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Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms

Edited by Sandeep Kumar, Rohit Raja, Shrikant Tiwari and Shilpa Rani
Copyright: 2022   |   Status: Published
ISBN: 9781119791607  |  Hardcover  |  
400 pages | 125 illustrations
Price: $225 USD
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One Line Description
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.

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.

Description
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.

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Author / Editor Details
Sandeep Kumar, PhD is a Professor in the Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India. He has published more than 100 research papers in various international/national journals and 6 patents. He has been awarded the “Best Excellence Award” in New Delhi, 2019.

Rohit Raja, PhD is an associate professor in the IT Department at the Guru Ghasidas, Vishwavidyalaya, Bilaspur (Central University-CG). He gained his PhD in Computer Science and Engineering in 2016 from C. V. Raman University India. He has filed successfully 10 (9 national + 1 international) patents and published more than 80 research papers in various international/national journals.

Shrikant Tiwari, PhD is an assistant professor in the Department of Computer Science & Engineering (CSE) at Shri Shankaracharya Technical Campus, Junwani, Bhilai, Distt. Chattisgarh, India. He received his PhD from the Department of Computer Science & Engineering (CSE) from the Indian Institute of Technology (Banaras Hindu University), Varanasi (India) in 2012.

Shilpa Rani, PhD is an assistant professor in the Department of Computer Science & Engineering, Neil Gogte Institute of Technology, Hyderabad, India.

