E-Book, Englisch, Band 545, 1453 Seiten, eBook
Sridhar / Padma / Rao Emerging Research in Electronics, Computer Science and Technology
1. Auflage 2019
ISBN: 978-981-13-5802-9
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
Proceedings of International Conference, ICERECT 2018
E-Book, Englisch, Band 545, 1453 Seiten, eBook
Reihe: Lecture Notes in Electrical Engineering
ISBN: 978-981-13-5802-9
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book presents the proceedings of the International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) organized by PES College of Engineering in Mandya. Featuring cutting-edge, peer-reviewed articles from the field of electronics, computer science and technology, it is a valuable resource for members of the scientific research community.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1;Preface;6
2;Acknowledgements;8
3;Contents;9
4;Editors and Contributors;19
5;An Approach for Estimation of Distance Information Between Two Persons from Single 2D Image;36
5.1;1 Introduction;36
5.2;2 Related Works;37
5.3;3 Proposed Methodology;38
5.3.1;3.1 Human Presence Detection;38
5.3.2;3.2 Segmentation;40
5.3.3;3.3 Estimating Depth Information;43
5.3.4;3.4 Find the Distortion Between Humans;43
5.4;4 Results;44
5.5;5 Conclusion;45
5.6;References;46
6;A Reliable Routing Protocol with Backup Scheme in Wired Computer Networks;47
6.1;1 Introduction and Motivation;47
6.1.1;1.1 Proposed Work;48
6.2;2 Related Work;48
6.3;3 Proposed Approach for Routing Protocol Implementation;49
6.3.1;3.1 Cost Function;49
6.3.2;3.2 Algorithm;50
6.3.3;3.3 Example;50
6.3.4;3.4 Flowchart for Proposed Protocol;52
6.4;4 Experiments and Results;52
6.5;5 Conclusions and Future Work;55
6.6;References;56
7;Anomaly-Based NIDS Using Artificial Neural Networks Optimised with Cuckoo Search Optimizer;57
7.1;1 Introduction;57
7.2;2 Literature Review;58
7.3;3 Problem Description;60
7.4;4 The Proposed Solution;60
7.4.1;4.1 Artificial Neural Network (ANN);61
7.4.2;4.2 Cuckoo Search Algorithm;62
7.4.3;4.3 Construction of ANN-CSO Model;63
7.5;5 Experimental Result;64
7.5.1;5.1 Dataset;64
7.5.2;5.2 Performance of the Model;64
7.5.3;5.3 Analysis of Performance;66
7.6;6 Conclusion and Future Work;68
7.7;References;68
8;WSN-Based Electronic Livestock of Dairy Cattle and Physical Parameters Monitoring;70
8.1;1 Introduction;70
8.2;2 Background;71
8.3;3 Literature Review;72
8.4;4 System Architecture;73
8.4.1;4.1 Temperature Sensor (LM35);73
8.4.2;4.2 Accelerometer Sensor (ADXL335);74
8.4.3;4.3 Heart Rate Sensor (Pulse);75
8.4.4;4.4 RL78 Microcontroller;75
8.4.5;4.5 Working of System;75
8.5;5 Results;76
8.6;6 Conclusion;76
8.7;7 Future Enhancement;78
8.8;References;78
9;A Sequence-Based Machine Comprehension Modeling Using LSTM and GRU;79
9.1;1 Introduction;79
9.2;2 Dataset;81
9.3;3 Methodology;82
9.3.1;3.1 Preprocessing;83
9.3.2;3.2 Long Short-Term Memory Network;83
9.3.3;3.3 Gated Recurrent Unit;83
9.3.4;3.4 Answer Prediction;84
9.4;4 Result;84
9.5;5 Conclusion;86
9.6;6 Future Work;86
9.7;References;86
10;Deep-Learning-Based Stance Detection for Indian Social Media Text;88
10.1;1 Introduction;88
10.1.1;1.1 Selection of Targets for Stances;89
10.2;2 Related Works;90
10.3;3 Creation and Details of Dataset;90
10.4;4 Our Approaches;91
10.4.1;4.1 Word Embeddings;92
10.4.2;4.2 Bag of Tricks;92
10.4.3;4.3 Bidirectional Long Short-Term Memory (Bi-LSTM);92
10.4.4;4.4 Convolutional Neural Network (CNN);94
10.5;5 Experiments and Observation;94
10.5.1;5.1 Unsupervised Learning of Word Vectors;95
10.5.2;5.2 System Experiment and Observation;95
10.6;6 Conclusions;97
10.7;References;98
11;Integration of Wireless Sensor Network and Cloud Computing Using Trust and Reputation Technique;99
11.1;1 Introduction;99
11.1.1;1.1 Wireless Sensor Network (WSN);99
11.1.2;1.2 Cloud Computing (CC);100
11.1.3;1.3 CC-WSN Integration;100
11.1.4;1.4 Cloud Security with Encryption of Data;100
11.1.5;1.5 Motivation;101
11.1.6;1.6 Research Contribution;102
11.1.7;1.7 Organization of This Paper;103
11.2;2 Related Work;103
11.2.1;2.1 Authentication and Data Security;103
11.2.2;2.2 CC-WSN Integration;104
11.2.3;2.3 Trust and Reputation Calculation;104
11.3;3 System Architecture;104
11.3.1;3.1 Overall Description of System Architecture;105
11.3.2;3.2 Factors of Trust and Reputation Calculation of CSP;105
11.4;4 Proposed Schemes;107
11.4.1;4.1 Entry List;107
11.4.2;4.2 Trust List;107
11.5;5 Algorithms;107
11.5.1;5.1 Choosing Desired CSPs for the Service;107
11.5.2;5.2 Choosing Desired SNPs for the Service;108
11.5.3;5.3 Securing Data in the Cloud;108
11.6;6 Performance Evaluation;109
11.6.1;6.1 Preliminaries;109
11.6.2;6.2 Simulation Setup;110
11.6.3;6.3 Performance Analysis;110
11.7;7 Conclusions;111
11.8;References;112
12;Classwise Clustering for Classification of Imbalanced Text Data;113
12.1;1 Introduction;114
12.2;2 Background and Motivation;115
12.3;3 Representation of Documents in Lower-Dimensional Space;115
12.4;4 Proposed Method;116
12.4.1;4.1 Clustering;116
12.4.2;4.2 Representation of Documents of Each Cluster;118
12.4.3;4.3 Creation of Knowledgebase of Interval-Valued Representatives;118
12.4.4;4.4 Classification;119
12.5;5 Experimentation and Results;120
12.5.1;5.1 Dataset and Experimental Setup;120
12.5.2;5.2 Results and Analysis;121
12.6;6 Conclusion;123
12.7;References;123
13;Limited Number of Materials Scene Reconstruct from Mojette Projections;125
13.1;1 Introduction;125
13.2;2 Binary Image Reconstructed from Line Mojette;127
13.2.1;2.1 Materials Line Mojette Algorithm;127
13.3;3 3 and 4 Materials Scene Reconstruction from Line Mojette Projections;129
13.4;4 Farey–Haros Series and Stern–Brocot Tree;129
13.5;5 Experiments;131
13.5.1;5.1 Binary Image Analysis;131
13.5.2;5.2 Experimental Results Comparison Between the 2, 3, and 4 Materials Line Mojette;132
13.6;6 Conclusion;135
13.7;References;135
14;Automatic Pattern Discovery of Neonatal Brain Tumor Segmentation and Abnormalities in MRI Sequence;137
14.1;1 Introduction;137
14.2;2 Related Work;138
14.3;3 Proposed Architecture;140
14.3.1;3.1 Segmentation;141
14.3.2;3.2 Program Code;142
14.4;4 Experimental Results;142
14.4.1;4.1 Detection of Brain Tumor;144
14.4.2;4.2 Area Calculation of Tumor Region;144
14.5;5 Conclusion;146
14.6;References;146
15;Theme-Based Partitioning Approach to Decision Tree: An Extended Experimental Analysis;147
15.1;1 Introduction;147
15.2;2 Literature Review;148
15.3;3 Review of Theme-Based Partitioning [8];149
15.4;4 Results and Analysis;150
15.5;5 Conclusion;156
15.6;References;156
16;Feedback-Based Swarm Optimization for Optimized Decision Making in Unsecured Mobile Cloud Coordinated Service;158
16.1;1 Introduction;159
16.1.1;1.1 Mobile Cloud;159
16.1.2;1.2 Swarm Intelligence;160
16.2;2 Swarm Framework for Unsecured Mobile Cloud;160
16.3;3 Proposed Feedback-Based Swarm Optimization for Mobile Cloud [FBSO];161
16.3.1;3.1 General Flock Network;162
16.3.2;3.2 Proposed FBSO Algorithm for Cloud;162
16.3.3;3.3 FBSO Algorithm for Mobile Cloud;163
