Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications: AISGSC 2019

Preface
Contents
Abbreviations
Chapter 1: Fractional-Order PID Controller Optimized by SCA for Solar System
	1.1 Introduction
	1.2 Literature Survey
	1.3 Proposed PV System
		1.3.1 PV Module
		1.3.2 FOPID (PIλ Dμ) Controller
	1.4 Sine Cosine Algorithm (SCA)
	1.5 Results and Discussion
	1.6 Conclusion
	References
Chapter 2: LVRT Capability Improvement in a Grid-Connected DFIG Wind Turbine System Using Neural Network-Based Dynamic Voltage...
	2.1 Introduction
	2.2 Wind Turbine System
	2.3 Low-Voltage Ride Through (LVRT)
	2.4 Dynamic Voltage Restorer (DVR)
	2.5 Control Strategy of DVR
		2.5.1 Control Based on PI Controller
		2.5.2 Control Based on ANN Controller
	2.6 Simulation Results and Discussions
	2.7 Conclusion
	References
Chapter 3: Detection and Classification of Power Quality Events Using Wavelet Energy Difference and Support Vector Machine
	3.1 Introduction
	3.2 Principal Component Analysis
		3.2.1 Steps to Reduce the Dimension of the Matrix with PCA
	3.3 Support Vector Machine
	3.4 Hilbert-Huang Transform
	3.5 Power Quality Analysis Using SVM
		3.5.1 Mathematical Models of PQE
		3.5.2 Multi-resolution Analysis (MRA)
		3.5.3 Energy Difference of MRA of PQE
		3.5.4 Optimization of Parameters and Classification of Testing Set
	3.6 Conclusion
	References
Chapter 4: PMSM Drive Using Predictive Current Control Technique for HVAC Applications
	4.1 Introduction
	4.2 Proposed Predictive Current Control
	4.3 Results and Discussions
	4.4 Comparison of Predictive Current Control with Classical Control Techniques
	4.5 Conclusion
	References
Chapter 5: Grid-Connected 5 kW Mono-crystalline Solar PV System
	5.1 Introduction
	5.2 Solar PV System Elements
		5.2.1 Solar PV Module
		5.2.2 Module Mounting Structures
		5.2.3 DC Combiner Box (Junction Box)
		5.2.4 DC Distribution Box (DC DB)
		5.2.5 Power Conditioning Unit (Inverter)
		5.2.6 AC Distribution Box
		5.2.7 Cables
		5.2.8 Protection: Earthing, Lightning and Grid Islanding
	5.3 Sizing of Solar PV System
		5.3.1 Solar Array Capacity
		5.3.2 Inverter Sizing
	5.4 Solar PV-Grid Interfacing
	5.5 Results and Discussion
	5.6 Conclusion
	References
Chapter 6: An Add-in Tool for BIM-Based Electrical Load Forecast for Multi-building Microgrid Design
	6.1 Introduction
	6.2 Literature Review
	6.3 Methodology
	6.4 Case Study and Inferences
	6.5 Conclusion
	References
Chapter 7: An Investigation on Torque Ripple Minimization of Switched Reluctance Motor Using Different Power Converter Topolog...
	7.1 Introduction
	7.2 SRM Drive Modelling
		7.2.1 Configuration
		7.2.2 Torque Production Mechanism
	7.3 Modelling of SRM Drive with Various Power Converters
		7.3.1 Asymmetric Power Converter
		7.3.2 R-Dump Power Converter
		7.3.3 C-Dump Power Converter
		7.3.4 Fuzzy Logic Controller
	7.4 Results and Discussions
	7.5 Conclusion
	References
Chapter 8: Design of Half-Ring MIMO Antenna to Reduce the Mutual Coupling
	8.1 Introduction
	8.2 Antenna Construction and Its Geometry
	8.3 Results Analysis and Discussion
	8.4 Conclusion
	References
Chapter 9: Optimal Allocation of Distributed Generation Using Clustered Firefly Algorithm
	9.1 Introduction
	9.2 Problem Formulation
		9.2.1 The Objective Function
		9.2.2 Voltage Limits
		9.2.3 Current Limits
	9.3 Application of CFFA for Power Loss Minimization
	9.4 Simulation Results and Discussion
	9.5 Performance Comparison
	9.6 Conclusion
	References
Chapter 10: CDM-Based Two-Degree-of-Freedom PID Controller Tuning Rules for Unstable FOPTD Processes
	10.1 Introduction
	10.2 Coefficient Diagram Method
	10.3 Proposed CDM-PID Tuning Rules
	10.4 Simulation Results
		10.4.1 Set Point Tracking and Disturbance Rejection
		10.4.2 Robustness Test
		10.4.3 Stability Analysis
	10.5 Conclusion
	References
Chapter 11: Real-Time Energy Management System for Solar-Wind-Battery fed Base Transceiver Station
	11.1 Introduction
	11.2 Block Diagram and System Description
		11.2.1 Flow Diagram of the Control Logic
	11.3 Backup Facilities
		11.3.1 Battery Specifications
		11.3.2 SOC Estimation of Battery
		11.3.3 Performance of SOC Estimator
		11.3.4 Performance of Controller
	11.4 Hardware Description
	11.5 Conclusions
	References
Chapter 12: IOT-Based Adaptive Protection of Microgrid
	12.1 Introduction
	12.2 Proposed IOT-Based Microgrid Protection System
		12.2.1 Methodology
		12.2.2 Modules
	12.3 Design Approach
	12.4 Results and Discussion
	12.5 Conclusion
	References
Chapter 13: Performance Comparison Between Sensor and Sensorless Control of Permanent Magnet Synchronous Motor with Wide Speed...