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Table of Contents
Preface
1. Cognitive Behavior: Different Human-Computer Interaction Types
S. Venkata Achyuth Rao, Sandeep Kumar and GVRK Acharyulu
1.1 Introduction: Cognitive Models and Human-Computer User Interface Management Systems
1.1.1 Interactive User Behavior Predicting Systems
1.1.2 Adaptive Interaction Observatory Changing Systems
1.1.3 Group Interaction Model Building Systems
1.1.4 Human-Computer User Interface Management Systems
1.1.5 Different Types of Human-Computer User Interfaces
1.1.6 The Role of User Interface Management Systems
1.1.7 Basic Cognitive Behavioral Elements of Human- Computer User Interface Management Systems
1.2 Cognitive Modeling: Decision Processing User Interacting Device System (DPUIDS)
1.2.1 Cognitive Modeling Automation of Decision Process Interactive Device Example
1.2.2 Cognitive Modeling Process in the Visualization Decision Processing User Interactive Device System
1.3 Cognitive Modeling: Decision Support User Interactive Device Systems (DSUIDS)
1.3.1 The Core Artifacts of the Cognitive Modeling of User Interaction
1.3.2 Supporting Cognitive Model for Interaction Decision Supportive Mechanism
1.3.3 Representational Uses of Cognitive Modeling for Decision Support User Interactive Device Systems
1.4 Cognitive Modeling: Management Information User Interactive Device System (MIUIDS)
1.5 Cognitive Modeling: Environment Role With User Interactive Device Systems
1.6 Conclusion and Scope
References
2. Classification of HCI and Issues and Challenges in Smart Home HCI Implementation
Pramod Vishwakarma, Vijay Kumar Soni, Gaurav Srivastav and Abhishek Jain
2.1 Introduction
2.2 Literature Review of Human-Computer Interfaces
2.2.1 Overview of Communication Styles and Interfaces
2.2.2 Input/Output
2.2.3 Older Grown-Ups
2.2.4 Cognitive Incapacities
2.3 Programming: Convenience and Gadget Explicit Substance
2.4 Equipment: BCI and Proxemic Associations
2.4.1 Brain-Computer Interfaces
2.4.2 Ubiquitous Figuring-Proxemic Cooperations
2.4.3 Other Gadget-Related Angles
2.5 CHI for Current Smart Homes
2.5.1 Smart Home for Healthcare
2.5.2 Savvy Homie for Energy Efficiency
2.5.3 Interface Design and Human-Computer Interaction
2.5.4 A Summary of Status
2.6 Four Approaches to Improve HCI and UX
2.6.1 Productive General COntrol Panel
2.6.2 Compelling User Interface
2.6.3 Variable Accessibility
2.6.4 Securer Privacy
2.7 Conclusion and Discussion
References
3. Teaching-Learning Process and Brain-Computer Interaction Using ICT Tools
Rohit Raja, Neelam Sahu and Sumati Pathak
3.1 The Concept of Teaching
3.2 The Concept of Learning
3.2.1 Deficient Visual Perception in a Student
3.2.2 Proper Eye Care (Vision Management)
3.2.3 Proper Ear Care (Hearing Management)
3.2.4 Proper Mind Care (Psychological Management)
3.3 The Concept of Teaching-Learning Process
3.4 Use of ICT Tools in Teaching-Learning Process
3.4.1 Digital Resources as ICT Tools
3.4.2 Special ICT Tools for Capacity Building of Students and Teachers
3.4.2.1 CogniFit
3.4.2.2 Brain-Computer Interface
3.5 Conclusion
References
4. Denoising of Digital Images Using Wavelet-Based Thresholding Techniques: A Comparison
Devanand Bhonsle
4.1 Introduction
4.2 Literature Survey
4.3 Theoretical Analysis
4.3.1 Wavelet Transform
4.3.1.1 Continuous Wavelet Transform
4.3.1.2 Discrete Wavelet Transform
4.3.1.3 Dual-Tree Complex Wavelet Transform
4.3.2 Types of Thresholding
4.3.2.1 Hard Thresholding
4.3.2.2 Soft Thresholding
4.3.2.3 Thresholding Techniques
4.3.3 Performance Evaluation Parameters
4.3.3.1 Mean Squared Error
4.3.3.2 Peak Signal–to-Noise Ratio
4.3.3.3 Structural Similarity Index Matrix
4.4 Methodology
4.5 Results and Discussion
4.6 Conclusions
References
5. Smart Virtual Reality–Based Gaze-Perceptive Common Communication System for Children With Autism Spectrum Disorder
Karunanithi Praveen Kumar and Perumal Sivanesan
5.1 Need for Focus on Advancement of ASD Intervention Systems
5.2 Computer and Virtual Reality–Based Intervention Systems
5.3 Why Eye Physiology and Viewing Pattern Pose Advantage for Affect Recognition of Children With ASD
5.4 Potential Advantages of Applying the Proposed Adaptive Response Technology to Autism Intervention
5.5 Issue
5.6 Global Status
5.7 VR and Adaptive Skills
5.8 VR for Empowering Play Skills
5.9 VR for Encouraging Social Skills
5.10 Public Status
5.11 Importance
5.12 Achievability of VR-Based Social Interaction to Cause Variation in Viewing Pattern of Youngsters With ASD
5.13 Achievability of VR-Based Social Interaction to Cause Variety in Eye Physiological Indices for Kids With ASD
5.14 Possibility of VR-Based Social Interaction to Cause Variations in the Anxiety Level for Youngsters With ASD
References
6. Construction and Reconstruction of 3D Facial and Wireframe Model Using Syntactic Pattern Recognition
Shilpa Rani, Deepika Ghai and Sandeep Kumar
6.1 Introduction
6.1.1 Contribution
6.2 Literature Survey
6.3 Proposed Methodology
6.3.1 Face Detection
6.3.2 Feature Extraction
6.3.2.1 Facial Feature Extraction
6.3.2.2 Syntactic Pattern Recognition
6.3.2.3 Dense Feature Extraction
6.3.3 Enhanced Features
6.3.4 Creation of 3D Model
6.4 Datasets and Experiment Setup
6.5 Results
6.6 Conclusion
References
7. Attack Detection Using Deep Learning–Based Multimodal Biometric Authentication System
Nishant Kaushal, Sukhwinder Singh and Jagdish Kumar
7.1 Introduction
7.2 Proposed Methodology
7.2.1 Expert One
7.2.2 Expert Two
7.2.3 Decision Level Fusion
7.3 Experimental Analysis