16.4;4 Implementation;164
16.5;5 Conclusion;166
16.6;References;167
17;Development of Hybrid Algorithm for Masquerading Sink Node Location in WSN;168
17.1;1 Introduction;168
17.2;2 Literature Review;170
17.3;3 Proposed Algorithm;171
17.3.1;3.1 Simulation Model;173
17.4;4 Results and Discussions;174
17.5;5 Conclusions;176
17.6;References;176
18;A Data-Driven Model Approach for DayWise Stock Prediction;178
18.1;1 Introduction;179
18.2;2 Methodology;180
18.3;3 Results and Discussion;183
18.4;4 Conclusion;186
18.5;References;186
19;Automatic English to Kannada Back-Transliteration Using Combination-Based Approach;188
19.1;1 Introduction;188
19.2;2 Related Work;190
19.3;3 Analysis of English-Kannada Script;190
19.3.1;3.1 Complexities in Transliteration;191
19.4;4 Syllabification;193
19.5;5 Bilingual Kannada–English Corpus;194
19.6;6 Methodology;195
19.6.1;6.1 Knowledge Base Creation;195
19.6.2;6.2 Back-Transliteration;196
19.7;7 Evaluation;196
19.8;8 Conclusion and Future Work;197
19.9;References;198
20;A Comparison of Warnsdorff’s Rule and Backtracking for Knight’s Tour on Square Boards;200
20.1;1 Introduction;200
20.1.1;1.1 Knight’s Tour;200
20.1.2;1.2 Algorithms to Compute Knight’s Tour;201
20.1.3;1.3 Square Boards of Even Dimensions and Odd Dimensions;201
20.2;2 Warnsdorff’s Rule;202
20.2.1;2.1 Boards of Even Dimensions;203
20.2.2;2.2 Boards of Odd Dimensions;205
20.3;3 Backtracking;206
20.3.1;3.1 Boards of Even Dimensions;208
20.3.2;3.2 Boards of Odd Dimensions;211
20.4;4 Results;213
20.5;References;214
21;Performance Evaluation of Fetal ECG Extraction Algorithms;215
21.1;1 Introduction;215
21.2;2 LMS-Based Adaptive Filtering Algorithm;216
21.3;3 Adaptive Filtering Combined with Neural Network;217
21.3.1;3.1 Input Unit;218
21.3.2;3.2 Hidden Unit;218
21.3.3;3.3 Output Unit;218
21.4;4 Results and Discussions;219
21.5;5 Conclusion;219
21.6;References;221
22;Implementation of Maximum Flow Algorithm in an Undirected Network;223
22.1;1 Introduction;223
22.2;2 Maximum Flow Algorithm;224
22.3;3 Illustrative Example;225
22.4;4 Limitation;229
22.5;5 Research;230
22.6;6 Conclusion;230
22.7;References;230
23;A Review on Trust Models of Social Internet of Things;231
23.1;1 Introduction;231
23.2;2 Social Internet of Things Network Structure;232
23.3;3 Quantifying Trust Model;232
23.4;4 Attribute Selection;233
23.5;5 Related Work;233
23.6;6 Discussion and Comparison;235
23.7;7 Limitations and Future Work;236
23.8;8 Conclusion;236
23.9;References;237
24;A Deep Learning-Based Stacked Generalization Method to Design Smart Healthcare Solution;238
24.1;1 Introduction;239
24.2;2 Related Works;239
24.3;3 Methodology;240
24.3.1;3.1 Dataset Summary and Statistics;240
24.3.2;3.2 Data Preprocessing;242
24.3.3;3.3 Feature Extraction and Feature Selection;243
24.3.4;3.4 Predictive Modeling;243
24.3.5;3.5 Base Learners;244
24.3.6;3.6 Stacked Ensemble Built on Deep Learning;245
24.4;4 Results;246
24.4.1;4.1 Performance on Different Models;246
24.5;5 Conclusion;247
24.6;References;248
25;Hadoop as a Service in OpenStack;250
25.1;1 Introduction;251
25.2;2 Literature Survey;251
25.2.1;2.1 OpenStack;252
25.2.2;2.2 Hadoop;254
25.3;3 Proposed Work;255
25.4;4 Implementation;256
25.4.1;4.1 Deploying OpenStack Private Cloud;256
25.4.2;4.2 Hadoop on OpenStack;257
25.5;5 Results and Discussion;258
25.6;6 Conclusion;259
25.7;7 Limitations and Future Scope;259
25.8;References;260
26;Load Balancing for Software-Defined Networks;261
26.1;1 Introduction;262
26.2;2 Related Work;263
26.2.1;2.1 OpenFlow;263
26.2.2;2.2 POX Controller;264
26.3;3 Proposed Work;264
26.3.1;3.1 Process Flow Diagram;265
26.3.2;3.2 Algorithm 1: Probing the Servers;266
26.3.3;3.3 Algorithm 2: Balancing Load on Servers;266
26.3.4;3.4 Algorithm 3: Balancing Load on Servers with Segregation;266
26.4;4 Emulation Model;267
26.4.1;4.1 Mininet;267
26.4.2;4.2 Performance Metrics;267
26.4.3;4.3 Simulation Scenario;268
26.5;5 Results and Discussion;269
26.6;6 Conclusion and Future Work;270
26.7;References;270
27;Paddy Yield Predictor Using Temperature, Rainfall, Soil pH, and Nitrogen;271
27.1;1 Introduction;271
27.2;2 Literature Survey;272
27.3;3 Methodology;273
27.3.1;3.1 Data Collection;273
27.3.2;3.2 Data Preprocessing;273
27.3.3;3.3 Applying Association Rule Mining Algorithm;273
27.3.4;3.4 Analyzing Association Rules;275
27.4;4 Experimentation;275
27.4.1;4.1 Dataset;275
27.4.2;4.2 Experimental Setup;276
27.4.3;4.3 Result and Discussion;276
27.5;5 Conclusion and Future Enhancement;278
27.6;References;278
28;Reshaping the Real Estate Industry Using Blockchain;280
28.1;1 Introduction;281
28.1.1;1.1 Why Blockchain?;281
28.2;2 Working of Blockchain—A Technical Framework;282
28.3;3 Problems in Existing Real Estate Industry;283
28.4;4 Opportunity of Blockchain in Real Estate Industry;284
28.4.1;4.1 Improved Property Search Through Blockchain-Enabled Multiple Listing Service (MLS);284
28.4.2;4.2 Property Visit and Inspection (Manual Phase);286
28.4.3;4.3 Negotiation of Terms/Value and Signing of the Letter of Intent (Manual Phase);286
28.4.4;4.4 Prepurchase/Lease Due Diligence by Using Smart Identities;286
28.4.5;4.5 Automated Agreement, Payments, and Cash Flow Using Smart Contracts;287
28.4.6;4.6 Execution of Sale and Real-Time Data Analysis;287
28.5;5 Conclusion;287
28.6;References;288
29;Assessment of Weld Bead Mechanical Properties During Destructive Testing Using Image Processing by Multivision Technique;289
29.1;1 Introduction;289
29.2;2 Selection of Work Material, Preparation and Description;291
29.3;3 Processing of Weld Bead Images Using Machine Vision (MV);294
29.3.1;3.1 Weld Bead Features;294
29.4;4 Results and Discussions;295
29.5;5 Conclusion;298
29.6;References;298
30;Design of Syntax Analyzer for Kannada Sentences Using Rule-Based Approach;300
30.1;1 Introduction;300
30.2;2 Previous Work;301
30.3;3 4691281En26FigbPrint (Kaaraka) Relations;301
30.4;4 Proposed Syntax Analyzer for Kannada Sentences;302
30.5;5 System Performance and Result Analysis;304
30.6;6 Comparison of Proposed Syntax Analyzer with Existing Kannada Syntax Analyzers;305
30.7;7 Conclusion and Future Enhancement;306
30.8;References;306
31;An Efficient Public Auditing Method with Periodic Verification for Data Integrity in Cloud;307
31.1;1 Introduction;307
31.2;2 Literature Review;308
31.3;3 Problem Statement;309
31.3.1;3.1 System Model;309
31.3.2;3.2 Definition of Our Scheme;310
31.4;4 The Proposed Scheme;310
31.5;5 Performance Evaluation;311
31.6;6 Conclusion;312
31.7;References;312
32;Web Service Ranking and Selection Based on QoS;314
32.1;1 Introduction;314
32.2;2 Literature Review;316
32.2.1;2.1 QoS-Based Web Service Selection;316
32.2.2;2.2 Web Service Ranking;317
32.2.3;2.3 Learning Algorithm for Ranking;317
32.2.4;2.4 Decision Strategies;318
32.2.5;2.5 Issues in Literature Review;318
32.2.6;2.6 Inferences Drawn from Literature;318
32.3;3 Proposed Work;320
32.3.1;3.1 Proposed System Architecture;321
32.4;4 Implementation Strategy;321
32.5;5 Conclusion and Future Plan;324
