دانلود کتاب Intelligent Data Analysis and Applications: Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, Ecc 2015

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Preface......Page 6
Organization......Page 7
Contents......Page 14
Part IData Analysis and Applications......Page 20
1 Introduction......Page 21
2 Related Work......Page 22
4.1 Random Walk......Page 24
4.2 Transition Matrix......Page 25
5 Experiment......Page 27
6 Conclusion and Future Work......Page 28
References......Page 29
1 Introduction......Page 30
2.2 Related Works......Page 31
3.1 Data Preprocess......Page 32
3.3 Specially Designed CNN for Recognition of Motion Sequence......Page 33
4.1 Gradients in Convolutional Layers......Page 34
4.2 Gradients in Sub-sampling Layers......Page 35
5.1 Introduction to 6DMG......Page 36
5.3 Deep Belief Network......Page 37
5.4 The DBCNN Model......Page 38
6 Conclusion......Page 39
References......Page 40
1 Introduction......Page 41
2 The Vibrating System......Page 43
3 Main Results......Page 46
References......Page 50
1 Introduction......Page 52
2 Related Work......Page 53
3.1 Preprocessing Stage......Page 54
3.2 Feature Extraction......Page 55
4 Experimental Results......Page 58
4.1 Benchmarks......Page 59
References......Page 61
1 Introduction......Page 63
2.1 Metabolic Data Acquisition......Page 64
2.3 Principal Component Analysis......Page 65
3 Testing......Page 66
3.1 Parametric Normalization......Page 67
3.2 Classification of New Patients......Page 68
3.4 Membership Degree Limits Definition......Page 69
3.5 Statistical Hypothesis Testing......Page 70
4 Results......Page 71
References......Page 72
A Hybrid Approach for Predicting River Runoff......Page 74
1 Introduction......Page 75
2 Background on Chaotic Theory......Page 76
3 Hybrid Model of K-means and RFNN......Page 77
4 RFNN......Page 79
5 Experiment......Page 80
References......Page 83
Critical Evaluation of Seven Lactation Curve Estimation Models......Page 85
3 The Approximating Function......Page 86
4 Numerical Study......Page 88
5 Quality of Modelling......Page 93
6 Discussion......Page 94
References......Page 95
1 Introduction......Page 97
2 Mathematical Modelling of Dynamics and Kinematics of Mobile Robot......Page 98
2.1 Computational Form of the Model......Page 101
3 Model Predictive Control......Page 102
3.3 Predictive Control of Mobile Robot......Page 104
4 Simulation Results......Page 105
References......Page 107
1 Introduction......Page 108
2.2 Generalized Linear Model......Page 110
2.3 A Hybrid Binary Classification Method......Page 111
2.4 Performance Evaluation Criteria......Page 112
3.1 Dataset Description......Page 113
3.3 Results......Page 114
4 Conclusions and Future Work......Page 116
References......Page 117
Abstract......Page 119
2 Big Data Definitions and Characteristics......Page 120
3 Big Data Sources......Page 121
4 Big Data Tools......Page 122
5 Data Mining Concept and Types......Page 123
6 Data Mining Techniques......Page 124
7.1 Companies......Page 125
7.3 Telecommunications......Page 126
8 Conclusion and Future Work......Page 127
References......Page 128
Abstract......Page 130
3 The Current State of Research......Page 131
4 Results of the Antiphase Eliminator Model......Page 133
5 Results of the Antiphase Eliminator Model......Page 134
6.1 Conditions Acoustic Field and Boundary Conditions......Page 135
6.3 Wave Absorption Conditions (Surface of Model)......Page 137
6.4 Models of Acoustic Antiphase......Page 138
References......Page 139
1 Introduction......Page 141
2.1 Discrete Wavelet Transform......Page 142
2.2 Genetic Programming......Page 143
2.3 Neural Network......Page 144
2.4 Backpropagation Learning Algorithm......Page 146
3 Results......Page 147
4 Conclusions......Page 148
References......Page 149
