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Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms

1. Aufl.

von: Sandeep Kumar, Rohit Raja, Shrikant Tiwari, Shilpa Rani
190,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	23.11.2021
ISBN/EAN:	9781119792086
Sprache:	englisch
Anzahl Seiten:	400

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Beschreibungen

Titelbeschreibung

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.

Inhaltsverzeichnis

Preface xv 1 Cognitive Behavior: Different Human-Computer Interaction Types 1 S. Venkata Achyuth Rao, Sandeep Kumar and GVRK Acharyulu 1.1 Introduction: Cognitive Models and Human-Computer User Interface Management Systems 2 1.1.1 Interactive User Behavior Predicting Systems 2 1.1.2 Adaptive Interaction Observatory Changing Systems 3 1.1.3 Group Interaction Model Building Systems 4 1.1.4 Human-Computer User Interface Management Systems 5 1.1.5 Different Types of Human-Computer User Interfaces 5 1.1.6 The Role of User Interface Management Systems 6 1.1.7 Basic Cognitive Behavioral Elements of Human- Computer User Interface Management Systems 7 1.2 Cognitive Modeling: Decision Processing User Interacting Device System (DPUIDS) 9 1.2.1 Cognitive Modeling Automation of Decision Process Interactive Device Example 9 1.2.2 Cognitive Modeling Process in the Visualization Decision Processing User Interactive Device System 11 1.3 Cognitive Modeling: Decision Support User Interactive Device Systems (DSUIDS) 12 1.3.1 The Core Artifacts of the Cognitive Modeling of User Interaction 13 1.3.2 Supporting Cognitive Model for Interaction Decision Supportive Mechanism 13 1.3.3 Representational Uses of Cognitive Modeling for Decision Support User Interactive Device Systems 14 1.4 Cognitive Modeling: Management Information User Interactive Device System (MIUIDS) 17 1.5 Cognitive Modeling: Environment Role With User Interactive Device Systems 19 1.6 Conclusion and Scope 20 References 20 2 Classification of HCI and Issues and Challenges in Smart Home HCI Implementation 23 Pramod Vishwakarma, Vijay Kumar Soni, Gaurav Srivastav and Abhishek Jain 2.1 Introduction 23 2.2 Literature Review of Human-Computer Interfaces 26 2.2.1 Overview of Communication Styles and Interfaces 33 2.2.2 Input/Output 37 2.2.3 Older Grown-Ups 37 2.2.4 Cognitive Incapacities 38 2.3 Programming: Convenience and Gadget Explicit Substance 40 2.4 Equipment: BCI and Proxemic Associations 41 2.4.1 Brain-Computer Interfaces 41 2.4.2 Ubiquitous Figuring—Proxemic Cooperations 43 2.4.3 Other Gadget-Related Angles 44 2.5 CHI for Current Smart Homes 45 2.5.1 Smart Home for Healthcare 45 2.5.2 Savvy Home for Energy Efficiency 46 2.5.3 Interface Design and Human-Computer Interaction 46 2.5.4 A Summary of Status 48 2.6 Four Approaches to Improve HCI and UX 48 2.6.1 Productive General Control Panel 49 2.6.2 Compelling User Interface 50 2.6.3 Variable Accessibility 52 2.6.4 Secure Privacy 54 2.7 Conclusion and Discussion 55 References 56 3 Teaching-Learning Process and Brain-Computer Interaction Using ICT Tools 63 Rohit Raja, Neelam Sahu and Sumati Pathak 3.1 The Concept of Teaching 64 3.2 The Concept of Learning 65 3.2.1 Deficient Visual Perception in a Student 67 3.2.2 Proper Eye Care (Vision Management) 68 3.2.3 Proper Ear Care (Hearing Management) 68 3.2.4 Proper Mind Care (Psychological Management) 69 3.3 The Concept of Teaching-Learning Process 70 3.4 Use of ICT Tools in Teaching-Learning Process 76 3.4.1 Digital Resources as ICT Tools 77 3.4.2 Special ICT Tools for Capacity Building of Students and Teachers 77 3.4.2.1 CogniFit 77 3.4.2.2 Brain-Computer Interface 78 3.5 Conclusion 80 References 81 4 Denoising of Digital Images Using Wavelet-Based Thresholding Techniques: A Comparison 85 Devanand Bhonsle 4.1 Introduction 85 4.2 Literature Survey 87 4.3 Theoretical Analysis 89 4.3.1 Wavelet Transform 90 4.3.1.1 Continuous Wavelet Transform 90 4.3.1.2 Discrete Wavelet Transform 91 4.3.1.3 Dual-Tree Complex Wavelet Transform 94 4.3.2 Types of Thresholding 95 4.3.2.1 Hard Thresholding 96 4.3.2.2 Soft Thresholding 96 4.3.2.3 Thresholding Techniques 97 4.3.3 Performance Evaluation Parameters 102 4.3.3.1 Mean Squared