	13.1 Introduction
	13.2 MRAS Using MTPA and Flux Weakening Operations
	13.3 Results and Discussion
		13.3.1 MTPA with Variable Speed and Variable Load
		13.3.2 Flux Weakening with Variable Speed and Variable Load
	13.4 Conclusion
	References
Chapter 14: Passive Fault-Tolerant Control Based on Interval Type-2 Fuzzy Controller for Coupled Tank System
	14.1 Introduction
	14.2 Model Description of the Coupled-Tank System
		14.2.1 Coupled-Tank-Level Control System
		14.2.2 CTLCS Mathematical Modelling
	14.3 PFTIT2FLC design and Background of Type-2 FLC
		14.3.1 Background of the Type-2 Fuzzy Logic Control
			14.3.1.1 Fuzzifier
			14.3.1.2 Type Reducer
			14.3.1.3 Deffuzzifier
		14.3.2 PFTIT2FLC Design for Coupled-Tank-Level Control System
	14.4 Simulation Results
		14.4.1 Tracking Response with System Component Faults
	14.5 Conclusion
	References
Chapter 15: Enhanced Isolated Boost DC-DC Converter with Reduced Number of Switches
	15.1 Introduction
		15.1.1 A Subsection Sample
	15.2 Operating Principle
		15.2.1 Operating Intervals of Proposed Converter
		15.2.2 Voltage Gain
	15.3 Simulation Results
	15.4 Conclusions
	References
Chapter 16: Harmonic Intensity Reduction Technique for Three Phase VSI Drive through Double Randomness
	16.1 Introduction
	16.2 Study of Harmonic Distribution in SPWM VSI Drive
	16.3 Proposed HIRDRPWM
	16.4 Simulation Study
	16.5 Conclusion
	References
Chapter 17: PV-Based Multilevel Inverter-Fed Three-Phase Induction Motor with Improved Time and Speed of Response
	17.1 Introduction
		17.1.1 Specification of the Systems
		17.1.2 Sizing of the Photovoltaic Panel
	17.2 Buck-Boost Converter
	17.3 Cascaded Multilevel Inverter
		17.3.1 Feedback Circuit
	17.4 Response of PI Controller
		17.4.1 PI Controller
	17.5 Proposed System with FOPID Controller and Time Response
		17.5.1 FOPID Controller
	17.6 Harmonic Reduction
		17.6.1 Comparison of Harmonics Parameter
	17.7 Simulation Results
		17.7.1 Closed Loop with PI Controller
		17.7.2 Closed Loop with FOPID Controller
	17.8 Comparison of Output Parameters
	17.9 Conclusion
	References
Chapter 18: Adaptive Disturbance Observers for Building Thermal Model
	18.1 Introduction
	18.2 Preliminaries
		18.2.1 Notation and Definitions
		18.2.2 Thermal Dynamics of the Air Conditioner
		18.2.3 Problem Definition
	18.3 Adaptive Disturbance Estimator
		18.3.1 Conditions for Observability of Disturbance Estimator
	18.4 Results
	18.5 Conclusions
	References
Chapter 19: HTSA Optimized PID-Based MPPT for Solar PV System
	19.1 Introduction
	19.2 System Investigation
		19.2.1 Boost Converter
	19.3 Controllers
		19.3.1 PID Controller
	19.4 Maximum Power Point Tracking (MPPT)
	19.5 Heat Transfer Search Algorithm (HTSA)
	19.6 Result and Discussion
	19.7 Conclusion
	References
Chapter 20: Performance Analysis of UFMC System with Different Prototype Filters for 5G Communication
	20.1 Introduction
	20.2 Literature Survey
	20.3 Proposed UFMC System with Prototype Filter
		20.3.1 Prototype Filter
		20.3.2 UFMC System Model
	20.4 Results and Discussion
		20.4.1 Parameters for Simulation
		20.4.2 Performance Analysis of the Hermite Filter
			20.4.2.1 PSD Evaluation
			20.4.2.2 BLER Analysis
			20.4.2.3 BER Analysis
			20.4.2.4 PAPR Analysis
		20.4.3 Performance Analysis of PHYDYAS Filter
			20.4.3.1 PSD Evaluation
			20.4.3.2 BLER Analysis
			20.4.3.3 BER Analysis
			20.4.3.4 PAPR Analysis
		20.4.4 Performance Analysis of Root-Raised-Cosine (RRC) Filter
			20.4.4.1 PSD Evaluation
			20.4.4.2 BLER Analysis
			20.4.4.3 BER Analysis
			20.4.4.4 PAPR Analysis
		20.4.5 Performance Analysis of Rectangular Filter
			20.4.5.1 PSD Evaluation
			20.4.5.2 BLER Analysis
			20.4.5.3 BER Analysis
			20.4.5.4 PAPR Analysis
	20.5 Conclusion
	References
Chapter 21: Fully Convolved Neural Network-Based Retinal Vessel Segmentation with Entropy Loss Function
	21.1 Introduction
	21.2 Proposed Method
		21.2.1 Overview
		21.2.2 Green Channel Extraction
		21.2.3 Local Normalization
		21.2.4 Contrast Limited Adaptive Histogram Equalization
		21.2.5 Convolutional Neural Network
		21.2.6 Cross-Entropy Loss Function
	21.3 Experiment Evaluation
		21.3.1 Dataset
		21.3.2 Evaluation Metric
	21.4 Result and Conclusion
		21.4.1 Result
	21.5 Conclusion
	References
Chapter 22: Solar Power Forecasting Using Adaptive Curve-Fitting Algorithm
	22.1 Introduction
	22.2 Proposed Algorithm: Adaptive Curve Fitting (ACF)
	22.3 Result
	22.4 Conclusions
