Data Science and Big Data Analytics

Preface......Page 6
Contents......Page 7
About the Editors......Page 11
 Analysis and Evaluation of Big Data Video Quality in Wireless Networks......Page 13
 Publishing Ethics and Author Services......Page 14
 Applying Machine Learning Techniques for Big Data Analytics......Page 15
 Outlier Detection in Big Data......Page 17
 Identification and Prevention of Internet Addiction in Youth......Page 18
 Android: Security Issues......Page 19
 Tweaking Big Data Analysis for Malicious Intents......Page 21
 Big Data and Web Mining......Page 22
 Big Data and Data Science Trends: Challenges and Opportunities......Page 23
 1 Introduction......Page 25
 2 Literature Review......Page 26
  3.2 Measures and Data Collection......Page 28
  3.3 Data Analysis......Page 29
  4.2 Correlations Between Variables......Page 30
 References......Page 32
Genetic Algorithm Approach for Optimization of Biomass Estimation at LiDAR......Page 34
 1 Introduction......Page 35
  2.3 Spaceborne LiDAR (ICESat/GLAS)......Page 36
 4 Methodology......Page 37
  5.1 Satellite Data......Page 38
 6 Genetic Approach for Optimization of LiDAR Biomass Estimation......Page 40
  7.1 Calculate of RMSE......Page 41
  8.1 Analysis of GLAS Derived Waveform Parameters......Page 43
  8.2 Results from Genetic Algorithm......Page 44
  8.3 Biomass Equation Generated......Page 48
 References......Page 49
E-alive: An Integrated Platform Based on Machine Learning Techniques to Aware and Educate Common People with the Current Statistics of Maternal and Child Health Care......Page 51
 1 Introduction......Page 52
 2 Motivation......Page 53
  3.1 Maternal and Child Health Care Parameters......Page 54
  3.2 Government Schemes......Page 55
  3.3 Techniques of Data Mining......Page 56
 4 System Implementation......Page 58
 6 Conclusion and Future Scope......Page 61
 References......Page 64
An Effective TCP’s Congestion Control Approach for Cross-Layer Design in MANET......Page 65
  1.1 Mobile Ad Hoc Network......Page 66
  1.2 Congestion Control......Page 67
  1.3 Cross-Layer Design......Page 68
 2 Literature Survey......Page 69
  3.1 Problem Identification......Page 70
  3.2 Methodology......Page 71
  4.2 Result Comparisons......Page 72
 References......Page 77
 1 Introduction......Page 79
 2 Related Work......Page 80
 3 Implementation......Page 81
  3.2 Hypothesis......Page 82
  3.3 Machine Learning......Page 83
  3.4 K-Fold Validation (K = 10)......Page 84
 4 Results......Page 85
 References......Page 86
 1 Introduction......Page 88
 2 Related Work......Page 90
 3 Routing Protocols......Page 91
 4 Simulation Table and Results......Page 93
 References......Page 98
Review on Internet Traffic Sharing Using Markov Chain Model in Computer Network......Page 101
 2 Markov Chain in Big Data Environment......Page 102
  2.1 Markov Chain Model......Page 103
  2.2 General Internet Traffic Sharing......Page 104
  3.3 Quality of Service Based......Page 105
  3.6 Disconnectivity Based......Page 106
  3.7 Iso-share Curve Based......Page 107
  3.8 Two Market Based......Page 108
  3.10 Cyber Crime Based......Page 110
  3.11 Re-attempt Based......Page 111
  3.13 Least Square Curve Fitting Based......Page 112
  3.15 Curve Fitting and Two Market Environment......Page 113
  3.16 Bounded Area Based......Page 114
  3.18 Two-Call Index Based......Page 115
  3.21 Multi-operator Environment Based......Page 116
 References......Page 117
Anomaly Detection Using Dynamic Sliding Window in Wireless Body Area Networks......Page 119
 1 Introduction......Page 120
 2 Dynamic Sliding Window Approach......Page 121
 3 Weighted Moving Average (WMA)......Page 123
 4 Proposed Anomaly Detection Algorithm......Page 124
  5.2 Results......Page 125
 6 Conclusions......Page 126
 References......Page 127
 1 Introduction......Page 129
 3 System Design and€Implementation......Page 130
