Hybrid Computational Intelligence

Book description

Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development.

Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems.

  • Provides insights into the latest research trends in hybrid intelligent algorithms and architectures
  • Focuses on the application of hybrid intelligent techniques for pattern mining and recognition, in big data analytics, and in human-computer interaction
  • Features hybrid intelligent applications in biomedical engineering and healthcare informatics

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. List of contributors
  7. Preface
  8. Chapter 1. Application and techniques of opinion mining
    1. Abstract
    2. 1.1 Introduction
    3. 1.2 Fundamentals of opinion mining
    4. 1.3 Feature extraction and its impact on opinion mining
    5. 1.4 Deep learning and its relation to opinion mining
    6. 1.5 Techniques of opinion mining
    7. 1.6 Tools of opinion mining
    8. 1.7 Ontology-based opinion mining
    9. 1.8 Applications of opinion mining
    10. 1.9 Conclusion
    11. References
  9. Chapter 2. Influence of big data in smart tourism
    1. Abstract
    2. 2.1 Introduction to smart tourism
    3. 2.2 Introduction to big data
    4. 2.3 Applications of big data
    5. 2.4 Use of big data in the travel sector
    6. 2.5 Big data is transforming the travel industry
    7. 2.6 Key findings for the travel sector
    8. 2.7 Tools for big data analysis for smart tourism
    9. 2.8 Applying PROPHET and ARIMA prediction models
    10. 2.9 Challenges in data-intensive tourism
    11. 2.10 Conclusion and future scope
    12. References
    13. Further reading
  10. Chapter 3. Deep learning and its applications for content-based video retrieval
    1. Abstract
    2. 3.1 Introduction
    3. 3.2 Video retrieval techniques
    4. 3.3 Video querying
    5. 3.4 Deep learning for video analysis
    6. 3.5 A multitier deep learning-based video classification using C3D
    7. References
  11. Chapter 4. A computationally intelligent agent for detecting fake news using generative adversarial networks
    1. Abstract
    2. 4.1 Fake news
    3. 4.2 Deep learning
    4. 4.3 Generative adversarial networks
    5. 4.4 A case study on generative adversarial networks
    6. 4.5 Experiment and results
    7. 4.6 Summary
    8. References
    9. Further reading
  12. Chapter 5. Hybrid computational intelligence for healthcare and disease diagnosis
    1. Abstract
    2. 5.1 Introduction
    3. 5.2 Medical image segmentation and classification
    4. 5.3 Disease and diagnosis approach
    5. 5.4 Identifying brain activity using a state classifier
    6. 5.5 Genomics
    7. 5.6 Health bioinformatics
    8. 5.7 Discussion
    9. 5.8 Conclusion
    10. References
  13. Chapter 6. Application of hybrid computational intelligence in health care
    1. Abstract
    2. 6.1 Introduction
    3. 6.2 Need for computational intelligence in health care
    4. 6.3 Need for hybrid computational intelligence in health care
    5. 6.4 Use cases for hybrid computational intelligence in health care
    6. 6.5 Conclusion
    7. References
  14. Chapter 7. Utility system for premature plant disease detection using machine learning
    1. Abstract
    2. 7.1 Introduction
    3. 7.2 Literature survey
    4. 7.3 Design and implementation
    5. 7.4 Results
    6. 7.5 Conclusion
    7. References
    8. Further reading
  15. Chapter 8. Artificial intelligence-based computational fluid dynamics approaches
    1. Abstract
    2. 8.1 Introduction
    3. 8.2 AI-based computational fluid dynamic approaches
    4. 8.3 Conclusion
    5. References
    6. Appendix
  16. Chapter 9. Real-time video segmentation using a vague adaptive threshold
    1. Abstract
    2. 9.1 Introduction
    3. 9.2 Temporal video segmentation (shot boundary detection)
    4. 9.3 Basic concepts and preliminaries
    5. 9.4 Approach for real-time video segmentation
    6. 9.5 Experimental results and analysis
    7. 9.6 Future directions and conclusion
    8. References
  17. Index

Product information

  • Title: Hybrid Computational Intelligence
  • Author(s): Siddhartha Bhattacharyya, Vaclav Snasel, Deepak Gupta, Ashish Khanna
  • Release date: March 2020
  • Publisher(s): Academic Press
  • ISBN: 9780128187005