	References
Chapter 23: A Review of Electric Vehicle Technologies
	23.1 Introduction
	23.2 Classification of Vehicles
		23.2.1 Internal Combustion Engine Vehicles (ICEVs)
		23.2.2 Hybrid Electric Vehicles (HEV)
		23.2.3 All-Electric Vehicles
	23.3 Electric Propulsion System
	23.4 Energy Storage System
		23.4.1 Battery
		23.4.2 Ultracapacitor
		23.4.3 Flywheel
	23.5 Energy Management
	23.6 Conclusion
	References
Chapter 24: Gabor Filter-Based Tonsillitis Analysis Using VHDL
	24.1 Introduction
	24.2 Related Work
		24.2.1 Region-Based Segmentation
		24.2.2 Threshold-Based Segmentation
		24.2.3 Cluster-Based Segmentation
		24.2.4 Filter-Based Segmentation
	24.3 Proposed Image Segmentation Using Gabor Filter and CORDIC Algorithm
	24.4 Simulated Results and Discussions
		24.4.1 Results Obtained in MATLAB
		24.4.2 Results Obtained Using VHDL
	24.5 Conclusion
	References
Chapter 25: Incorporation of Modified Second-Order Adaptive Filter in MFGCI for Harmonic Mitigation of Microgrid
	25.1 Introduction
	25.2 SSS Configured MFGCI
	25.3 Modified Second-Order Adaptive Filter
	25.4 Simulation Results
	25.5 Conclusion
	References
Chapter 26: Optimal DAU Placement for Smart Distribution Grid Communication Network
	26.1 Introduction
	26.2 Clustering Smart Devices
	26.3 System Model
	26.4 Improved K-Means Algorithm
	26.5 Conclusion
	References
Chapter 27: Long-Term Forecasting of Hybrid Renewable Energy Potential Using Weibull Distribution Method in Coimbatore
	27.1 Introduction
	27.2 Renewable Energy Potential of Coimbatore
		27.2.1 Scenario of Coimbatore
		27.2.2 Characteristics of Wind
		27.2.3 Characteristics of Solar Energy
	27.3 Weibull Distribution Method
		27.3.1 Energy Pattern Factor Method
		27.3.2 Description of Data Set
	27.4 Result and Discussions
		27.4.1 Wind Speed Prediction
		27.4.2 Prediction of Solar Irradiance
	27.5 Conclusion
	References
Chapter 28: Efficient and Improved ANN-Based Voltage-Rise Mitigation Strategy in Distribution Network with Distributed Solar P...
	28.1 Introduction
	28.2 System Model
	28.3 Proposed Control Algorithm
	28.4 Simulation Results
	28.5 Conclusions
	References
Chapter 29: Control of Buck Converter by Fuzzy Controller for Wind Energy: Battery System
	29.1 Introduction
	29.2 Overview of the Proposed System
	29.3 Buck Converter
	29.4 Fuzzy Logic Controller (FLC)
	29.5 Simulation Block Diagram
	29.6 Simulation Results
	29.7 Conclusion
	References
Chapter 30: A Survey on Secure Beamforming in MIMO-NOMA-Based Cognitive Radio Network
	30.1 Introduction
	30.2 Study of Securing MIMO NOMA in 5G Technology
		30.2.1 Defence for MIMO-NOMA-Based CRN
		30.2.2 A General Power Allocation Scheme for NOMA
		30.2.3 Enhancement of NOMA´s Physical Layer Security in Large-Scale Networks
		30.2.4 Secrecy Capability Maximization
		30.2.5 Physical Layer Security of Cooperative NOMA Systems
		30.2.6 Secure Beamforming in MIMO-NOMA
	30.3 Future Guidance to Secure NOMA
	30.4 Conclusion
	References
Chapter 31: Hybrid Optimization of Cuckoo Search and Differential Evolution Algorithm for Privacy-Preserving Data Mining
	31.1 Introduction
	31.2 Related Work
	31.3 Methodology
		31.3.1 The Adult Dataset
		31.3.2 k-anonymity
		31.3.3 Cuckoo Search (CS)
		31.3.4 Differential Evolution
	31.4 Results and Discussion
	31.5 Conclusion
	References
Chapter 32: Using Sliding Window Algorithm for Rainfall Forecasting
	32.1 Introduction
	32.2 Proposed Work
		32.2.1 Methodology
		32.2.2 Sliding Window
		32.2.3 Statistical Measures
			32.2.3.1 Mean
			32.2.3.2 Variation
			32.2.3.3 Jaccard distance
			32.2.3.4 Root-Mean-Square Error
		32.2.4 Sliding Window Algorithm
		32.2.5 Dataset Description
	32.3 Results and Discussion
	32.4 Conclusion
	References
Chapter 33: Air Pollution-Level Estimation in Smart Cities Using Machine Learning Algorithms
	33.1 Introduction
	33.2 Literature Survey
	33.3 Proposed Model
		33.3.1 Classification of Air Quality Level
			33.3.1.1 K-Nearest Neighbours
			33.3.1.2 Random Decision Forest
			33.3.1.3 Support Vector Machine
	33.4 Experimental Analysis
		33.4.1 Study Area
		33.4.2 Performance Metrics
	33.5 Conclusion
	References
Chapter 34: Implicit Continuous User Authentication Using Swipe Actions on Mobile Touch Screen with ANN Classifier
	34.1 Introduction
	34.2 Related Works
	34.3 Materials and Methods
	34.4 Feature Explanation
		34.4.1 x value, y value, and z value
		34.4.2 Size
		34.4.3 Raw x, raw y
		34.4.4 Touch Major, Touch Minor