Abstract......Page 151
2 State of the Art......Page 152
3.1 Measured Parameters......Page 153
4 Results......Page 157
4.1 Configuration of Network Components from Implemented SW......Page 158
References......Page 160
1 Introduction......Page 162
3.1 Objective Assessment......Page 163
3.2 Subjective Assessment......Page 165
5.2 Subjective Assessment......Page 167
References......Page 170
1 Introduction......Page 172
2 Self-organizing Maps on Interval Variables......Page 174
3 Data Description and Preprocessing......Page 177
4 Implementation of the SOM and Localization Algorithm......Page 178
5 Experiments......Page 179
6 Discussion......Page 181
Acknowledgments......Page 183
References......Page 184
1 Introduction......Page 185
2 Related Work......Page 186
3.2 Preprocessing......Page 188
3.3 Spelling Error Detection......Page 189
3.4 Normalization......Page 190
4.1 3-Gram Language Model and Data Using for it......Page 192
5 Conclusion......Page 193
References......Page 194
1 Introduction......Page 196
2 Background and Related Work......Page 197
3 Information Theory......Page 198
4 Analysis of the Musical Message......Page 199
5 The Obtained Results......Page 203
References......Page 205
1 Introduction......Page 207
2.2 Social Information Retrieval......Page 208
2.3 Source Selection Approach......Page 209
3.2 Document Profile Definition......Page 210
3.3 User Profile Adaptation......Page 211
3.4 Source Selection Process......Page 212
4.1 Experimental Setup......Page 213
4.2.1 Results of Personalization Approach......Page 214
5 Conclusion and Future Work......Page 216
References......Page 217
1 Introduction......Page 218
2.1 Student Absenteeism in the Arab World......Page 220
2.2 Student Absenteeism: The Efforts......Page 221
2.3 Student Absenteeism: Scientific and Technological Endeavours......Page 222
3 The Case Study......Page 223
4 Conclusion......Page 225
References......Page 226
1 Introduction......Page 228
2.1 The Methodology of the Facility......Page 229
2.2 Production Costs......Page 230
3.1 Genetic Algorithm......Page 231
4.2 Fitness Function Evaluation......Page 232
4.4 Crossover......Page 234
5 Case Study......Page 235
6 Conclusions......Page 237
References......Page 238
1 Introduction......Page 239
2 Problem Statement......Page 240
2.1 Essential Components......Page 241
3 Similar Works......Page 243
4 Proposed Model and Methodology......Page 244
4.1 Learning Phenomena......Page 245
5 Results and Discussion......Page 249
References......Page 251
1 Introduction......Page 253
2 Tapping Background......Page 254
3 Related Work......Page 255
4.1 Data Collection......Page 257
4.2 Feature Extraction......Page 259
5 Results and Analysis......Page 261
6 Conclusion and Future Work......Page 262
References......Page 263
Part IISecure Multimedia Applications......Page 265
Abstract......Page 266
1 Introduction......Page 267
2.1 Image Selection Stage......Page 268
2.3 Iris Normalization Process......Page 270
2.4 Template Quality Test......Page 272
2.5 Multi-sample Dynamic Fusion......Page 273
2.6 Iris Encoding and Matching......Page 274
3 Results and Discussions......Page 275
4 Conclusion......Page 277
References......Page 278
1 Introduction......Page 280
2 Related Work......Page 281
3.3 Speeded Up Robust Features (SURF)......Page 282
4 Fusion Techniques......Page 283
6 Performance Metrics......Page 285
7 Experimental Results......Page 286
8 Conclusions......Page 288
References......Page 289
1 Introduction......Page 291
1.2 Android: An Overview......Page 293
1.4 Goals and Challenge......Page 294
3 The System Model......Page 295
3.2 ELM327-OBD2......Page 296
4 Development Environment......Page 297
5 Results and Discussions......Page 299
References......Page 302
1 Introduction......Page 304
2.2.1 Voice over IP (VOIP)......Page 306
