Smart and Sustainable Intelligent Systems (Sustainable Computing and Optimization)

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Dedication
Contents
Preface
	Organization of the Book
Acknowledgement
Part 1: MACHINE LEARNINGAND ITS APPLICATION
	1 Single Image Super-Resolution Using GANs for High-Upscaling Factors
		1.1 Introduction
		1.2 Methodology
			1.2.1 Architecture Details
			1.2.2 Loss Function
		1.3 Experiments
			1.3.1 Environment Details
			1.3.2 Training Dataset Details
			1.3.3 Training Parameters
		1.4 Experiments
		1.5 Conclusions
		1.6 Related Work
		References
	2 Landmark Recognition Using VGG16 Training
		2.1 Introduction
		2.2 Related Work
			2.2.1 ImageNet Classification
			2.2.2 Deep Local Features
			2.2.3 VGG Architecture
		2.3 Proposed Solution
			2.3.1 Revisiting Datasets
		2.4 Results and Conclusions
		2.5 Discussions
		References
	3 A Comparison of Different Techniques Used for Classification of Bird Species From Images
		3.1 Introduction
		3.2 CUB_200_2011 Birds Dataset
		3.3 Machine Learning Approaches
			3.3.1 Softmax Regression
			3.3.2 Support Vector Machine
			3.3.3 K-Means Clustering
		3.4 Deep Learning Approaches
			3.4.1 CNN
			3.4.2 RNN
			3.4.3 InceptionV3
			3.4.4 ImageNet
		3.5 Conclusion
		3.6 Conclusion and Future Scope
		References
	4 Road Lane Detection Using Advanced Image Processing Techniques
		4.1 Introduction
		4.2 Related Work
		4.3 Proposed Approach
		4.4 Analysis
			4.4.1 Dataset
			4.4.2 Camera Calibration and Distortion Correction
			4.4.3 Threshold Binary Image
			4.4.4 Perspective Transform
			4.4.5 Finding the Lane Lines—Sliding Window
			4.4.6 Radius of Curvature and Central Offset
		4.5 Annotation
		4.6 Illustrations
		4.7 Results and Discussions
		4.8 Conclusion and Future Work
		References
	5 Facial Expression Recognition in Real Time Using Convolutional Neural Network
		5.1 Introduction
			5.1.1 Need of Study
		5.2 Related Work
		5.3 Methodology
			5.3.1 Applying Transfer Learning using VGG-16
			5.3.2 Modeling and Training
		5.4 Results
		5.5 Conclusion and Future Scope
		References
	6 Feature Extraction and Image Recognition of Cursive Handwritten English Words Using Neural Network and IAM Off-Line Database
		6.1 Introduction
			6.1.1 Scope of Discussion
		6.2 Literature Survey
			6.2.1 Early Scanners and the Digital Age
			6.2.2 Machine Learning
		6.3 Methodology
			6.3.1 Dataset
			6.3.2 Evaluation Metric
			6.3.3 Pre-Processing
			6.3.4 Implementation and Training
		6.4 Results
			6.4.1 CNN Output
			6.4.2 RNN Output
			6.4.3 Model Analysis
		6.5 Conclusion and Future Work
			6.5.1 Image Pre-Processing
			6.5.2 Extend the Model to Fit Text-Lines
			6.5.3 Integrate Word Beam Search Decoding
		References
	7 License Plate Recognition System Using Machine Learning
		7.1 Introduction
			7.1.1 Machine Learning
		7.2 Related Work
		7.3 Classification Models
			7.3.1 Logistic Regression
			7.3.2 Decision Trees
			7.3.3 Random Forest
			7.3.4 K Means Clustering
			7.3.5 Support Vector Machines
		7.4 Proposed Work and Methodology
			7.4.1 Detect License Plate
			7.4.2 Segmentation
			7.4.3 Training the Model
			7.4.4 Prediction and Recognition
		7.5 Result
		7.6 Conclusion
		7.7 Future Scope
		References
	8 Prediction of Disease Using Machine Learning Algorithms
		8.1 Introduction
		8.2 Datasets and Evaluation Methodology
			8.2.1 Datasets
		8.3 Algorithms Used
			8.3.1 Decision Tree Classifier
			8.3.2 Random Forest Classifier
			8.3.3 Support Vector Machines
			8.3.4 K Nearest Neighbors
		8.4 Results
		8.5 Conclusion
		References
Part 2: DEEP LEARNING AND ITS APPLICATION
	9 Brain Tumor Prediction by Binary Classification Using VGG-16
		9.1 Introduction
		9.2 Existing Methodology
