Bhatia / Mishra / Tiwari Advances in Computer and Computational Sciences

Proceedings of ICCCCS 2016, Volume 2
1. Auflage 2018
ISBN: 978-981-10-3773-3
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 554, 689 Seiten

Reihe: Advances in Intelligent Systems and Computing

Exchange of information and innovative ideas are necessary to accelerate the development of technology. With advent of technology, intelligent and soft computing techniques came into existence with a wide scope of implementation in engineering sciences. Keeping this ideology in preference, this book includes the insights that reflect the 'Advances in Computer and Computational Sciences' from upcoming researchers and leading academicians across the globe. It contains high-quality peer-reviewed papers of 'International Conference on Computer, Communication and Computational Sciences (ICCCCS 2016), held during 12-13 August, 2016 in Ajmer, India'. These papers are arranged in the form of chapters. The content of the book is divided into two volumes that cover variety of topics such as intelligent hardware and software design, advanced communications, power and energy optimization, intelligent techniques used in internet of things, intelligent image processing, advanced software engineering, evolutionary and soft computing, security and many more. This book helps the perspective readers' from computer industry and academia to derive the advances of next generation computer and communication technology and shape them into real life applications.

Dr. Sanjiv K. Bhatia received his Ph.D. in Computer Science from the University of Nebraska, Lincoln in 1991. He presently works as Professor and Graduate Director (Computer Science) in the University of Missouri, St. Louis. His primary areas of research include image databases, digital image processing, and computer vision. He has published over 40 articles in those areas. He has also consulted extensively with industry for commercial and military applications of computer vision. He is an expert in system programming and has worked on real-time and embedded applications. He serves on the organizing committee of a number of conferences and on the editorial board of international journals. He has taught a broad range of courses in computer science and was the recipient of Chancellor's Award for Excellence in Teaching in 2015. He is a senior member of ACM. Dr. Krishn K. Mishra is currently works as a Visiting Faculty, Department of Mathematics & Computer Science, University of Missouri, St. Louis, USA. He is an alumnus of Motilal Nehru National Institute of Technology Allahabad, India which is also his base working institute. His primary area of research includes evolutionary algorithms, optimization techniques, and design and analysis of algorithms. He has published more than 50 publications in International Journals and in Proceedings of International Conferences of repute. He has served as a program committee member of several conferences and also edited Scopus and SCI-indexed journals. He has 15 years of teaching and research experience during which he made all his efforts to bridge the gaps between teaching and research. Dr. Shailesh Tiwari is currently works as a Professor in Computer Science and Engineering Department, ABES Engineering College, Ghaziabad, India. He is also administratively heading the department. He is an alumnus of Motilal Nehru National Institute of Technology Allahabad, India. He has more than 15 years of experience in teaching, research and academic administration. His primary areas of research are software testing, implementation of optimization algorithms and machine learning techniques in software engineering. He has also published more than 40 publications in International Journals and in Proceedings of International Conferences of repute. He has served as a program committee member of several conferences and edited Scopus and E-SCI-indexed journals. He has also organized several international conferences under the banner of IEEE and Springer. He is a Senior Member of IEEE, member of IEEE Computer Society and Executive Committee member of IEEE Uttar Pradesh section. He is a member of reviewer and editorial board of several International Journals and Conferences. Dr. Vivek Kumar Singh is Assistant Professor at Department of Computer Science, Banaras Hindu University, India. His major research interest lies in the area of text analytics. Currently, he is working on scientometrics, sentiment analysis, social network analysis; altmetrics which are the broader research area of text analytics. He has developed and coordinated a text analytics laboratory, which works in various text analytics tasks. He is an alumnus of Allahabad University, Allahabad, India. He has published more than 30 publications in International Journals and in Proceedings of International Conferences of repute. He has also served in South Asian University, Delhi, India as an Assistant Professor for more than 4 years. He has also associated with several research projects such as Indo-Mexican Joint Research Project funded jointly by the Department of Science and Technology, Government of India along with the National Council for Science and Technology (CONACYT) of the United Mexican States.

Autoren/Hrsg.

Bhatia, Sanjiv K.

Mishra, Krishn K.

