Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms

Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms

by Sandeep Kumar, Rohit Raja, Shrikant Tiwari, Shilpa Rani
Released December 2021
Publisher(s): Wiley-Scrivener
ISBN: 9781119791607

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Book description

COGNITIVE BEHAVIOR AND HUMAN COMPUTER INTERACTION BASED ON MACHINE LEARNING ALGORITHMS

The objective of this book is to provide the most relevant information on Human-Computer Interaction to academics, researchers, and students and for those from industry who wish to know more about the real-time application of user interface design.

Human-computer interaction (HCI) is the academic discipline, which most of us think of as UI design, that focuses on how human beings and computers interact at ever-increasing levels of both complexity and simplicity. Because of the importance of the subject, this book aims to provide more relevant information that will be useful to students, academics, and researchers in the industry who wish to know more about its real-time application. In addition to providing content on theory, cognition, design, evaluation, and user diversity, this book also explains the underlying causes of the cognitive, social and organizational problems typically devoted to descriptions of rehabilitation methods for specific cognitive processes. Also described are the new modeling algorithms accessible to cognitive scientists from a variety of different areas.

This book is inherently interdisciplinary and contains original research in computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Since machine learning research has already been carried out for a decade in various applications, the new learning approach is mainly used in machine learning-based cognitive applications. Since this will direct the future research of scientists and researchers working in neuroscience, neuroimaging, machine learning-based brain mapping, and modeling, etc., this book highlights the framework of a novel robust method for advanced cross-industry HCI technologies. These implementation strategies and future research directions will meet the design and application requirements of several modern and real-time applications for a long time to come.

Audience: A wide range of researchers, industry practitioners, and students will be interested in this book including those in artificial intelligence, machine learning, cognition, computer programming and engineering, as well as social sciences such as psychology and linguistics.