7.3.1 Datasets
7.3.2 Setup
7.3.3 Results
7.4 Conclusion and Future Scope
References
8. Feature Optimized Machine Learning Framework for Unbalanced Bioassays
Dinesh Kumar, Anuj Kumar Sharma, Rohit Bajaj and Lokesh Pawar
8.1 Introduction
8.2 Related Work
8.3 Proposed Work
8.3.1 Class Balancing Using Class Balancer
8.3.2 Feature Selection
8.3.3 Ensemble Classification
8.4 Experimental
8.4.1 Dataset Description
8.4.2 Experimental Setting
8.5 Result and Discussion
8.5.1 Performance Evaluation
8.6 Conclusion
References
9. Predictive Model and Theory of Interaction
Raj Kumar Patra, Srinivas Konda, M. Varaprasad Rao, Kavitarani Balmuri and G. Madhukar
9.1 Introduction
9.2 Related Work
9.3 Predictive Analytics Process
9.3.1 Requirement Collection
9.3.2 Data Collection
9.3.3 Data Analysis and Massaging
9.3.4 Statistics and Machine Learning
9.3.5 Predictive Modeling
9.3.6 Prediction and Monitoring
9.4 Predictive Analytics Opportunities
9.5 Classes of Predictive Analytics Models
9.6 Predictive Analytics Techniques
9.6.1 Decision Tree
9.6.2 Regression Model
9.6.3 Artificial Neural Network
9.6.4 Bayesian Statistics
9.6.5 Ensemble Learning
9.6.6 Gradient Boost Model
9.6.7 Support Vector Machine
9.6.8 Time Series Analysis
9.6.9 k-Nearest Neighbors (k-NN)
9.6.10 Principle Component Analysis
9.7 Dataset Used in Our Research
9.8 Methodology
9.8.1 Comparing Link-Level Features
9.8.2 Comparing Feature Models
9.9 Results
9.10 Discussion
9.11 Use of Predictive Analytics
9.11.1 Banking and Financial Services
9.11.2 Retail
9.11.3 Well-Being and Insurance
9.11.4 Oil Gas and Utilities
9.11.5 Government and Public Sector
9.12 Conclusion and Future Work
References
10. Advancement in Augmented and Virtual Reality
Omprakash Dewangan, Latika Pinjarkar, Padma Bonde and Jaspal Bagga
10.1 Introduction
10.2 Proposed Methodology
10.2.1 Classification of Data/Information Extracted
10.2.2 The Phase of Searching of Data/Information
10.3 Results
10.3.1 Original Copy Publication Evolution
10.3.2 General Information/Data Analysis
10.3.2.1 Nations
10.3.2.2 Themes
10.3.2.3 R&D Innovative Work
10.3.2.4 Medical Services
10.3.2.5 Training and Education
10.3.2.6 Industries
10.4 Conclusion
References
11. Computer Vision and Image Processing for Precision Agriculture Narendra Khatri and Gopal U Shinde
11.1 Introduction
11.2 Computer Vision
11.3 Machine Learning
11.3.1 Support Vector Machine
11.3.2 Neural Networks
11.3.3 Deep Learning
11.4 Computer Vision and Image Processing in Agriculture
11.4.1 Plant/Fruit Detection
11.4.2 Harvesting Support
11.4.3 Plant Health Monitoring Along With Disease Detection
11.4.4 Vision-Based Vehicle Navigation System for Precision Agriculture
11.4.5 Vision-Based Mobile Robots for Agriculture Applications
11.5 Conclusion
References
12. A Novel Approach for Low-Quality Fingerprint Image Enhancement Using Spatial and Frequency Domain Filtering Techniques
Mehak Sood and Akshay Girdhar
12.1 Introduction
12.2 Existing Works for the Fingerprint Ehancement
12.2.1 Spatial Domain
12.2.2 Frequency Domain
12.2.3 Hybrid Approach
12.3 Design and Implementation of the Proposed Algorithm
12.3.1 Enhancement in the Spatial Domain
12.3.2 Enhancement in the Frequency Domain
12.4 Results and Discussion
12.4.1 Visual Analysis
12.4.2 Texture Descriptor Analysis
12.4.3 Minutiae Ratio Analysis
12.4.4 Analysis Based on Various Input Modalities
12.5 Conclusion and Future Scope
References
13. Elevate Primary Tumor Detection Using Machine Learning
Lokesh Pawar, Pranshul Agrawal, Gurjot Kaur and Rohit Bajaj
13.1 Introduction
13.2 Related Works
13.3 Proposed Work
13.3.1 Class Balancing
13.3.2 Classification
13.3.3 Eliminating Using Ranker Algorithm
13.4 Experimental Investigation
13.4.1 Dataset Description
13.4.2 Experimental Settings
13.5 Result and Discussion
13.5.1 Performance Evaluation
13.5.2 Analytical Estimation of Selected Attributes
13.6 Conclusion
13.7 Future Work
References
14. Comparative Sentiment Analysis Through Traditional and Machine Learning-Based Approach
Sandeep Singh and Harjot Kaur
14.1 Introduction to Sentiment Analysis
14.1.1 Sentiment Definition
14.1.2 Challenges of Sentiment Analysis Tasks
14.2 Four Types of Sentiment Analyses
14.3 Working of SA System
14.4 Challenges Associated With SA System
14.5 Real-Life Applications of SA
14.6 Machine Learning Methods Used for SA
14.7 A Proposed Method
14.8 Results and Discussions
14.9 Conclusion
References
15. Application of Artificial Intelligence and Computer Vision to Identify Edible Bird’s Nest
Weng Kin Lai, Mei Yuan Koay, Selina Xin Ci Loh, Xiu Kai Lim and Kam Meng Goh
15.1 Introduction
15.2 Prior Work
15.2.1 Low-Dimensional Color Features
15.2.2 Image Pocessing for Automated Grading
15.2.3 Automated Classification
15.3 Auto Grading of Edible Birds Nest
15.3.1 Feature Extraction
15.3.2 Curvature as a Feature
15.3.3 Amount of Impurities
15.3.4 Color of EBNs
15.3.5 Size—Total Area
15.4 Experimental Results
15.4.1 Data Pre-Processing
15.4.2 Auto Grading
15.4.3 Auto Grading of EBNs
15.5 Conclusion
Acknowledgments
References
16. Enhancement of Satellite and Underwater Image Utilizing Luminance Model by Color Correction Method
Sandeep Kumar, E. G. Rajan and Shilpa Rani
16.1 Introduction
16.2 Related Work
16.3 Proposed Methodology
16.3.1 Color Correction
16.3.2 Contrast Enhancement
16.3.3 Multi-Fusion Method
16.4 Investigational Findings and Evaluation
16.4.1 Mean Square Error
16.4.2 Peak Signal–to-Noise Ratio
16.4.3 Entropy
16.5 Conclusion
References
Index

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