Tiwari, Shailesh

Singh, Vivek Kumar

Inhaltsverzeichnis

1;Preface;6
2;Organizing Committee;8
2.1;Technical Program Committee;9
3;Contents;15
4;About the Editors;22
5;Advanced Software Engineering;24
6;1 Approach for an Opinion Wrapping System–Using Focused Web Crawler;25
6.1;Abstract;25
6.2;1 Introduction;26
6.3;2 Literature Review;26
6.4;3 Proposed Methodology;27
6.5;4 Proposed Algorithms;28
6.5.1;4.1 URL Retriever;28
6.5.2;4.2 URL Screener;29
6.5.3;4.3 URL Updater;29
6.5.4;4.4 Opinion Hunter;29
6.5.5;4.5 Opinion Screener;30
6.6;5 Comparison of Existing Framework with Our Proposed Framework;31
6.7;6 Limitation and Future Work;33
6.8;7 Conclusion;33
6.9;References;33
7;Improved Environmental Adaption Method with Real Parameter Encoding for Solving Optimization Problems;35
7.1;1 Introduction;35
7.2;2 Background Details;36
7.2.1;2.1 EAM;36
7.2.2;2.2 IEAM;37
7.3;3 Proposed Approach;37
7.3.1;3.1 Algorithms;38
7.3.2;3.2 Details of Algorithm;39
7.4;4 Experimental Setup;41
7.5;5 Result Analysis;41
7.6;6 Conclusion;42
7.7;References;42
8;Grouping-Aware Data Placement in HDFS for Data-Intensive Applications Based on Graph Clustering;43
8.1;1 Introduction;44
8.2;2 Related Works;45
8.3;3 Optimal Data Placement Strategy---Proposed work;46
8.4;4 Experimental Results and Analysis;49
8.5;5 Conclusion and Future work;52
8.6;References;53
9;4 Parameter Estimation for PID Controller Using Modified Gravitational Search Algorithm;54
9.1;Abstract;54
9.2;1 Introduction;54
9.3;2 Gravitational Search Algorithm (GSA);55
9.4;3 Modification on GSA-PID Controller;57
9.4.1;3.1 Drawback of GSA;57
9.4.2;3.2 Modified GSA (MGSA);57
9.5;4 Numerical Experiments;59
9.5.1;4.1 Induction Motor System;59
9.5.2;4.2 Simulation Results and Analysis;59
9.6;5 Conclusion;61
9.7;References;61
10;5 Auto Improved-PSO with Better Convergence and Diversity;63
10.1;Abstract;63
10.2;1 Introduction;63
10.3;2 Related Work;65
10.3.1;2.1 Variants of PSO;65
10.4;3 Auto Improved-PSO with Better Convergence and Diversity (AI-PSO);66
10.4.1;3.1 AI-PSO Procedure;67
10.5;4 Test Functions and Experimental Setup;67
10.5.1;4.1 Benchmark Functions;67
10.5.2;4.2 Experimental Setup;68
10.6;5 Experimental Results and Discussion;69
10.6.1;5.1 Comparison of Convergence Rate and Solution Accuracy;69
10.7;6 Conclusion;70
10.8;References;70
11;6 A Novel Hybrid PSO–WOA Algorithm for Global Numerical Functions Optimization;72
11.1;Abstract;72
11.2;1 Introduction;73
11.3;2 Particle Swarm Optimization;73
11.4;3 Whale Optimization Algorithm;74
11.4.1;3.1 Encircling Prey Equation;74
11.4.2;3.2 Bubble-Net Attacking Method;75
11.5;4 The Hybrid PSO–WOA Algorithm;77
11.6;5 Simulation Results for Unconstraint Test Benchmark Function;78
11.7;6 Conclusion;79
11.8;References;79
12;7 Moth-Flame Optimizer Method for Solving Constrained Engineering Optimization Problems;80
12.1;Abstract;80
12.2;1 Introduction;81
12.3;2 Moth-Flame Optimizer;81
12.4;3 Constrained Engineering Design Problem;82
12.4.1;3.1 Car Side Impact Design;82
12.4.2;3.2 Multiple Disk Clutch Brake Design Problem;84
12.4.3;3.3 Rolling Element Bearing Design Problem;84
12.4.4;3.4 Speed Reducer Design Problem;84
12.4.5;3.5 Tubular Column Design Problem;85
12.4.6;3.6 Belleville Spring Design Problem;86
12.5;4 Conclusion;86
12.6;References;87
13;8 Training Multilayer Perceptrons in Neural Network Using Interior Search Algorithm;88
13.1;Abstract;88
13.2;1 Introduction;89
13.3;2 Feedforward Neural Network and Multilayer Perceptron;89
13.4;3 Interior Search Algorithm;90
13.4.1;3.1 Algorithm Description;90
13.4.2;3.2 Parameter Tuning;91
13.4.3;3.3 Nonlinear Constraint Manipulation;92
13.4.4;3.4 ISA-Based MLP Trainer;92
13.5;4 Results and Discussion;93
13.6;5 Conclusion;95
13.7;References;96
14;9 Sequence Generation of Test Case Using Pairwise Approach Methodology;97
14.1;Abstract;97
14.2;1 Introduction;98
14.3;2 Metaheuristic Algorithms;98
14.3.1;2.1 N-IPO (Novel-IPO);99
14.3.2;2.2 Pairwise Testing;99
14.4;3 Combination Sequence Generation Algorithm (Proposed Algorithm);99
14.4.1;3.1 Test Case Origination Algorithm;101
14.5;4 Experimental Outcomes;102
14.6;5 Discussion and Conclusion;102
14.7;References;103
15;10 A Rule Extraction for Outsourced Software Project Risk Classification;104
15.1;Abstract;104
15.2;1 Introduction;105
15.3;2 IVIFS and Its Distance;106
15.4;3 Application Example;106
15.5;4 Algorithm Steps;109
15.6;5 Experimental Results Analysis;112
15.7;6 Conclusion;114
