In the digital era, novel applications and techniques in the realm of computer science are increasing constantly. These innovations have led to new techniques and developments in the field of cybernetics.
The **Handbook of Research on Applied Cybernetics and Systems Science** is an authoritative reference publication for the latest scholarly information on complex concepts of more adaptive and self-regulating systems. Featuring exhaustive coverage on a variety of topics such as infectious disease modeling, clinical imaging, and computational modeling, this publication is an ideal source for researchers and students in the field of computer science seeking emerging trends in computer science and computational mathematics.

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Snehanshu Saha, Abhyuday Mandal, Anand Narasimhamurthy, V. Sarasvathi, Shivappa Sangam

Snehanshu Saha; IGI Global

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Snehanshu Saha et al.

Advances in Computational Intelligence and Robotics, Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, PA 17033, USA), 2017

Advances in computational intelligence and robotics (ACIR) book series, Hershey, PA, 2017

United States, United States of America

2, 20170417

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Handbook of Research on Applied Cybernetics and Systems Science 2
List of Contributors 7
Table of Contents 9
Detailed Table of Contents 12
Preface 19
Section 1: Signal Processing and Communications 20
1 A Survey of Parallel Community Detection Algorithms 22
INTRODUCTION 22
Applications of Community Detection 24
Methods of Community Detection 25
Quality Metrics for Community 26
EXISTING COMMUNITY DETECTION TAXONOMIES 27
A NOVEL TAXONOMY OF PARALLEL COMMUNITY DETECTION 28
Classification Based on Core Algorithm 29
Label Propagation 30
Agglomerative Method 30
InfoMap Algorithm 32
Classification Based on Communication Model 32
Classification Based on Computation Model 32
PARALLEL COMMUNITY DETECTION ALGORITHMS 33
Core Algorithm Based 33
Parallel Algorithms Based on Label Propagation 34
Parallel Algorithms Based on Agglomerative Method 35
Parallel Algorithms Based on InfoMap 38
Communication Model Based 38
Shared Memory Based 38
Message Passing Based 38
Computation Model Based 39
CPU Implementation 39
GPU Implementation 40
MISCELLANEOUS 40
Tensor Based Method 41
Propinquity Based Method 41
OPEN PROBLEMS 42
CONCLUSION 43
ACKNOWLEDGMENT 43
REFERENCES 43
KEY TERMS AND DEFINITIONS 47
2 Towards Automation of IoT Analytics: An Ontology-Driven Approach 48
INTRODUCTION 48
A MODEL DRIVEN APPROACH IN IoT APPLICATION DEVELOPMENT 50
USE OF KNOWLEDGE MODELS: AN IoT SCENARIO 51
CREATION OF SENSOR KNOWLEDGE MODEL 52
CREATION OF ALGORITHM KNOWLEDGE MODEL 61
WORKFLOW CREATION USING KNOWLEDGE MODELS 66
FUTURE WORKS AND CONCLUSION 69
REFERENCES 70
3 Image Processing for Surveillance and Security 73
INTRODUCTION 73
IMAGE PROCESSING FOR VISUAL TRACKING 74
CONCLUSION 95
REFERENCES 95
4 Application of Corona Product of Graphs in Computing Topological Indices of Some Special Chemical Graphs 103
INTRODUCTION 103
BACKGROUND 104
MAIN RESULTS 107
CONCLUSION 119
REFERENCES 119
Section 2: Systems and Computational Biology 123
5 Statistical Analysis of Functional Magnetic Resonance Imaging Data 124
INTRODUCTION 124
DATA COLLECTION 126
STATISTICAL ISSUES 129
OVERVIEW OF APPROACHES TO STATISTICAL ANALYSIS 130
DISCUSSION 134
REFERENCES 134
6 Modeling Associations: Sensor Fusion and Signaling Bar Codes 136
INTRODUCTION 136
SIGNALING MODELS 138
SIGNAL DECOMPOSITION AND BAR CODES 149
SIGNAL GRAMMARS 153
SIGNAL DECOMPOSITION FROM HOMOLOGY 155
LAMINAR PROCESSING AND PERSISTENT HOMOLOGY 160
LAMINAR PROCESSING AS A DECOMPOSITION ALGORITHM IMPLEMENTATION 166
COMPUTATION IN NEURAL SYSTEMS 168
CONCLUSION 170
REFERENCES 170
Section 3: Machine Learning and Data Sciences 174
7 Graph-Based Semi-Supervised Learning With Big Data 175
1. INTRODUCTION 175
2. STATISTICAL MACHINE LEARNING PROBLEM SETUP 177
3. AN OVERVIEW OF SEMI-SUPERVISED LEARNING 180