			9.2.1 Dataset Description
			9.2.2 Data Import and Preprocessing
		9.3 Augmentation
			9.3.1 For CNN Model
			9.3.2 For VGG 16 Model
		9.4 Models Used
			9.4.1 CNN Model
			9.4.2 VGG 16 Model
		9.5 Results
		9.6 Comparison
		9.7 Conclusion and Future Scope
		References
	10 Study of Gesture-Based Communication Translator by Deep Learning Technique
		10.1 Introduction
		10.2 Literature Review
		10.3 The Proposed Recognition System
			10.3.1 Image Acquisition
			10.3.2 Pre-Processing
			10.3.3 Classification and Recognition
			10.3.4 Post-Processing
		10.4 Result and Discussion
		10.5 Conclusion
		10.6 Future Work
		References
	11 Transfer Learning for 3-Dimensional Medical Image Analysis
		11.1 Introduction
		11.2 Literature Survey
			11.2.1 Deep Learning
			11.2.2 Transfer Learning
			11.2.3 PyTorch and Keras (Our Libraries)
		11.3 Related Works
			11.3.1 Convolution Neural Network
			11.3.2 Transfer Learning
		11.4 Dataset
			11.4.1 Previously Used Dataset
			11.4.2 Data Acquiring
			11.4.3 Cleaning the Data
			11.4.4 Understanding the Data
		11.5 Description of the Dataset
		11.6 Architecture
		11.7 Proposed Model
			11.7.1 Model 1
			11.7.2 Model 2
			11.7.3 Model 3
		11.8 Results and Discussion
			11.8.1 Coding the Model
		11.9 Conclusion
		11.10 Future Scope
		Acknowledgement
		References
	12 A Study on Recommender Systems
		12.1 Introduction
		12.2 Background
			12.2.1 Popularity-Based
			12.2.2 Content-Based
			12.2.3 Collaborative Systems
		12.3 Methodology
			12.3.1 Input Parameters
			12.3.2 Implementation
			12.3.3 Performance Measures
		12.4 Results and Discussion
		12.5 Conclusions and Future Scope
		References
	13 Comparing Various Machine Learning Algorithms for User Recommendations Systems
		13.1 Introduction
		13.2 Related Works
		13.3 Methods and Materials
			13.3.1 Content-Based Filtering
			13.3.2 Collaborative Filtering
			13.3.3 User–User Collaborative Filtering
			13.3.4 Item–Item Collaborative Filtering
			13.3.5 Random Forest Algorithm
			13.3.6 Neural Networks
			13.3.7 ADA Boost Classifier
			13.3.8 XGBoost Classifier
			13.3.9 Trees
			13.3.10 Regression
			13.3.11 Dataset Description
		13.4 Experiment Results and Discussion
		13.5 Future Enhancements
		13.6 Conclusion
		References
	14 Indian Literacy Analysis Using Machine Learning Algorithms
		14.1 Introduction
		14.2 Related Work
		14.3 Solution Approaches
			14.3.1 Preparation of Dataset
			14.3.2 Data Reduction
			14.3.3 Data Visualization
			14.3.4 Prediction Models
		14.4 Proposed Approach
		14.5 Result Analysis
		14.6 Conclusion and Future Scope
			14.6.1 Conclusion
			14.6.2 Future Scope
		References
	15 Motion Transfer in Videos using Deep Convolutional Generative Adversarial Networks
		15.1 Introduction
		15.2 Related Work
		15.3 Methodology
			15.3.1 Pre-Processing
			15.3.2 Pose Detection and Estimation
		15.4 Pose to Video Translation
		15.5 Results and Analysis
		15.6 Conclusion and Future Scope
		References
	16 Twin Question Pair Classification
		16.1 Introduction
		16.2 Literature Survey
			16.2.1 Duplicate Quora Questions Detection by Lei Guo, Chong Li & Haiming Tian
			16.2.2 Natural Language Understanding with the Quora Question Pairs Dataset by Lakshay Sharma, Laura Graesser, Nikita Nangia, Utku Evci
			16.2.3 Duplicate Detection in Programming Question Answering Communities by Wei Emma Zhang and Quan Z. Sheng, Macquarie University
			16.2.4 Exploring Deep Learning in Semantic Question Matching by Arpan Poudel and Ashwin Dhakal [1]
		16.3 Methods Applied for Training
			16.3.1 Count Vectorizer
			16.3.2 TF-IDF Vectorizer
			16.3.3 XG Boosting
			16.3.4 Random Forest Classifier
		16.4 Proposed Methodology
			16.4.1 Data Collection
			16.4.2 Data Analysis