15.8;Acknowledgements;115
15.9;References;115
16;Prediction of Market Movement of Gold, Silver and Crude Oil Using Sentiment Analysis;117
16.1;1 Introduction;117
16.2;2 Problem Statement and Proposed Methodology;119
16.2.1;2.1 Generating Public Mood Time Series;120
16.3;3 Results and Discussions;122
16.4;4 Conclusion and Future Work;123
16.5;References;124
17;12 Social Influence and Learning Pattern Analysis: Case Studies in Stackoverflow;126
17.1;Abstract;126
17.2;1 Introduction;127
17.3;2 Related Works;127
17.4;3 HITS Algorithm;128
17.5;4 Methodology;129
17.6;5 Result;131
17.7;6 Conclusion;135
17.8;References;135
18;13 Classification Approach to Extract Strongly Liked and Disliked Features Through Online User Opinions;137
18.1;Abstract;137
18.2;1 Introduction;137
18.3;2 Background;139
18.4;3 Experiment Design and Methodology;140
18.4.1;3.1 Crawl Reviews;140
18.4.2;3.2 Preprocessing;140
18.4.3;3.3 POS Tagging;140
18.4.4;3.4 Seed List Preparation;142
18.4.5;3.5 Extracting Opinionated Sentences;142
18.4.6;3.6 Handling Negation;142
18.4.7;3.7 Identifying the Polarity of Reviews;142
18.4.8;3.8 Merging All of the Reviews into a Single File;143
18.4.9;3.9 Extracting the Features;143
18.4.10;3.10 Extracting the Strongly Liked/Disliked Feature;143
18.5;4 Experiment Results and Discussion;144
18.6;5 Conclusion;148
18.7;References;148
19;Internet of Things;150
20;14 A Multicriteria Decision-Making Method for Cloud Service Selection and Ranking;151
20.1;Abstract;151
20.2;1 Introduction;151
20.3;2 Related Work;152
20.4;3 Service Measurement Index (SMI) of Cloud;153
20.5;4 Cloud Service Selection;154
20.6;5 Case Study: Using TOPSIS Method to Ranking Cloud Service Based on QoS Requirement;155
20.7;6 Conclusion and Future Work;158
20.8;References;159
21;15 Development and Analysis of IoT Framework for Healthcare Application;160
21.1;Abstract;160
21.2;1 Introduction;161
21.3;2 State of Art Techniques;161
21.4;3 Proposed Work;162
21.4.1;3.1 Architecture;163
21.4.2;3.2 Use Case;163
21.4.3;3.3 Proposed Design;164
21.5;4 Conclusion and Future Work;167
21.6;References;167
22;16 An Effective and Empirical Review on Internet of Things and Real-Time Applications;170
22.1;Abstract;170
22.2;1 Introduction;171
22.3;2 Usage of Sensor Data in Cloud Environment;172
22.4;3 Real Life Implementations and Applications of IoT;173
22.5;4 Protocols Associated with Internet of Things;174
22.6;5 Literature Review;174
22.7;6 Conclusion;177
22.8;References;178
23;17 Operations on Cloud Data (Classification and Data Redundancy);179
23.1;Abstract;179
23.2;1 Introduction;179
23.3;2 Related Work;180
23.4;3 Problem Statement;181
23.5;4 Proposed Approach;183
23.6;5 Results and Analysis;186
23.7;6 Conclusion and Future Scope;188
23.8;References;189
24;18 Load Balancing Tools and Techniques in Cloud Computing: A Systematic Review;190
24.1;Abstract;190
24.2;1 Introduction;190
24.3;2 Cloud Virtualization;191
24.3.1;2.1 Full Virtualization;191
24.3.2;2.2 Para Virtualization;192
24.4;3 Existing Techniques for Load Balancing;192
24.4.1;3.1 LBVS;192
24.4.2;3.2 Honeybee Foraging Behavior;192
24.4.3;3.3 CLBVM;192
24.4.4;3.4 SBLB for Internet Distributed Services;193
24.4.5;3.5 Join-Idle Queue;193
24.4.6;3.6 Decentralized Content Aware LB;193
24.4.7;3.7 Index Name Server;194
24.4.8;3.8 Stochastic Hill Climbing;194
24.4.9;3.9 HBB-LB;194
24.4.10;3.10 Cloud Server Optimization;194
24.4.11;3.11 Response Time Based LB;195
24.4.12;3.12 Ant Colony Optimization;195
24.4.13;3.13 PLBS;195
24.4.14;3.14 A2LB;196
24.5;4 Metrics of Cloud Load Balancing;196
24.5.1;4.1 Throughput;196
24.5.2;4.2 Overhead;196
24.5.3;4.3 Fault Tolerance;197
24.5.4;4.4 Transfer Time;197
24.5.5;4.5 Reaction Time;197
24.5.6;4.6 Resource Utilization;197
24.5.7;4.7 Scalability;197
24.5.8;4.8 Performance;197
24.6;5 Comparison of Existing Techniques for Load Balancing;198
24.7;6 Tools for Load Balancing;198
24.7.1;6.1 CloudAnalyst;198
24.7.2;6.2 GroudSim;198
24.7.3;6.3 GreenCloud;198
24.8;7 Conclusion;203
24.9;References;203
25;19 A Hybrid Optimization Approach for Load Balancing in Cloud Computing;205
25.1;Abstract;205
25.2;1 Introduction;205
25.3;2 Related Work;206
25.4;3 Motivation;208
25.5;4 Ant Colony Optimization;208
25.6;5 Artificial Bee Colony;209
25.7;6 Proposed Scheme;209
25.7.1;6.1 Modified ACO Algorithm;210
25.7.2;6.2 Modified Bee Colony;210
25.7.3;6.3 Hybrid Algorithm;211
25.8;7 Results and Discussions;211
25.8.1;7.1 Response Time;212
