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E-raamat: Smart Sustainable Intelligent Systems [Wiley Online]

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"The world is experiencing an unprecedented period of change and growth through all the electronic and technological developments and everyone on the planet has been impacted. What was once "science fiction', today it is a reality. This book explores the world of many of once unthinkable advancements by explaining current technologies in great detail. Each chapter focuses on a different aspect--Machine Vision, Pattern Analysis and Image Processing--Advanced Trends in Computational Intelligence and DataAnalytics--Futuristic Communication Technologies--Disruptive Technologies for Future Sustainability. The chapters include the list of topics that spans all the areas of smart intelligent systems and computing such as: Data Mining with Soft Computing, Evolutionary Computing, Quantum Computing, Expert Systems, Next Generation Communication, Blockchain and Trust Management, Intelligent Biometrics, Multi-Valued Logical Systems, Cloud Computing and security etc. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her"--

The world is experiencing an unprecedented period of change and growth through all the electronic and technilogical developments and everyone on the planet has been impacted.  What was once ‘science fiction’, today it is a reality.

This book explores the world of many of once unthinkable advancements by explaining current technologies in great detail.  Each chapter focuses on a different aspect - Machine Vision, Pattern Analysis and Image Processing - Advanced Trends in Computational Intelligence and Data Analytics - Futuristic Communication Technologies - Disruptive Technologies for Future Sustainability. The chapters include the list of topics that spans all the areas of smart intelligent systems and computing such as: Data Mining with Soft Computing, Evolutionary Computing, Quantum Computing, Expert Systems, Next Generation Communication, Blockchain and Trust Management, Intelligent Biometrics, Multi-Valued Logical Systems, Cloud Computing and security etc.  An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her.

Preface xxv
Acknowledgement xxix
Part 1 Machine Learning and Its Application
1(126)
1 Single Image Super-Resolution Using GANs for High-Upscaling Factors
3(14)
Harshit Singhal
Aman Kumar
Shubham Khandelwal
Anupatn Kumar
Mini Agarwal
1.1 Introduction
3(2)
1.2 Methodology
5(2)
1.2.1 Architecture Details
5(2)
1.2.2 Loss Function
7(1)
1.3 Experiments
7(1)
1.3.1 Environment Details
7(1)
1.3.2 Training Dataset Details
7(1)
1.3.3 Training Parameters
7(1)
1.4 Experiments
8(2)
1.5 Conclusions
10(1)
1.6 Related Work
10(7)
References
13(4)
2 Landmark Recognition Using VGG16 Training
17(24)
Ruchi Jha
Prerna Jain
Sandeep Tayal
Ashish Sharma
2.1 Introduction
17(2)
2.2 Related Work
19(7)
2.2.1 ImageNet Classification
19(3)
2.2.2 Deep Local Features
22(3)
2.2.3 VGG Architecture
25(1)
2.3 Proposed Solution
26(7)
2.3.1 Revisiting Datasets
26(2)
2.3.1.1 Data Pre-Processing
28(2)
2.3.1.2 Model Training
30(3)
2.4 Results and Conclusions
33(4)
2.5 Discussions
37(4)
References
37(4)
3 A Comparison of Different Techniques Used for Classification of Bird Species From Images
41(10)
Sourabh Kumar
Vatsal Dhoundiyal
Nishant Raj
Neha Sharma
3.1 Introduction
41(1)
3.2 CUB 200 2011 Birds Dataset
42(1)
3.3 Machine Learning Approaches
42(4)
3.3.1 Softmax Regression
44(1)
3.3.2 Support Vector Machine