32.6;References;324
33;Campus Vehicle Monitoring Through Image Processing;326
33.1;1 Introduction;326
33.2;2 Literature Survey;328
33.3;3 System Design;330
33.3.1;3.1 Detection of Motion Using PIR Sensor;331
33.3.2;3.2 Image Processing Methods;331
33.4;4 Results;332
33.5;5 Conclusion;334
33.6;References;335
34;CL-PKA: Key Management in Dynamic Wireless Sensor Network: A Novel Framework;337
34.1;1 Introduction;338
34.1.1;1.1 Overview;338
34.1.2;1.2 Major Features;339
34.2;2 Literature Survey;339
34.3;3 Related Work;340
34.3.1;3.1 System Architecture;341
34.3.2;3.2 Overview of Architecture;341
34.4;4 Implementation;342
34.4.1;4.1 Service Provider Module;343
34.4.2;4.2 Routing Module;343
34.4.3;4.3 Clustering Modules;343
34.4.4;4.4 Receiver Module;344
34.4.5;4.5 Attacker Module;344
34.4.6;4.6 Flow of Architecture;344
34.5;5 Results;344
34.5.1;5.1 Encryption;344
34.5.2;5.2 Initializing Mac Address;344
34.5.3;5.3 Initializing Energy of Each Node;345
34.5.4;5.4 Selecting Destination Node;345
34.5.5;5.5 Transfer of Data Through Nodes;348
34.5.6;5.6 Transfer of Data Through Nodes When Node is Affected by Virus;348
34.6;6 Conclusion;348
34.7;References;349
35;Convolutional Neural Network Approach for Extraction and Recognition of Digits from Bank Cheque Images;351
35.1;1 Introduction;351
35.2;2 Preliminaries;353
35.3;3 Proposed Method;355
35.4;4 Data sets;357
35.5;5 Results and Discussions;358
35.6;6 Conclusion;359
35.7;References;360
36;A Novel Routing Protocol for Security Over Wireless Adhoc Networks;362
36.1;1 Introduction;362
36.2;2 Related Work;363
36.3;3 Proposed Method;364
36.3.1;3.1 Network Topology;364
36.3.2;3.2 Attack Detection;365
36.3.3;3.3 Attack Prevention;366
36.4;4 Results and Discussions;367
36.4.1;4.1 Simulation Environment;367
36.5;5 Conclusion and Enhancements;371
36.6;References;372
37;A Study on Personalized Early Detection of Breast Cancer Using Modern Technology;373
37.1;1 Introduction;373
37.1.1;1.1 Breast Cancer;374
37.1.2;1.2 Early Detection of Breast Cancer;374
37.2;2 Wearable Device for Early Detection of Breast Cancer;375
37.3;3 Conclusion and Future Enhancement;379
37.4;References;380
38;Smartphone Price Prediction in Retail Industry Using Machine Learning Techniques;381
38.1;1 Introduction;381
38.2;2 Existing System;382
38.3;3 Proposed System;383
38.3.1;3.1 Dataset;383
38.3.2;3.2 Feature Selection;384
38.3.3;3.3 Methodology;384
38.3.4;3.4 Models;385
38.3.5;3.5 Results and Graphical Analysis;387
38.4;4 Conclusion;391
38.5;5 Future Scope;391
38.6;References;391
39;Weighted Round-Robin Load Balancing Algorithm for Software-Defined Network;392
39.1;1 Introduction;392
39.2;2 Literature Survey;394
39.2.1;2.1 Review of SDN Architecture;394
39.2.2;2.2 Study on Load Balancing Strategy in SDN;394
39.2.3;2.3 Survey on Distributed Load Balancing in SDN;395
39.2.4;2.4 SDN Frameworks;396
39.3;3 Proposed Work;398
39.3.1;3.1 Analytical Model for WRR;398
39.3.2;3.2 Weighed Round-Robin (WRR) Algorithm;399
39.4;4 Result Analysis;400
39.4.1;4.1 Simulation Scenario;400
39.4.2;4.2 Results and Discussion;401
39.5;5 Conclusion;403
39.6;References;403
40;Assessing Human Stress Through Smartphone Usage;405
40.1;1 Introduction;405
40.2;2 Related Works;406
40.3;3 Methodology;408
40.4;4 Implementation and Results;411
40.5;5 Conclusion and Future Work;414
40.6;References;415
41;Virtual Map-Based Approach to Optimize Storage and Perform Analytical Operation on Educational Big Data;416
41.1;1 Introduction;416
41.2;2 Related Work;417
41.3;3 Problem Description;418
41.4;4 Proposed Methodology;418
41.5;5 Algorithm Design;419
41.5.1;5.1 Algorithm Snapshot;419
41.5.2;5.2 Algorithm Description;420
41.6;6 Results Discussion;422
41.7;7 Conclusion;424
41.8;References;425
42;Comparison of Rainfall Forecasting Using Artificial Neural Network and Chaos Theory;428
42.1;1 Introduction;429
42.2;2 Methodology;430
42.2.1;2.1 Concept of Artificial Neural Network;430
42.2.2;2.2 Algorithms;431
42.2.3;2.3 Concept of Chaos Theory;431
42.2.4;2.4 Procedure Adapted;432
42.3;3 Results and Discussion;433
42.4;4 Conclusion;435
42.5;References;436
43;Analysis of Vital Signals Acquired from Wearable Device;438
43.1;1 Introduction;439
43.2;2 Methodology;440
43.2.1;2.1 Physiological Parameters;440
43.2.2;2.2 Block Diagram for Physiological Monitoring;440
43.2.3;2.3 PPG Signal Acquisition and Analysis;440
43.2.4;2.4 ECG Signal Acquisition and Analysis;441
43.2.5;2.5 Temperature;445
43.2.6;2.6 Galvanic Skin Response and Analysis;445
43.2.7;2.7 Accelerometer and Activity Analysis;447
43.2.8;2.8 Wireless Communication Setup;447
43.2.9;2.9 Signal Processing;447
43.3;3 Results and Discussion;449
43.4;4 Conclusion;449
43.5;References;451
44;Experimental Investigations on Quality of Water Used in Poultry Farm Using Sensors;452
44.1;1 Introduction;452
44.2;2 Literature Survey;453
44.3;3 Parameters Measured and Their Effect on Layers;455
44.3.1;3.1 pH (Power of Hydrogen);455
44.3.2;3.2 Turbidity;455
44.3.3;3.3 Temperature;455
44.4;4 Experimental Setup;456
44.5;5 Experimental Study and Results;457
44.6;6 Conclusions and Future Work;460
44.7;References;460
45;Empirical Study to Evaluate the Performance of Classification Algorithms on Public Datasets;461
45.1;1 Introduction;461
45.2;2 Literature Review;462
45.3;3 Methodology;463
45.4;4 Experiments and Results;464
45.4.1;4.1 Datasets Information;464
45.4.2;4.2 Experimental Procedure;465
45.4.3;4.3 Results;466
45.5;5 Conclusion;468
45.6;References;469
46;Performance Analysis of Job Scheduling Algorithms on Hadoop Multi-cluster Environment;470
46.1;1 Introduction;471
46.2;2 Related Work;473
46.3;3 Proposed Work;474
46.4;4 Implementation;475
46.4.1;4.1 Master and Slave Connection Using SSH;476
46.4.2;4.2 YouTube Dataset Extractions;476
46.4.3;4.3 FIFO Scheduling Algorithm;477
46.4.4;4.4 FAIR Scheduling Algorithm;477
46.4.5;4.5 Capacity Scheduling Algorithm;477
46.5;5 Results and Discussion;478
46.5.1;5.1 Performance Analysis of Scheduling Algorithm Considering CPU Cycles;478
46.5.2;5.2 Performance Analysis of Scheduling Algorithm Considering Physical Memory;478
46.5.3;5.3 Performance Analysis of Scheduling Algorithm Considering Virtual Memory;479
46.5.4;5.4 Performance Analysis of FAIR, FIFO, and CAPACITY Schedulers;480
46.6;6 Conclusion;480
46.7;Appendix;481
46.8;References;483
47;A Movie Recommender System Using Modified Cuckoo Search;484
47.1;1 Introduction;484
47.1.1;1.1 Parts of Filtering Algorithm [2];485
47.1.2;1.2 Challenges in Recommender System;486
47.2;2 Related Work;487
47.2.1;2.1 Clustering;487
47.2.2;2.2 Optimization Using Nature-Inspired Algorithm;488
47.3;3 Integrating Nature-Inspired Optimization Algorithms to K-Means Clustering;488
47.3.1;3.1 Cuckoo Search;490
47.3.2;3.2 Parameter Set for Algorithms;490
47.4;4 Experiment and Results;490
47.5;5 Conclusion and Future Work;494
47.6;References;495
48;Machine Learning Approaches for Potential Blood Donors Prediction;496
48.1;1 Introduction;497
48.2;2 Literature Review;498
48.3;3 Methodology;499
48.3.1;3.1 Online Questionnaire;499