25.8.2;7.2 Data Center Processing Time;212
25.8.3;7.3 Cost;213
25.9;8 Conclusion;213
25.10;References;213
26;20 A Comparative Analysis of Cloud Forensic Techniques in IaaS;215
26.1;Abstract;215
26.2;1 Introduction;215
26.3;2 Cloud Forensics Investigation Challenges;216
26.4;3 Literature Survey of IaaS-Based Cloud Forensics Techniques;217
26.4.1;3.1 Enabling Cloud Forensic [6];217
26.4.2;3.2 Cloud Forensic Using VM Snapshot [7];218
26.4.3;3.3 Introspection of Virtual Machine [12];218
26.4.4;3.4 Forensically Enabled IaaS Cloud [13];219
26.5;4 Comparative Study;221
26.6;5 Conclusion and Future Scope;222
26.7;References;223
27;21 Cloud Detection: A Systematic Review and Evaluation;224
27.1;Abstract;224
27.2;1 Introduction and Motivation;224
27.2.1;1.1 Motivation for Work;225
27.3;2 Background;225
27.3.1;2.1 Various Segmentation Methods;225
27.3.2;2.2 Types of Cloud;226
27.3.3;2.3 Why Segmentation;227
27.4;3 Review Method;227
27.4.1;3.1 Research Questions;227
27.4.2;3.2 Systematic Count of Publications;231
27.5;4 Evaluation of Cloud Detection Techniques;231
27.5.1;4.1 Evaluation Trends;232
27.5.2;4.2 Evaluation Metrics;232
27.5.3;4.3 Experimental Results;233
27.6;5 Discussion;234
27.7;6 Conclusion;235
27.8;References;235
28;22 Sentiment Classification for Chinese Micro-blog Based on the Extension of Network Terms Feature;237
28.1;Abstract;237
28.2;1 Introduction;237
28.3;2 Related Work;238
28.4;3 Method Overview;239
28.4.1;3.1 Construction of Network Terms Lexicon;240
28.4.2;3.2 Word Segmentation Operation and Optimization;240
28.4.3;3.3 Extended Lexicon and Creation of Sentence Feature;242
28.4.4;3.4 Fusion Sentiment Features;243
28.5;4 Experiment;243
28.5.1;4.1 Experiment 1;244
28.5.1.1;4.1.1 Analysis of the Results of Experiment 1;244
28.5.2;4.2 Experiment 2;244
28.5.2.1;4.2.1 Analysis of the Results of Experiment 2;245
28.6;5 Conclusion;246
28.7;References;246
29;23 Implementation of Stress Measurement System Based on Technology of Internet of Things;248
29.1;Abstract;248
29.2;1 Introduction;248
29.3;2 “Comprehensive Sensing” in Internet of Things;249
29.4;3 “Interconnection” of the Internet of Things;251
29.5;4 “Smart Operation” of the Network of Things;252
29.6;5 System Test;252
29.7;6 Conclusions;253
29.8;References;254
30;24 Social Media Big Data Analysis for Global Sourcing Realization;255
30.1;Abstract;255
30.2;1 Introduction;255
30.3;2 Research Methodology;256
30.3.1;2.1 Risk and Uncertainty Ontology (RUO);257
30.3.2;2.2 Data Extraction;258
30.4;3 Results;259
30.5;4 Conclusions;260
30.6;References;260
31;25 Based on Hidden Markov Model to Identify the Driver Lane-Changing Behavior of Automobile OBD Internet of Vehicles Research and Design;261
31.1;Abstract;261
31.2;1 Introduction;261
31.3;2 Hidden Markov Model to Identify the Driver Lane-Changing Behavior;262
31.3.1;2.1 The Pilot Lane-Changing Behavior HMM Structure Definition;262
31.3.2;2.2 The HMM Model Parameter Set Training;264
31.4;3 Car OBD Car Network Design and Implementation;265
31.5;4 The System Evaluation;265
31.6;5 Conclusion;267
31.7;Acknowledgements;267
31.8;References;267
32;26 The Research on Key Technique of Raw Coal Management Information System;268
32.1;Abstract;268
32.2;1 Introduction;268
32.3;2 System Design;269
32.3.1;2.1 System Structure Design;269
32.3.2;2.2 System Database Design;269
32.3.3;2.3 System Functional Design;271
32.4;3 Key Technologies;271
32.4.1;3.1 The Implementation of User Interface Based on Freemarker and Jquery;271
32.4.2;3.2 The Implementation of Report and Graphics Based on Jxl and JfreeChart;272
32.4.3;3.3 The Implementation of User Permission Control with Configurable Function;273
32.5;4 Conclusion;273
32.6;Acknowledgements;274
32.7;References;274
33;27 Structural Modeling of Implementation Enablers of Cloud Computing;275
33.1;Abstract;275
33.2;1 Introduction;275
33.3;2 Literature Review;277
33.4;3 Methodology;278
33.4.1;3.1 Interpretive Structural Modeling Steps;279
33.4.2;3.2 Structural Self-Interaction Matrix (SSIM);279
33.4.3;3.3 Development of Reachability Matrix;279
33.4.4;3.4 Level Partitions;282
33.4.5;3.5 MICMAC Analysis and TISM Diagraph;282
33.5;4 Key Conclusions and Future Directions;282
33.6;References;288
34;28 Labelling and Encoding Hierarchical Addressing for Scalable Internet Routing;289
34.1;Abstract;289
34.2;1 Introduction;289
34.3;2 Variable-Length Encoding Hierarchical Addressing;290
34.4;3 Performance Evaluation;292
34.5;4 Conclusion;293