45(1)
3.3.3 K-Means Clustering
45(1)
3.4 Deep Learning Approaches
46(2)
3.4.1 CNN
46(1)
3.4.2 RNN
46(1)
3.4.3 InceptionV3
47(1)
3.4.4 ImageNet
47(1)
3.5 Conclusion
48(1)
3.6 Conclusion and Future Scope
49(2)
References
49(2)
4 Road Lane Detection Using Advanced Image Processing Techniques
51(22)
Prateek Sawhney
Varun Goel
4.1 Introduction
51(1)
4.2 Related Work
52(1)
4.3 Proposed Approach
52(1)
4.4 Analysis
53(14)
4.4.1 Dataset
53(3)
4.4.2 Camera Calibration and Distortion Correction
56(3)
4.4.3 Threshold Binary Image
59(2)
4.4.4 Perspective Transform
61(3)
4.4.5 Finding the Lane Lines--Sliding Window
64(3)
4.4.6 Radius of Curvature and Central Offset
67(1)
4.5 Annotation
67(1)
4.6 Illustrations
68(2)
4.7 Results and Discussions
70(1)
4.8 Conclusion and Future Work
70(3)
References
70(3)
5 Facial Expression Recognition in Real Time Using Convolutional Neural Network
73(18)
Vashi Dhankar
Anu Rathee
5.1 Introduction
73(2)
5.1.1 Need of Study
75(1)
5.2 Related Work
75(1)
5.3 Methodology
76(4)
5.3.1 Applying Transfer Learning using VGG-16
77(1)
5.3.2 Modeling and Training
78(2)
5.4 Results
80(6)
5.5 Conclusion and Future Scope
86(5)
References
87(4)
6 Feature Extraction and Image Recognition of Cursive Handwritten English Words Using Neural Network and IAM Off-Line Database
91(12)
Arushi Sharma
Shikha Gupta
6.1 Introduction
91(2)
6.1.1 Scope of Discussion
92(1)
6.2 Literature Survey
93(1)
6.2.1 Early Scanners and the Digital Age
93(1)
6.2.2 Machine Learning
93(1)
6.3 Methodology
94(4)
6.3.1 Dataset
95(1)
6.3.2 Evaluation Metric
96(1)
6.3.3 Pre-Processing
96(1)
6.3.4 Implementation and Training
97(1)
6.4 Results
98(2)
6.4.1 CNN Output
98(1)
6.4.2 RNN Output
98(1)
6.4.3 Model Analysis
99(1)
6.5 Conclusion and Future Work
100(3)
6.5.1 Image Pre-Processing
100(1)
6.5.2 Extend the Model to Fit Text-Lines
100(1)
6.5.3 Integrate Word Beam Search Decoding
100(1)
References
101(2)
7 License Plate Recognition System Using Machine Learning
103(12)
Ratan Gupta
Arpan Gupta
Amit Kumar
Rachna Jain
Preeti Nagrath
7.1 Introduction
103(2)
7.1.1 Machine Learning
105(1)
7.2 Related Work
105(1)
7.3 Classification Models
106(2)
7.3.1 Logistic Regression
107(1)
7.3.2 Decision Trees
107(1)
7.3.3 Random Forest
107(1)
7.3.4 K Means Clustering
107(1)
7.3.5 Support Vector Machines
107(1)
7.3.5.1 Linear Classification
108(1)
7.3.5.2 Nonlinear Classification
108(1)
7.4 Proposed Work and Methodology
108(4)
7.4.1 Detect License Plate
110(1)
7.4.2 Segmentation
110(1)
7.4.3 Training the Model
111(1)
7.4.4 Prediction and Recognition
111(1)
7.5 Result
112(1)
7.6 Conclusion
112(1)
7.7 Future Scope
112(3)
References
112(3)
8 Prediction of Disease Using Machine Learning Algorithms
115(12)
Annu Dhankhar
Shashank Jain
8.1 Introduction
115(1)
8.2 Datasets and Evaluation Methodology
116(2)
8.2.1 Datasets
117(1)
8.3 Algorithms Used
118(5)
8.3.1 Decision Tree Classifier
118(1)
8.3.2 Random Forest Classifier
119(1)
8.3.3 Support Vector Machines
120(1)
8.3.4 K Nearest Neighbors
121(2)
8.4 Results
123(1)
8.5 Conclusion
123(4)
References
123(4)
Part 2 Deep Learning and Its Application
127(180)
9 Brain Tumor Prediction by Binary Classification Using VGG-16
129(10)
Vaibhav Singh
Sarthak Sharma
Shubham Goel
Shivay Lamba
Neetu Garg
9.1 Introduction
130(1)
9.2 Existing Methodology
130(2)
9.2.1 Dataset Description
130(1)
9.2.2 Data Import and Preprocessing
131(1)
9.3 Augmentation
132(1)
9.3.1 For CNN Model
133(1)
9.3.2 For VGG 16 Model
133(1)
9.4 Models Used
133(1)
9.4.1 CNN Model
133(1)
9.4.2 VGG 16 Model
133(1)
