Deep Learning in Internet of Things for Next Generation Healthcare

 
Edition number: 1
Publisher: Chapman and Hall
Date of Publication:
 
Normal price:

Publisher's listprice:
GBP 110.00
Estimated price in HUF:
53 130 HUF (50 600 HUF + 5% VAT)
Why estimated?
 
Your price:

47 817 (45 540 HUF + 5% VAT )
discount is: 10% (approx 5 313 HUF off)
The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
Click here to subscribe.
 
Availability:

Not yet published.
 
  Piece(s)

 
 
 
 
Product details:

ISBN13:9781032586106
ISBN10:1032586109
Binding:Hardback
No. of pages:310 pages
Size:254x178 mm
Language:English
Illustrations: 82 Illustrations, black & white; 12 Halftones, black & white; 70 Line drawings, black & white; 5 Tables, black & white
700
Category:
Short description:

This book presents latest developments in deep learning-enabled healthcare tools and technologies and offers practical ideas of using IoT with deep learning (motion-based object data) to deal with human dynamics, and challenges.

Long description:

This book presents the latest developments in deep learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with deep learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of deep learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with deep learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, Alzheimer's disease, and genomes.



  • Presents practical approaches of using the IoT with deep learning vision and how it deals with human dynamics

  • Offers novel solution for medical imaging including skin lesion detection, cancer detection, enhancement techniques for MRI images, automated disease prediction, fraud detection, genomes, and many more

  • Includes the latest technological advances in the IoT and deep learning with their implementations in healthcare

  • Combines deep learning and analysis in the unified framework to understand both IoT and deep learning applications

  • Covers the challenging issues related to data collection by sensors, detection and tracking of moving objects and solutions to handle relevant challenges

Postgraduate students and researchers in the departments of computer science, working in the areas of the IoT, deep learning, machine learning, image processing, big data, cloud computing, and remote sensing will find this book useful.

Table of Contents:

1. Rise of communications devices to IoT 2. Architecture framework for IoT and deep learning system 3. Deep learning and human vision in IoT 4. Impact of IoT on big data analytics and applications in Medical Images 5. Geospatial data collection tools in healthcare 6. Geospatial technologies in healthcare 7. Advancement of Geospatial Technology in Healthcare systems 8. Implementation of Deep Learning in Assessment of Health Hazardous Air Pollutants 9. Technological interventions in Healthcare 10. Disaster and emergency healthcare 11. Cloud Frameworks for Deep Learning and IoT based Applications in Healthcare Domain 12. Improvement of Patient Care using Robotics in the Healthcare Industry 13. Deep learning Processes in MRI images 14. Artificial Intelligence and Robotics in Healthcare: Transforming the Indian Landscape 15. Medical Insurance Fraud Detection 16. Privacy and Security Issues for IoT and Deep Learning in Next Generation Healthcare: An Indian Perspective 17. A Systematic Review on the Future of Internet of Things Applications in Healthcare 18. 6G Network Development and 3D Holography in Future Healthcare 19. Tracking of disease- A review of state of the art of technology for next generation healthcare 20. Disease detection using Tensor Flow Methodology 21. AI and Deep Learning: Applications in Healthcare