		34.4.5 x, y
		34.4.6 x velocity, y velocity, and Distance
	34.5 Results and Discussion
	34.6 Conclusion
	References
Chapter 35: A Review on Graph Analytics-Based Approaches in Protein-Protein Interaction Network
	35.1 Introduction
	35.2 Essential Protein Identification
	35.3 Computational Methods
		35.3.1 Degree Centrality
		35.3.2 Betweenness Centrality
		35.3.3 Closeness Centrality
		35.3.4 Eigen Vector Centrality
		35.3.5 Modularity and Community Structure in Networks
		35.3.6 Centrality and Modularity
	35.4 Protocol
	35.5 Conclusion
	References
Chapter 36: A Survey on Emotion Detection Using EEG Signals
	36.1 Introduction
	36.2 Related Work
	36.3 Comparison Study
		36.3.1 Comparison of Physiological Signals to Detect Emotions
		36.3.2 Comparison of Methods to Extract Emotions from EEG
	36.4 Conclusion
	References
Chapter 37: A Smart Agricultural Model Using IoT, Mobile, and Cloud-Based Predictive Data Analytics
	37.1 Introduction
	37.2 Literature Review
	37.3 Proposed Methodology
	37.4 Conclusion
	References
Chapter 38: Machine Translation Using Deep Learning: A Comparison
	38.1 Introduction
	38.2 Various Approaches for Machine Translation
		38.2.1 Rule-Based Machine Translation
			38.2.1.1 Direct Machine Translation
			38.2.1.2 Interlingual Machine Translation
			38.2.1.3 Transfer-Based Machine Translation
		38.2.2 Corpus-Based Machine Translation
			38.2.2.1 Statistical Machine Translation
			38.2.2.2 Example-Based Machine Translation
		38.2.3 Neural Machine Translation
	38.3 Comparison Among the Different Machine Translation Techniques
	38.4 Evaluation Metrics
		38.4.1 Bleu Score
		38.4.2 NIST Score
		38.4.3 Translation Error Rate
	38.5 Conclusion
	References
Chapter 39: Societal Impact of Framework for Energy-Efficient Clustering Algorithms in Mobile Wireless Sensor Networks
	39.1 Introduction
	39.2 Challenges in Real-Time MWSN Applications
		39.2.1 Localization
		39.2.2 Network Coverage
		39.2.3 Dynamic Network Topology
		39.2.4 Energy Consumption
		39.2.5 Mobility of Sensor Nodes and Sink
	39.3 FEECA: Framework for Energy-Efficient Clustering Algorithms in MWSN
	39.4 Services Offered by FEECA
		39.4.1 Single Area-Based Services
		39.4.2 Grid Area-Based Services
		39.4.3 High Density Area-Based Services
	39.5 Societal Impact of FEECA
		39.5.1 Single Area-Based Applications Served by FEECA
			39.5.1.1 Health Monitoring
			39.5.1.2 Vehicle Monitoring
			39.5.1.3 Animal Monitoring
			39.5.1.4 Machine Monitoring
		39.5.2 Grid Area-Based Applications Served by FEECA
			39.5.2.1 Environmental Monitoring
			39.5.2.2 Patient Monitoring
			39.5.2.3 Disaster Monitoring
			39.5.2.4 Structural Monitoring
		39.5.3 High Density Area-Based Applications Served by FEECA
			39.5.3.1 Traffic Monitoring
			39.5.3.2 Accident Monitoring
			39.5.3.3 Undersea Monitoring
			39.5.3.4 Forest Monitoring
	39.6 Conclusion and Future Work
	References
Chapter 40: Energy Demand Prediction Using Linear Regression
	40.1 Introduction
	40.2 Literature Survey
	40.3 Sources of Energy Big Data
	40.4 Visualization Process
	40.5 Linear Regression Predictive Modeling
	40.6 Results
	40.7 Conclusion and Future Works
	References
Chapter 41: Risk Prediction Analysis of Cardiovascular Disease Using Supervised Machine Learning Techniques
	41.1 Introduction
	41.2 Literature Survey
	41.3 Proposed System
		41.3.1 Dataset
		41.3.2 Preprocessing
		41.3.3 Classification
		41.3.4 EDC-AIRS
		41.3.5 Decision Tree (DT)
		41.3.6 Support Vector Machine (SVM)
	41.4 Experimental Results
	41.5 Conclusion
	References
Chapter 42: Safest Secure and Consistent Data Services in the Storage of Cloud Computing
	42.1 Introduction
	42.2 Related Works
		42.2.1 Problem and System Model Definitions
	42.3 Overview
		42.3.1 Design Objective
		42.3.2 Preliminaries and Notations (Table 42.1)
	42.4 Extension to Data Storage Security
	42.5 Experimental Results
	42.6 Conclusion
	References
Chapter 43: Agile Supply Chain Management Enabled by the Internet of Things and Microservices
	43.1 Introduction
	43.2 Background
	43.3 Empowering Supply Chains with IoT
	43.4 Agile Development with Microservices
	43.5 Microservices for IoT Applications
	43.6 Connecting the Dots: IoT-Enabled Supply Chain Solutions with Microservices
	43.7 Conclusion and Future Work
	References
Chapter 44: Production and Characterization of Bio-Fertilizers from Tree Leaves Utilizing an Automated Hot Composting Chamber ...