4. ANCHOR GRAPHS IN SEMI-SUPERVISED LEARNING 189
5. EMPIRICAL DEMONSTRATIONS 199
6. DISCUSSION 201
ACKNOWLEDGMENT 203
REFERENCES 203
8 Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms 207
INTRODUCTION 207
METHODS 209
EXPERIMENT RESULTS 223
DISCUSSION 225
REFERENCES 226
9 Machine Learning Approaches for Supernovae Classification 228
INTRODUCTION 228
BACKGROUND 230
CATEGORIZATION OF SUPERNOVA 231
MACHINE LEARNING TECHNIQUES 233
SUPERNOVAE DATA SOURCE AND CLASSIFICATION 237
RESULTS AND ANALYSIS 238
FUTURE RESEARCH DIRECTIONS 238
CONCLUSION 239
REFERENCES 239
10 Supervised Learning in Absence of Accurate Class Labels: A Multi-Instance Learning Approach 241
1. INTRODUCTION 242
2. LITERATURE REVIEW 243
3. METHODOLOGY 244
4. EMPIRICAL VALIDATION 250
CONCLUSION AND DIRECTIONS FOR FUTURE WORK 252
REFERENCES 253
11 Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach 255
INTRODUCTION 255
BACKGROUND AND SIGNIFICANCE 259
LITERATURE SURVEY 260
METHODS 262
DATASET AND EXPERIMENTS 267
FUTURE WORK 270
REFERENCES 271
KEY TERMS AND DEFINITIONS 273
12 Support Vector Machines and Applications 275
INTRODUCTION TO PATTERN RECOGNITION 275
WHY SUPPORT VECTOR MACHINES 276
HARD MARGIN SVM 278
KERNEL METHODS 280
CONCLUSION 283
KEY TERMS AND DEFINITIONS 283
Section 4: Statistical Models and Designs in Computing 284
13 Design and Analysis of Computer Experiments 285
1 INTRODUCTION 285
2 EXPERIMENTAL DESIGNS FOR COMPUTER EXPERIMENTS 286
3 MODELING OF COMPUTER EXPERIMENTS 290
4 CONCLUSION 296
ACKNOWLEDGMENT 296
REFERENCES 296
14 Effective Statistical Methods for Big Data Analytics 301
1. INTRODUCTION 301
2. STATISTICAL FORMULATION OF BID DATA PROBLEM 304
3. LEVERAGE-BASED SAMPLING METHOD 307
4. NOVEL LEVERAGING-BASED SAMPLING METHOD 312
5. SOFTWARE IMPLEMENTATION 314
6. DEMONSTRATION: TWO CASE STUDIES 316
7. SUMMARY 318
ACKNOWLEDGMENT 318
REFERENCES 319
Section 5: Scientometrics and Cybernetics 321
15 Measuring Complexity of Chaotic Systems With Cybernetics Applications 322
INTRODUCTION 322
COMPLEX SYSTEMS 323
INFORMATION AND COMPLEXITY 328
MEASURING COMPLEXITY OF CHAOTIC SYSTEMS 333
PRACTICAL APPLICATIONS IN CYBERNETICS 342
SUMMARY AND FUTURE RESEARCH DIRECTIONS 349
ACKNOWLEDGMENT 350
REFERENCES 350
16 Cyber-Physical Systems: An Overview of Design Process, Applications, and Security 356
1. INTRODUCTION 356
2. CYBER-PHYSICAL SYSTEMS: A BRIEF OVERVIEW 358
3. APPLICATIONS OF CYBER-PHYSICAL SYSTEMS 361
4. THE DESIGN PROCESS 365
5. SECURITY OF CYBER-PHYSICAL SYSTEMS 370
6. SUMMARY 376
REFERENCES 376
17 Scientometrics: A Study of Scientific Parameters and Metrics 379
INTRODUCTION 380
BACKGROUND 381
“INFLUENCE” AS A METRIC: JOURNAL LEVEL 383
CASE STUDY 386
INTERNATIONALITY: AT JOURNAL LEVEL 389
COBB DOUGLAS MODEL 393
SCHOLASTIC MEASURES 395
INTERNATIONAL COLLABORATION 400
OTHER CITATIONS QUOTIENT: MEASURING DIVERSITY OF AN AUTHOR 401
CONCLUSION 403
REFERENCES 404
18 Design of Assistive Speller Machine Based on Brain Computer Interfacing 406
1. INTRODUCTION 406
2. BCI DESIGN PRINCIPLES 410
3. METHODOLOGY 414
4. FUTURE RESEARCH DIRECTIONS 438
5. CONCLUSION 438
REFERENCES 439
Compilation of References 440
About the Contributors 476
Index 482

"[This book] includes the latest scholarly information on complex concepts of more adaptive and self-regulating systems. Featuring exhaustive coverage on a variety of topics such as infectious disease modeling, clinical imaging, and computational modeling, this publication is an ideal source for researchers and students in the field of computer science seeking emerging trends in computer science and computational mathematics"--Provided by publisher

"This book provides articles written on Applied Computing and should serve as pilot pointer to young researchers in Computer, Information and System sciences. The book volume contains content on the following topics areas: Signal processing and communications, Systems and Computational Biology; Machine learning and Data Sciences; Statistical Models and Designs in Computing and Scientometrics and Cybernetics"-- Provided by publisher

2018-05-19