9.4.2.1 Pre-Trained Model Approach
134(1)
9.5 Results
134(2)
9.6 Comparison
136(1)
9.7 Conclusion and Future Scope
136(3)
References
137(2)
10 Study of Gesture-Based Communication Translator by Deep Learning Technique
139(12)
Rishabh Agarwal
Shubham Bansal
Abhinav Aggarwal
Neetu Garg
Akanksha Kochhar
10.1 Introduction
139(2)
10.2 Literature Review
141(1)
10.3 The Proposed Recognition System
142(6)
10.3.1 Image Acquisition
143(2)
10.3.2 Pre-Processing
145(1)
10.3.3 Classification and Recognition
146(1)
10.3.4 Post-Processing
147(1)
10.4 Result and Discussion
148(1)
10.5 Conclusion
149(1)
10.6 Future Work
150(1)
References
150(1)
11 Transfer Learning for 3-Dimensional Medical Image Analysis
151(22)
Sanket Singh
Sarthak Jain
Akshit Khanna
Anupam Kumar
Ashish Sharma
11.1 Introduction
151(1)
11.2 Literature Survey
152(3)
11.2.1 Deep Learning
152(1)
11.2.2 Transfer Learning
153(1)
11.2.3 PyTorch and Keras (Our Libraries)
154(1)
11.3 Related Works
155(4)
11.3.1 Convolution Neural Network
156(1)
11.3.2 Transfer Learning
157(2)
11.4 Dataset
159(3)
11.4.1 Previously Used Dataset
159(1)
11.4.2 Data Acquiring
159(1)
11.4.3 Cleaning the Data
160(1)
11.4.4 Understanding the Data
160(2)
11.5 Description of the Dataset
162(1)
11.6 Architecture
162(3)
11.7 Proposed Model
165(2)
11.7.1 Model 1
167(1)
11.7.2 Model 2
167(1)
11.7.3 Model 3
167(1)
11.8 Results and Discussion
167(2)
11.8.1 Coding the Model
169(1)
11.9 Conclusion
169(1)
11.10 Future Scope
169(4)
Acknowledgement
170(1)
References
170(3)
12 A Study on Recommender Systems
173(8)
Agrima Mehandiratta
Pooja Gupta
Alok Kumar Sharma
12.1 Introduction
173(1)
12.2 Background
174(2)
12.2.1 Popularity-Based
175(1)
12.2.2 Content-Based
175(1)
12.2.3 Collaborative Systems
175(1)
12.3 Methodology
176(2)
12.3.1 Input Parameters
176(1)
12.3.2 Implementation
177(1)
12.3.3 Performance Measures
178(1)
12.4 Results and Discussion
178(2)
12.5 Conclusions and Future Scope
180(1)
References
180(1)
13 Comparing Various Machine Learning Algorithms for User Recommendations Systems
181(10)
Rahul Garg
Shivay Latnba
Sachin Garg
13.1 Introduction
181(1)
13.2 Related Works
182(1)
13.3 Methods and Materials
182(3)
13.3.1 Content-Based Filtering
182(1)
13.3.2 Collaborative Filtering
182(1)
13.3.3 User-User Collaborative Filtering
182(1)
13.3.4 Item-Item Collaborative Filtering
183(1)
13.3.5 Random Forest Algorithm
183(1)
13.3.6 Neural Networks
183(1)
13.3.7 ADA Boost Classifier
184(1)
13.3.8 XGBoost Classifier
184(1)
13.3.9 Trees
184(1)
13.3.10 Regression
185(1)
13.3.11 Dataset Description
185(1)
13.4 Experiment Results and Discussion
185(4)
13.5 Future Enhancements
189(1)
13.6 Conclusion
189(2)
References
190(1)
14 Indian Literacy Analysis Using Machine Learning Algorithms
191(14)
Shubhi Jain
Sakshi Bindal
Ruchi Goeland Gaurav Aggarwal
14.1 Introduction
191(2)
14.2 Related Work
193(1)
14.3 Solution Approaches
194(3)
14.3.1 Preparation of Dataset
195(1)
14.3.2 Data Reduction
195(1)
14.3.3 Data Visualization
195(1)
14.3.4 Prediction Models
196(1)
14.3.4.1 KNN (K-Nearest Neighbors)
196(1)
14.3.4.2 ElasticNet Regression
196(1)
14.3.4.3 Artificial Neural Networks
197(1)
14.3.4.4 Random Forest
197(1)
14.4 Proposed Approach
197(2)
14.5 Result Analysis
199(3)
14.6 Conclusion and Future Scope
202(3)
14.6.1 Conclusion
202(1)
14.6.2 Future Scope
202(1)
References
203(2)
15 Motion Transfer in Videos using Deep Convolutional Generative Adversarial Networks
205(10)
Savitoj Singh
Bittoo Aggarwal
Vipin Bhardwaj
Anupam Kumar
15.1 Introduction
205(1)
15.2 Related Work