	44.1 Introduction
	44.2 Materials and Methods
		44.2.1 Sampling
		44.2.2 Design of Composting Chamber
		44.2.3 Physicochemical Parameters of Composting Materials
	44.3 Results and Discussion
		44.3.1 Moisture Content
		44.3.2 Influence of Temperature
		44.3.3 Electrical Conductivity
		44.3.4 pH Measurements
	44.4 Conclusions
	References
Chapter 45: Spectrum Sensing Based on Cascaded Approach for Cognitive Radios
	45.1 Introduction
	45.2 Related Work
	45.3 Detection Method
		45.3.1 Energy Detection
		45.3.2 Cyclic Prefix Autocorrelation Detection
		45.3.3 Fusion Scheme
	45.4 Hardware and Simulation Results
		45.4.1 Energy Detection Technique
		45.4.2 Autocorrelation-Based Detection Technique
		45.4.3 Performance of Energy Detection Technique and Autocorrelation Detection Technique
		45.4.4 Cascaded Approach
	45.5 Conclusion
	References
Chapter 46: Remote Process Monitoring and Control Through IIoT
	46.1 Introduction
	46.2 Internet of Things
		46.2.1 Conventional Closed-Loop Control
		46.2.2 Closed-Loop Control Through IIoT
			46.2.2.1 Process: Nonlinear CSTR
			46.2.2.2 Controller: CC3200
			46.2.2.3 Software Requirements: Energia, ThingSpeak Cloud, and MQTT
			46.2.2.4 Implementation of IoT-Based Closed-Loop Control
	46.3 Benefits in IoT-Based Control
	46.4 IoT for Process Data Analytics
	46.5 Conclusion
	References
Chapter 47: Case-Based Reasoning (CBR)-Based Smart Indoor Navigation
	47.1 Motivation
	47.2 Context of the Work
	47.3 Related Work
		47.3.1 Existing Work in the Area of Smart City
		47.3.2 Existing Work in the Area of Smart Indoor Navigation
		47.3.3 Existing Work in the Area of Case-Based Reasoning
	47.4 Lessons Learned
	47.5 Objective
	47.6 Case-Based Reasoning
	47.7 Synergy Between CBR and Smart Navigation System
		47.7.1 Representation of Cases in Case-Based Reasoning
	47.8 Conceptual Architecture
	47.9 Observations
	47.10 Sample Testing
	47.11 Discussion and Conclusion
	Appendix
	References
Chapter 48: A Survey on Medical Image Registration Using Deep Learning Techniques
	48.1 Introduction
	48.2 General Workflow of Image Registration (Fig. 48.1)
		48.2.1 Classification of Image Registration (Fig. 48.2)
	48.3 Overview of Convolutional Neural Networks
	48.4 Literature Survey
	48.5 Conclusion
	References
Chapter 49: Agent-Based Temperature Monitoring System
	49.1 Introduction
	49.2 Multi-agent Systems
	49.3 Characteristics of MAS
		49.3.1 Robust
		49.3.2 Accuracy
		49.3.3 Mobility
	49.4 Identification of Tool Support
		49.4.1 JADE
	49.5 Need for an Agent-Based Model
		49.5.1 Monitoring Agent
		49.5.2 Actuating Agent
		49.5.3 Controller Agent
		49.5.4 Network Layer
	49.6 Agent-Based Temperature Monitoring System
		49.6.1 Agent Creation Using JADE Framework
	49.7 Conclusion
	49.8 Future Work
	References
Chapter 50: Classification of Phonemes Using EEG
	50.1 Introduction
	50.2 Previous Work
	50.3 Dataset
	50.4 Methodology
		50.4.1 Pre-processing
		50.4.2 Feature Extraction
		50.4.3 Feature and Channel Selection
		50.4.4 Classification
	50.5 Experimental Results
	50.6 Conclusion
	References
Chapter 51: Attribute Table-Based Multipath Routing Protocol to Improve Network Lifetime in Multi-hop WSN
	51.1 Introduction
	51.2 Related Works
	51.3 Attribute Table-Based Energy-Efficient QoS Multipath Routing
		51.3.1 Block Diagram
		51.3.2 Attribute Table
		51.3.3 Optimized Pair Shortest Path Algorithm
		51.3.4 Network Design and Synchronization of Multiple Nodes
		51.3.5 Energy Consumption
		51.3.6 Data Collection Process
	51.4 Result and Discussion
		51.4.1 Data Delivery Fraction
	51.5 Conclusion
	References
Chapter 52: Application of Subjective and Objective Integrated Weightage (SOIW) Method for Decision-Making (MADM) in Distribut...
	52.1 Introduction
	52.2 Decision-Making Methods
		52.2.1 Multiple Attribute Decision-Making (MADM)
		52.2.2 Subjective and Objective Integrated Weightage MADM Method
	52.3 Implementation and Results
	52.4 Conclusion
	References
Chapter 53: Visual Importance Identification of Natural Images Using Location-Based Feature Selection Saliency Map
	53.1 Introduction
	53.2 Related Work
	53.3 Proposed Location-Based Visual Saliency Map (LBVSM)
		53.3.1 Visual Saliency Map Computation
		53.3.2 Transformation Function for Image Feature Selection
	53.4 Implementation Stages for Proposed Visual Saliency Method
	53.5 Proposed Saliency Map Efficiency
	53.6 Experimental Results
	53.7 Comparison of Results with the State-of-the-Art Methods
		53.7.1 Processing Time Comparison
		53.7.2 Evaluation Metrics Receiver Operating Characteristics (ROC)
	53.8 Conclusions
	53.9 Future Works
	References
Chapter 54: Missing Values and Class Prediction Based on Mutual Information and Supervised Similarity
	54.1 Introduction
	54.2 Related Works
	54.3 Proposed Method
		54.3.1 Mutual Information
		54.3.2 Supervised Similarity
		54.3.3 Cosine Similarity
		54.3.4 Class Prediction
	54.4 Implementation and Result
	54.5 Conclusion
	References