			16.4.3 Data Cleaning and Pre-Processing
			16.4.4 Embedding
			16.4.5 Feature Extraction
			16.4.6 Data Splitting
			16.4.7 Modeling
		16.5 Observations
		16.6 Conclusion
		References
	17 Exploration of Pixel-Based and Object-Based Change Detection Techniques by Analyzing ALOS PALSAR and LANDSAT Data
		17.1 Introduction
		17.2 Classification of Pixel-Based and Object-Based Change Detection Methods
			17.2.1 Image Ratio
			17.2.2 Image Differencing
			17.2.3 Image Regression
			17.2.4 Vegetation Index Differencing
			17.2.5 Minimum Distance Classification
			17.2.6 Maximum Likelihood Classification
			17.2.7 Spectral Angle Mapper (SAM)
			17.2.8 Support Vector Machine
		17.3 Experimental Results
			17.3.1 Omission Error
			17.3.2 Commission Error
			17.3.3 User Accuracy
			17.3.4 Producer Accuracy
			17.3.5 Overall Accuracy
		17.4 Conclusion
		Acknowledgment
		References
	18 Tracing Bad Code Smells Behavior Using Machine Learning with Software Metrics
		18.1 Introduction
		18.2 Related Work and Motivation
		18.3 Methodology
			18.3.1 Data Collection
			18.3.2 Static Code Analysis
			18.3.3 Sampling
			18.3.4 Machine Learning Approach
		18.4 Result Analysis and Manual Validation
		18.5 Threats, Limitation and Conclusion
		References
	19 A Survey on Various Negation Handling Techniques in Sentiment Analysis
		19.1 Introduction
		19.2 Methods for Negation Identification
			19.2.1 Bag of Words
			19.2.2 Contextual Valence Shifters
			19.2.3 Semantic Relations
			19.2.4 Relations and Dependency-Based or Syntactic-Based
		19.3 Word Embedding
		19.4 Conclusion
		References
	20 Mobile-Based Bilingual Speech Corpus
		20.1 Introduction
		20.2 Overview of Multilingual Speech Corpus for Indian Languages
		20.3 Methodology for Speech Corpus Development
			20.3.1 Recording Setup
			20.3.2 Capturing
			20.3.3 Segregation and Editing
		20.4 Description of Bilingual Speech Corpus
		20.5 Conclusion and Future Scope
		References
	21 Intrusion Detection using Nature-Inspired Algorithms and Automated Machine Learning
		21.1 Introduction
		21.2 Related Work
		21.3 Methodology
			21.3.1 Nature Inspired Algorithms for Feature Selection
			21.3.2 Automated Machine Learning
			21.3.3 Architecture Search using Bayesian Search
			21.3.4 Hyperparameter Optimization Through Particle Swarm Optimization (HPO-PSO)
		21.4 Results
		21.5 Conclusion
		References
Part 3: SECURITY AND BLOCKCHAIN
	22 Distributed Ownership Model for Non-Fungible Tokens
		22.1 Introduction
		22.2 Background
			22.2.1 Blockchain Technology
			22.2.2 Ownership
		22.3 Proposed Architecture
			22.3.1 Overview
			22.3.2 Implementation
			22.3.3 Rationale for Smart Contract
			22.3.4 Smart Contract Tables
		22.4 Use-Cases
			22.4.1 Transaction Volume
			22.4.2 Comparison Between NFT Tokens
		22.5 Example Usage
			22.5.1 Current Scenario
			22.5.2 Solution by Distributed NFT
		22.6 Results
		22.7 Conclusion and Future Work
		References
	23 Comparative Analysis of Various Platforms of Blockchain
		23.1 Introduction to Blockchain
		23.2 Important Terms of Blockchain
			23.2.1 Decentralization
			23.2.2 Ledger
			23.2.3 Consensus Algorithm
			23.2.4 51% Attack
			23.2.5 Merkle Tree
			23.2.6 Cryptography
			23.2.7 Smart Contract
		23.3 Bitcoin or Blockchain
			23.3.1 Primary Key and Public Key
			23.3.2 Workflow of Bitcoin
		23.4 Platforms of Blockchain
			23.4.1 Ethereum
			23.4.2 Hyperledger
			23.4.3 R3 Corda
			23.4.4 Stellar
			23.4.5 Multichain
		23.5 Blockchain Platforms and Comparative Analysis
		23.6 Conclusion
		References
	24 Smart Garbage Monitoring System
		24.1 Introduction
		24.2 Literature Review
		24.3 System Design
		24.4 System Specifications
			24.4.1 Components
			24.4.2 Simulation Tool
