Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data
eBook - ePub

Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data

  1. 294 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data

About this book

Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data discusses the insight of data processing applications in various domains through soft computing techniques and enormous advancements in the field. The book focuses on the cross-disciplinary mechanisms and ground-breaking research ideas on novel techniques and data processing approaches in handling structured and unstructured healthcare data. It also gives insight into various information-processing models and many memories associated with it while processing the information for forecasting future trends and decision making. This book is an excellent resource for researchers and professionals who work in the Healthcare Industry, Data Science, and Machine learning. - Focuses on data-centric operations in the Healthcare industry - Provides the latest trends in healthcare data analytics and practical implementation outcomes of the proposed models - Addresses real-time challenges and case studies in the Healthcare industry

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Yes, you can access Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data by Akash Kumar Bhoi,Victor Hugo Costa de Albuquerque,Parvathaneni Naga Srinivasu,Goncalo Marques in PDF and/or ePUB format, as well as other popular books in Business & Business Intelligence. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Academic Press
Year
2022
Print ISBN
9780323857512
eBook ISBN
9780323903486
Topic
Business
Subtopic
Business Intelligence
Index
Business

Table of contents

  1. Title of Book
  2. Cover image
  3. Title page
  4. Table of Contents
  5. Copyright
  6. Contributors
  7. Preface
  8. Chapter 1 Artificial intelligence and machine learning for the healthcare sector: performing predictions and metrics evaluation of ML classifiers on a diabetic diseases data set
  9. Chapter 2 Cognitive technology for a personalized seizure predictive and healthcare analytic device
  10. Chapter 3 Cognitive Internet of Things (IoT) and computational intelligence for mental well-being
  11. Chapter 4 Artificial neural network-based approaches for computer-aided disease diagnosis and treatment
  12. Chapter 5 AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions
  13. Chapter 6 Universal intraensemble method using nonlinear AI techniques for regression modeling of small medical data sets
  14. Chapter 7 Comparisons among different stochastic selections of activation layers for convolutional neural networks for health care
  15. Chapter 8 Natural computing and unsupervised learning methods in smart healthcare data-centric operations
  16. Chapter 9 Optimized adaptive tree seed Kalman filter for a diabetes recommendation system—bilevel performance improvement strategy for healthcare applications
  17. Chapter 10 Unsupervised deep learning-based disease diagnosis using medical images
  18. Chapter 11 Probabilistic approaches for minimizing the healthcare diagnosis cost through data-centric operations
  19. Chapter 12 Effects of EEG-sleep irregularities and its behavioral aspects: review and analysis
  20. Index