Chapter 55: Fake Product Review Detection and Removal Using Opinion Mining Through Machine Learning
	55.1 Introduction
	55.2 Related Work
	55.3 Methodology
		55.3.1 Implementation Process
	55.4 Machine Learning Algorithms
		55.4.1 Convolutional Neural Network
		55.4.2 Recurrent Neural Network (RNN)
	55.5 Detection Processes
	55.6 Experimental Results
	55.7 Conclusion
	References
Chapter 56: Ask Less: Scale Market Research Without Annoying Your Customers
	56.1 Introduction
	56.2 Proposed Approach
		56.2.1 Preparatory Phase
		56.2.2 Scaling Phase
	56.3 Results
	56.4 Conclusion
	References
Chapter 57: Preferential Resource Selection and Scheduling of Cloud Resources Pivot on Value of Information
	57.1 Introduction
	57.2 Related Work
	57.3 Standard Factors Affecting Resource Selection
	57.4 Proposed Workflow
	57.5 Resource Selection Algorithm
	57.6 Resource Selection Algorithm
	57.7 Experimental Studies
	57.8 Experimental Studies
	References
Chapter 58: A Survey on Supervised and Unsupervised Learning Techniques
	58.1 Introduction (Fig. 58.1)
	58.2 Literature Survey
		58.2.1 Supervised Learning
			58.2.1.1 SVM
			58.2.1.2 Decision Tree
			58.2.1.3 Naïve Bayes
			58.2.1.4 KNN
			58.2.1.5 Regression
		58.2.2 Unsupervised Learning
			58.2.2.1 K-means
			58.2.2.2 Agglomerative
			58.2.2.3 Divisive
			58.2.2.4 Neural Network
	58.3 Conclusion
	References
Chapter 59: Performance Study of IPv6/IPv4 MANET (64MANET) Architecture
	59.1 Introduction
	59.2 Review of Literature Review
	59.3 Proposed 64MANET Architecture
	59.4 Performance Study of 64MANET
		59.4.1 Simulation Setup
		59.4.2 Simulation Scenario
		59.4.3 Simulation Results and Performance Analysis
			59.4.3.1 Data Loss in Byte-Per-Rate (ByER) Study
			59.4.3.2 Max Payload Overhead Data Rate Study
			59.4.3.3 Throughput Study
			59.4.3.4 Latency Study
			59.4.3.5 Response Time Study
			59.4.3.6 End-to-End Delay Study
			59.4.3.7 Packet Delivery Ratio (PDR) Study
			59.4.3.8 Handover Latency Study
	59.5 Conclusion and Future Work
	References
Chapter 60: Internet of Things: A Technical Perspective Survey
	60.1 Introduction
	60.2 Architectural Building Blocks
	60.3 Enabling Technologies
	60.4 Application Domains
		60.4.1 Smart Home and Building
		60.4.2 Smart Grid
		60.4.3 Smart City
		60.4.4 Healthcare
	60.5 Challenges and Barriers
	60.6 Conclusion
	References
Chapter 61: Analysis on DGHV and NTRU Fully Homomorphic Encryption Schemes
	61.1 Introduction
	61.2 Literature Survey
	61.3 Homomorphic Encryption
		61.3.1 Homomorphic Scheme
		61.3.2 Homomorphic Properties
			61.3.2.1 Addition
			61.3.2.2 Multiplication
	61.4 DGHV Scheme
		61.4.1 Symmetric Scheme
			61.4.1.1 Homomorphic Behavior
		61.4.2 Asymmetric Scheme
			61.4.2.1 Key Generation
			61.4.2.2 Encryption
			61.4.2.3 Decryption
	61.5 NTRU Scheme
		61.5.1 NTRU
		61.5.2 Key Generation
		61.5.3 NTRU Encryption
		61.5.4 NTRU Decryption
		61.5.5 Homomorphic Property of NTRU
	61.6 Conclusion
	References
Chapter 62: Automated Image Captioning for Flickr8K Dataset
	62.1 Introduction
	62.2 Convolutional Neural Networks
		62.2.1 Working of CNN
		62.2.2 Feature Extraction
		62.2.3 Classification
		62.2.4 Fine-Tuning of Data
	62.3 Recurrent Neural Network
		62.3.1 Training of RNN
	62.4 Region-Based CNN
		62.4.1 Segmentation
		62.4.2 Selective Search
		62.4.3 Generating Captions
	62.5 Conclusion
	References
Chapter 63: RAkEL Algorithm and Mahalanobis Distance-Based Intrusion Detection System Against Network Intrusions
	63.1 Introduction
	63.2 Related Work
	63.3 Proposed Methodology
		63.3.1 Dataset Collection
		63.3.2 Pre-processing and Feature Extraction
		63.3.3 Feature Ranking and Feature Selection
		63.3.4 Rules Generation
		63.3.5 Attack Detection and Classification
	63.4 Experimental Results and Analysis
	63.5 Conclusion and Future Work
	References
Chapter 64: Vaguely Node Classification Scheme for Wireless Networks to Design an Intrusion Detection System
	64.1 Introduction
	64.2 Related Work
	64.3 Vaguely Node Classification Scheme
		64.3.1 System Model
		64.3.2 Proposed Scheme for the Classification of Vaguely Nodes
	64.4 Simulation Environment
	64.5 Simulation Results and Discussion
	64.6 Conclusion
	References
Chapter 65: Dynamic Traffic Light Scheduling for Emergency Vehicles Using Fog Computing
	65.1 Introduction
	65.2 Related Works
	65.3 Proposed Work
		65.3.1 XBee Module S2C
		65.3.2 Arduino
	65.4 Working Model
		65.4.1 Function at Emergency Vehicle
		65.4.2 Function at Traffic Pole
		65.4.3 SD Card Module
	65.5 Implementation Details
	65.6 Conclusion
	References
Chapter 66: Cloud Database - A Technical Review
	66.1 Introduction
	66.2 Cloud Models
		66.2.1 Deployment Model
		66.2.2 Service Model
	66.3 Enabling Technologies
	66.4 Architecture of Cloud
	66.5 Cloud Database
	66.6 Rationale and Significance of the Cloud Databases Study
		66.6.1 Cloud DBMS Wish List
		66.6.2 Cloud Service Provider Challenges
	66.7 Database Infrastructures as a Service
