrapping Up and Future Directions 14. Other Books You May Enjoy

Use case two – image-based smart solid waste separation

Solid waste is a global challenge. The management of solid waste is expensive, and improper waste management is also seriously impacting the global economy, public health, and the environment. Generally, solid waste, such as plastic, glass bottles, and paper, are recyclable, and they need an effective recycling method to become economically and environmentally beneficial. However, in most countries, the existing recycling processes are done manually. In addition, citizens or consumers often become confused about the recycling method.

In this context, IoT with the support of machine learning and deep learning, especially image-based object recognition, can identify the type of waste and help sort it accordingly without any human intervention.

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