Big Data Intelligence for Smart Applications (Studies in Computational Intelligence, 994)

Today, the use of machine intelligence, expert systems, and analytical technologies combined with Big Data is the natural evolution of both disciplines. As a result, there is a pressing need for new and innovative algorithms to help us find effective and practical solutions for smart applications such as smart cities, IoT, healthcare, and cybersecurity. This book presents the latest advances in big data intelligence for smart applications. It explores several problems and their solutions regarding computational intelligence and big data for smart applications. It also discusses new models, practical solutions,and technological advances related to developing and transforming cities through machine intelligence and big data models and techniques. This book is helpful for students and researchers as well as practitioners. Preface 6 Contents 8 About the Editors 10 Data Quality in the Era of Big Data: A Global Review 13 1 Introduction 14 2 Big Data Characteristics 15 3 Big Data Quality 16 4 Big Data Value Chain 20 4.1 Data Generation 21 4.2 Data Acquisition 21 4.3 Data Pre-processing 21 4.4 Data Storage 22 4.5 Data Analysis 22 4.6 Data Visualization 23 4.7 Data Exposition 23 5 Research Methodology 23 6 Big Data Quality Approaches 25 7 Data Quality and Big Data Applications Domains 32 8 Conclusion 34 References 35 Adversarial Machine Learning, Research Trends and Applications 38 1 Introduction 38 2 Literature Review 40 2.1 The Tolerance for False Positives Versus False Negatives 47 3 Black Versus White-Box Attacks 47 4 Defenses Against Adversarial Attacks 49 4.1 Adversary Attacks and Language Comprehension 50 4.2 White Versus Black-Box Attacks and Defenses 50 5 Sequence Generative Models 51 6 Adversarial Training Techniques 54 7 Generation Models/Tasks/Applications 55 7.1 Next-Word Prediction 56 7.2 Dialog Generation 56 7.3 Neural Machine Translation 56 8 Text Generation Metrics 57 9 Memory-Based Models 59 10 Summary and Conclusion 61 References 61 Multi-agent Systems for Distributed Data Mining Techniques: An Overview 67 1 Introduction 67 2 Distributed Data Mining 69 2.1 DDM Approach 73 2.2 DDM Information Sharing 75 3 Multi-agent System 76 3.1 MAS Features 81 3.2 MAS Applications 82 3.3 MAS Platform 84 3.4 MAS with Distributed Data Mining 88 3.5 Key Characteristics of DDM with MAS 92 3.6 DDM with MAS Approaches 94 4 Conclusion 96 References 98 Time Series Data Analysis Using Deep Learning Methods for Smart Cities Monitoring 103 1 Introduction 103 2 Times Series Basic Concepts 105 2.1 Autoregressive Model (AR) 110 2.2 Moving Average Model 110 2.3 Autoregressive Moving Average Model 111 3 Machine Learning-Based Methods 111 3.1 Artificial Neural Networks 112 3.2 Big Data Analytics 114 3.3 Deep Learning Algorithms 116 4 Implementing an LSTM to Forecast the Traffic Noise 117 4.1 Introduction 117 4.2 Materials and Methods 118 4.3 Recurrent Neural Network (RNN) 119 4.4 Results and Discussion 120 5 Conclusions 123 References 124 A Low-Cost IMU-Based Wearable System for Precise Identification of Walk Activity Using Deep Convolutional Neural Network 127 1 Introduction 127 2 Related Works 129 3 System Details 131 3.1 Deployment Details 131 3.2 Data Acquisition 132 3.3 Data Standardization and Feature Extraction 133 3.4 Data Processing 137 4 Learning Techniques 138 4.1 kNN Classifier 138 4.2 SVM Classifier 138 4.3 GNB Classifier 139 4.4 DT Classifier 139 4.5 CNN Classifier 139 5 Results and Discussion 141 5.1 Experimental Setup 141 5.2 Evaluation Metrics 142 5.3 Results of Classification 142 5.4 CNN Learning Curve 145 5.5 Comparison with Related Works 146 6 Conclusion and Future Scope 147 References 147 Facial Recognition Application with Hyperparameter Optimisation 151 1 Introduction 151 2 Problem Statement 153 3 Theoretical Framework 153 3.1 Artificial Neural Network 153 3.2 K-Fold Cross-Validation 154 3.3 Back-Propagation 154 3.4 Grid Search 155 3.5 Random Search 155 4 Related Work 155 5 Methodology 156 6 Part 1 159 6.1 Designing a Solution 159 6.2 MLPClassifier 160 6.3 Experiments 161 6.4 Results 168 6.5 Analysis 170 7 Part 2 174 7.1 Grid Search 174 7.2 Random Search 175 7.3 Further Optimisation 177 8 Results 178 9 Analysis 178 10 Common Issues in Face Recognition 179 11 Conclusion 180 References 181 Internet-Assisted Data Intelligence for Pandemic Prediction: An Intelligent Framework 183 1 Introduction 183 1.1 The Role of Big Data in Smart Cities 185 2 Literature Review 186 3 Development of the Framework 188 3.1 Development of the SEIR Algorithm 190 3.2 Development of the A&P Algorithm 192 4 Experiment Results 193 4.1 Results of the Pandemic Prediction SEIR Algorithm 194 4.2 Results of the Air Quality Based