48.3.2;3.2 Classification Using Machine Learning Algorithms;500
48.4;4 Experimental Results;502
48.5;5 Conclusion;503
48.6;6 Future Works;504
48.7;References;504
49;Analysis of Various CNN Models for Locating Keratin Pearls in Photomicrographs;505
49.1;1 Introduction;505
49.2;2 Background;506
49.3;3 Training Procedure;507
49.4;4 Models Used;507
49.5;5 Performance Comparison;508
49.6;6 Limitations;511
49.7;7 Discussion and Conclusion;511
49.8;8 Future Work;511
49.9;References;512
50;A New Approach for Book Recommendation Using Opinion Leader Mining;513
50.1;1 Introduction;513
50.2;2 Recommendation Process;514
50.2.1;2.1 Techniques of Book Recommendation;515
50.3;3 Related Work in Book Recommendation;516
50.3.1;3.1 Challenges Associated with Existing Work;517
50.4;4 Proposed Approach;519
50.4.1;4.1 Evaluation Measure of the Proposed Method;521
50.5;5 Experimental Analysis;522
50.6;6 Discussion;524
50.7;7 Conclusion;525
50.8;References;526
51;A Deep Learning-Based Named Entity Recognition in Biomedical Domain;528
51.1;1 Introduction;528
51.2;2 Related Works;529
51.3;3 Background;530
51.3.1;3.1 Data set;531
51.3.2;3.2 RNN;531
51.3.3;3.3 Long Short-Term Memory (LSTM);532
51.3.4;3.4 Gated Recurrent Unit (GRU);533
51.4;4 Experimentation and Results;533
51.4.1;4.1 Sequence Labelling;534
51.4.2;4.2 Keras;534
51.5;5 Conclusion and Future Work;534
51.6;References;536
52;Convolutional Neural Network with SVM for Classification of Animal Images;538
52.1;1 Introduction;539
52.2;2 Convolution Neural Network Configuration;541
52.3;3 Proposed Methodology;542
52.3.1;3.1 CNN with SVM-Based Classification;542
52.3.2;3.2 Multiclass SVM Classifier;543
52.4;4 Experimentation;543
52.4.1;4.1 Dataset;544
52.4.2;4.2 Experimental Setup;544
52.4.3;4.3 Results;544
52.5;5 Conclusion;545
52.6;References;548
53;Surface Roughness Measurement of WEDM Components Using Machine Vision System;549
53.1;1 Introduction;549
53.2;2 Experimental Work;551
53.3;3 Software Development;552
53.3.1;3.1 Surface Roughness Measurement;553
53.4;4 Results and Discussion;555
53.5;5 Conclusion;556
53.6;References;557
54;Performance Analysis of Students by Evaluating Their Examination Answer Scripts by Using Soft Computing Techniques;558
54.1;1 Introduction;559
54.2;2 Related Work;560
54.3;3 Methodology;561
54.3.1;3.1 Features Used for Student Performance Analysis;561
54.3.2;3.2 Manual Grouping: (Supervised Grouping);568
54.3.3;3.3 Use of MATLAB Tool;570
54.3.4;3.4 Validation and Test Data;571
54.3.5;3.5 Final Result Analysis;572
54.4;4 Conclusion;576
54.5;References;576
55;Performance Analysis of IPv6 and NDN Internet Architecture in IoT Environment;578
55.1;1 Introduction;578
55.1.1;1.1 IPv6 for Internet of Things;579
55.1.2;1.2 NDN for Internet of Things;580
55.1.3;1.3 Key Challenges for Internet of Things;582
55.2;2 Related Work;583
55.3;3 Implementation Strategy;584
55.3.1;3.1 Implementation;584
55.3.2;3.2 Performance Metrics;585
55.4;4 Results and Discussion;586
55.5;5 Conclusion;587
55.5.1;5.1 Future Scope;588
55.6;References;588
56;Machine Learning Approach for Preterm Birth Prediction Based on Maternal Chronic Conditions;589
56.1;1 Introduction;589
56.2;2 Literature Survey;590
56.3;3 Methodology;591
56.4;4 Results and Discussions;593
56.5;5 Conclusion;595
56.6;References;595
57;A Review on Biometric Template Security;597
57.1;1 Introduction;597
57.2;2 Effects of Template Protection;599
57.3;3 Biometric Template Protection Issues;600
57.4;4 Open Challenges in Biometric Template Protection;601
57.5;5 Conclusion;602
57.6;References;602
58;CD2A: Concept Drift Detection Approach Toward Imbalanced Data Stream;605
58.1;1 Introduction;606
58.2;2 Related Work;608
58.3;3 Proposed Method;610
58.3.1;3.1 Imbalanced Data Stream Online Algorithm;610
58.3.2;3.2 Detecting and Handling Concept Drift in the Model;611
58.3.3;3.3 Incremental Learning of the Classifier;612
58.4;4 Experiments;613
58.4.1;4.1 Datasets;613
58.4.2;4.2 Evaluation Metrics;614
58.4.3;4.3 Experimental Setup;615
58.4.4;4.4 Results and Discussion;615
58.5;5 Conclusion;618
58.6;References;618
59;A Literature Review on Energy Harvesting for Internet of Things Applications;621
59.1;1 Introduction;621
59.2;2 Energy Harvesting for IoT Application;623
59.3;3 Protocol Stack;635
59.4;4 Conclusion and Future Work;637
59.5;References;637
60;Review on Recent Methods for Segmentation of Liver Using Computed Tomography and Magnetic Resonance Imaging Modalities;639
60.1;1 Introduction;639
60.2;2 Literature Review;640
60.2.1;2.1 Liver Segmentation Using Computed Tomography (CT) Images;641
60.2.2;2.2 Liver Segmentation Using Magnetic Resonance Imaging (MRI) Images;641
60.3;3 Methodologies;642
60.3.1;3.1 Liver Segmentation Methods Using Computed Tomography Images;642
60.3.2;3.2 Liver Segmentation Methods Using Magnetic Resonance Imaging Images;645
60.4;4 Discussion;647
60.5;5 Conclusion;647
60.6;References;654
61;A Survey: On Network Forensic Data Acquisition and Analysis Tools;656
61.1;1 Introduction;656
61.2;2 Literature Review;657
61.2.1;2.1 Digital Forensic;659
61.2.2;2.2 Live Forensics;660
61.2.3;2.3 Network Devices and Forensic Value;661
61.2.4;2.4 Network Forensic Tools;661
61.3;3 Investigation Strategy;662
61.4;4 Conclusion and Future Work;665
61.5;References;665
62;A Study on Sentiment Analysis on Social Media Data;667
62.1;1 Introduction;667
62.2;2 Importance of Social Media Data and Its Sources;668
62.3;3 Machine Learning in Sentiment Analysis;668
62.4;4 Data Collection and Storage;669
62.5;5 Ontology for Sentiment Analysis;669
62.6;6 Related Work;670
62.7;7 Existing Techniques for Sentiment Analysis;671
62.8;8 Research Challenges in the Domain of Sentimental Analysis;672
62.9;9 Conclusion;672
62.10;References;672
63;Character Recognition on Palm-Leaf Manuscripts—A Survey;674
63.1;1 Introduction;674
63.2;2 Related Work;675
63.3;3 Methodology;676
63.3.1;3.1 Document Preprocessing;676
63.4;4 Analysis;685
63.4.1;4.1 Binarization;685
63.4.2;4.2 Text Line Segmentation;686
63.4.3;4.3 Character Segmentation;687
63.5;5 Conclusion and Future Work;688
63.6;References;688
64;Sentiment Analysis of Restaurant Reviews Using Machine Learning Techniques;691
64.1;1 Introduction;691
64.2;2 Related Work;693
64.3;3 Architecture and Modeling;694
64.3.1;3.1 Data Preprocessing;695
64.3.2;3.2 Preparation of Bag of Words;695
64.3.3;3.3 Segregation of Training and Test Data;696
64.3.4;3.4 Classification;696
64.3.5;3.5 Performance Evaluation of Classification;697
64.3.6;3.6 Class Prediction;698
64.4;4 Implementation and Performance Analysis;698
64.5;5 Conclusion;699
64.6;References;699
65;Diabetic Retinopathy Risk Prediction for Diabetics Using Nearest Neighbour Approach;701
65.1;1 Introduction;701
65.2;2 Methodology;703
65.2.1;2.1 Diabetic Retinopathy Risk Prediction Using Machine Learning Model;703
65.2.2;2.2 Data Acquisition;703
65.3;3 Proposed Model;704
65.3.1;3.1 Data Collection;704
65.3.2;3.2 Preprocessing;704
65.3.3;3.3 Naïve Bayes Classification for Diabetes Detection;704
65.3.4;3.4 Feature Selection;705
