Applications of Machine Learning (eBook)

Prashant Johri, Jitendra Kumar Verma, Sudip Paul (Herausgeber)

eBook Download: PDF

2020 | 1st ed. 2020
XXII, 394 Seiten
Springer Singapore (Verlag)
978-981-15-3357-0 (ISBN)

Infos

This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.

Dr. Prashant Johri is a Professor at the School of Computing Science & Engineering, Galgotias University, Greater Noida, India. He received his MCA from Aligarh Muslim University and Ph.D. in Computer Science from Jiwaji University, Gwalior, India. He has also worked as a Professor and Director (MCA), Noida Institute of Engineering and Technology, (NIET). His research interests include big data, data analytics, data retrieval and predictive analytics, information security, privacy protection, big data open platforms, etc. He is actively publishing in these areas.

Dr. Jitendra Kumar Verma is Assistant Professor (Grade III) of Computer Science & Engineering at Amity School of Engineering & Technology, Amity University Haryana, Gurugram (Manesar), India. He received the degree of Ph.D. from Jawaharlal Nehru University (JNU), New Delhi, India in 2017, degree of M.Tech from JNU in 2013 and degree of B.Tech in Computer Science & Engineering from Kamla Nehru Institute of Technology (KNIT), Sultanpur, Uttar Pradesh, India in 2008. Dr. Verma is awardee of prestigious DAAD 'A new Passage to India' Fellowship (2015-16) funded by Federal Ministry of Education and Research - BMBF, Germany and German Academic Exchange Service (DAAD). He worked at JULIUS-MAXIMILIAN UNIVERSITY OF WÜRZBURG, GERMANY (mother of 14 Nobel Laureate) as a Visiting Research Scholar. Dr. Verma is member of several technical societies e.g. IEEE, IEEE IAS, and ACM. Over his short career, he published several research papers in proceedings of various international conferences and peer-reviewed International Journals of repute. He also contributed numerous book chapters to the several books published with publishers of high international repute. Apart from scholarly contribution towards scientific community, he organized several Conferences/Workshops/Seminars at the national and international levels. He voluntarily served as reviewer for various International Journals, conferences, and workshops. He also served as Guest Editor and Editorial Board Member of numerous international journals.  His research interest includes cloud computing, Mobile cloud, Machine learning, AR & VR, Soft computing, Fuzzy systems, Healthcare, Pattern recognition, Bio-inspired phenomena, and advanced optimization model & computation.

Dr. Sudip Paul is an Assistant Professor at the Department of Biomedical Engineering, School of Technology, North-Eastern Hill University (NEHU), Shillong, India. He received his Ph.D. from the Indian Institute of Technology (Banaras Hindu University), Varanasi, with a specialization in Electrophysiology and Brain Signal Analysis. He was selected as a Postdoc Fellow in 2017-18 under the Biotechnology Overseas Associateship for scientists working in the Northeastern States of India, supported by the Department of Biotechnology, Government of India. Dr. Sudip has published more than 90 international journal and conference papers and has filed four patents. Recently, he completed three book projects and is currently serving as Editor for a further two. Dr. Sudip is a member of numerous societies and professional bodies, e.g. the APSN, ISN, IBRO, SNCI, SfN, and IEEE. He received First Prize in the Sushruta Innovation Award 2011, sponsored by the Department of Science and Technology, Government of India, and various other awards, including a World Federation of Neurology (WFN) Travelling Fellowship, Young Investigator Award, and IBRO and ISN Travel Awards. Dr. Sudip has also served as an editorial board member for a variety of international journals.

