Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms
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.
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.
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Persons
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.
Content
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