65.3.5;3.5 Z-Ordering with KNN;705
65.4;4 Results and Discussion;707
65.5;5 Conclusions and Future Work;708
65.6;References;708
66;A Revised Auditing and Survey on Mobile Application Analytics;710
66.1;1 Introduction;710
66.1.1;1.1 Types of Data;711
66.1.2;1.2 Elements of Big Data;712
66.2;2 Big Data Analytics;713
66.3;3 Mobile Analytics;714
66.3.1;3.1 Comparison Between Web and Mobile Analytics;714
66.4;4 Working of Mobile Analytics;715
66.5;5 Types of Mobile Analytics;716
66.5.1;5.1 Advertising/Marketing Analytics;716
66.5.2;5.2 In-App Analytics;717
66.5.3;5.3 Performance Analytics;718
66.6;6 Measurements of Mobile Analytics;720
66.6.1;6.1 User Acquisition Metrics;720
66.6.2;6.2 Engagement Metrics;720
66.6.3;6.3 Revenue Metrics;720
66.7;7 Mobile Analytics Tools;721
66.8;8 Conclusion;722
66.9;References;722
67;Detection of Breast Cancer Using Digital Breast Tomosynthesis;724
67.1;1 Introduction;724
67.2;2 Literature Survey;725
67.2.1;2.1 Limitations;726
67.3;3 Proposed Method;726
67.3.1;3.1 Image Acquisition;726
67.3.2;3.2 Preprocessing;726
67.3.3;3.3 Segmentation;728
67.3.4;3.4 Feature Extraction;729
67.3.5;3.5 Classification;731
67.4;4 Conclusion;732
67.5;5 Future Enhancement;732
67.6;References;733
68;Cross-Spectral Periocular Recognition: A Survey;734
68.1;1 Introduction;735
68.2;2 Methodologies Used in Cross-Spectral Periocular Recognition;737
68.3;3 Cross-Spectral Periocular Databases;739
68.3.1;3.1 IMP Database;740
68.3.2;3.2 PolyU Cross-Spectral Iris Database;740
68.3.3;3.3 Cross-Eyed Periocular Database;741
68.4;4 Conclusion and Future Work;742
68.5;References;743
69;An Automated Method Using MATLAB to Identify the Adductor Sesamoid for Determining the Onset of Puberty and Assessing the Skeletal Age in Children;745
69.1;1 Introduction;746
69.2;2 Literature Review;746
69.3;3 Methodology;747
69.4;4 Experimental Results and Discussion;751
69.5;5 Conclusion;752
69.6;References;752
70;Anomaly Detection in Surveillance Video Using Pose Estimation;754
70.1;1 Introduction;754
70.2;2 Related Work;755
70.3;3 Methodology;756
70.3.1;3.1 An Overview of Open Pose;756
70.3.2;3.2 Proposed Method;757
70.3.3;3.3 Program Code;757
70.4;4 Results;760
70.5;5 Performance Evaluation;765
70.6;References;767
71;Design of Low-Power Square Root Carry Select Adder and Wallace Tree Multiplier Using Adiabatic Logic;768
71.1;1 Introduction;769
71.2;2 Literature Review;770
71.3;3 Design and Implementation of Adders and Multipliers;771
71.3.1;3.1 Square Root Carry Select Adder;771
71.3.2;3.2 Wallace Tree Multiplier;772
71.3.3;3.3 Adiabatic Logic;774
71.4;4 Results and Discussions;776
71.5;5 Conclusions and Future Scope;781
71.6;References;781
72;Implementation of Doppler Beam Sharpening Technique for Synthetic-Aperture Radars;783
72.1;1 Introduction;783
72.2;2 DBS Algorithm;784
72.2.1;2.1 Pulse Compression;785
72.2.2;2.2 Matched Filter;786
72.3;3 Ambiguities in DBS;787
72.3.1;3.1 Range Ambiguity;787
72.3.2;3.2 Doppler Ambiguity;787
72.4;4 Simulation Results;788
72.5;5 Conclusion;791
72.6;References;791
73;Capturing Discriminative Attributes Using Convolution Neural Network Over ConceptNet Numberbatch Embedding;792
73.1;1 Introduction;792
73.2;2 Dataset;794
73.3;3 Methodology;795
73.3.1;3.1 Representation;795
73.3.2;3.2 ConceptNet Numberbatch;797
73.3.3;3.3 Rule-Based Method;797
73.3.4;3.4 Hyperparameter Tuning;798
73.4;4 Conclusion and Future Scope;799
73.5;References;800
74;Prediction of Gold Stock Market Using Hybrid Approach;802
74.1;1 Introduction;802
74.2;2 Related Work;803
74.3;3 Proposed System;804
74.4;4 Result Analysis;809
74.5;5 Conclusion and Future Work;810
74.6;References;811
75;Analysis of Digit Recognition in Kannada Using Kaldi Toolkit;812
75.1;1 Introduction;812
75.2;2 Related Work;814
75.3;3 The Kaldi ASR Toolkit;814
75.4;4 Recognition Phases;816
75.4.1;4.1 Voice Data Preparation;816
75.4.2;4.2 Acoustic Data Preparation;816
75.4.3;4.3 Feature Extraction;816
75.4.4;4.4 Language Data Creation;817
75.4.5;4.5 Language Model Creation;817
75.4.6;4.6 Training;818
75.4.7;4.7 Decoding;818
75.5;5 Experimental Results;818
75.6;6 Conclusion;819
75.7;References;820
76;Real-Time Traffic Management Using RF Communication;821
76.1;1 Introduction;821
76.2;2 Proposed Scheme;822
76.3;3 Proposed Design;823
76.3.1;3.1 Tag;823
76.3.2;3.2 Reader;824
76.3.3;3.3 Co-ordinator;824
76.4;4 Results;827
76.4.1;4.1 Results Obtained from Prototype Model;827
76.4.2;4.2 Simulation Analysis;828
76.5;5 Conclusion and Scope for Further Improvement;832
76.6;References;833
77;SDN Security: Challenges and Solutions;834
77.1;1 Introduction;834
77.2;2 Software-Defined Network;835
77.3;3 Vulnerabilities in the Open Interface;837
77.3.1;3.1 Securing the Controller;837
77.3.2;3.2 Securing the Data Plane;839
77.3.3;3.3 Securing the Channel;840
77.3.4;3.4 Security in Virtual Environments;840
77.3.5;3.5 Security in Wireless Environment;840
77.4;4 Conclusion;843
77.5;References;843
78;Comparative Performance Analysis of PID and Fuzzy Logic Controllers for 150hp Three-Phase Induction Motor;846
78.1;1 Introduction;847
78.2;2 Background Information;847
78.3;3 Existing PID Controller in Cable Industry;848
78.4;4 Proposed Fuzzy Logic Controller for Three-Phase Induction Motor;851
78.4.1;4.1 Implementation of the Fuzzy Logic Controller;851
78.5;5 Results and Discussion;854
78.6;6 Conclusion;860
78.7;Appendix;861
78.8;References;861
79;A Hybrid Progressive Image Compression, Transmission, and Reconstruction Architecture;863
79.1;1 Introduction;863
79.1.1;1.1 Need for Progressive Compression;865
79.2;2 Implementation;865
79.3;3 Experimental Results;868
79.4;4 Conclusions;870
79.5;References;870
80;Design and Development of Non-volatile Multi-threshold Schmitt Trigger SRAM Cell;872
80.1;1 Introduction;872
80.2;2 Proposed Cell;873
80.3;3 Results and Analysis of the Proposed Cell;875
80.4;4 Conclusion;877
80.5;References;878
81;Intelligent Phase-Locked Loops for Automotive Applications;880
81.1;1 Introduction;881
81.2;2 Overview of Automotive Systems;881
81.3;3 Powertrain Sensor-Signal Processing;882
81.4;4 Implementation of Intelligent Phase-Locked Loops—IPLL;885
81.5;5 Simulation Results—IPLL VHDL Model;887
81.6;6 Summary with Conclusion;887
81.7;References;888
82;Design and Implementation of Logarithmic Multiplier Using FinFETs for Low Power Applications;890
82.1;1 Introduction;890
82.2;2 Background;891
82.2.1;2.1 Motivation;891
82.2.2;2.2 Mitchell Algorithm and Logarithmic Multiplier;891
82.2.3;2.3 FinFETs;892
82.3;3 Design and Implementation;893
82.4;4 Results and Discussion;894
82.5;5 Conclusion;895
82.6;References;897
83;Design of Ternary SRAM Cell Based on Level Shift Ternary Inverter;898
83.1;1 Introduction;899
83.2;2 Traditional Ternary Inverter;899
83.3;3 Traditional Ternary SRAM;900
83.4;4 Level Shift Ternary Inverter;901
83.5;5 Level Shift Ternary SRAM;904
83.6;6 Results and Discussion;905
83.7;7 Conclusion;908
83.8;References;909
84;Machine-Vision-Assisted Performance Monitoring in Turning Inconel 718 Material Using Image Processing;910