206(2)
15.3 Methodology
208(1)
15.3.1 Pre-Processing
209(1)
15.3.2 Pose Detection and Estimation
209(1)
15.4 Pose to Video Translation
209(1)
15.5 Results and Analysis
210(2)
15.6 Conclusion and Future Scope
212(3)
References
213(2)
16 Twin Question Pair Classification
215(14)
Ashish Sharma
Sachin Sourav Jha
Sahil Arora
Shubham Garg
Sandeep Tayal
16.1 Introduction
215(1)
16.2 Literature Survey
216(2)
16.2.1 Duplicate Quora Questions Detection by Lei Guo, Chong Li & Haiming Tian
216(1)
16.2.2 Natural Language Understanding with the Quora Question Pairs Dataset by Lakshay Sharma, Laura Graesser, Nikita Nangia, UtkuEvci
217(1)
16.2.3 Duplicate Detection in Programming Question Answering Communities by Wei Emma Zhang and Quan Z. Sheng, Macquarie University
217(1)
16.2.4 Exploring Deep Learning in Semantic Question Matching by Arpan Poudel and Ashwin Dhakal
218(1)
16.3 Methods Applied for Training
218(6)
16.3.1 Count Vectorizer
218(1)
16.3.2 TF-IDF Vectorizer
219(1)
16.3.3 XG Boosting
220(2)
16.3.4 Random Forest Classifier
222(2)
16.4 Proposed Methodology
224(1)
16.4.1 Data Collection
224(1)
16.4.2 Data Analysis
224(1)
16.4.3 Data Cleaning and Pre-Processing
224(1)
16.4.4 Embedding
225(1)
16.4.5 Feature Extraction
225(1)
16.4.6 Data Splitting
225(1)
16.4.7 Modeling
225(1)
16.5 Observations
225(1)
16.6 Conclusion
226(3)
References
226(3)
17 Exploration of Pixel-Based and Object-Based Change Detection Techniques by Analyzing ALOS PALSAR and LANDSAT Data
229(16)
Amit Kumar Shakya
Ayushman Ramola
Anurag Vidyarthi
17.1 Introduction
229(2)
17.2 Classification of Pixel-Based and Object-Based Change Detection Methods
231(6)
17.2.1 Image Ratio
231(1)
17.2.2 Image Differencing
232(1)
17.2.3 Image Regression
233(1)
17.2.4 Vegetation Index Differencing
233(2)
17.2.5 Minimum Distance Classification
235(1)
17.2.6 Maximum Likelihood Classification
235(1)
17.2.7 Spectral Angle Mapper (SAM)
235(1)
17.2.8 Support Vector Machine
236(1)
17.3 Experimental Results
237(5)
17.3.1 Omission Error
237(1)
17.3.2 Commission Error
238(1)
17.3.3 User Accuracy
238(1)
17.3.4 Producer Accuracy
238(1)
17.3.5 Overall Accuracy
238(4)
17.4 Conclusion
242(3)
Acknowledgment
242(1)
References
242(3)
18 Tracing Bad Code Smells Behavior Using Machine Learning with Software Metrics
245(14)
Aakanshi Gupta
Bharti Suri
Lakshay Lamba
18.1 Introduction
245(2)
18.2 Related Work and Motivation
247(1)
18.3 Methodology
248(3)
18.3.1 Data Collection
248(1)
18.3.2 Static Code Analysis
249(1)
18.3.3 Sampling
250(1)
18.3.4 Machine Learning Approach
251(1)
18.4 Result Analysis and Manual Validation
251(4)
18.5 Threats, Limitation and Conclusion
255(4)
References
255(4)
19 A Survey on Various Negation Handling Techniques in Sentiment Analysis
259(22)
Sarita Bansal Garg
V.V. Subrahmanyam
19.1 Introduction
259(2)
19.2 Methods for Negation Identification
261(6)
19.2.1 Bag of Words
261(1)
19.2.2 Contextual Valence Shifters
261(1)
19.2.3 Semantic Relations
262(2)
19.2.4 Relations and Dependency-Based or Syntactic-Based
264(3)
19.3 Word Embedding
267(11)
19.4 Conclusion
278(3)
References
279(2)
20 Mobile-Based Bilingual Speech Corpus
281(14)
Nivedita Palia
Deepali Kamthania
Ashish Pahwa
20.1 Introduction
281(2)
20.2 Overview of Multilingual Speech Corpus for Indian Languages
283(1)
20.3 Methodology for Speech Corpus Development
283(8)
20.3.1 Recording Setup
287(1)
20.3.1.1 Text Selection
287(1)
20.3.1.2 Speaker Selection
288(1)
20.3.1.3 Device Selection
288(1)
20.3.1.4 Recording Environment
289(1)
20.3.2 Capturing
289(1)