  3.1 Sensor......Page 131
  3.3 Server and€Database......Page 132
  3.4 Cellular Phone or€Personal Digital Assistant (PDA)......Page 133
  3.5 Intelligent Software Agent......Page 134
 References......Page 135
 1 Introduction......Page 137
 2 Related Work......Page 138
  3.1 DEAP Database......Page 139
  3.2 Wavelet Coefficients as Features......Page 140
 4 Implementation......Page 141
 5 Conclusion......Page 142
 References......Page 143
Development of Early Prediction Model for Epileptic Seizures......Page 145
  2.1 Database......Page 146
  2.3 Dataset Preparation and Experimental Work......Page 147
  2.4 Learning Predictive Model......Page 149
 3 Results......Page 154
 4 Conclusion......Page 155
 References......Page 156
 1 Introduction......Page 159
  1.2 Anonymization Techniques......Page 160
 2 Challenges to Privacy in Data Anonymization......Page 164
 3 Existing Solution and Open Issue for Privacy of Data Anonymization......Page 165
 4 Open Research Problem......Page 166
 References......Page 167
 1 Introduction......Page 169
  2.1 Data Collection......Page 173
  2.2 Methods......Page 174
  3.1 Results......Page 175
  3.2 Discussions......Page 178
 References......Page 185
 1 Introduction......Page 188
 2 Related Work......Page 189
  3.1 Advantages......Page 190
  4.1 Advantages......Page 191
 5 Proposed Algorithm DBSDCT......Page 192
 6 Result Analysis......Page 193
 7 Conclusion and Future Work......Page 194
 References......Page 195
 1 Introduction......Page 196
 3 Brief Literature Survey......Page 198
 4 Comparative Study of Well-Known Wireless Charging Techniques for Sensor Nodes in WSN......Page 202
 5 Gaps in Study......Page 203
 6 Problem Formulation......Page 204
 8 Methodology for Enhancement of Existing Work......Page 205
 References......Page 206
Leeway of Lean Concept to Optimize Big Data in Manufacturing Industry:  An Exploratory Review......Page 208
 2 Literature Review......Page 209
  2.2 Lean Principle from Big Data Perspectives......Page 210
  3.1 Meager Lean Initiative......Page 212
  3.3 Lack of Visualization......Page 213
 4 Lean Paradigm: A Case to Make Big Data Small......Page 214
 5 Conclusion......Page 217
 References......Page 218
Monitoring Public Participation  in Multilateral Initiatives Using Social Media Intelligence......Page 219
  1.2 Social Media and Public Participation......Page 220
  1.3 Challenges......Page 221
 2 Related Work......Page 222
  3.2 Lexical and Quantitative Analysis......Page 223
  3.5 Open-Source and Commercial Tools Used......Page 224
  4.1 Case Study......Page 225
 5 Architecture of the Automated Monitoring  and Evaluation Tool for Multilateral Development Agency......Page 226
 6 Solution Architecture......Page 227
 8 Working of Consultative Hub......Page 228
 9 Text Analysis......Page 229
 10 Conclusion......Page 230
 References......Page 231
 1 Introduction......Page 233
 2 Related Work......Page 235
  3.1 Preprocessing......Page 236
  3.2 Prediction......Page 238
  4.2 Experimental Design and Performance Measure......Page 240
  4.3 Experimental Analysis of Proposed Method Using Traditional CF Methods......Page 241
 References......Page 245
 1 Introduction......Page 247
  2.1 Urdhva Tiryakbhayam......Page 248
  2.2 Reversible Urdhva Tiryakbhayam Multiplier......Page 249
  3.2 Reversible Logic Gates Used for Adders......Page 250
 4 Results, Analysis, and Comparison......Page 251
 5 Conclusion......Page 253
 References......Page 256
 1 Overview of Healthcare Sector of India......Page 257
 2 Advantages of Big Data in Healthcare......Page 259
 3 Model of Big Data for Healthcare Sector of India......Page 262
 References......Page 263
 1 Introduction......Page 265
 2 Literature Survey......Page 266
  3.1 Linguistic Uncertainty......Page 267
  3.2 Ways of Handling Uncertainty......Page 268
 5 Conclusion......Page 271
 References......Page 272