Pandemic Prediction Algorithm 195 4.3 Results of Parking Data Based Pandemic Prediction Algorithm 197 5 Conclusion 199 References 199 NHS Big Data Intelligence on Blockchain Applications 201 1 Introduction 202 2 Background of Blockchain 202 2.1 About Blockchain 202 2.2 Blockchain for NHS 206 2.3 PBFT Algorithm in Application 209 3 Blockchain Security Applied Solution 210 3.1 Blockchain Algorithms 210 3.2 Anti-False Transaction 212 3.3 Anti-Alteration Integrity 213 4 Blockchain Security Problem-Solving 213 5 Evaluation 214 5.1 Discussion 214 5.2 Discussion of Blockchain Examples 215 5.3 Evaluation 215 6 Conclusions and Future Development 216 6.1 Summary 216 6.2 Suggestion for Future Development 217 References 217 Depression Detection from Social Media Using Twitter's Tweet 219 1 Introduction 220 2 Literature Review 221 3 Research Methodology 224 3.1 Data Collection and Preparation 224 3.2 Data Preprocessing 226 3.3 Word Analysis 227 3.4 Tokenization 227 3.5 Extract Features 228 3.6 Depression Detection 228 3.7 Output 229 4 Experimental Results and Discussions 229 4.1 Performance Metrics 230 4.2 Depression Prediction Performance 230 4.3 Comparison the Algorithms' Results 232 5 Limitations and Future Research Direction 234 6 Conclusions 234 References 234 A Conceptual Analysis of IoT in Healthcare 237 1 Introduction 237 2 Related Work 239 3 Wireless Body Area Network 241 4 Proposed Model 242 4.1 Data Sets and Feature Selection 242 4.2 Selection of Algorithm 243 4.3 Fusion of Deep Neural Networks and Fuzzy Logic 243 4.4 Implementation 244 4.5 Parallelization of Machine Learning Algorithms 244 5 Conceptual Analysis of Proposed Model 244 5.1 Algorithms 245 5.2 Results and Discussion 246 6 Discussion on Research Questions 248 7 Conclusion and Future Work 248 References 249 Securing Big Data-Based Smart Applications Using Blockchain Technology 251 1 Introduction 252 2 Research Methodology 253 3 An Overview of Blockchain Technology 254 4 Blockchain in the Service of Big Data 256 4.1 Healthcare Field 256 4.2 Banking Field 257 4.3 Smart Applications 258 4.4 Game Theory 259 4.5 Internet of Things 259 4.6 Big Data and Blockchain in the Service of VANETs 260 4.7 The Fifth Generation Based Applications (5G) 261 5 Discussion 262 6 Conclusion 271 References 272 Overview of Blockchain-Based Privacy Preserving Machine Learning for IoMT 275 1 Introduction 275 2 Related Works 276 3 Preliminaries 277 3.1 Machine Learning 277 3.2 Homomorphic Cryptosystem 279 3.3 Differential Privacy 279 3.4 Blockchain 281 4 System Overview & Model Construction 281 4.1 System Model 281 4.2 Threat Model 282 4.3 Data Sharing via Blockchain 283 4.4 Model Construction 284 5 Experimental Setup & Result Analysis 284 6 Conclusion 286 References 287 Big Data Based Smart Blockchain for Information Retrieval in Privacy-Preserving Healthcare System 289 1 Introduction 290 2 Related Work 291 3 Proposed Framework 295 3.1 Design of Multi-transaction Mode Consortium Blockchain 295 3.2 Proposed Blockchain Based Privacy Algorithm 297 3.3 Enhanced Storage Model of Improved Redis Cache 298 4 Experimental Evaluations of the Proposed RS-IMTMCB-PIR Scheme 300 5 Conclusion 304 References 304 Classification of Malicious and Benign Binaries Using Visualization Technique and Machine Learning Algorithms 307 1 Introduction 307 2 Related Works 310 3 Methodology 311 3.1 PE as Grayscale Image 313 3.2 Database 313 3.3 Local and Global Image Descriptors 315 4 Proposed Solution 318 5 Results and Discussion 319 6 Conclusion 323 References 324 FakeTouch: Machine Learning Based Framework for Detecting Fake News 326 1 Introduction 327 2 Methodology 328 2.1 Collect Data 328 2.2 Data Pre-processing 330 2.3 Features Extraction 330 2.4 Model Generation 331 3 Evaluation Metrics 331 3.1 Performance Parameters 331 4 Results and Discussion 334 4.1 Environments and Tools 334 4.2 Result Analysis 334 4.3 Comparative Discussion 339 5 Conclusion 341 References 341 Today, the use of machine intelligence, expert systems, and analytical technologies combined with Big Data is the natural evolution of both disciplines. As a result, there is a pressing need for new and innovative algorithms to help us find effective and practical solutions for smart applications such as smart cities, IoT, healthcare, and cybersecurity. This book presents the latest advances in big data intelligence for smart applications. It explores several problems and their solutions regarding computational intelligence and big data for smart applications. It also discusses new models, practical solutions, and technological advances related to developing and transforming cities through machine intelligence and big data models and techniques. This book is helpful for students and researchers as well as practitioners