84.1;1 Introduction;910
84.2;2 Experimental Details;911
84.3;3 Performance Monitoring;912
84.3.1;3.1 Characterizations of Cutting Tool Wear Using Image Processing;912
84.3.2;3.2 Machined Surface Characterization Using Image Histogram;912
84.4;4 Result and Discussion;916
84.5;5 Conclusion;919
84.6;References;919
85;Improved WEMER Protocol for Data Aggregation in Wireless Sensor Networks;921
85.1;1 Introduction;921
85.2;2 Literature Review;922
85.3;3 Proposed Algorithm;923
85.3.1;3.1 Cluster Head Selection;924
85.3.2;3.2 Leader Node Selection;925
85.3.3;3.3 Gateway Node Selection;925
85.4;4 Results and Discussions;927
85.5;5 Conclusions;928
85.6;References;929
86;Analysis of Speckle Diminution in Ultrasound Images—A Review;930
86.1;1 Introduction;930
86.2;2 Related Work;931
86.3;3 Noise Models;932
86.4;4 Various Methods;933
86.5;5 Results;935
86.6;6 Conclusion;936
86.7;References;939
87;Comparative Study of gm/ID Methodology for Low-Power Applications;941
87.1;1 Introduction;941
87.2;2 gm/ID Figure of Merits;942
87.2.1;2.1 Transconductor Efficiency (gm/ID);942
87.2.2;2.2 Transit Frequency (FT);943
87.2.3;2.3 Intrinsic Gain (gm* Ro);945
87.2.4;2.4 gm/ID Versus fT Trade-Off;945
87.3;3 gm/ID Design Methodology;946
87.4;4 Design of Differential Amplifier;948
87.5;5 Experiment Result;949
87.6;6 Conclusion;950
87.7;7 Future Scope;950
87.8;References;951
88;Vehicle Speed Warning System and Wildlife Detection Systems to Avoid Wildlife-Vehicle Collisions;952
88.1;1 Introduction;952
88.2;2 Literature Survey;953
88.3;3 Design Methodology;954
88.4;4 Flowchart;955
88.5;5 Result;956
88.6;6 Conclusion and Future Work;957
88.7;References;959
89;Camera Raw Image: A Study, Processing and Quality Analysis;960
89.1;1 Introduction;960
89.1.1;1.1 Color Filter Array (CFA);961
89.2;2 Proposed Workflow;962
89.2.1;2.1 Linearization;963
89.2.2;2.2 White Balancing;963
89.2.3;2.3 Demosaicking;964
89.2.4;2.4 Color Space Conversion;965
89.2.5;2.5 Brightness and Gamma Correction;966
89.3;3 Experimental Results;967
89.4;4 Conclusion;970
89.5;References;971
90;An Image Processing Approach for Compression of ECG Signals Based on 2D RLE and SPIHT;972
90.1;1 Introduction;972
90.2;2 Methodology;974
90.2.1;2.1 Removal of Baseline Wander;975
90.2.2;2.2 Detecting QRS Complexes;977
90.2.3;2.3 Construction of 2D ECG Array;978
90.2.4;2.4 Run Length Encoding;980
90.2.5;2.5 2D Discrete Wavelet Transform;980
90.2.6;2.6 Thresholding of Wavelet Coefficients;981
90.2.7;2.7 SPIHT;981
90.3;3 Results;982
90.4;4 Conclusion;984
90.5;References;985
91;Classification of Service Robot Environments Using Multimodal Sequence Data;987
91.1;1 Introduction;987
91.2;2 Related Work;988
91.3;3 Proposed Method;989
91.3.1;3.1 Environmental Setup and Scenario Creation;989
91.3.2;3.2 Data Collection;990
91.3.3;3.3 Preprocessing;992
91.3.4;3.4 Classification;994
91.4;4 Results and Analysis;995
91.4.1;4.1 Evaluation;995
91.4.2;4.2 Results;995
91.5;5 Conclusion;999
91.6;References;1000
92;Comparative Analysis of Existing Latest Microcontroller Development Boards;1001
92.1;1 Introduction;1001
92.2;2 Case 1: Automatic Auditorium System Using Arduino Board;1005
92.3;3 CASE 2: IoT-Based LCD Gadget Using MBED LPC1768;1007
92.3.1;3.1 Methodology;1008
92.4;4 Case 3: Home Automation Using Intel Galileo Board;1009
92.4.1;4.1 Methodology;1012
92.5;5 Results;1012
92.6;6 Conclusion;1014
92.7;References;1015
93;Dynamic Routing in Software-Defined Networks;1016
93.1;1 Introduction;1017
93.2;2 Related Work;1018
93.2.1;2.1 Mininet Emulator;1018
93.2.2;2.2 POX Controller;1018
93.2.3;2.3 Drawbacks of the Conventional Network;1019
93.2.4;2.4 Literature Survey;1019
93.3;3 Proposed Work;1020
93.3.1;3.1 System Model;1020
93.3.2;3.2 Bellman–Ford Algorithm;1020
93.4;4 Implementation;1021
93.4.1;4.1 Topologies Deployed;1022
93.5;5 Results and Performance Estimation;1023
93.6;6 Conclusion;1025
93.7;7 Future Scope;1025
93.8;References;1026
94;Design of an Energy-Efficient Routing Protocol Using Adaptive PSO Technique in Wireless Sensor Networks;1027
94.1;1 Introduction;1028
94.2;2 Proposed Model;1029
94.2.1;2.1 WSN System Model;1030
94.2.2;2.2 Problem Formulation;1031
94.2.3;2.3 Cluster Head Selection;1032
94.2.4;2.4 PSO-Based Routing;1032
94.3;3 Results and Discussion;1036
94.3.1;3.1 Performance Measurement Metrics;1036
94.3.2;3.2 Experimental Scenario 1;1037
94.4;4 Conclusion;1040
94.5;References;1041
95;Detection of Retinal Disease Screening Using Local Binary Patterns;1042
95.1;1 Introduction;1042
95.2;2 Literature Survey;1043
95.3;3 Methods;1046
95.3.1;3.1 Local Binary Patterns;1046
95.3.2;3.2 Random Forest;1047
95.4;4 System Methodology;1047
95.4.1;4.1 Preprocessing;1049
95.4.2;4.2 Feature Extraction;1050
95.4.3;4.3 Classification;1050
95.5;5 Results and Analysis;1050
95.6;6 Conclusion;1053
95.7;References;1054
96;Comparative Performance Analysis of Hybrid PAPR Reduction Techniques in OFDM Systems;1055
96.1;1 Introduction;1055
96.2;2 OFDM System, PAPR, and CCDF;1056
96.3;3 Discrete Cosine Transform (DCT) and Companding-Based PAPR Reduction Technique;1057
96.4;4 Companding Technique;1058
96.5;5 Proposed Hybrid PAPR Reduction Technique;1059
96.6;6 Simulation Results;1060
96.7;7 Conclusion;1062
96.8;References;1062
97;A Hardware Accelerator Based on Quantized Weights for Deep Neural Networks;1064
97.1;1 Introduction;1065
97.2;2 Related Work;1065
97.3;3 System Model;1066
97.3.1;3.1 Architecture;1066
97.3.2;3.2 Algorithm Optimization;1070
97.4;4 Experiments;1072
97.5;5 Results;1074
97.6;6 Conclusion;1075
97.7;References;1076
98;Effective Protocols for Industrial Communication;1077
98.1;1 Introduction;1077
98.2;2 Industrial Communication;1078
98.2.1;2.1 Hierarchical Levels in Industrial Communication Network;1079
98.3;3 Design Principles of Industry 4.0;1080
98.4;4 Industrial Communication Technologies;1080
98.4.1;4.1 Traditional Fieldbus Technology;1080
98.4.2;4.2 Industrial Ethernet Technology;1081
98.4.3;4.3 Industrial IOT;1084
98.5;5 Case Study on EtherCAT;1084
98.6;6 Conclusion;1087
98.7;References;1089
99;Optimal Resource Allocation and Binding in High-Level Synthesis Using Nature-Inspired Computation;1090
99.1;1 Introduction;1090
99.2;2 Previous Work;1091
99.3;3 Nature-Inspired Computations Method (Genetic Algorithm (GA), Particle Swarm Optimization (PSO));1091
99.3.1;3.1 Genetic Algorithm (GA);1092
99.3.2;3.2 Particle Swarm Optimization (PSO);1092
99.4;4 Methodology;1092
99.4.1;4.1 Problem Formulation;1092
99.4.2;4.2 Benchmark Problem for Resource Allocation;1092
99.4.3;4.3 Integer Linear Programming (ILP) Formulation for Allocation and Binding as a Constraint Optimization;1093
99.5;5 Experimental Analysis and Data;1095
99.6;6 Results and Discussion;1096
99.6.1;6.1 Resource Binding Performance Analysis Using Genetic Algorithm;1096
99.6.2;6.2 Discussion;1097
99.6.3;6.3 Resource Binding Performance Analysis Using Particle Swarm Optimization;1099
99.6.4;6.4 Discussion;1099
99.7;7 Conclusion;1101
99.8;References;1101