		66.7.1 DbaaS
		66.7.2 Query Optimization in Cloud Databases
	66.8 Cloud Database - Challenges and Issues
		66.8.1 Open Research Issues
	66.9 Conclusion
	References
Chapter 67: Projection of Population and Prediction of Food Demand Through Mining and Forecasting Techniques
	67.1 Introduction
	67.2 Related Work
		67.2.1 Taylor Series
		67.2.2 Maximum Likelihood Estimation (MLE)
		67.2.3 Gaussian Distribution
	67.3 Proposed Method
		67.3.1 Population Estimation
		67.3.2 Food Requisite Distributions
	67.4 Results and Discussion
	67.5 Conclusion
	References
Chapter 68: Detection of Hairline Fracture Foot Using Canny Operator and Wavelet Packet Transform
	68.1 Introduction
	68.2 Materials and Methods
	68.3 Results and Discussions
	68.4 Conclusion
	References
Chapter 69: Image Encryption-Then-Compression System for Secure Transmission via Hybrid Henon Chaotic Map
	69.1 Introduction
	69.2 Related Works
	69.3 Proposed Methodology
		69.3.1 Image Encryption
			69.3.1.1 Confusion
			69.3.1.2 Shuffling
			69.3.1.3 Diffusion
		69.3.2 Compression using Asymmetric Numeral Method
	69.4 Experimental Analysis
		69.4.1 Number of Pixel Change Rate (NPCR)
		69.4.2 Unified Average Changing Intensity (UACI)
		69.4.3 Computation Time
	69.5 Conclusion
	References
Chapter 70: Analysis of Primary Emulsion Attack in Cognitive Radio Using Distributed On-Demand Routing Protocol
	70.1 Introduction
	70.2 Previous Works
	70.3 Proposed Work
	70.4 Implementation
		70.4.1 Initial Setup
		70.4.2 Distributed On-Demand Routing Protocol
		70.4.3 RSA Algorithm
	70.5 Comparisons
	70.6 Conclusions
	References
Chapter 71: Heart Disease Prediction Using Retinal Fundus Image
	71.1 Introduction
	71.2 Related Work
	71.3 Methodology
		71.3.1 Pre-processing Techniques
		71.3.2 Segmentation Techniques
		71.3.3 Edge Detection Techniques
	71.4 Implementation
		71.4.1 Segmentation
		71.4.2 Edge Detection
		71.4.3 Calculating of A/V Ratio
	71.5 Result
	71.6 Conclusion
	References
Chapter 72: Blind Speech Enhancement Using Adaptive Algorithms
	72.1 Introduction
	72.2 Adaptive Algorithms for Blind Speech Enhancement
		72.2.1 Least Mean Square
		72.2.2 Normalized Least Mean Square
		72.2.3 Dual Fast Normalized Least Mean Square (DFNLMS)
	72.3 Analysis of Simulation Results
		72.3.1 Segmental SNR (SegSNR) Evaluation
		72.3.2 Segmental MSE (SegMSE) Evaluation
	72.4 Conclusion
	References
Chapter 73: Android Malware Detection
	73.1 Introduction
	73.2 Literature Survey
	73.3 Proposed Work
		73.3.1 Dataset Collection
		73.3.2 Methodologies
	73.4 Result Analysis
	73.5 Conclusion
	References
Chapter 74: Test Data Compression Methods: A Review
	74.1 Introduction
	74.2 Test Data Compression Strategies
	74.3 Linear Decompression-Based Schemes
		74.3.1 Combinational Decompressors
		74.3.2 Sequential Decompressor
		74.3.3 Combined Linear and Nonlinear Decompressor
	74.4 Code-Based Test Data Compression Techniques
		74.4.1 Dictionary-Based Code
		74.4.2 Huffman Code
		74.4.3 Run-Length Code
	74.5 Broadcast-Scan-Based Schemes
	74.6 Other Compression Techniques and Classification by DFT Vendors
	74.7 Conclusion
	References
Chapter 75: Piripori: Morphological Analyser for Tamil
	75.1 Introduction
	75.2 Literature Review
	75.3 Foundation of Piripori
		75.3.1 Patterns
		75.3.2 Rules
		75.3.3 Exceptions
	75.4 Architecture of Piripori
	75.5 Results and Discussion
		75.5.1 Analysis Time
		75.5.2 Precision and Recall
	75.6 Conclusion
	References
Chapter 76: A Comprehensive Survey on Strategies in Multicore Architectures, Design Considerations and Challenges
	76.1 Introduction
	76.2 Challenges and Issues in Multicore Architecture
	76.3 Design Models of Multicore Processors
	76.4 Conclusion
	References
Chapter 77: Blockchain-based e-Voting as a Service
	77.1 Introduction
	77.2 Blockchain Fundamentals
		77.2.1 Blockchain Applications
			77.2.1.1 Cost Reduction
			77.2.1.2 Enhancement of Accountability and Transparency
			77.2.1.3 Service Delivery Improvement and Facilitation of e-Society
	77.3 Characteristics of Voting
		77.3.1 Accessibility
		77.3.2 Accuracy
		77.3.3 Privacy
		77.3.4 Mobility
	77.4 Synergy Between Blockchain and e-Voting
		77.4.1 Confidentiality and Integrity
		77.4.2 Auditability and Accuracy
		77.4.3 Privacy
		77.4.4 Authentication
		77.4.5 Mobility of Ballots and Voters
	77.5 Agent-Based Model and Its Need
		77.5.1 Registration Agent
		77.5.2 Authentication Agent
		77.5.3 Authorization Agent
		77.5.4 Monitoring Agent
		77.5.5 Alert Agent
	77.6 The Need for Blockchain-Based e-Voting and Its Advantages
		77.6.1 Distributed Denial-of-Service (DDoS) Attack
		77.6.2 Man-in-the-Middle Attack
	77.7 Conclusion
	References
Chapter 78: Optimum Resource Allocation Techniques for Enhancing Quality of Service Parameters in Cloud Environment
	78.1 Introduction
	78.2 Related Works
	78.3 Proposed Works
		78.3.1 Multi-Agent-Based Dynamic Resource Allocation
		78.3.2 Multistage Framework for Improving QoS in Resource Allocation