3.1 LTE Physical Layer......Page 307
4.1.2 Real-Time Transport Control Protocol [RTCP]......Page 308
4.2.2 Delay Jitter......Page 309
6.1 End-to-from End Delay......Page 310
7.1 RRM Over LTE Networks......Page 311
8.1 Network Topology and Simulated Scenarios......Page 312
8.2 Simulation Results......Page 313
9 Conclusion......Page 316
References......Page 317
Abstract......Page 318
1 Introduction......Page 319
2 Related Rate Adaption Algorithms......Page 320
2.2 Closed-Loop Approach Rate Adaptation Schemes......Page 321
3.1 Evaluation View Point......Page 322
3.3 Experiment Scenarios......Page 323
4.2 Modified RRAA Algorithm......Page 326
4.3 Performance of Proposed Algorithm Analysis......Page 327
References......Page 332
1 Introduction......Page 334
2 IPv6 Adoption: Opportunities and Challenges......Page 335
3 Case Study......Page 337
3.1 Data Collection and Analysis......Page 338
3.2 Implementation and Adoption of IPv6......Page 340
4 Conclusion......Page 344
References......Page 345
Software Engineering for Security as a Non-functional Requirement......Page 346
1 Introduction......Page 347
2 Engineering the Software Non-functional Requirements......Page 348
3 The Case Study......Page 350
4 Conclusion and Future Work......Page 355
References......Page 356
Part IIISecurity and Privacy......Page 357
Localization for Jamming Attack in Wireless Sensor Networks......Page 358
1 Introduction......Page 359
2 Related Work......Page 360
3 Algorithm of α-MCC......Page 361
4 Evaluation......Page 363
References......Page 365
1 Introduction......Page 367
2 Related Work......Page 368
3.1 Generation of Cloud Watermarking Sequence......Page 370
3.3 Extracting Cloud Watermark......Page 371
3.4 Similarity Cloud Judgment......Page 372
4 Experimental Results......Page 373
5 Conclusions......Page 374
References......Page 375
1 Introduction......Page 376
2.1 Subsampling......Page 377
2.2 Compressive Sensing......Page 378
3.1 Data Embedding......Page 379
3.2 Data Extracting......Page 380
4 Experimental Results......Page 381
5 Conclusion......Page 383
References......Page 384
Part IVSignal Processing and Applications......Page 385
1 Introduction......Page 386
2.1 The Haze Removal Algorithm Based on Physical Model......Page 387
2.2 The Haze Removal Algorithm Based on Image Enhancement......Page 390
2.3 Contrast Results......Page 392
References......Page 395
1 Introduction......Page 397
2 Signal Model......Page 398
3 MVM Estimators......Page 400
4.1 Wideband Focusing......Page 401
4.2.1 Amplify Intersubarray Distortions......Page 402
5 Simulations......Page 404
5.1 Simulation 1 Precision of the Methods Versus SNR (Amplify Intersubarray Distortions)......Page 405
5.2 Simulation 2 Precision of the Methods Versus SNR (Phase Intersubarray Distortions)......Page 406
References......Page 407
1 Introduction......Page 409
2 Related Work......Page 411
3 Proposed Method......Page 412
4 Experiments and the Results......Page 415
5 Conclusions......Page 417
References......Page 418
1 Introduction......Page 419
2 Definition of Cloud Computing......Page 420
3.2 Google Cloud......Page 421
3.3 Salesforce Cloud......Page 422
3.5 Comparison......Page 423
4 Experiments......Page 424
Acknowledgments......Page 426
References......Page 427
Part VApplications of the Big Dataand the Connected Vehicles......Page 428
1 Introduction......Page 429
2 The Concept and the Application Status of Transportation IoT......Page 430
3 System Requirements and the Function Model of Transportation IoT......Page 431
4 System Framework and the Core Technologies of Transportation IoT......Page 432
4.2 The Network Layer......Page 433
4.3.1 Cloud Computing Platform......Page 434
4.3.2 Cloud Computing and Big Data Processing Technology......Page 435
5 Conclusion......Page 436