34.6;Acknowledgements;294
34.7;References;294
35;29 A Cuckoo Search Algorithm-Based Task Scheduling in Cloud Computing;295
35.1;Abstract;295
35.2;1 Introduction;296
35.3;2 Literature Survey;296
35.4;3 Proposed Methodology;297
35.5;4 Problem Formulation and Our Solution;298
35.5.1;4.1 Task Scheduling;298
35.5.2;4.2 Mathematical Model;299
35.6;5 Performance Evaluation;299
35.7;6 Conclusion;300
35.8;References;300
36;30 Performance Optimization in Cloud Computing Through Cloud Partitioning-Based Load Balancing;302
36.1;Abstract;302
36.2;1 Introduction;303
36.3;2 Load Balancing;304
36.4;3 Literature Review;304
36.5;4 Proposed Approach for Load Balancing;306
36.6;5 Performance Evaluation and Results;309
36.7;6 Conclusion;310
36.8;Acknowledgements;311
36.9;References;311
37;Intelligent Image Processing;313
38;31 An Optimistic Approach of Locking Strategy in Progress Fourth Generation Language;314
38.1;Abstract;314
38.2;1 Introduction;314
38.3;2 Literature Survey;315
38.4;3 Overview of Transaction Management;316
38.4.1;3.1 Transaction Failures;317
38.5;4 Database Locking;317
38.6;5 Traditional Mechanism;318
38.6.1;5.1 Optimistic Locking Strategy;318
38.7;6 Simulation Experiments;319
38.8;7 Conclusions;320
38.9;References;321
39;32 Combating Clickjacking Using Content Security Policy and Aspect Oriented Programming;322
39.1;Abstract;322
39.2;1 Introduction;323
39.3;2 Related Work and Existing Clickjacking Defenses;323
39.3.1;2.1 Frame Busting;324
39.3.2;2.2 X-Frame-Options HTTP Response Header;324
39.3.3;2.3 Browser Plugins and Add-Ons;324
39.4;3 Motivation and Principles;324
39.4.1;3.1 Content Security Policy;324
39.4.2;3.2 Aspect Oriented Programming;325
39.4.3;3.3 X-Frame-Options HTTP Header Versus Content Security Policy Header;326
39.5;4 Proposed Work;326
39.6;5 Implementation and Results;326
39.6.1;5.1 Designing Content Security Policy for Blocking Clickjacking Attempts;326
39.6.2;5.2 Adding the Content Security Policy Header to a J2EE Aspect and Defining Point Cuts to Bind the Header to GET and Post Calls;327
39.6.3;5.3 Testing Different Policy Combinations on Different Browsers;328
39.6.4;5.4 Implementation Issues;329
39.7;6 Conclusion and Future Scope;329
39.8;References;329
40;33 A Conceptual Framework for Analysing the Source Code Dependencies;331
40.1;Abstract;331
40.2;1 Introduction;331
40.3;2 Related Work;333
40.4;3 Research Gaps;334
40.5;4 Need of the Proposed Framework;334
40.6;5 Scope of Proposed Framework;334
40.7;6 Working of the Framework;335
40.7.1;6.1 Phase1;335
40.7.2;6.2 Phase 2;336
40.7.3;6.3 Phase 3;336
40.8;7 Applications of the Tool;337
40.9;8 Conclusion and Future Work;337
40.10;References;337
41;34 DWT-SVD-Based Color Image Watermarking Using Dynamic-PSO;340
41.1;Abstract;340
41.2;1 Introduction;340
41.3;2 Particle Swarm Optimization and Its Variants;341
41.4;3 Application of Dynamic-PSO in DWT-SVD-Based Watermarking for Color Images;342
41.4.1;3.1 DWT-SVD-Based Watermarking Scheme;343
41.4.2;3.2 Dynamic-PSO (DPSO);343
41.4.3;3.3 Fitness Function;343
41.4.4;3.4 Finding Scaling Factor Using Dynamic-PSO;344
41.5;4 Results and Discussion;344
41.6;5 Conclusion;347
41.7;References;347
42;35 Semi-supervised Spatiotemporal Classification and Trend Analysis of Satellite Images;349
42.1;Abstract;349
42.2;1 Introduction;349
42.3;2 Proposed Work;351
42.4;3 Experimental Results;353
42.4.1;3.1 Semi-supervised Classification of Satellite Images;353
42.4.2;3.2 Accuracy Assessment;354
42.4.3;3.3 Kappa Statistic;356
42.4.4;3.4 Trend Analysis;356
42.5;4 Conclusion;357
42.6;References;358
43;36 Improved Content-Based Image Classification Using a Random Forest Classifier;360
43.1;Abstract;360
43.2;1 Introduction;360
43.3;2 Materials and Model;362
43.3.1;2.1 Image Features;362
43.3.1.1;2.1.1 Local Binary Pattern;362
43.3.1.2;2.1.2 Colour Percentiles;363
43.3.1.3;2.1.3 Colour Moment;364
43.3.1.4;2.1.4 Colour Histogram;364
43.3.2;2.2 Image Classification;364
43.4;3 Results Analysis;367
43.5;4 Conclusions;370
43.6;References;370
44;37 An Advanced Approach of Face Recognition Using HSV and Eigen Vector;372
44.1;Abstract;372
44.2;1 Introduction;372
44.3;2 Related Work;373
44.4;3 Color Base Face Detection;374
44.5;4 Principal Component;375
44.6;5 Proposed Methodology;376
44.7;6 Experiment and Graphical Approach;376
44.7.1;6.1 Efficiency Observed Over Proposed Algorithm;376
44.8;7 Conclusion;380
44.9;References;380