Inhalt

Preface 6
Editorial Advisory Board 13
Technical Program Committee 13
Contents 17
About the Editors 20
1 Statistical Learning Process for the Reduction of Sample Collection Assuring a Desired Level of Confidence 22
1 Introduction 22
2 Sampling Protocol Proposal 23
2.1 Mathematical Base 23
2.2 New Sampling Protocol 28
3 Study Cases 29
3.1 Simulation 1 29
3.2 Simulation 2 32
4 Future Research Lines and Conclusions 33
References 35
2 Sentiment Analysis on Google Play Store Data Using Deep Learning 36
1 Introduction 36
2 Literature Review 37
3 Data 38
3.1 Data Extraction 38
3.2 App Details and Review Details Specifics 40
4 Dataset Description 41
5 Data Exploration 43
6 Methods 43
6.1 Data Processing 44
6.2 Numerical Data Columns 45
7 Modelling Results 46
8 Discussion 49
9 Conclusion 50
References 50
3 Managing the Data Meaning in the Data Stream Processing: A Systematic Literature Mapping 52
1 Introduction 52
2 Research Method 53
2.1 Aim and Research Questions 54
2.2 Search Strategy 54
2.3 Filtering Results 55
2.4 Data Extraction Process 56
2.5 Synthesis Process 56
3 Performing the Systematic Literature Mapping 57
4 Summary of Results: Data-Meaning Strategies and Real-Time Data Processing 58
5 Conclusions 64
References 65
4 Tracking an Object Using Traditional MS (Mean Shift) and CBWH MS (Mean Shift) Algorithm with Kalman Filter 68
1 Introduction 68
2 Literature Survey 69
3 Proposed Methodology 71
3.1 Tracking the MS 73
3.2 Normalized Centroid Distance (NCD) 74
3.3 BWH MS Tracking 75
3.4 Similarities for Representing the BWH with Usual Representation 76
3.5 CBWH Algorithm 78
3.6 Applications 79
4 Results 79
5 Conclusion 81
References 85
5 Transfer Learning and Domain Adaptation for Named-Entity Recognition 87
1 Introduction 87
2 Related Work 89
3 Procedure 89
4 Results and Analysis 92
Reference 93
6 Knowledge Graph from Informal Text: Architecture, Components, Algorithms and Applications 94
1 Introduction 94
2 Knowledge Graph Development Pipeline and Components 95
3 Knowledge Extraction 96
3.1 Entity Extraction 96
3.2 Relation Extraction (RE) and Attribute Extraction (AE) 98
4 Knowledge Graph Construction 99
4.1 Entity Resolution 99
4.2 Link Prediction 100
4.3 Node Labeling 100
5 Knowledge Graph Challenges in Sparse Corpus 101
5.1 CRF with Automatic Feature Engineering-Based NER Model 102
5.2 AugmentedIE 103
6 Industrial Applications 104
6.1 Phase 1—KG Creation 106
6.2 Phase 2—KG Based Req2Test Application 106
7 Summary 107
References 108
7 Neighborhood-Based Collaborative Recommendations: An Introduction 110
1 Introduction 110
2 Notations 111
3 Neighborhood-Based Recommendations 112
3.1 User-Based Recommendations 114
3.2 Item-Based Recommendations 116
3.3 User-Based Versus Item-Based Methods 117
4 Neighborhood-Based Methods in Action 118
4.1 Rating Normalization 119
4.2 Similarity Computation 119
4.3 Variations in Selecting Peer Groups 121
5 Rating Matrix 121
5.1 Continuous Ratings 122
5.2 Interval-Scaled Ratings 122
5.3 Ordinal Ratings 123
5.4 Binary Ratings 123
5.5 Unary Ratings 123
6 Characteristics of the Rating Matrix 124
6.1 Sparsity 124
6.2 The Long-Tail Property 125
6.3 Cold-Start Problem 126
References 128
8 Classification of Arabic Text Using Singular Value Decomposition and Fuzzy C-Means Algorithms 130
1 Introduction 130
2 Literature Review 131
3 Methodology 134
3.1 Text Preprocessing 135
3.2 Feature Extraction 135
3.3 Applying Fuzzy C-Means Classifier 137
4 Experimental Results 137
4.1 Feature Reduction 137
4.2 Arabic Datasets 137
4.3 Performance Evaluation Measures 138
4.4 Comparison with Other Approaches 139
5 Conclusions and Future Work 140
References 141
9 Echo State Network Based Nonlinear Channel Equalization in Wireless Communication System 143
1 Introduction 143