Error 102 4.3.3.2 Peak Signal–to-Noise Ratio 103 4.3.3.3 Structural Similarity Index Matrix 103 4.4 Methodology 103 4.5 Results and Discussion 105 4.6 Conclusions 112 References 112 5 Smart Virtual Reality–Based Gaze-Perceptive Common Communication System for Children With Autism Spectrum Disorder 117 Karunanithi Praveen Kumar and Perumal Sivanesan 5.1 Need for Focus on Advancement of ASD Intervention Systems 118 5.2 Computer and Virtual Reality–Based Intervention Systems 118 5.3 Why Eye Physiology and Viewing Pattern Pose Advantage for Affect Recognition of Children With ASD 120 5.4 Potential Advantages of Applying the Proposed Adaptive Response Technology to Autism Intervention 121 5.5 Issue 122 5.6 Global Status 123 5.7 VR and Adaptive Skills 124 5.8 VR for Empowering Play Skills 125 5.9 VR for Encouraging Social Skills 125 5.10 Public Status 126 5.11 Importance 127 5.12 Achievability of VR-Based Social Interaction to Cause Variation in Viewing Pattern of Youngsters With ASD 128 5.13 Achievability of VR-Based Social Interaction to Cause Variety in Eye Physiological Indices for Kids With ASD 129 5.14 Possibility of VR-Based Social Interaction to Cause Variations in the Anxiety Level for Youngsters With ASD 132 References 133 6 Construction and Reconstruction of 3D Facial and Wireframe Model Using Syntactic Pattern Recognition 137 Shilpa Rani, Deepika Ghai and Sandeep Kumar 6.1 Introduction 138 6.1.1 Contribution 139 6.2 Literature Survey 140 6.3 Proposed Methodology 143 6.3.1 Face Detection 143 6.3.2 Feature Extraction 143 6.3.2.1 Facial Feature Extraction 143 6.3.2.2 Syntactic Pattern Recognition 143 6.3.2.3 Dense Feature Extraction 147 6.3.3 Enhanced Features 148 6.3.4 Creation of 3D Model 148 6.4 Datasets and Experiment Setup 148 6.5 Results 149 6.6 Conclusion 152 References 154 7 Attack Detection Using Deep Learning–Based Multimodal Biometric Authentication System 157 Nishant Kaushal, Sukhwinder Singh and Jagdish Kumar 7.1 Introduction 158 7.2 Proposed Methodology 160 7.2.1 Expert One 160 7.2.2 Expert Two 160 7.2.3 Decision Level Fusion 161 7.3 Experimental Analysis 162 7.3.1 Datasets 162 7.3.2 Setup 162 7.3.3 Results 163 7.4 Conclusion and Future Scope 163 References 164 8 Feature Optimized Machine Learning Framework for Unbalanced Bioassays 167 Dinesh Kumar, Anuj Kumar Sharma, Rohit Bajaj and Lokesh Pawar 8.1 Introduction 168 8.2 Related Work 169 8.3 Proposed Work 170 8.3.1 Class Balancing Using Class Balancer 171 8.3.2 Feature Selection 171 8.3.3 Ensemble Classification 171 8.4 Experimental 172 8.4.1 Dataset Description 172 8.4.2 Experimental Setting 173 8.5 Result and Discussion 173 8.5.1 Performance Evaluation 173 8.6 Conclusion 176 References 176 9 Predictive Model and Theory of Interaction 179 Raj Kumar Patra, Srinivas Konda, M. Varaprasad Rao, Kavitarani Balmuri and G. Madhukar 9.1 Introduction 180 9.2 Related Work 181 9.3 Predictive Analytics Process 182 9.3.1 Requirement Collection 182 9.3.2 Data Collection 184 9.3.3 Data Analysis and Massaging 184 9.3.4 Statistics and Machine Learning 184 9.3.5 Predictive Modeling 185 9.3.6 Prediction and Monitoring 185 9.4 Predictive Analytics Opportunities 185 9.5 Classes of Predictive Analytics Models 187 9.6 Predictive Analytics Techniques 188 9.6.1 Decision Tree 188 9.6.2 Regression Model 189 9.6.3 Artificial Neural Network 190 9.6.4 Bayesian Statistics 191 9.6.5 Ensemble Learning 192 9.6.6 Gradient Boost Model 192 9.6.7 Support Vector Machine 193 9.6.8 Time Series Analysis 194 9.6.9 k-Nearest Neighbors (k-NN) 194 9.6.10 Principle Component Analysis 195 9.7 Dataset Used in Our Research 196 9.8 Methodology 198 9.8.1 Comparing Link-Level Features 199 9.8.2 Comparing Feature Models 200 9.9 Results 201 9.10 Discussion 202 9.11 Use of Predictive Analytics 204 9.11.1 Banking and Financial Services 205 9.11.2 Retail 205 9.11.3 Well-Being and Insurance 205 9.11.4 Oil Gas and Utilities 206 9.11.5 Government and Public Sector 206 9.12 Conclusion and Future Work 206 References 208 10 Advancement in Augmented and Virtual Reality 211 Omprakash Dewangan, Latika Pinjarkar, Padma Bonde and Jaspal Bagga 10.1 Introduction 212 10.2 Proposed Methodology 214 10.2.1 Classification