References......Page 437
Abstract......Page 439
1 Introduction......Page 440
2.2 Evolved Bat Algorithm......Page 441
3 Applying the EBA into Wireless Sensor Network......Page 442
4 Experimental Result......Page 443
References......Page 444
1 Introduction......Page 446
2 Multiband Parallel Interleaver CUWB System Model......Page 447
3 Subband Pulse Generator......Page 448
5.1 Pulse Correlation Property......Page 450
5.2 Information Transmission Rate......Page 451
5.3 System Bit Error Rate......Page 452
Acknowledgments......Page 453
References......Page 454
Part VI Machine Learning Algorithmsfor Big Data......Page 455
1 Introduction......Page 456
2 Uninorm-Based Neural Network......Page 458
3.1 Initialization of New Fuzzy Rules Based on 025B-Completeness Criterion......Page 459
3.2 Rule Updating with ESOM......Page 460
3.3 Pruning of Inconsequential Rules and Merger of Similar Fuzzy Sets......Page 461
4.1 Mackey-Glass Time Series......Page 462
References......Page 464
1 Introduction......Page 466
2.1 Multi-feature Extraction......Page 467
2.2.1 Quaternion and Quaternion Matrix......Page 468
2.2.2 Orthogonal Eigenvectors......Page 470
2.2.3 Expand to Quaternion Field......Page 471
3 Experiment Results and Analysis......Page 472
References......Page 473
1 Introduction......Page 475
2 The Concumption of Node Energy and Energy-Saving Measures......Page 476
3 Energy Assessment of Neighboring Nodes......Page 477
4 SESP Self-adaptive in-Cluster Route Algorithm......Page 479
5 Simulation......Page 480
References......Page 481
Part VIIIntelligent Data Analysisand Processing......Page 483
1 Introduction......Page 484
2.2 Sequence Alignment Technique......Page 486
3 The Proposed Enzyme Function Classification Approach......Page 490
4 Experimental Results and Discussion......Page 491
4.2 Experimental Scenarios......Page 492
4.3 Discussion......Page 493
References......Page 494
1 Introduction......Page 496
2 Related Work......Page 498
3.1 Dataset and Data Pre-Processing......Page 499
3.2 Text Features Extraction......Page 501
3.3 Personality Traits Classification......Page 503
4 Results and Discussion......Page 504
References......Page 505
Machine Learning-Based Measurement System for Spinal Cord Injuries Rehabilitation Length of Stay......Page 507
1 Introduction......Page 508
2 Related Work......Page 509
3.1 Support Vector Machines (SVMs)......Page 511
4 Proposed Automated System for SCI Rehabilitation LOS......Page 512
4.1 Preprocessing Phase......Page 513
4.3 Rehabilitation Length of Stay Measurement Phase......Page 514
5.2 Results and Discussions......Page 515
References......Page 517
Region-based Image Fusion Approach of Panchromatic and Multi-spectral Images......Page 519
1 Introduction......Page 520
2.2 Marker-Controlled Watershed Segmentation......Page 521
3.2 Histogram Matching and SWT Decomposition......Page 522
3.3 Segmentation Using Marker Controlled Watershed Segmentation......Page 523
3.4 Fusion Rules......Page 524
4.1 Data Sets: Panchromatic and Multi-spectral Images......Page 526
5 Conclusions......Page 528
References......Page 529
Biomarker-Based Water Pollution Assessment System Using Case-Based Reasoning......Page 530
1 Introduction......Page 531
2.2 Distance-based similarity measure......Page 532
3 The Proposed Case-Based Reasoning System......Page 533
3.1 Case Representation Phase......Page 535
3.4 Revise Phase......Page 536
4 Experimental Analysis and Discussion......Page 537
References......Page 539
Erratum to: Chapter `PERSONALIZED Source Selection Process: A Social Profile Adaptation Technique\' in: A. Abraham et al. (eds.), Intelligent Data Analysis and Applications, Advances in Intelligent Systems and Computing 370, DOI 10.1007/978-3-319-21206-7_18......Page 541
Author Index......Page 542