2 System Model and Equalization 145
3 Echo State Network 146
3.1 Architecture of ESN 146
3.2 Training 147
3.3 Testing 148
4 Reservoir Design Considerations 148
4.1 Reservoir 148
5 Channel Equalization Using ESN 150
6 Simulation Results 151
6.1 Effect of ESN Parameters 151
6.2 Equalizer Performance Comparison 154
7 Conclusion 156
References 156
10 Melody Extraction from Music: A Comprehensive Study 158
1 Introduction 158
2 Melody Extraction Techniques 159
2.1 Salience-Based Approaches 160
2.2 Source Separation-Based Approaches 162
2.3 Data-Driven Approaches 164
3 Datasets 166
4 Performance Measures 167
4.1 Voice Recall (VR) 167
4.2 Voicing False Alarm (VFA) 167
4.3 Raw Pitch Accuracy (RPA) 168
4.4 Raw Chroma Accuracy (RCA) 168
4.5 Overall Accuracy (OA) 168
5 Melody Extraction Applications 169
6 Challenges 170
7 Conclusion and Future Perspective 170
References 171
11 Comparative Analysis of Combined Gas Turbine–Steam Turbine Power Cycle Performance by Using Entropy Generation and Statistical Methodology 173
1 Introduction 173
2 Brief of Combined GT-ST Power Generation System 175
3 Plant Operation Condition 178
3.1 Thermodynamic Analysis and Statistical Modeling of Combined GT-ST Power Plant 178
4 Results and Discussion 184
4.1 Effect of Performance Parameters on Plant 185
5 Conclusions 189
References 190
12 Data Mining—A Tool for Handling Huge Voluminous Data 192
1 Introduction 192
1.1 Unavailability of Past Data 193
1.2 Background 194
1.3 Overview of Data Mining 196
1.4 Patterns to Accommodate Different Applications 199
1.5 Data Mining to Big Data Mining: Key Idea 201
2 Conclusion 202
References 202
13 Improving the Training Pattern in Back-Propagation Neural Networks Using Holt-Winters’ Seasonal Method and Gradient Boosting Model 204
1 Introduction 204
2 Related Works 206
3 Proposed Method 206
4 Results and Discussions 209
5 Conclusions 212
References 212
14 Ensemble of Multi-headed Machine Learning Architectures for Time-Series Forecasting of Healthcare Expenditures 214
1 Introduction 214
2 Background 216
3 Method 217
3.1 Data 217
3.2 Evaluation Metrics 218
3.3 Experiment Design for Multi-headed LSTM 218
3.4 Experiment Design for Multi-headed ConvLSTM 220
3.5 Experiment Design for Multi-headed CNN-LSTM 220
3.6 Ensemble Model 221
4 Results 222
4.1 Multi-headed LSTM Model 222
4.2 Multi-headed ConvLSTM Model 223
4.3 Multi-headed CNN-LSTM Model 225
4.4 Ensemble Model 226
5 Discussion and Conclusions 227
References 230
15 Soft Computing Approaches to Investigate Software Fault Proneness in Agile Software Development Environment 232
1 Introduction 232
2 Related Work 233
2.1 Soft Computing Approaches 234
3 Dataset and Metrics Definitions 236
4 Proposed Methodology 237
5 Results and Discussion 241
6 Conclusion and Future Research Scope 242
References 245
16 Week Ahead Time Series Prediction of Sea Surface Temperature Using Nonlinear Autoregressive Network with and Without Exogenous Inputs 249
1 Introduction 249
2 Literature Review 251
3 Time Series Models 254
3.1 Basic Nonlinear Autoregressive Models 254
3.2 Error Measures 256
4 Dataset Used 258
5 Proposed Methods 258
5.1 SSTA 259
5.2 SSTANAR 260
5.3 SSTAAirT 261
5.4 SSTAZ 262
5.5 SSTAM 263
6 Results 263
7 Summary 264
References 269
17 Regression Model of Frame Rate Processing Performance for Embedded Systems Devices 271
1 Introduction 271
2 Background and Related Works 272
3 Model of Performance Estimation 273
4 Results 276
5 Discussions 278
6 Conclusions 278
References 278
18 Time Series Data Representation and Dimensionality Reduction Techniques 280
1 Introduction 280
2 A Taxonomy of Representation/Dimensionality Reduction Techniques 281
3 Piecewise Linear Methods 283
3.1 PAA 283
3.2 APCA 284
3.3 DPAA 284
3.4 PLA 285
3.5 PTA 285
4 Symbolic-Based Methods 286