20.3.3 Segregation and Editing
289(1)
20.3.3.1 Annotation
290(1)
20.4 Description of Bilingual Speech Corpus
291(1)
20.5 Conclusion and Future Scope
292(3)
References
292(3)
21 Intrusion Detection using Nature-Inspired Algorithms and Automated Machine Learning
295(12)
Vasudev Awatramani
Pooja Gupta
21.1 Introduction
295(3)
21.2 Related Work
298(2)
21.3 Methodology
300(4)
21.3.1 Nature Inspired Algorithms for Feature Selection
300(1)
21.3.2 Automated Machine Learning
301(1)
21.3.3 Architecture Search using Bayesian Search
301(2)
21.3.4 Hyperparameter Optimization Through Particle Swarm Optimization (HPO-PSO)
303(1)
21.4 Results
304(1)
21.5 Conclusion
304(3)
References
304(3)
Part 3 Security and Blockchain
307(66)
22 Distributed Ownership Model for Non-Fungible Tokens
309(14)
Jaspreet Singh
Prashant Singh
22.1 Introduction
309(1)
22.2 Background
310(1)
22.2.1 Blockchain Technology
310(1)
22.2.2 Ownership
311(1)
22.3 Proposed Architecture
311(4)
22.3.1 Overview
311(1)
22.3.2 Implementation
312(1)
22.3.3 Rationale for Smart Contract
313(1)
22.3.4 Smart Contract Tables
314(1)
22.4 Use-Cases
315(3)
22.4.1 Transaction Volume
316(1)
22.4.2 Comparison Between NFT Tokens
317(1)
22.5 Example Usage
318(2)
22.5.1 Current Scenario
318(1)
22.5.2 Solution by Distributed NFT
318(2)
22.6 Results
320(1)
22.7 Conclusion and Future Work
321(2)
References
321(2)
23 Comparative Analysis of Various Platforms of Blockchain
323(18)
Nitin Mittal
Srishty Pal
Anjali Joshi
Ashish Sharma
Sandeep Tayal
Yogesh Sharma
23.1 Introduction to Blockchain
323(2)
23.2 Important Terms of Blockchain
325(2)
23.2.1 Decentralization
325(1)
23.2.2 Ledger
325(1)
23.2.3 Consensus Algorithm
325(1)
23.2.4 51% Attack
326(1)
23.2.5 Merkle Tree
326(1)
23.2.6 Cryptography
326(1)
23.2.7 Smart Contract
326(1)
23.3 Bitcoin or Blockchain
327(1)
23.3.1 Primary Key and Public Key
327(1)
23.3.2 Workflow of Bitcoin
327(1)
23.4 Platforms of Blockchain
328(6)
23.4.1 Ethereum
328(1)
23.4.1.1 History
328(1)
23.4.1.2 Ethereum and Bitcoin
328(1)
23.4.1.3 Gas and Ether
329(1)
23.4.1.4 Workflow
329(1)
23.4.2 Hyperledger
329(1)
23.4.2.1 Purpose of Introducing Hyperledger
330(1)
23.4.2.2 Features
330(1)
23.4.2.3 PBFT
330(1)
23.4.2.4 Framework Use Cases
330(1)
23.4.3 R3Corda
331(1)
23.4.3.1 History
331(1)
23.4.3.2 Purpose
331(1)
23.4.3.3 Corda vs Bitcoin
331(1)
23.4.3.4 Consensus
332(1)
23.4.4 Stellar
332(1)
23.4.4.1 History
332(1)
23.4.4.2 Assets and Anchors
332(1)
23.4.4.3 Features
333(1)
23.4.4.4 Stellar Consensus
333(1)
23.4.5 Multichain
333(1)
23.4.5.1 History
333(1)
23.4.5.2 Features
334(1)
23.4.5.3 Consensus Algorithm for Multichain
334(1)
23.4.5.4 Smart Filters
334(1)
23.5 Blockchain Platforms and Comparative Analysis
334(4)
23.6 Conclusion
338(3)
References
338(3)
24 Smart Garbage Monitoring System
341(14)
Akshita Goel
Amita Goel
24.1 Introduction
341(2)
24.2 Literature Review
343(1)
24.3 System Design
344(1)
24.4 System Specifications
345(1)
24.4.1 Components
345(1)
24.4.2 Simulation Tool
346(1)
24.4.3 Analytics Tool
346(1)
24.5 Circuit Diagram
346(1)
24.6 Proposed Approach
347(1)
24.7 Implementation
348(4)
24.8 Result
352(1)
24.9 Conclusion
352(1)
24.10 Future Scope
352(3)
References
353(2)
25 Study of Various Intrusion Detection Systems: A Survey
355(18)
Minakshi Chauhan
Mohit Agarwal
25.1 Introduction
355(1)
25.2 Structure of IDS
356(1)
25.3 Intrusion Detection Systems
356(3)
25.3.1 Host-Based IDS (HIDS)
357(1)
25.3.2 Network-Based IDS (NIDS)
357(1)
25.3.3 Types of Network-Based Detection Technique
358(1)
