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Goel / Yadav

Internet of Things enabled Machine Learning for Biomedical Application

Medium: Buch
ISBN: 978-1-032-55082-4
Verlag: Taylor & Francis Ltd
Erscheinungstermin: 30.09.2024
vorbestellbar, Erscheinungstermin ca. September 2024
The text begins by highlighting the benefits of the internet of things enabled machine learning in the healthcare sector, examines the diagnosis of diseases using machine learning algorithms, and examines security and privacy issues in the healthcare systems using the internet of things. The text elaborates on image processing implementation for medical images to detect and classify diseases based on magnetic resonance imaging and ultrasound images.

This book:

·       Covers the procedure to recognize emotions using image processing and the internet of things-enabled machine learning.

·       Highlights security and privacy issues in the healthcare system using the internet of things.                                                                                       

·       Discusses classification and implementation techniques of image segmentation.

·       Explains different algorithms of machine learning for image processing in a comprehensive manner.                                                                                            

·       Provides computational intelligence on the internet of things for future biomedical applications including lung cancer.

It is primarily written for graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.

Produkteigenschaften


  • Artikelnummer: 9781032550824
  • Medium: Buch
  • ISBN: 978-1-032-55082-4
  • Verlag: Taylor & Francis Ltd
  • Erscheinungstermin: 30.09.2024
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2024
  • Produktform: Gebunden
  • Seiten: 512
  • Format (B x H): 156 x 234 mm
  • Ausgabetyp: Kein, Unbekannt

Autoren/Hrsg.

Herausgeber

Goel, Neha

Yadav, Ravindra Kumar

1. ML and IoT coupled Bio-Medical applications in Healthcare: Smart Growth and Upcoming Challenges. 2. Recent Advances in Ubiquitous Sustainable Healthcare Systems. 3. IoT enabled Healthcare System using Machine Learning. 4. An Efficient Architecture for Classification of Super Resolution Enhanced Human Chromosome Images. 5. Applications of Machine Learning to the Impact of IoT in Biomedical Applications. 6. Ovarian Cancer Detection Using IoT-Based Intelligent Assistant and Blockchain Technology. 7. Blood oxygen level and pulse rate measurement using hemodialysis using IoT and Computational Intelligence. 8. Dental Shade Matching using machine Learning Models. 9. Brain Tumor Detection for Recognising Critical Brain Damage in Patients Using Computer Vision. 10. Smart Therapist: The Mental Health detector. 11. Medical Image Analysis based on Deep Learning Approach and Internet of Medical Things (IoMT) for early Diagnosis of Retinal disease. 12. Intelligent E-Learning Platform Consolidating Web of Things and Chat-GPT. 13. Issues and Challenges in security and privacy with E-health care: a thorough literature analysis. 14. Harnessing the Power of Distributed Cloud and Edge Computing for Advanced Healthcare Systems. 15. Securing Cloud-Based IoT: Exploring the Significance of Lightweight Cryptography for Enhanced Security. 16. Security and Privacy in the Internet of Medical Things (IoMT)-Based Healthcare: Ensuring Trust and Safety. 17. A Comprehensive Study of the Problem and Challenges Associated with Machine Learning Enabled IOT in Biomedical Applications. 18. A Machine Learning-enabled Internet of Things Model for Cloud-based Biomedical Applications. 19. Machine Learning Enabled IoT for Biomedical applications: Problem and challenges. 20. IOT driven Machine learning mechanisms for Healthcare Applications.