 1 Introduction......Page 273
 2 QoS Matrices in Cloud......Page 274
 3 Related Work on Quality of Services Management Techniques......Page 275
 5 Discussion......Page 279
 References......Page 282
 1 Introduction......Page 284
 2 Background and Related Work......Page 285
 3 The SkyQP Technique......Page 286
  3.1 Processing Correlated Skyline Queries......Page 287
  3.2 The Overview of the SkyQP Technique......Page 288
  4.1 Experimental Work......Page 289
 5 Conclusions and Future Extensions......Page 291
 References......Page 292
Human Face Detection Enabled Smart Stick for Visually Impaired People......Page 294
 1 Introduction......Page 295
 3 Face Detection......Page 296
  3.1 Haar-Like Features......Page 297
  3.2 Classification Function......Page 298
  3.3 Cascade of Classifiers......Page 299
  5.1 Experimental Setup......Page 300
  5.2 Implementation......Page 301
  5.3 Results and Discussion......Page 303
 6 Conclusion and Future Work......Page 304
 References......Page 305
Web-Based Service Recommendation System by Considering User Requirements......Page 307
  2.1 System Overview......Page 308
  2.2 Methodology......Page 309
  3.1 Memory Consumption......Page 312
  3.2 Time Consumption......Page 315
 4 Conclusion and Future Work......Page 316
  4.1 Conclusion......Page 317
 References......Page 318
Unsupervised Machine Learning for Clustering the Infected Leaves Based on the Leaf-Colors......Page 319
 2 Related Work......Page 320
  3.1 Image Processing Task......Page 322
  3.2 Clustering......Page 323
 4 Results and Discussion......Page 326
 References......Page 328
 1 Introduction......Page 329
 3 Real-Time System......Page 330
 5 Type Real-Time Processing......Page 331
 6 Real-Time Big Analysis Data Stack......Page 332
 7 Five-Phase Process Model [2]......Page 334
 8 Application of RTBDA......Page 335
 References......Page 336
 1 Introduction......Page 337
  2.1 Fuzzification of Records......Page 339
  2.2 Missing Value Estimation......Page 341
 3 Results and Discussion......Page 343
 4 Conclusions......Page 345
 References......Page 346
Recommendation Framework for Diet and Exercise Based on Clinical Data: A Systematic Review......Page 348
 1 Introduction......Page 349
 2 Recommendation Frameworks......Page 350
  2.3 Knowledge-Based Recommendation Systems......Page 351
  3.1 Food Recommendation Frameworks......Page 352
  3.2 Diet and Exercise Recommendation Frameworks......Page 353
  4.3 Physical Activity Data......Page 357
 5 Conclusion......Page 358
 References......Page 359
 1 Introduction......Page 362
 2 AODV and SAODV Working......Page 363
 4 Security Attacks on MANET......Page 364
 6 Performance Parameter......Page 366
 7 Simulation......Page 367
 8 Conclusion......Page 368
 References......Page 369
 1 Introduction......Page 371
 2 Related Work......Page 373
  3.2 Formal Description of the Protocol......Page 374
 5 Conclusion and Future Work......Page 376
 References......Page 377
 1 Introduction......Page 378
 4 Proposed Work......Page 380
  5.3 Tivoli Workload Scheduler (OPCA)......Page 381
  6.1 Decision Tree......Page 382
  6.3 K-Nearest Neighborhood Algorithm......Page 383
  6.5 Support Vector Machines......Page 384
  6.6 Apriori Algorithm......Page 385
 7 Background......Page 386
 9 Result Analysis......Page 387
 References......Page 391
 1 Introduction......Page 392
 3 MUD System Design for Proposed System......Page 395
 4 Results......Page 396
 5 Conclusion......Page 397
 References......Page 399
 1 Introduction......Page 401
 2 Artificial Neural Networks (ANN)......Page 403
 4 Proposed Methodology......Page 404
  4.1 Levenberg–Marquardt (LM) Algorithm......Page 405
 5 Results......Page 408
 6 Conclusion......Page 409
 References......Page 411
 1 Introduction......Page 413
 2 Current Scenario......Page 414
 3 Proposed Scenario......Page 415
 References......Page 416
Author Index......Page 417