25.3.3.1 Signature-Based (or Pattern-Matching) Intrusion Detection Systems (SIDS)
358(1)
25.3.3.2 Anomaly-Based Intrusion Detection Systems (AIDS)
358(1)
25.3.3.3 Hybrid Intrusion Detection System
358(1)
25.4 Types of Attacks
359(1)
25.5 Recent Improved Solutions to Intrusion Detection
360(1)
25.5.1 Based on Data Mining and Machine Learning Methods
361(1)
25.5.2 Knowledge-Based
361(1)
25.5.3 Evolutionary Methods and Optimization Techniques
361(1)
25.6 Analysis of Exiting IDS Based on Technique Used
361(4)
25.7 Analysis of Existing IDS in Different Domains
365(3)
25.7.1 IDS for IoT
366(1)
25.7.2 IDS in Cloud Computing Environment
367(1)
25.7.3 IDS in Web Applications
367(1)
25.7.4 IDS for WSN (Wireless Sensor Network)
368(1)
25.8 Conclusion
368(5)
References
368(5)
Part 4 Communication and Networks
373(70)
26 Green Communication Technology Management for Sustainability in Organization
375(12)
Shivangi Sahay
Anju Bharti
26.1 Introduction
375(3)
26.2 Sustainability of Green ICT
378(1)
26.3 Going Green and Sustainability
378(1)
26.4 ICT: Green and Sustainability
379(1)
26.5 Benefits: Green IT Practices
380(1)
26.6 Management Perspective: Green IT
381(1)
26.7 Biodegradable Device Components
381(3)
26.8 Conclusion
384(3)
References
385(2)
27 A Means of Futuristic Communication: A Review
387(14)
Vivek
Deepika Kukreja
Deepak Kumar Sharma
27.1 Introduction
387(5)
27.1.1 Internet of Things
387(1)
27.1.1.1 Characteristics of IoT
387(1)
27.1.1.2 Different Forms of IoT
388(1)
27.1.1.3 IoT Applications
388(1)
27.1.1.4 Challenges in IoT
388(1)
27.1.2 IoT and Cloud Computing
389(1)
27.1.2.1 Issues With IoT Cloud Platforms
390(1)
27.1.3 Fog Computing
390(1)
27.1.3.1 Analysis of Data in Fog Computing
391(1)
27.1.4 Edge Computing
391(1)
27.1.5 Comparative Analysis of Cloud, Fog and Edge Computing
391(1)
27.2 Literature Review
392(2)
27.3 IoT Simulators
394(1)
27.4 IoT Test Beds
394(2)
27.5 Conclusion and Future Scope
396(5)
References
397(4)
28 Experimental Evaluation of Security and Privacy in GSM Network Using RTL-SDR
401(12)
Hardik Manocha
Utkarsh Upadhyay
Deepika Kumar
28.1 Introduction
401(1)
28.2 Literature Review
402(1)
28.3 Privacy in Telecommunication
403(1)
28.4 A Take on User Privacy: GSM Exploitation
404(1)
28.4.1 IMSI Catching
404(1)
28.4.1.1 Active Attacks
405(1)
28.4.1.2 Soft Downgrade to GSM
405(1)
28.4.2 Eavesdropping
405(1)
28.5 Experimental Setup
405(1)
28.5.1 Hardware and Software
405(1)
28.5.2 Implementation Algorithm and Procedure
405(1)
28.6 Results and Analysis
406(4)
28.7 Conclusion
410(3)
References
410(3)
29 A Novel Consumer-Oriented Trust Model in E-Commerce
413(14)
Jyoti Malik
Suresh Kumar
29.1 Introduction
413(1)
29.2 Literature Surveys
414(1)
29.3 Trust Pyramid
415(3)
29.3.1 Trust Scenarios
416(1)
29.3.2 Statistics of E-Commerce
417(1)
29.3.2.1 Case Study: Consumer Trust Violation
418(1)
29.4 Categorization of E-Commerce in Different Spheres
418(3)
29.4.1 Hyperlocal
418(1)
29.4.2 Travel and Hospitality
419(1)
29.4.3 Business to Customer (B2C)
419(1)
29.4.4 Education Technology
419(1)
29.4.5 Payments and Wallets
419(1)
29.4.6 Business to Business (B2B)
419(1)
29.4.7 Mobility
420(1)
29.4.8 Financial Technology
420(1)
29.4.9 Health Technology
420(1)
29.4.10 Social Commerce
420(1)
29.4.11 Gaming
420(1)
29.4.12 Logistics Technology
421(1)
29.4.13 Online Classified and Services
421(1)
29.5 Categorization of E-Commerce in Different Spheres and Investment in Last Five Years
421(1)
29.6 Proposed Model
422(2)
29.6.1 Different Components of Web Trust Model
422(1)