			24.4.3 Analytics Tool
		24.5 Circuit Diagram
		24.6 Proposed Approach
		24.7 Implementation
		24.8 Result
		24.9 Conclusion
		24.10 Future Scope
		References
	25 Study of Various Intrusion Detection Systems: A Survey
		25.1 Introduction
		25.2 Structure of IDS
		25.3 Intrusion Detection Systems
			25.3.1 Host-Based IDS (HIDS)
			25.3.2 Network-Based IDS (NIDS)
			25.3.3 Types of Network-Based Detection Technique
		25.4 Types of Attacks
		25.5 Recent Improved Solutions to Intrusion Detection
			25.5.1 Based on Data Mining and Machine Learning Methods
			25.5.2 Knowledge-Based
			25.5.3 Evolutionary Methods and Optimization Techniques
		25.6 Analysis of Exiting IDS Based on Technique Used
		25.7 Analysis of Existing IDS in Different Domains
			25.7.1 IDS for IoT
			25.7.2 IDS in Cloud Computing Environment
			25.7.3 IDS in Web Applications
			25.7.4 IDS for WSN (Wireless Sensor Network)
		25.8 Conclusion
		References
Part 4: COMMUNICATION AND NETWORKS
	26 Green Communication Technology Management for Sustainability in Organization
		26.1 Introduction
		26.2 Sustainability of Green ICT
		26.3 Going Green and Sustainability
		26.4 ICT: Green and Sustainability
		26.5 Benefits: Green IT Practices
		26.6 Management Perspective: Green IT
		26.7 Biodegradable Device Components
		26.8 Conclusion
		References
	27 A Means of Futuristic Communication: A Review
		27.1 Introduction
			27.1.1 Internet of Things
			27.1.2 IoT and Cloud Computing
			27.1.3 Fog Computing
			27.1.4 Edge Computing
			27.1.5 Comparative Analysis of Cloud, Fog and Edge Computing
		27.2 Literature Review
		27.3 IoT Simulators
		27.4 IoT Test Beds
		27.5 Conclusion and Future Scope
		References
	28 Experimental Evaluation of Security and Privacy in GSM Network Using RTL-SDR
		28.1 Introduction
		28.2 Literature Review
		28.3 Privacy in Telecommunication
		28.4 A Take on User Privacy: GSM Exploitation
			28.4.1 IMSI Catching
			28.4.2 Eavesdropping
		28.5 Experimental Setup
			28.5.1 Hardware and Software
			28.5.2 Implementation Algorithm and Procedure
		28.6 Results and Analysis
		28.7 Conclusion
		References
	29 A Novel Consumer-Oriented Trust Model in E-Commerce
		29.1 Introduction
		29.2 Literature Surveys
		29.3 Trust Pyramid
			29.3.1 Trust Scenarios
			29.3.2 Statistics of E-Commerce
		29.4 Categorization of E-Commerce in Different Spheres
			29.4.1 Hyperlocal
			29.4.2 Travel and Hospitality
			29.4.3 Business to Customer (B2C)
			29.4.4 Education Technology
			29.4.5 Payments and Wallets
			29.4.6 Business to Business (B2B)
			29.4.7 Mobility
			29.4.8 Financial Technology
			29.4.9 Health Technology
			29.4.10 Social Commerce
			29.4.11 Gaming
			29.4.12 Logistics Technology
			29.4.13 Online Classified and Services
		29.5 Categorization of E-Commerce in Different Spheres and Investment in Last Five Years
		29.6 Proposed Model
			29.6.1 Different Components of Web Trust Model
			29.6.2 A Novel Consumer-Oriented Trust Model
		29.7 Conclusion
		References
	30 Data Mining Approaches for Profitable Business Decisions
		30.1 Introduction to Data Mining and Business Intelligence
		30.2 Outline of Data Mining and BI
			30.2.1 CRISP-DM
		30.3 Leading Techniques used for Data Mining in BI
			30.3.1 Classification Analysis
			30.3.2 Clustering
			30.3.3 Regression Analysis
			30.3.4 Anomaly Detection
			30.3.5 Induction Rule
			30.3.6 Summarization
			30.3.7 Sequential Patterns
			30.3.8 Decision Tree
			30.3.9 Neural Networks
			30.3.10 Association Rule Mining
		30.4 Some Implementations of Data Mining in Business
			30.4.1 Banking and Finance
			30.4.2 Relationship Management
			30.4.3 Targeted Marketing
			30.4.4 Fraud Detection
			30.4.5 Manufacturing and Production
			30.4.6 Market Basket Analysis
