Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms
- Length: 384 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2022-01-06
- ISBN-10: 111979160X
- ISBN-13: 9781119791607
- Sales Rank: #0 (See Top 100 Books)
The book focuses on the way that human beings and computers interact to ever increasing levels of both complexity and simplicity. Assuming very little knowledge, the book provides content on theory, cognition, design, evaluation, and user diversity. It aims to explain the underlying causes of the cognitive, social and organizational problems typically are devoted to descriptions of rehabilitation methods for specific cognitive processes. This book describes new algorithms for modeling accessible to cognitive scientists of all varieties.
The book is inherently interdisciplinary, publishing original research in the fields of computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization, as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Machine learning research has been being carried out for a decade at international level in various applications. The new learning approach is mostly used in machine learning based cognitive applications. This will give direction for future research to scientists and researchers working in neuroscience, neuro-imaging, machine learning based brain mapping and modeling etc.
Cover Table of Contents Title Page Copyright Preface 1 Cognitive Behavior: Different Human-Computer Interaction Types 1.1 Introduction: Cognitive Models and Human-Computer User Interface Management Systems 1.2 Cognitive Modeling: Decision Processing User Interacting Device System (DPUIDS) 1.3 Cognitive Modeling: Decision Support User Interactive Device Systems (DSUIDS) 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 2.3 Programming: Convenience and Gadget Explicit Substance 2.4 Equipment: BCI and Proxemic Associations 2.5 CHI for Current Smart Homes 2.6 Four Approaches to Improve HCI and UX 2.7 Conclusion and Discussion References 3 Teaching-Learning Process and Brain-Computer Interaction Using ICT Tools 3.1 The Concept of Teaching 3.2 The Concept of Learning 3.3 The Concept of Teaching-Learning Process 3.4 Use of ICT Tools in Teaching-Learning Process 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 4.4 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 6.4 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 7.3 Experimental Analysis 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 8.4 Experimental 8.5 Result and Discussion 8.6 Conclusion References 9 Predictive Model and Theory of Interaction 9.1 Introduction 9.2 Related Work 9.3 Predictive Analytics Process 9.4 Predictive Analytics Opportunities 9.5 Classes of Predictive Analytics Models 9.6 Predictive Analytics Techniques 9.7 Dataset Used in Our Research 9.8 Methodology 9.9 Results 9.10 Discussion 9.11 Use of Predictive Analytics 9.12 Conclusion and Future Work References 10 Advancement in Augmented and Virtual Reality 10.1 Introduction 10.2 Proposed Methodology 10.3 Results 10.4 Conclusion References 11 Computer Vision and Image Processing for Precision Agriculture 11.1 Introduction 11.2 Computer Vision 11.3 Machine Learning 11.4 Computer Vision and Image Processing in Agriculture 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 12.3 Design and Implementation of the Proposed Algorithm 12.4 Results and Discussion 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 13.4 Experimental Investigation 13.5 Result and Discussion 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 14.2 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 15.3 Auto Grading of Edible Birds Nest 15.4 Experimental Results 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 16.4 Investigational Findings and Evaluation 16.5 Conclusion References Index End User License Agreement
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