29.6.2 A Novel Consumer-Oriented Trust Model
422(2)
29.7 Conclusion
424(3)
References
424(3)
30 Data Mining Approaches for Profitable Business Decisions
427(16)
Harshita Belwal
Sandeep Tayal
Yogesh Sharma
Ashish Sharma
30.1 Introduction to Data Mining and Business Intelligence
427(1)
30.2 Outline of Data Mining and BI
428(3)
30.2.1 CRISP-DM
430(1)
30.3 Leading Techniques used for Data Mining in BI
431(3)
30.3.1 Classification Analysis
431(1)
30.3.2 Clustering
431(1)
30.3.3 Regression Analysis
431(1)
30.3.4 Anomaly Detection
432(1)
30.3.5 Induction Rule
432(1)
30.3.6 Summarization
432(1)
30.3.7 Sequential Patterns
432(1)
30.3.8 Decision Tree
433(1)
30.3.9 Neural Networks
433(1)
30.3.10 Association Rule Mining
433(1)
30.4 Some Implementations of Data Mining in Business
434(2)
30.4.1 Banking and Finance
434(1)
30.4.2 Relationship Management
434(1)
30.4.3 Targeted Marketing
434(1)
30.4.4 Fraud Detection
435(1)
30.4.5 Manufacturing and Production
435(1)
30.4.6 Market Basket Analysis
435(1)
30.4.7 Propensity to Buy
435(1)
30.4.8 Customer Profitability
435(1)
30.4.9 Customer Attrition and Channel Optimization
436(1)
30.5 Tabulated Attributes of Popular Data Mining Technique
436(4)
30.5.1 Classification Analysis
436(1)
30.5.2 Clustering
436(2)
30.5.3 Anomaly or Outlier Detection
438(1)
30.5.4 Regression Analysis
438(1)
30.5.5 Induction Rule
438(1)
30.5.6 Summarization
438(1)
30.5.7 Sequential Pattern
439(1)
30.5.8 Decision Tree
439(1)
30.5.9 Neural Networks
439(1)
30.5.10 Association Rule Learning
439(1)
30.6 Conclusion
440(3)
References
440(3)
Part 5 Latest Trends in Sustainable Computing Techniques
443(80)
31 Survey on Data Deduplication Techniques for Securing Data in Cloud Computing Environment
445(16)
Ashima Arya
Vikas Kuchhal
Karon Gulati
31.1 Cloud Computing
445(6)
31.1.1 Introduction
445(1)
31.1.2 Cloud Computing Features
446(1)
31.1.3 Services Provided by Cloud Computing
446(1)
31.1.4 Types of Clouds Based on Deployment Model
447(1)
31.1.5 Cloud Computing Security Challenges
447(1)
31.1.5.1 Infrastructure-as-a-Service (IaaS)
447(2)
31.1.5.2 Platform-as-a-Service (PaaS)
449(1)
31.1.5.3 Software-as-a-Service (SaaS)
449(1)
31.1.5.4 Hardware-as-a-Service (HaaS)
450(1)
31.1.5.5 Data-as-a-Service (DaaS)
450(1)
31.2 Data Deduplication
451(2)
31.2.1 Data Deduplication Introduction
451(1)
31.2.2 Key Design Criteria for Deduplication Techniques
451(1)
31.2.2.1 Information Granularity
451(1)
31.2.2.2 Deduplication Area
452(1)
31.2.2.3 System Architecture
452(1)
31.2.2.4 Duplicate Check Boundary
453(1)
31.3 Literature Review
453(1)
31.4 Assessment Rules of Secure Deduplication Plans
454(1)
31.5 Open Security Problems and Difficulties
455(2)
31.5.1 Data Ownership the Board
455(1)
31.5.2 Achieving Semantically Secure Deduplication
456(1)
31.5.3 POW in Decentralized Deduplication Structures
456(1)
31.5.4 New Security Risks on Deduplication
457(1)
31.6 Conclusion
457(4)
References
457(4)
32 Procedural Music Generation
461(8)
Punya Aachman
Piyush Aggarwal
Pushkar Goel
32.1 Introduction
461(1)
32.2 Related Work
462(1)
32.3 Experimental Setup
463(1)
32.4 Methodology
463(2)
32.5 Result
465(2)
32.6 Conclusion
467(2)
References
467(2)
33 Detecting Photoshopped Faces Using Deep Learning
469(12)
Siddharth Aggarwal
Ajay Kumar Tiwari
33.1 Introduction
469(2)
33.2 Related Literature
471(1)
33.3 Dataset Generation
472(4)
33.3.1 Generating Dataset of Fake Images
473(3)
33.4 Methodolody
476(2)
33.4.1 Details of the Training Procedure
477(1)
33.5 Results
478(1)
33.6 Conclusion
479(1)
33.7 Future Scope
479(2)