45;38 RMI Approach to Cluster Based Image Decomposition for Filtering Techniques;382
45.1;Abstract;382
45.2;1 Introduction;383
45.3;2 Literature Survey;384
45.4;3 Logical Programming Structure;385
45.4.1;3.1 Median Filter;385
45.5;4 Workflow of Given System;386
45.6;5 Benefits of Current Approach;388
45.7;6 Experimental Setup and Workload Characterization;388
45.8;7 Performance Measurements;390
45.9;8 Conclusion and Future Scope;393
45.10;References;393
46;39 Segregation of Composite Document Images into Textual and Non-Textual Content;395
46.1;Abstract;395
46.2;1 Introduction;396
46.3;2 Literature Survey;396
46.4;3 Image Frame Sets from Videos;397
46.5;4 Challenges;400
46.6;5 Proposed Approach;401
46.7;6 Results;403
46.8;7 Applications;405
46.9;References;405
47;40 Optimization of Automatic Test Case Generation with Cuckoo Search and Genetic Algorithm Approaches;406
47.1;Abstract;406
47.2;1 Introduction;406
47.2.1;1.1 Software Testing Background;407
47.2.2;1.2 Unit Testing;407
47.2.3;1.3 Path Testing;408
47.3;2 Genetic Algorithm;409
47.4;3 Cuckoo Search;409
47.5;4 Genetic Algorithm in Software Testing;410
47.6;5 Proposed Method;410
47.7;6 Experimental Setup;411
47.8;7 Conclusion;415
47.9;References;415
48;41 Impact Analysis of Contributing Parameters in Audio Watermarking Using DWT and SVD;417
48.1;Abstract;417
48.2;1 Introduction;417
48.3;2 Related Work and Contribution;418
48.3.1;2.1 DWT Based Algorithms;418
48.3.2;2.2 SVD-Based Algorithms;419
48.4;3 Proposed Algorithm;419
48.4.1;3.1 Watermark Embedding Procedure;419
48.4.2;3.2 Watermark Extracting Procedure;421
48.5;4 Experimental Results and Contribution;421
48.6;5 Conclusion and Future Work;423
48.7;References;424
49;42 Digital Audio Watermarking: A Survey;425
49.1;Abstract;425
49.2;1 Introduction;425
49.3;2 Classification of Digital Watermarking;426
49.3.1;2.1 Based on Working Domain;426
49.3.2;2.2 Based on Extraction;426
49.3.3;2.3 Based on Human Perception;427
49.3.4;2.4 Based on Data;427
49.3.5;2.5 Based on Key;427
49.4;3 Audio Watermarking;427
49.5;4 Techniques of Audio Watermarking;428
49.5.1;4.1 Time Domain Methods;428
49.5.2;4.2 Transform Domain Methods;429
49.6;5 Literature Survey;430
49.7;6 Conclusion;431
49.8;References;434
50;43 Brain CT and MR Image Fusion Framework Based on Stationary Wavelet Transform;436
50.1;Abstract;436
50.2;1 Introduction;437
50.3;2 Methodology of the Proposed Fusion Scheme;438
50.4;3 Experimental Setup;441
50.5;4 Results and Discussions;443
50.6;5 Conclusion;443
50.7;References;443
51;44 A Feature-Based Semi-fragile Watermarking Algorithm for Digital Color Image Authentication Using Hybrid Transform;445
51.1;Abstract;445
51.2;1 Introduction;445
51.3;2 Related Work;446
51.4;3 Proposed Algorithm;447
51.4.1;3.1 Generating Watermark and Scrambling Process;448
51.4.2;3.2 Embedment Process;448
51.4.3;3.3 Extraction Process;449
51.4.4;3.4 Reverse Scrambling Process;449
51.5;4 Experimental Results;450
51.6;5 Conclusion;453
51.7;Acknowledgements;454
51.8;References;454
51.9;Data Set;455
52;45 Inventory Control Using Fuzzy-Aided Decision Support System;456
52.1;Abstract;456
52.2;1 Introduction;457
52.3;2 Proposed Model of ANFIS-Based DSS for Inventory Control;459
52.4;3 Experimental Details and Results;461
52.5;4 Conclusion;464
52.6;References;464
53;46 Assessment of Examination Paper Quality Using Soft Computing Technique;466
53.1;Abstract;466
53.2;1 Introduction;466
53.3;2 Literature Review;467
53.4;3 Fuzzy Logic;469
53.5;4 Proposed Model;469
53.6;5 Result;472
53.7;6 Conclusion;473
53.8;References;473
54;47 Moving Shadow Detection Using Fusion of Multiple Features;475
54.1;Abstract;475
54.2;1 Introduction;475
54.3;2 The Proposed Shadow Detection Method;476
54.3.1;2.1 The Description of Flowchart;476
54.3.2;2.2 Adaptive Shadow Detection by Improved Local Ternary Pattern;478
54.3.3;2.3 The Fusion of Multiple Features by Genetic Programming Model;479
54.4;3 Experimental Results;480
54.4.1;3.1 Dataset and Evaluation Metric;480
54.4.2;3.2 Qualitative and Quantitative Results;481
54.5;4 Conclusion;481
54.6;References;482
55;48 Caption Text Extraction from Color Image Based on Differential Operation and Morphological Processing;483
55.1;Abstract;483
55.2;1 Introduction;483
55.3;2 Edge Detection for Caption Text;484
55.4;3 Morphological Processing;486
55.5;4 Caption Text Location;486
55.6;5 Conclusion;489
55.7;References;489
56;49 Reversible Data Hiding Based on Dynamic Image Partition and Multilevel Histogram Modification;491