			30.4.7 Propensity to Buy
			30.4.8 Customer Profitability
			30.4.9 Customer Attrition and Channel Optimization
		30.5 Tabulated Attributes of Popular Data Mining Technique
			30.5.1 Classification Analysis
			30.5.2 Clustering
			30.5.3 Anomaly or Outlier Detection
			30.5.4 Regression Analysis
			30.5.5 Induction Rule
			30.5.6 Summarization
			30.5.7 Sequential Pattern
			30.5.8 Decision Tree
			30.5.9 Neural Networks
			30.5.10 Association Rule Learning
		30.6 Conclusion
		References
Part 5: LATEST TRENDS IN SUSTAINABLECOMPUTING TECHNIQUES
	31 Survey on Data Deduplication Techniques for Securing Data in Cloud Computing Environment
		31.1 Cloud Computing
			31.1.1 Introduction
			31.1.2 Cloud Computing Features
			31.1.3 Services Provided by Cloud Computing
			31.1.4 Types of Clouds Based on Deployment Model
			31.1.5 Cloud Computing Security Challenges
		31.2 Data Deduplication
			31.2.1 Data Deduplication Introduction
			31.2.2 Key Design Criteria for Deduplication Techniques
		31.3 Literature Review
		31.4 Assessment Rules of Secure Deduplication Plans
		31.5 Open Security Problems and Difficulties
			31.5.1 Data Ownership the Board
			31.5.2 Achieving Semantically Secure Deduplication
			31.5.3 POW in Decentralized Deduplication Structures
			31.5.4 New Security Risks on Deduplication
		31.6 Conclusion
		References
	32 Procedural Music Generation
		32.1 Introduction
		32.2 Related Work
		32.3 Experimental Setup
		32.4 Methodology
		32.5 Result
		32.6 Conclusion
		References
	33 Detecting Photoshopped Faces Using Deep Learning
		33.1 Introduction
		33.2 Related Literature
		33.3 Dataset Generation
			33.3.1 Generating Dataset of Fake Images
		33.4 Methodolody
			33.4.1 Details of the Training Procedure
		33.5 Results
		33.6 Conclusion
		33.7 Future Scope
		References
	34 A Review of SQL Injection Attack and Various Detection Approaches
		34.1 Introduction
		34.2 SQL Injection Attack and Its Types
		34.3 Literature Survey
		34.4 Summary
		34.5 Conclusion
		References
	35 Futuristic Communication Technologies
		35.1 Introduction
		35.2 Types of Communication Medium
			35.2.1 Wired Medium
		35.3 Types of Wired Connections
			35.3.1 Implementation of Wired (Physical Mode) Technology
			35.3.2 Limitations of Wired Technology
		35.4 Wireless Communication
			35.4.1 Types of Wireless Technology
			35.4.2 Applications of Wireless Technology
			35.4.3 Limitations of Wireless Technology
		35.5 Optical Fiber Communication
			35.5.1 Types of Optical Fiber Communication
			35.5.2 Applications of Optical Fiber Communication
			35.5.3 Limitations of Optical Fiber Communication
		35.6 Radar Communication
			35.6.1 Types of Radar Communication
			35.6.2 Applications of RADAR Communication
			35.6.3 Limitations of RADAR Communication
		35.7 Green Communication Technology, Its Management and Its Sustainability
		35.8 Space Air Ground Integrated Communication
		35.9 Ubiquitous Communication
		35.10 Network Planning, Management, Security
		35.11 Cognitive Radio Communication
		35.12 Types of Cognitive Radio Communication
		35.13 Next Generation Communications and Applications
		35.14 Smart Energy Management
		References
	36 An Approach for Load Balancing Through Genetic Algorithm
		36.1 Introduction
		36.2 Motivation
		36.3 Background and Related Technology
			36.3.1 Load Balancing
			36.3.2 Load Balancing Metrics
			36.3.3 Classification of Load Balancing Algorithms
		36.4 Related Work
		36.5 Proposed Solution
			36.5.1 Genetic Algorithm
			36.5.2 Flowchart of Proposed Strategy
		36.6 Experimental Setup and Results Analysis
			36.6.1 Data Pre-Processing
			36.6.2 Experimental Setup
			36.6.3 Result Analysis
		36.7 Conclusion
		References
Index
EULA