Big Data Analytics and Intelligent Techniques for Smart Cities

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Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgments
Editors
Contributors
Chapter 1 Big Data for Smart Education
	1.1 Introduction
		1.1.1 Big Data (BD)
			1.1.1.1 Big Data Analytics (BDA)
		1.1.2 Big Data Architecture Aimed at Learning Analytics
		1.1.3 Role of Big Data in Smart Education
	1.2 Fruition of Smart Learning
		1.2.1 Insight of Smart Learning
		1.2.2 Smart Learning Environment
		1.2.3 Denotation of Smart in Smart Learning
		1.2.4 Smart Learner
	1.3 Framework of Smart Education
		1.3.1 Smart Pedagogy
		1.3.2 Smart Learning Environments
		1.3.3 Technical Architecture of a Smart Education Environment
		1.3.4 3-Tier Architecture of Smart Computing
		1.3.5 Key Function of Smart Computing
	1.4 Big Data Subsequent Revolution in Education
		1.4.1 Big Data is Making Education Smarter
	1.5 Technique Educators Are Improving in Learning Process
		1.5.1 Measure, Monitor, and Respond
		1.5.2 Epitomize Learning Experience
		1.5.3 Designing New Courses
	1.6 Big Data Components in Smart Education
	1.7 Big Data Tools in Smart Education
	1.8 Big Data Applications for Smart Education
		1.8.1 Higher Education Analysis
		1.8.2 Student Engagement
		1.8.3 Bookstore Effectiveness
	1.9 How BD and Education Could Work Together to Benefit Student Success
		1.9.1 Customized Curricula Aimed at Improved Learning Outcome
		1.9.2 Big Data to Expand Student’s Performance
		1.9.3 New Paths of Learning Potentials
	1.10 Big Data Analytics Consequensces in Advanced Education
	1.11 Opportunities along with Challenges of Big Data in Smart Education
	1.12 Conclusion: Challenge of Simplifying Smart Education
	References
Chapter 2 Big Data Analytics Using R for Offline Voltage Prediction in an Electric Power System
	2.1 Introduction
	2.2 Data Analytics
	2.3 The Process Flow
	2.4 R - As a Programming Language
		2.4.1 R-STUDIO Description
		2.4.2 Need for R-Studio
	2.5 Analytics Functions Using R
		2.5.1 Linear Regression in R
		2.5.2 Understanding the Summary of the Model
	2.6 Proposed Methodology
		2.6.1 The Standard IEEE Test System with 6 Buses
		2.6.2 Formulation of Voltage Prediction for Load Bus 4
		2.6.3 Formulation of Voltage Prediction for Load Bus 5
		2.6.4 Formulation of Voltage Prediction for Load Bus 6
	2.7 Analysis of Results
	2.8 Conclusion
	References
Chapter 3 Intelligent Face Recognition Based on Regularized Robust Coding with Deep Learning Process
	3.1 Introduction
	3.2 Concepts Used in Existing Methods
		3.2.1 The Modeling of Regularized Robust Coding (RRC)
		3.2.2 RRC via Iteratively Reweighting
		3.2.3 The Weights W
		3.2.4 Two Significant Cases in RRC
	3.3 Proposed Method
		3.3.1 Algorithm of Regularized Robust Coding
		3.3.2 The Convergence of IR[sup(3)]C
		3.3.3 Complexity Analysis of the Proposed Algorithm
	3.4 Experimental Results
		3.4.1 Calculation of Parameters
		3.4.2 Face Recognition without Occlusion
			3.4.2.1 Extended Yale B Database
			3.4.2.2 AR Database
			3.4.2.3 Multi PIE Database
		3.4.3 Face Recognition with Occlusion
			3.4.3.1 Face Recognition with Pixel Corruption
			3.4.3.2 Face Recognition with Block Occlusion
			3.4.3.3 Face Recognition with Real Face Disguise
		3.4.4 Face Validation
		3.4.5 Running Time Comparison
		3.4.6 Parameter Discussion
	3.5 Conclusion
	References
Chapter 4 Big Data Analysis, Interpretation, and Management for Secured Smart Health Care
	4.1 Introduction
	4.2 5 V’s of Big Data in Health Care Systems
		4.2.1 Volume
		4.2.2 Variety
		4.2.3 Velocity
		4.2.4 Veracity
		4.2.5 Value
	4.3 Successful Related Works
	4.4 Impact of Big Data in Health Care
		4.4.1 Right Living
		4.4.2 Right Care
		4.4.3 Right Provider
		4.4.4 Right Innovation
		4.4.5 Right Value
	4.5 Applications of Big Data
	4.6 Big Data Sources for Health Care
	4.7 Mining Using Big Data