56.1;Abstract;491
56.2;1 Introduction;491
56.3;2 Proposed Scheme;492
56.3.1;2.1 Dynamic Image Partition;492
56.3.2;2.2 EMD Mechanism;494
56.3.3;2.3 Overflow and Underflow Prevention;494
56.3.4;2.4 Data Embedding;495
56.3.5;2.5 Data Extraction and Image Restoration;496
56.4;3 Experimental Results;496
56.5;4 Conclusion;497
56.6;Acknowledgements;498
56.7;References;498
57;ADBMS and Security;499
58;50 Threshold-Based Hierarchical Visual Cryptography Using Minimum Distance Association;500
58.1;Abstract;500
58.2;1 Introduction;500
58.3;2 Hierarchical Visual Cryptography;502
58.3.1;2.1 The Model;502
58.3.2;2.2 Thresholding Approach;503
58.4;3 Minimum Distance Association of Shares;505
58.5;4 Conclusion;507
58.6;References;507
59;51 Security in IoT-Based Smart Grid Through Quantum Key Distribution;509
59.1;Abstract;509
59.2;1 Introduction;509
59.2.1;1.1 Smart Grid;510
59.3;2 Related Work;510
59.4;3 Security Issues of IoT-Based Smart Grid;510
59.5;4 Preliminaries of Quantum Key Distribution (QKD);511
59.5.1;4.1 How Quantum Key Distribution Helps to Solve Security Issues;511
59.6;5 Proposed Protocol;511
59.7;6 Experimental Setup;513
59.7.1;6.1 Key Generation Without Eavesdropping;513
59.7.2;6.2 Key Generation with Eavesdropping;514
59.7.3;6.3 Comparison of Different Authentication Schemes;514
59.8;7 Conclusion;515
59.9;References;516
60;52 A Comparative Study on Face Detection Techniques for Security Surveillance;517
60.1;Abstract;517
60.2;1 Introduction;517
60.2.1;1.1 Face Recognition Approaches;518
60.3;2 Literature Review;519
60.3.1;2.1 Face Detection Techniques;519
60.3.2;2.2 Face Recognition Algorithms;520
60.3.3;2.3 Color Images;521
60.3.4;2.4 Classification Methods;521
60.3.5;2.5 Face Recognition Algorithms;522
60.4;3 Comparative Result;524
60.5;4 Conclusion;526
60.6;Acknowledgements;526
60.7;References;526
61;53 Proposed Approach for Book Recommendation Based on User k-NN;528
61.1;Abstract;528
61.2;1 Introduction;528
61.3;2 Theoretical Background;529
61.3.1;2.1 Recommendation System Overview;529
61.3.2;2.2 Performance Measures;530
61.3.3;2.3 Similarity Measures;531
61.3.4;2.4 Collaborative Recommendation;531
61.3.5;2.5 k-NN Algorithm;532
61.4;3 Methodology;532
61.4.1;3.1 Data Integration;534
61.4.2;3.2 Data Pre-processing;534
61.5;4 Experimental Setup;535
61.5.1;4.1 Dataset Used;535
61.5.2;4.2 Tool Used;535
61.5.3;4.3 Model Construction for Training;535
61.5.4;4.4 Model Construction for Testing;536
61.6;5 Result and Analysis;537
61.6.1;5.1 Output;537
61.6.2;5.2 Work Flow of Proposed Model;537
61.6.3;5.3 Performance Measures;539
61.6.4;5.4 Analysis;539
61.7;6 Conclusion;543
61.8;Acknowledgements;543
61.9;References;543
62;54 Improved FP-Linked List Algorithm for Association Rule Mining;544
62.1;Abstract;544
62.2;1 Introduction;545
62.3;2 FP-Growth Algorithm;546
62.4;3 FPBitLink Algorithm;548
62.5;4 Transactions-Based FPBitLink Algorithm;550
62.6;5 Algorithm Selector;553
62.7;6 Conclusion;553
62.8;References;554
63;55 On Hierarchical Visualization of Event Detection in Twitter;555
63.1;Abstract;555
63.2;1 Introduction;555
63.2.1;1.1 Contribution;556
63.3;2 Related Work;557
63.4;3 Framework;557
63.4.1;3.1 Preprocessing;557
63.4.2;3.2 Event Detection;558
63.4.3;3.3 Visualization;560
63.5;4 Experimental Results;560
63.5.1;4.1 Evaluation Methodology;560
63.5.2;4.2 Parameter Tuning;561
63.5.3;4.3 Results and Discussions;561
63.6;5 Conclusion;562
63.7;References;562
64;56 Audio Steganography Techniques: A Survey;564
64.1;Abstract;564
64.2;1 Introduction;565
64.3;2 Audio Steganography;565
64.3.1;2.1 Characteristics;565
64.4;3 Techniques of Data Hiding in Audio;566
64.4.1;3.1 Spatial Domain Technique;566
64.4.2;3.2 Transform Domain Technique;569
64.4.3;3.3 Compressed Domain Technique;570
64.4.4;3.4 Coded Domain Technique;571
64.5;4 Conclusion;571
64.6;References;572
65;57 Role of Clustering in Crime Detection: Application of Fuzzy K-means;573
65.1;Abstract;573
65.2;1 Introduction;573
65.2.1;1.1 Fuzzy K-means Approach;574
65.2.2;1.2 Working Steps of Fuzzy K-Means;574
65.3;2 Literature Survey;575
65.4;3 Proposed Work;576
65.5;4 Result Analysis;577
65.5.1;4.1 Results of FKMC;580
65.5.2;4.2 Comparison Results;580
65.6;5 Conclusion;581
65.7;References;581
66;58 Implementation of Modified K-means Approach for Privacy Preserving in Data Mining;582
66.1;Abstract;582
66.2;1 Introduction;583
66.3;2 Related Work;583
66.4;3 Proposed Solution;584