of Data/Information Extracted 215 10.2.2 The Phase of Searching of Data/Information 216 10.3 Results 218 10.3.1 Original Copy Publication Evolution 218 10.3.2 General Information/Data Analysis 224 10.3.2.1 Nations 224 10.3.2.2 Themes 227 10.3.2.3 R&D Innovative Work 227 10.3.2.4 Medical Services 229 10.3.2.5 Training and Education 230 10.3.2.6 Industries 232 10.4 Conclusion 233 References 235 11 Computer Vision and Image Processing for Precision Agriculture 241 Narendra Khatri and Gopal U Shinde 11.1 Introduction 242 11.2 Computer Vision 243 11.3 Machine Learning 244 11.3.1 Support Vector Machine 245 11.3.2 Neural Networks 245 11.3.3 Deep Learning 245 11.4 Computer Vision and Image Processing in Agriculture 246 11.4.1 Plant/Fruit Detection 249 11.4.2 Harvesting Support 252 11.4.3 Plant Health Monitoring Along With Disease Detection 252 11.4.4 Vision-Based Vehicle Navigation System for Precision Agriculture 252 11.4.5 Vision-Based Mobile Robots for Agriculture Applications 257 11.5 Conclusion 259 References 259 12 A Novel Approach for Low-Quality Fingerprint Image Enhancement Using Spatial and Frequency Domain Filtering Techniques 265 Mehak Sood and Akshay Girdhar 12.1 Introduction 266 12.2 Existing Works for the Fingerprint Ehancement 269 12.2.1 Spatial Domain 269 12.2.2 Frequency Domain 270 12.2.3 Hybrid Approach 271 12.3 Design and Implementation of the Proposed Algorithm 272 12.3.1 Enhancement in the Spatial Domain 273 12.3.2 Enhancement in the Frequency Domain 279 12.4 Results and Discussion 282 12.4.1 Visual Analysis 283 12.4.2 Texture Descriptor Analysis 285 12.4.3 Minutiae Ratio Analysis 285 12.4.4 Analysis Based on Various Input Modalities 293 12.5 Conclusion and Future Scope 293 References 296 13 Elevate Primary Tumor Detection Using Machine Learning 301 Lokesh Pawar, Pranshul Agrawal, Gurjot Kaur and Rohit Bajaj 13.1 Introduction 301 13.2 Related Works 302 13.3 Proposed Work 303 13.3.1 Class Balancing 304 13.3.2 Classification 304 13.3.3 Eliminating Using Ranker Algorithm 305 13.4 Experimental Investigation 305 13.4.1 Dataset Description 305 13.4.2 Experimental Settings 306 13.5 Result and Discussion 306 13.5.1 Performance Evaluation 306 13.5.2 Analytical Estimation of Selected Attributes 311 13.6 Conclusion 311 13.7 Future Work 312 References 312 14 Comparative Sentiment Analysis Through Traditional and Machine Learning-Based Approach 315 Sandeep Singh and Harjot Kaur 14.1 Introduction to Sentiment Analysis 316 14.1.1 Sentiment Definition 316 14.1.2 Challenges of Sentiment Analysis Tasks 318 14.2 Four Types of Sentiment Analyses 319 14.3 Working of SA System 321 14.4 Challenges Associated With SA System 323 14.5 Real-Life Applications of SA 324 14.6 Machine Learning Methods Used for SA 324 14.7 A Proposed Method 326 14.8 Results and Discussions 328 14.9 Conclusion 333 References 334 15 Application of Artificial Intelligence and Computer Vision to Identify Edible Bird’s Nest 339 Weng Kin Lai, Mei Yuan Koay, Selina Xin Ci Loh, Xiu Kai Lim and Kam Meng Goh 15.1 Introduction 340 15.2 Prior Work 342 15.2.1 Low-Dimensional Color Features 342 15.2.2 Image Pocessing for Automated Grading 343 15.2.3 Automated Classification 343 15.3 Auto Grading of Edible Birds Nest 343 15.3.1 Feature Extraction 344 15.3.2 Curvature as a Feature 344 15.3.3 Amount of Impurities 344 15.3.4 Color of EBNs 345 15.3.5 Size—Total Area 346 15.4 Experimental Results 347 15.4.1 Data Pre-Processing 347 15.4.2 Auto Grading 349 15.4.3 Auto Grading of EBNs 353 15.5 Conclusion 355 Acknowledgments 356 References 356 16 Enhancement of Satellite and Underwater Image Utilizing Luminance Model by Color Correction Method 361 Sandeep Kumar, E. G. Rajan and Shilpa Rani 16.1 Introduction 362 16.2 Related Work 362 16.3 Proposed Methodology 364 16.3.1 Color Correction 364 16.3.2 Contrast Enhancement 365 16.3.3 Multi-Fusion Method 366 16.4 Investigational Findings and Evaluation 367 16.4.1 Mean Square Error 367 16.4.2 Peak Signal–to-Noise Ratio 368 16.4.3 Entropy 368 16.5 Conclusion 375 References 376 Index 381

Autorenportrait

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.

Back cover copy

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.