Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Cognitive Behavior: Different Human-Computer Interaction Types
	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
	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 Home 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 Secure Privacy
	2.7 Conclusion and Discussion
	References
3 Teaching-Learning Process and BrainComputer Interaction Using ICT Tools
	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
	4.1 Introduction
	4.2 Literature Survey
	4.3 Theoretical Analysis
		4.3.1 Wavelet Transform
		4.3.2 Types of Thresholding
		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
	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
	6.1 Introduction
	6.2 Literature Survey
	6.3 Proposed Methodology
		6.3.2.1 Facial Feature Extraction
		6.3.2.2 Syntactic Pattern Recognition
		6.3.2.3 Dense Feature Extraction
	6.4 Datasets and Experiment Setup
	6.5 Results
	6.6 Conclusion
	References
7 Attack Detection Using Deep Learning–Based Multimodal Biometric Authentication System
	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
	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
	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
	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
	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
	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
	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
	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
	15.1 Introduction
	15.2 Prior Work
		15.2.1 Low-Dimensional Color Features
		15.2.2 Image Processing 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
	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
EULA