100;Boundary Extraction and Tortuosity Calculation in Retinal Fundus Images;1102
100.1;1 Introduction;1102
100.2;2 Tortuosity Measurement as Differential Geometrical Problem;1103
100.2.1;2.1 Pre-processing;1104
100.2.2;2.2 Binary Image and Vascular Network;1105
100.2.3;2.3 Major Vessel Extraction;1105
100.3;3 Boundary Extraction;1107
100.4;4 Segmentation and Average Curvature Computation;1109
100.5;5 Results and Discussion;1110
100.6;6 Conclusion;1112
100.7;References;1112
101;Synthesis, Characterization of Hybrid Nanomaterials of Strontium, Yttrium, Copper Doped with Indole Schiff Base Derivatives Possessing Dielectric and Semiconductor Properties;1114
101.1;1 Introduction;1115
101.2;2 Experimental;1116
101.2.1;2.1 Synthesis of Sr-Y Nanocomposites;1116
101.2.2;2.2 Purification and Isolation of Sr-Y Nanocomposites;1116
101.2.3;2.3 Doping of Copper to Sr-Y Nanocomposites;1116
101.2.4;2.4 Synthesis of Substituted Derivatives of Carbohydrazide (2a–2e);1117
101.2.5;2.5 Synthesis of Various Analogues of Indole Schiff Base Derivatives (4a–4e);1117
101.2.6;2.6 Synthesis of Hydrochloride Salts of Indole Schiff Base Derivatives (5a–5e);1117
101.2.7;2.7 Dielectric and Semiconductor Properties;1117
101.2.8;2.8 Synthetic Reaction Scheme;1119
101.3;3 Materials and Methods;1120
101.3.1;3.1 Spectral Analysis Data;1120
101.4;4 Results and Discussion;1122
101.5;5 Conclusion;1122
101.6;References;1123
102;Study of Clustering Approaches in Wireless Sensor Networks;1124
102.1;1 Introduction;1124
102.2;2 Clustering Technique in WSN;1125
102.3;3 Challenges for Clustering in WSN;1125
102.4;4 Standard Clustering Algorithms;1127
102.5;5 Simulation and Analysis;1129
102.6;6 Conclusion;1131
102.7;References;1131
103;Novel Color Image Data Hiding Technique Based on DCT and Compressed Sensing Algorithm;1133
103.1;1 Introduction;1133
103.2;2 Literature Review;1134
103.3;3 Results and Discussions;1136
103.4;4 Conclusions;1137
103.5;References;1138
104;GSM-Based Advanced Multi-switching DTMF Controller for Remotely Monitoring of Electrical Appliances;1140
104.1;1 Introduction;1140
104.2;2 Basic Principles of DTMF Decoder;1141
104.3;3 Proposed DTMF Controller;1142
104.4;4 Results and Discussion;1147
104.5;5 Conclusion;1147
104.6;References;1148
105;Design and Development of 15-Level Asymmetrical Cascaded Multilevel Inverter;1149
105.1;1 Introduction;1150
105.2;2 Proposed Model;1150
105.3;3 Multilevel Inverters;1152
105.4;4 Simulation Results;1153
105.4.1;4.1 15-Level H-Bridge Symmetrical Multilevel Inverter;1155
105.4.2;4.2 15-Level H-Bridge Asymmetrical Multilevel Inverter;1155
105.4.3;4.3 Equal Area Criteria Method;1156
105.4.4;4.4 Simulink Model of a New 15-Level Asymmetrical Multilevel Inverter Topology with DC Source;1159
105.5;5 Hardware Implementation;1159
105.5.1;5.1 Controller: Arduino;1161
105.5.2;5.2 Boost Converter Integrated with 15-Level MLI;1162
105.6;6 Conclusion;1162
106;Comparative Study of 31-Level Symmetrical and Asymmetrical Cascaded H-Bridge Multilevel Inverter;1164
106.1;1 Introduction;1164
106.2;2 Symmetrical Cascaded H-Bridge Inverter;1165
106.3;3 Asymmetrical Cascaded H-Bridge Inverter;1166
106.4;4 Analysis and Simulation Results;1168
106.4.1;4.1 Symmetrical Cascaded H-Bridge 31-Level Inverter;1168
106.4.2;4.2 Asymmetrical Cascaded H-Bridge 31-Level Inverter;1170
106.4.3;4.3 Simulation Results;1173
106.5;5 Conclusion;1175
106.6;6 Future Scope;1175
106.7;References;1175
107;Three-Phase Shunt Active Filter for Cuk-Sepic Fused Converter with Solar–Wind Hybrid Sources;1177
107.1;1 Introduction;1177
107.2;2 Objective;1178
107.3;3 Proposed System;1179
107.4;4 Calculation for the DQO Compensation;1182
107.5;5 Simulation Results;1183
107.6;6 Results;1186
107.7;7 THD with Various Filters;1187
107.8;8 Conclusion;1187
107.9;References;1188
108;Study of Different Modelling Techniques of SMA Actuator and Their Validation Through Simulation;1189
108.1;1 Introduction;1189
108.2;2 Thermodynamics of SMA;1191
108.3;3 Phase Transformation of SMA;1194
108.3.1;3.1 Preisach Model;1194
108.3.2;3.2 Duhem Hysteresis Model;1195
108.3.3;3.3 Fermi–Dirac Statistics;1198
108.3.4;3.4 Ikuta Model;1199
108.3.5;3.5 Liang Constitutive Model;1201
108.3.6;3.6 Brinson Model;1203
108.3.7;3.7 Kinematic Model for SMA;1204
108.4;4 Conclusion;1204
108.5;References;1205
109;Maximum Power Point Tracking for an Isolated Wind Energy Conversion System;1207
109.1;1 Introduction;1207
109.2;2 Wind Energy Conversion System;1208
109.3;3 Wind Turbine Modeling;1209
109.4;4 Excitation Capacitor Design for SEIG;1210
109.5;5 Hill Climbing Search MPPT Algorithm;1212
109.6;6 Simulation Results;1212
109.7;7 Conclusions;1216
109.8;References;1218
110;Phase Shift Control Scheme of Modular Multilevel DC/DC Converters for HVDC-Based Systems;1220
110.1;1 Introduction;1221
110.2;2 Proposed System;1222
110.3;3 System Design;1222
110.3.1;3.1 Operation Analysis;1223
110.4;4 Voltage Auto-Balance Mechanism;1224
110.5;5 Operation Analysis of Proposed System;1226
110.5.1;5.1 Switching Sequence of the Three-Stage Converter;1227
110.5.2;5.2 Parameters for Design;1227
110.5.3;5.3 Simulation Diagram of DC-to-DC Converter with Filter and Without Filter;1228
110.5.4;5.4 Output Waveform;1229
110.5.5;5.5 Output Waveforms of Switches, Voltages, Current;1229
110.6;6 Comparison Analysis of Base Converter (Three-Stage Converter) and Proposed Converter (Two-Stage Converter);1229
110.7;7 Conclusion and Future Work;1229
110.8;References;1231
111;Improvement of Power Quality in an Electric Arc Furnace Using Shunt Active Filter;1232
111.1;1 Introduction;1232
111.2;2 Electric Arc Furnace;1234
111.3;3 Modeling of Electric Arc Furnace;1234
111.3.1;3.1 Hyperbolic Model;1235
111.3.2;3.2 Exponential Model;1235
111.3.3;3.3 Exponential-Hyperbolic Model;1236
111.4;4 Harmonic Suppression Techniques;1237
111.4.1;4.1 Active Power Filtering;1237
111.4.2;4.2 Shunt Active Power Filter;1237
111.5;5 Results and Discussion;1239
111.6;6 Harmonic Behavior of EAF System;1240
111.7;7 Conclusion;1246
111.8;References;1246
112;Wireless Power Transfer for LED Display System Using Class-DE Inverter;1247
112.1;1 Introduction;1247
112.2;2 Circuit Description;1248
112.2.1;2.1 Operating Mode During Interval 1 (0 le?le?/2);1249
112.2.2;2.2 Operating Mode During Interval 2 (?/2 le?le?);1251
112.2.3;2.3 Operating Mode During Interval 3 (?le?le3?/2);1251
112.2.4;2.4 Operating Mode During Interval 4 (3?/2 le?le2?);1252
112.3;3 Design Considerations;1253
112.3.1;3.1 Receiver-side Rectifier Specifications;1253
112.3.2;3.2 Transmitter-side Class-DE Inverter Design;1253
112.3.3;3.3 Supply-side Rectifier Design;1254
112.3.4;3.4 Transmitter Coil Design;1254
112.3.5;3.5 Receiver Coil Design;1255
112.4;4 Simulation Results;1255
112.5;5 Conclusion;1257
112.6;References;1258
113;Bidirectional Power Conversion by DC–AC Converter with Active Clamp Circuit;1259
113.1;1 Introduction;1259
113.2;2 Block Diagram;1260
113.3;3 Operation Principle;1261
113.4;4 Basic Operations of Converter;1262
113.5;5 Control Algorithm for the Bidirectional Converter;1264