4.1 SAX 286
4.2 SFA 287
4.3 Trend-Based SAX Reduction 289
5 Feature-Based Methods 290
5.1 BOP 290
5.2 BOSS 291
5.3 Shapelets 292
6 Transformed-Based Methods 293
6.1 DFT 293
6.2 DCT 294
6.3 DWT 294
7 State of the Art 295
8 Conclusion and Future Research Direction 295
References 296
19 Simultaneous Localization and Mapping with Gaussian Technique 298
1 Introduction 298
2 Statistical Estimation and Learning in Robotics 299
3 Simultaneous Localization and Mapping [SLAM] 300
4 Kalman Filter, Extended Kalman Filter 300
5 Future Work 303
References 303
20 Unsupervised Learning of the Sequences of Adulthood Transition Trajectories 305
1 Introduction 305
2 Literature Review on Unsupervised Learning of Sequences 307
2.1 Sequences of Biological Processes 307
2.2 Social Processes 309
3 Data and Methods 311
3.1 Method 312
4 Exploratory Analysis of Distribution of Adulthood Events by Geographical Region 314
4.1 Proportion of Events by Geographical Region 314
4.2 Failure Rate Function 316
5 Sequence Analysis 316
5.1 Index Plot 316
5.2 State Frequency Plot 318
5.3 State Distribution Plot 319
5.4 Entropy and Turbulence Measures 320
5.5 Cluster Analysis 322
6 Conclusion 323
References 329
21 A Quantile-Based Approach to Supervised Learning 332
1 Introduction 332
2 An Introduction to the Quantile-Based Distributions 334
3 Formulation of the Regression Model 336
4 Method of Fitting the Best Line of Regression 338
4.1 Convergence of the Algorithm 340
4.2 Validation 341
5 Regression Quantile Models Using Quantile-Based Distributions 342
5.1 Simulation Study 343
6 Empirical Applications 343
7 Conclusion 350
References 350
22 Feature Learning Using Random Forest and Binary Logistic Regression for ATDS 352
1 Introduction 352
2 Related Work 353
3 Background 354
3.1 Random Forest 354
3.2 Binary Logistic Regression 356
3.3 DUC 2002 Dataset 356
4 Proposed Approach 357
5 Experiment and Results 358
6 Concluding Remark 359
References 362
23 MLPGI: Multilayer Perceptron-Based Gender Identification Over Voice Samples in Supervised Machine Learning 364
1 Introduction 364
2 Gender Classification Related Work 365
3 Proposed Work 366
3.1 Used Dataset 366
3.2 Voice Preprocessing 367
3.3 Voice Features Extraction 367
3.4 Classification 368
3.5 Used Models for Classification 368
4 Experiment and Result 371
5 Conclusion and Future Scope 374
References 375
24 Scrutinize the Idea of Hadoop-Based Data Lake for Big Data Storage 376
1 Introduction 376
2 Research Methodology 378
2.1 Research Description or Definition 378
2.2 Searching Articles 379
2.3 Article Verification 380
2.4 Analyze Research 380
3 Research Classification 380
4 What Are Big Data and Big Data Storage? 381
4.1 Big Data Life Cycle 384
4.2 Tools and Technology to Process and Analysis Big Data 385
5 Data Lake 386
5.1 Data Lake Versus Data Swamp Versus Data Warehouse 392
5.2 The Need for Data Consumer and Producer 393
5.3 Application and Use Cases of the Data Lake 394
6 The Architecture of Data Lake 395
6.1 Technology Service Architecture 397
6.2 Data Architecture of Data Lake 397
7 Conclusion 399
8 Future Direction 399
References 400
Author Index 403

Produkt-Details

• Erscheint lt. Verlag: 4.5.2020
• Reihe/Serie: Algorithms for Intelligent Systems
• Reihe/Serie: Algorithms for Intelligent Systems
• Zusatzinfo: XXII, 394 p. 158 illus., 112 illus. in color.
• Sprache: englisch
• Themenwelt: Informatik ► Grafik / Design ► Digitale Bildverarbeitung
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: Artificial Intelligence • evolutionary computation • Fuzyy Logic • Image Processing • Learning theory • Neural networks • probabilistic methods • Soft Computing • Telemedicine and Robotics
• ISBN-10: 981-15-3357-1 / 9811533571
• ISBN-13: 978-981-15-3357-0 / 9789811533570