		78.3.3 Artificial Immune System-Directed Acyclic Graph Model-Based Resource Allocation
	78.4 Experimental Results and Discussion
	78.5 Conclusion
	References
Chapter 79: MIPGIOT: Monitoring and Improving the Productivity in Garment Unit Using IOT
	79.1 Introduction
	79.2 Literature Survey
	79.3 MIPGIOT: The Proposed Methodology for Measuring and Improving the Productivity in Garment Unit
		79.3.1 MIPGIOT: Proposed Idea
	79.4 Implementation of the Proposed Method
		79.4.1 MIPGIOT: Implementation Process
		79.4.2 MIPGIOT: Results Achieved
	79.5 Conclusion
	References
Chapter 80: Text and Audio Transfer Using LI-FI Technology
	80.1 Introduction
		80.1.1 Light Fidelity
		80.1.2 Problem Definition
		80.1.3 Objective
	80.2 Literature Review
		80.2.1 Drawbacks of Wi-Fi
	80.3 System Design
		80.3.1 Working of Data Transfer
		80.3.2 Working of Transmitter
		80.3.3 Working of Receiver
	80.4 Software Implementation
		80.4.1 Modules Description
		80.4.2 Home Page
		80.4.3 Transmitting of Binary Data
		80.4.4 Receiving the Data
	80.5 Audio Streaming Through Li-Fi
		80.5.1 Working of Audio Transfer
	80.6 Implementation
		80.6.1 Binary Data Transfer Through Li-Fi
		80.6.2 Audio Transfer Through Li-Fi
	80.7 Results and Discussion
	80.8 Conclusion and Future Work
	References
Chapter 81: Hyperspectral Image Segmentation Using Evolutionary Multifactorial Spectral Analysis for OMEGA Dataset
	81.1 Introduction
	81.2 Related Work
	81.3 Proposed Model of Evolutionary Multifactorial Spectral Analysis (ESA)
		81.3.1 A Subsection Sample Selection of Spectral Features: Mineral Selection for Classification
		81.3.2 Classification of the Hyperspectral Features
	81.4 Experimental Analysis
	81.5 Conclusion and Future Scope
	References
Chapter 82: Novel Lifting Filter Bank for Bird Call Analysis
	82.1 Classification of Lifting Techniques
	82.2 Cohen-Daubaches-Feauveau (CDF) Wavelet Filter
		82.2.1 Filters Used for Bird Call Analysis
		82.2.2 Lifting Scheme Algorithm
			82.2.2.1 Algorithm for First-Level Decomposition (Fig. 82.4)
		82.2.3 Bird Call Analysis
	82.3 Conclusion
	References
Chapter 83: Automatic Classification of Solid Waste Using Deep Learning
	83.1 Introduction
		83.1.1 Objective
		83.1.2 Scope of the Proposed Methodology
	83.2 Related Work
	83.3 Methodology
		83.3.1 Dataset Collection
		83.3.2 Processing Datasets
		83.3.3 Types of Wastes
	83.4 Implementation
		83.4.1 Algorithm Used: Convolution Neural Network
		83.4.2 Steps Involved
		83.4.3 Flow Chart (Fig. 83.3)
		83.4.4 Model Development
	83.5 Experimental Results
	83.6 Conclusion
	References
Chapter 84: Relevancy and Similarity Aware Drug Comment Classification Framework on Social Media Drug Posts
	84.1 Introduction
	84.2 Related Works
	84.3 Relevancy and Similarity Aware Drug Comment Classification Framework
	84.4 Experimental Results
	84.5 Conclusion
	References
Chapter 85: Enhanced Particle Swarm Optimization with Genetic Algorithm and Modified Artificial Neural Network for Efficient F...
	85.1 Introduction
	85.2 Literature Survey
	85.3 Proposed Methodology
		85.3.1 Dataset Collection
		85.3.2 Fuzzy C Means (FCM) Clustering Algorithm
		85.3.3 Enhanced Particle Swarm Optimization with Genetic Algorithm Based Attribute Selection
		85.3.4 Modified Artificial Neural Network (ANN)
	85.4 Experimental Results
	85.5 Conclusion
	References
Chapter 86: Feature Selection Techniques for Email Spam Classification: A Survey
	86.1 Introduction
		86.1.1 Filter Method
		86.1.2 Wrapper Method
		86.1.3 Embedded Method
	86.2 Overview of Email Spam Detection
		86.2.1 Dataset
		86.2.2 Evolutionary Algorithms
		86.2.3 Classification Algorithms
	86.3 Related Works
	86.4 Conclusion
	References
Chapter 87: A Novel Paradigm Towards Exploration of Rechargeable WSN Through Deep Learning Architecture for Prolonging Network...
	87.1 Introduction
		87.1.1 A Subsection Sample
	87.2 Related Works
		87.2.1 Distributed Cooperative Relaying Low Energy Adaptive Clustering Hierarchy Protocol
		87.2.2 Bayesian Compressive Data Gathering Protocol
	87.3 Proposed Model
		87.3.1 Network Model
		87.3.2 Mobile Sink
		87.3.3 Dynamic Source Routing
		87.3.4 Deep Belief Network
	87.4 Simulation Results
		87.4.1 Throughput
		87.4.2 Routing Overhead
	87.5 Conclusion
	References
Chapter 88: Why Feature Selection in Data Mining Is Prominent? A Survey
	88.1 Introduction
	88.2 Importance of Feature Selection
	88.3 Defining Relevancy and Redundancy of Features
		88.3.1 Feature Relevancy
		88.3.2 Feature Redundancy
	88.4 Literature Survey on Feature Selection Techniques in Various Applications
		88.4.1 Literature Survey on Feature Selection in the Year 2015
		88.4.2 Literature Survey on Feature Selection in the Year 2016
		88.4.3 Literature Survey on Feature Selection in the Year 2017
		88.4.4 Literature Survey on Feature Selection in the Year 2018
	88.5 Outcome of the Literature Survey
	88.6 Research Directions
	88.7 Conclusion
	References