113.6;6 Simulation Results;1265
113.7;7 Conclusion;1269
113.8;References;1270
114;Performance Study of DC–DC Resonant Converter Topologies for Solar PV Applications;1271
114.1;1 Introduction;1271
114.2;2 Computer Simulation Model of PV System;1272
114.3;3 Boost Converter;1273
114.4;4 Inverter;1273
114.5;5 DC–DC Resonant Converter;1275
114.5.1;5.1 Operating Modes of DC–DC Resonant Circuit;1275
114.6;6 Simulation, Results and Discussion;1277
114.6.1;6.1 DC–DC Series Resonant Circuit;1277
114.6.2;6.2 DC–DC Parallel Resonant Circuit;1278
114.6.3;6.3 DC–DC Series–Parallel Resonant Circuit;1278
114.6.4;6.4 DC–DC Without Resonant Circuit;1278
114.7;7 Conclusion;1282
114.8;8 Future Scope;1284
114.9;References;1286
115;Performance Analysis of SHEPWM Based on GA and PSO for CMLI;1287
115.1;1 Introduction;1287
115.1.1;1.1 Cascaded Multilevel Inverter;1288
115.1.2;1.2 Selective Harmonic Elimination (SHE);1290
115.1.3;1.3 Genetic Algorithm;1290
115.1.4;1.4 Structures of PSO;1292
115.2;2 Implementation;1294
115.2.1;2.1 Cascaded H-Bridge Multilevel Inverter;1294
115.2.2;2.2 Selective Harmonic Elimination PWM;1294
115.2.3;2.3 Genetic Algorithm (GA);1295
115.2.4;2.4 Particle Swarm Optimization;1298
115.2.5;2.5 Calculation of Switching;1298
115.2.6;2.6 Total Harmonic Distortion (THD);1299
115.3;3 Simulation Results;1299
115.3.1;3.1 Cascaded Multilevel Inverter;1299
115.3.2;3.2 Switching Angles Obtained Using GA Rule;1302
115.3.3;3.3 Switching Angles Obtained Using PSO Rule;1302
115.3.4;3.4 Simulation Result of Genetic Algorithm;1303
115.3.5;3.5 Simulation Result of PSO Algorithm;1304
115.4;4 Conclusion;1305
115.5;References;1305
116;Induction Motor Internal and External Fault Detection;1306
116.1;1 Introduction;1306
116.2;2 External Fault Detection;1309
116.2.1;2.1 Multi-layer Perceptron Neural Network;1309
116.2.2;2.2 Random Forest;1312
116.3;3 Internal Fault Detection;1312
116.3.1;3.1 Fast Fourier Transformation;1312
116.3.2;3.2 S-Transform;1313
116.4;4 Results;1314
116.5;5 Conclusion;1317
116.6;References;1319
117;Comparison of Maximum Power Point Tracking—Perturb and Observe and Fuzzy Logic Controllers for Single Phase Photovoltaic Systems;1322
117.1;1 Introduction;1322
117.2;2 PV Cell Design;1323
117.3;3 Maximum Power Point Tracking;1324
117.3.1;3.1 Perturb and Observe;1325
117.3.2;3.2 Fuzzy Logic Controller;1326
117.4;4 Results;1328
117.5;5 Conclusion;1330
117.6;References;1330
118;Comparative Study of Different High-Gain Converter;1332
118.1;1 Introduction;1332
118.2;2 Analysis of Different Converter;1334
118.2.1;2.1 Buck-Boost-Flyback Converter;1335
118.2.2;2.2 Boost–Cuk DC–DC Converter;1335
118.2.3;2.3 Converter with Coupled Inductor and Diode Capacitor;1336
118.2.4;2.4 Modified Converter with Coupled Inductor and Diode Capacitor;1336
118.3;3 Comparison of Different Converter;1337
118.4;4 Simulations Results;1338
118.5;5 Conclusion;1339
118.6;References;1340
119;Evolution in Solid-State Transformer and Power Electronic Transformer for Distribution and Traction System;1341
119.1;1 Introduction;1341
119.2;2 LFT and P.E.T. Technologies in Distribution System;1345
119.2.1;2.1 LFT Distribution System;1345
119.2.2;2.2 P.E.T. Distribution System;1345
119.3;3 CLFT and P.E.T. Technologies in Traction System;1346
119.3.1;3.1 CLFT Traction Technology;1346
119.3.2;3.2 P.E.T. Traction Technology;1346
119.3.3;3.3 Essential Requirements for P.E.T.T. Technology;1347
119.4;4 Converter Architecture and P.E.T. Technology;1348
119.4.1;4.1 Two-Stage (AC/HVAC) Power Conversion Topologies;1349
119.4.2;4.2 Single-Stage (AC-HFAC) Power Conversion Topologies;1350
119.4.3;4.3 P.E.T-Formed Auxiliary Power Converters;1352
119.5;5 Advancement in P.E.T. Modules and Medium-Frequency Design Consideration;1353
119.6;6 Conclusion;1355
119.7;References;1355
120;Optimized Control of VAR/Voltage in the Off-grid Hybrid Power System;1358
120.1;1 Introduction;1359
120.2;2 Off-grid Hybrid Power System Configurations and Voltage–Reactive Power Equation with Composite Load;1361
120.2.1;2.1 Voltage–Reactive Power Equation of the OGHPS, Considering Composite Load;1361
120.3;3 PI Controller Parameter Optimization by BFA and GA;1364
120.3.1;3.1 Bactria Foraging Algorithm (BFA);1365
120.3.2;3.2 Genetic Algorithm (GA);1367
120.4;4 Simulation Results and Discussion;1369
120.5;5 Conclusion;1376
120.6;Appendix;1376
120.7;References;1377
121;Methods to Optimize the Performance of an Existing Large-Scale On-grid Solar PV Plant;1379
121.1;1 Introduction;1380
121.2;2 Method to Improve PV Panel Cleaning Efficiency for Dry Tropical Regions;1380
121.2.1;2.1 Mist Nozzle;1381
121.2.2;2.2 Cleaning System Design;1384
121.2.3;2.3 Cleaning Procedure Working;1385
121.2.4;2.4 Conclusion;1385
121.3;3 Simulink-Based Estimation of Partial Shading Loss Reduction Using Herbicides for Vegetation Management;1386
121.3.1;3.1 Partial Shading;1387
121.3.2;3.2 Site-Specific Details;1387
121.3.3;3.3 Simulink-Based Estimation of Partial Shading Loss in a PV String;1388
121.3.4;3.4 Organic Contact Herbicide and Salt Spraying System to Reduce Vegetative Growth;1395
121.3.5;3.5 Results;1396
121.3.6;3.6 Conclusion;1396
121.4;4 Procedure to Suggest New Optimum Tilt for a Seasonal;1397
121.4.1;4.1 Suggestion of Additional Tilts Changing Tilt Angles Monthly;1397
121.4.2;4.2 Conclusion;1402
121.5;5 Automated Switching and Lighting Circuit Using Two-Way Relay Switch;1402
121.5.1;5.1 Prototype Design;1402
121.5.2;5.2 Working;1403
121.6;6 Conclusion;1406
121.7;7 Future Scope;1407
121.8;References;1407
122;Application of Hilbert–Huang Transform and SVM Classifier to Monitor the Power Quality Disturbances;1409
122.1;1 Introduction;1409
122.2;2 Literature Review;1410
122.3;3 Methodology;1411
122.3.1;3.1 Generation of PQDs;1412
122.4;4 Feature Detection;1414
122.4.1;4.1 Empirical Mode Decomposition;1414
122.4.2;4.2 Ensemble Empirical Mode Decomposition;1415
122.4.3;4.3 Complete Ensemble Empirical Mode Decomposition with Adaptive Noise;1416
122.4.4;4.4 Classification of Voltage Disturbances by Cross-Correlation;1416
122.5;5 Results;1417
122.5.1;5.1 Classification by Support Vector Machine;1419
122.6;6 Conclusion;1422
122.7;References;1424
123;Voltage Stability Enhancement in Radial Distribution System by Shunt Capacitor and STATCOM;1426
123.1;1 Introduction;1426
123.2;2 Line Stability Indicator (LSI);1427
123.3;3 Shunt Capacitor and STATCOM;1428
123.4;4 Problem Formulation;1429
123.5;5 Cuckoo Search with Levy Flight;1430
123.6;6 Proposed Methodology;1431
123.7;7 Results and Discussion;1431
123.8;8 Conclusion;1437
123.9;Appendix;1438
123.10;References;1439
124;Optimal Siting and Sizing of DG Employing Multi-objective Particle Swarm Optimization for Network Loss Reduction and Voltage Profile Improvement;1440
124.1;1 Introduction;1440
124.2;2 Present Study of the Work in This Area;1441
124.3;3 Problem Formulation;1441
124.3.1;3.1 Constraints;1442
124.3.2;3.2 Equations;1443
124.4;4 Particle Swarm Optimization;1444
124.5;5 Results and Discussion;1445
124.6;References;1449
125;Author Index;1450