66.4.1;3.1 Algorithm for Modified K-means;585
66.5;4 Experimental Setup;586
66.6;5 Results and Analysis;587
66.6.1;5.1 Graphs;588
66.7;6 Conclusion;591
66.8;References;591
67;59 Cross-Lingual Information Retrieval: A Dictionary-Based Query Translation Approach;592
67.1;Abstract;592
67.2;1 Introduction;592
67.3;2 Related Work;593
67.4;3 Proposed Approach;594
67.4.1;3.1 Pre-processing;594
67.4.2;3.2 Query Translation;595
67.4.3;3.3 OOV Terms Transliteration Mining (OOVTTM);595
67.4.4;3.4 Indexing, Retrieval and Evaluation;596
67.5;4 Experiment Results and Discussions;596
67.6;5 Conclusion;598
67.7;References;598
68;60 Predictive Classification of ECG Parameters Using Association Rule Mining;600
68.1;Abstract;600
68.2;1 Introduction;600
68.3;2 Literature Survey;601
68.3.1;2.1 Association Rule Mining;601
68.3.2;2.2 Apriori Association Rule Mining;602
68.3.3;2.3 Predictive Apriori Association Rule Mining;604
68.4;3 Experimental Setup;605
68.5;4 Results;605
68.6;5 Observations;607
68.7;6 Conclusion;608
68.8;References;608
69;61 Two-Level Diversified Classifier Ensemble for Classification of Credit Entries;609
69.1;Abstract;609
69.2;1 Introduction;609
69.3;2 Methodology;610
69.3.1;2.1 Experimental Protocol;611
69.3.2;2.2 Model Evaluation;611
69.3.3;2.3 Ensemble Models;611
69.4;3 Related Work;613
69.5;4 Constructing Two-Level Hybrid Ensemble;613
69.6;5 Experiments and Results;614
69.7;6 Conclusions and Further Extensions;615
69.8;References;616
70;62 P-RED: Probability Based Random Early Detection Algorithm for Queue Management in MANET;617
70.1;Abstract;617
70.2;1 Introduction;618
70.3;2 Literature Survey;618
70.4;3 The P-RED Algorithm;619
70.5;4 Simulation for Throughputs and Delay;620
70.6;5 Conclusions and Further Work;622
70.7;References;622
71;63 Analyzing Game Stickiness Using Clustering Techniques;624
71.1;Abstract;624
71.2;1 Introduction;624
71.3;2 Literature Review;625
71.3.1;2.1 k-Means;626
71.3.2;2.2 DBSCAN;627
71.4;3 Proposed System;627
71.5;4 Implementation;628
71.5.1;4.1 Data Collection;628
71.5.2;4.2 Tool Used;629
71.5.3;4.3 Procedure;629
71.6;5 Experimental Results;630
71.7;6 Conclusion;632
71.8;References;632
72;64 Automated Detection of Acute Leukemia Using K-mean Clustering Algorithm;634
72.1;Abstract;634
72.2;1 Introduction;635
72.3;2 Proposed Methodology;636
72.3.1;2.1 Image Preprocessing;636
72.3.2;2.2 Image Segmentation;637
72.4;3 Identification and Classification;641
72.4.1;3.1 Identification of Grouped Leucocytes;642
72.4.2;3.2 Nucleus and Cytoplasm Selection;643
72.4.3;3.3 Feature Extraction;644
72.4.4;3.4 Image Classification;645
72.5;4 Conclusion;647
72.6;References;647
73;65 Energy Data Analysis of Green Office Building;650
73.1;Abstract;650
73.2;1 Introduction;651
73.3;2 Basics;651
73.3.1;2.1 DBSCAN Clustering Algorithm;651
73.3.2;2.2 C4.5 Classification Algorithm;652
73.3.3;2.3 Dynamic LOF Algorithm;652
73.4;3 Examples of Verification;653
73.4.1;3.1 Data Sorting and Analyzing;653
73.4.2;3.2 Using DBSCAN Algorithm Clustering;654
73.4.3;3.3 Using C4.5 Algorithm to Classify the Clustering Data;654
73.4.4;3.4 Dynamic LOF Algorithms to Detect Outliers;655
73.5;4 Summary;657
73.6;References;658
74;66 Location Prediction Model Based on K-means Algorithm;659
74.1;Abstract;659
74.2;1 Introduction;659
74.3;2 Location Prediction Model Based on K-means;661
74.3.1;2.1 Basic Idea of K-means Algorithm;661
74.3.2;2.2 Location Prediction Method Based on K-means;661
74.4;3 Experiments and Results;663
74.5;4 Conclusion;664
74.6;References;665
75;Visual Tracking via Clustering-Based Patch Weighing and Masking;666
75.1;1 Introduction;667
75.2;2 The Proposed Method;667
75.2.1;2.1 Framework Overview;667
75.2.2;2.2 Appearance Modeling;667
75.2.3;2.3 Tracking;669
75.3;3 Experiments;670
75.3.1;3.1 Experimental Setup;670
75.3.2;3.2 Quantitative and Qualitative Analysis;670
75.4;4 Conclusions;671
75.5;References;672
76;68 A Presenter Discovery Method Based on Analysis of Reputation Record;673
76.1;Abstract;673
76.2;1 Introduction;674
76.3;2 Related Work;674
76.4;3 Presenter Discovery Method;676
76.4.1;3.1 Presenter Initial Set;676
76.4.2;3.2 Build Presenter Candidate Set;677
76.4.3;3.3 Calculation of the Trust Presenters;678
76.4.4;3.4 Recommendation Record Update;682
76.5;4 Experiment and Result Analysis;682
76.5.1;4.1 The Experimental Setting;683
76.5.2;4.2 The Experimental Results and Analysis;683
76.6;5 Conclusions;685
76.7;References;686
77;Author Index;687