Table of contents

  1. Cover
  2. Title page
  3. Copyright
  4. Preface
  5. 1 Cognitive Behavior: Different Human-Computer Interaction Types
    1. 1.1 Introduction: Cognitive Models and Human-Computer User Interface Management Systems
    2. 1.2 Cognitive Modeling: Decision Processing User Interacting Device System (DPUIDS)
    3. 1.3 Cognitive Modeling: Decision Support User Interactive Device Systems (DSUIDS)
    4. 1.4 Cognitive Modeling: Management Information User Interactive Device System (MIUIDS)
    5. 1.5 Cognitive Modeling: Environment Role With User Interactive Device Systems
    6. 1.6 Conclusion and Scope
    7. References
  6. 2 Classification of HCI and Issues and Challenges in Smart Home HCI Implementation
    1. 2.1 Introduction
    2. 2.2 Literature Review of Human-Computer Interfaces
    3. 2.3 Programming: Convenience and Gadget Explicit Substance
    4. 2.4 Equipment: BCI and Proxemic Associations
    5. 2.5 CHI for Current Smart Homes
    6. 2.6 Four Approaches to Improve HCI and UX
    7. 2.7 Conclusion and Discussion
    8. References
  7. 3 Teaching-Learning Process and Brain-Computer Interaction Using ICT Tools
    1. 3.1 The Concept of Teaching
    2. 3.2 The Concept of Learning
    3. 3.3 The Concept of Teaching-Learning Process
    4. 3.4 Use of ICT Tools in Teaching-Learning Process
    5. 3.5 Conclusion
    6. References
  8. 4 Denoising of Digital Images Using Wavelet-Based Thresholding Techniques: A Comparison
    1. 4.1 Introduction
    2. 4.2 Literature Survey
    3. 4.3 Theoretical Analysis
    4. 4.4 Methodology
    5. 4.5 Results and Discussion
    6. 4.6 Conclusions
    7. References
  9. 5 Smart Virtual Reality–Based Gaze-Perceptive Common Communication System for Children With Autism Spectrum Disorder
    1. 5.1 Need for Focus on Advancement of ASD Intervention Systems
    2. 5.2 Computer and Virtual Reality–Based Intervention Systems
    3. 5.3 Why Eye Physiology and Viewing Pattern Pose Advantage for Affect Recognition of Children With ASD
    4. 5.4 Potential Advantages of Applying the Proposed Adaptive Response Technology to Autism Intervention
    5. 5.5 Issue
    6. 5.6 Global Status
    7. 5.7 VR and Adaptive Skills
    8. 5.8 VR for Empowering Play Skills
    9. 5.9 VR for Encouraging Social Skills
    10. 5.10 Public Status
    11. 5.11 Importance
    12. 5.12 Achievability of VR-Based Social Interaction to Cause Variation in Viewing Pattern of Youngsters With ASD
    13. 5.13 Achievability of VR-Based Social Interaction to Cause Variety in Eye Physiological Indices for Kids With ASD
    14. 5.14 Possibility of VR-Based Social Interaction to Cause Variations in the Anxiety Level for Youngsters With ASD
    15. References
  10. 6 Construction and Reconstruction of 3D Facial and Wireframe Model Using Syntactic Pattern Recognition
    1. 6.1 Introduction
    2. 6.2 Literature Survey
    3. 6.3 Proposed Methodology
    4. 6.4 Datasets and Experiment Setup
    5. 6.5 Results
    6. 6.6 Conclusion
    7. References
  11. 7 Attack Detection Using Deep Learning–Based Multimodal Biometric Authentication System
    1. 7.1 Introduction
    2. 7.2 Proposed Methodology
    3. 7.3 Experimental Analysis
    4. 7.4 Conclusion and Future Scope
    5. References
  12. 8 Feature Optimized Machine Learning Framework for Unbalanced Bioassays
    1. 8.1 Introduction
    2. 8.2 Related Work
    3. 8.3 Proposed Work
    4. 8.4 Experimental
    5. 8.5 Result and Discussion
    6. 8.6 Conclusion
    7. References
  13. 9 Predictive Model and Theory of Interaction
    1. 9.1 Introduction
    2. 9.2 Related Work
    3. 9.3 Predictive Analytics Process
    4. 9.4 Predictive Analytics Opportunities
    5. 9.5 Classes of Predictive Analytics Models
    6. 9.6 Predictive Analytics Techniques
    7. 9.7 Dataset Used in Our Research
    8. 9.8 Methodology
    9. 9.9 Results
    10. 9.10 Discussion
    11. 9.11 Use of Predictive Analytics
    12. 9.12 Conclusion and Future Work
    13. References
  14. 10 Advancement in Augmented and Virtual Reality
    1. 10.1 Introduction
    2. 10.2 Proposed Methodology
    3. 10.3 Results
    4. 10.4 Conclusion
    5. References
  15. 11 Computer Vision and Image Processing for Precision Agriculture
    1. 11.1 Introduction
    2. 11.2 Computer Vision
    3. 11.3 Machine Learning
    4. 11.4 Computer Vision and Image Processing in Agriculture
    5. 11.5 Conclusion
    6. References
  16. 12 A Novel Approach for Low-Quality Fingerprint Image Enhancement Using Spatial and Frequency Domain Filtering Techniques
    1. 12.1 Introduction
    2. 12.2 Existing Works for the Fingerprint Ehancement
    3. 12.3 Design and Implementation of the Proposed Algorithm
    4. 12.4 Results and Discussion
    5. 12.5 Conclusion and Future Scope
    6. References
  17. 13 Elevate Primary Tumor Detection Using Machine Learning
    1. 13.1 Introduction
    2. 13.2 Related Works
    3. 13.3 Proposed Work
    4. 13.4 Experimental Investigation
    5. 13.5 Result and Discussion
    6. 13.6 Conclusion
    7. 13.7 Future Work
    8. References
  18. 14 Comparative Sentiment Analysis Through Traditional and Machine Learning-Based Approach
    1. 14.1 Introduction to Sentiment Analysis
    2. 14.2 Four Types of Sentiment Analyses
    3. 14.3 Working of SA System
    4. 14.4 Challenges Associated With SA System
    5. 14.5 Real-Life Applications of SA
    6. 14.6 Machine Learning Methods Used for SA
    7. 14.7 A Proposed Method
    8. 14.8 Results and Discussions
    9. 14.9 Conclusion
    10. References
  19. 15 Application of Artificial Intelligence and Computer Vision to Identify Edible Bird’s Nest
    1. 15.1 Introduction
    2. 15.2 Prior Work
    3. 15.3 Auto Grading of Edible Birds Nest
    4. 15.4 Experimental Results
    5. 15.5 Conclusion
    6. Acknowledgments
    7. References
  20. 16 Enhancement of Satellite and Underwater Image Utilizing Luminance Model by Color Correction Method
    1. 16.1 Introduction
    2. 16.2 Related Work
    3. 16.3 Proposed Methodology
    4. 16.4 Investigational Findings and Evaluation
    5. 16.5 Conclusion
    6. References
  21. Index
  22. End User License Agreement

Product information

  • Title: Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms
  • Author(s): Sandeep Kumar, Rohit Raja, Shrikant Tiwari, Shilpa Rani
  • Release date: December 2021
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119791607