References
479(2)
34 A Review of SQL Injection Attack and Various Detection Approaches
481(10)
Neha Bhateja
Dr. Sunil Sikka
Dr. Anshu Malhotra
34.1 Introduction
481(2)
34.2 SQL Injection Attack and Its Types
483(1)
34.3 Literature Survey
484(3)
34.4 Summary
487(1)
34.5 Conclusion
488(3)
References
488(3)
35 Futuristic Communication Technologies
491(20)
Sanika Singh
Aman Anand
Shubham Sharma
Tanupriya Choudhury
Saurabh Mukherjee
35.1 Introduction
491(2)
35.2 Types of Communication Medium
493(1)
35.2.1 Wired Medium
493(1)
35.3 Types of Wired Connections
493(2)
35.3.1 Implementation of Wired (Physical Mode) Technology
494(1)
35.3.2 Limitations of Wired Technology
494(1)
35.4 Wireless Communication
495(2)
35.4.1 Types of Wireless Technology
495(2)
35.4.2 Applications of Wireless Technology
497(1)
35.4.3 Limitations of Wireless Technology
497(1)
35.5 Optical Fiber Communication
497(1)
35.5.1 Types of Optical Fiber Communication
497(1)
35.5.2 Applications of Optical Fiber Communication
498(1)
35.5.3 Limitations of Optical Fiber Communication
498(1)
35.6 Radar Communication
498(2)
35.6.1 Types of Radar Communication
499(1)
35.6.2 Applications of RADAR Communication
500(1)
35.6.3 Limitations of RADAR Communication
500(1)
35.7 Green Communication Technology, Its Management and Its Sustainability
500(2)
35.8 Space Air Ground Integrated Communication
502(1)
35.9 Ubiquitous Communication
503(1)
35.10 Network Planning, Management, Security
504(2)
35.11 Cognitive Radio Communication
506(1)
35.12 Types of Cognitive Radio Communication
507(1)
35.13 Next Generation Communications and Applications
507(1)
35.14 Smart Energy Management
508(3)
References
509(2)
36 An Approach for Load Balancing Through Genetic Algorithm
511(12)
Mahendra Pratap Yadav
Harishchandra A. Akarte
Dharmendra Kumar Yadav
36.1 Introduction
511(1)
36.2 Motivation
512(1)
36.3 Background and Related Technology
513(3)
36.3.1 Load Balancing
513(1)
36.3.2 Load Balancing Metrics
514(1)
36.3.3 Classification of Load Balancing Algorithms
515(1)
36.4 Related Work
516(2)
36.5 Proposed Solution
518(2)
36.5.1 Genetic Algorithm
518(1)
36.5.2 Flowchart of Proposed Strategy
519(1)
36.6 Experimental Setup and Results Analysis
520(3)
36.6.1 Data Pre-Processing
520(1)
36.6.2 Experimental Setup
520(1)
36.6.3 Result Analysis
521(2)
36.7 Conclusion
523(1)
References 523(2)
Index 525
Namita Gupta is the Head of Computer Science and Engineering Department at Maharaja Agrasen Institute of Technology, GGSIP University, Delhi, India. She has more than 20 years of teaching experience and has played active role in research and project development. Her current areas of interest and research includes data mining, databases and machine learning.

Prasenjit Chatterjee is an associate professor in the Mechanical Engineering Department at MCKV Institute of Engineering, India. He has more than 80 research papers in various international SCI journals. Dr. Chatterjee is one of the developers of a new multiple-criteria decision-making method called Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS).

Tanupriya Choudhury received his PhD degree in the year 2016 and is an associate professor in Dept. of Computer Science and Engineering at UPES Dehradun, India. His areas of interests include human computing, soft computing, cloud computing, and data mining. He has filed 14 patents till date and received 16 copyrights from MHRD for his own software. He has authored more than 85 research papers.
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