	4.8 Big Data Analytics Drawbacks in Health Care Sectors
	4.9 Privacy and Security in Big Data
		4.9.1 Security in Health Care
		4.9.2 Life Cycle of Big Data Security
			4.9.2.1 Collection of Data
			4.9.2.2 Transformation
			4.9.2.3 Data Modeling
			4.9.2.4 Knowledge Creation
		4.9.3 Technologies to Provide Security and Privacy
			4.9.3.1 Authentication
		4.9.4 Privacy of Big Data in the Health Care Industry
	4.10 Conclusions
	References
Chapter 5 Big Data Handling for Smart Healthcare System: A Brief Review and Future Directions
	5.1 Introduction
	5.2 Artificial Intelligence in the Healthcare Sector
		5.2.1 Deep Learning in Smart Healthcare Applications
		5.2.2 Machine Learning in Smart Healthcare
		5.2.3 Application of Machine Learning in the Fields of Healthcare
			5.2.3.1 Cardiology
			5.2.3.2 Oncology
			5.2.3.3 Hematology
			5.2.3.4 Machine Learning and Patients in the intensive care unit
	5.3 Internet of Things and the Healthcare System
		5.3.1 IoT in the Field of Smart Healthcare
		5.3.2 Wearable Sensors and Central Nodes
			5.3.2.1 Pulse Sensors
			5.3.2.2 Respiratory Rate Sensor
			5.3.2.3 Body Temperature Sensor
			5.3.2.4 Blood Pressure Sensor
			5.3.2.5 Pulse Oximeter
		5.3.3 Communication Standards in IoT-Based Healthcare System
			5.3.3.1 Short-Range Communication
			5.3.3.2 Long-Range Communication
		5.3.4 Challenges Faced by IoT
			5.3.4.1 Challenge in Architecture
			5.3.4.2 Hardware and Technical Challenge
			5.3.4.3 Security Challenges
	5.4 Storage and Privacy of Medical Data
		5.4.1 IoT-Based Storage Systems
		5.4.2 Blockchain Technology for Data Security
		5.4.3 Other Associated Technologies
		5.4.4 Wireless Body Area Networks (WBANs)
		5.4.5 Models for Assessment and Security Threats
		5.4.6 Enabling Security Using Fog Computing
	5.5 Smart Healthcare: Big Data and Cloud Storage
		5.5.1 Safe Data Storage
		5.5.2 Mobile Devices and Cloud Computing
		5.5.3 5G Smart Systems in Healthcare Units
		5.5.4 Healthcare Cyber-Physical Systems
		5.5.5 Multi-structured Patient Data
		5.5.6 Wearable Healthcare Systems
		5.5.7 Data Integration and Sharing
	5.6 Conclusions
	References
Chapter 6 Big Data Analysis for Smart Energy System: An Overview and Future Directions
	6.1 Introduction
	6.2 Big Data Applications in Energy and Price Forecasting
	6.3 Big Data in Smart Grids
	6.4 Role of BDA in Smart Cities and Smart Meters
	6.5 Miscellaneous
	6.6 Conclusions
	References
Chapter 7 Optimum Placement of Multiple Distributed Generators in Distribution Systems for Loss Mitigation Considering Load Growth
	7.1 Introduction
	7.2 Problem Formulation
		7.2.1 Assumption Made for Multiple DG Placement
	7.3 Proposed Methodology
		7.3.1 SFLA (Shuffled Frog Leaping Algorithm)
		7.3.2 Load Flow Algorithm
		7.3.3 Algorithm for SFLA Approach
		7.3.4 Computational Procedure for Multiple DG Placement
		7.3.5 Voltage Stability Index and Load Growth Analysis
	7.4 Results and Discussions
		7.4.1 Test System
		7.4.2 Assumptions
	7.5 Conclusion
	References
Chapter 8 Big Data for Smart Energy
	8.1 Introduction
	8.2 Smart Energy
		8.2.1 What Makes a Grid “Smart?”
		8.2.2 What does a Smart Grid do?
			8.2.2.1 Smart Appliances
			8.2.2.2 Smart Meters
			8.2.2.3 Synchrophasors
		8.2.3 Smart Grid Applications
	8.3 Internet of Things (IoT)
		8.3.1 IoT Gateway
	8.4 IoT Gateway Feature Set
	8.5 Cloud Computing
		8.5.1 Cloud Applications for Energy Management
	8.6 Big Data
		8.6.1 Relationship with Cloud Computing and Big Data
		8.6.2 Relationship between IoT and Big Data
		8.6.3 Data-Driven Smart Energy Management
	8.7 Conclusion
	References
Chapter 9 An Intelligent Security Framework for Cyber-Physical Systems in Smart City
	9.1 Introduction
	9.2 Literature Review
	9.3 Proposed Method
	9.4 Data Set Description
		9.4.1 Data Preprocessing
	9.5 Experimental Setup and Result Analysis
		9.5.1 Evaluation Criteria
		9.5.2 Parameter Setting
		9.5.3 Result Analysis
	9.6 Conclusion
	References
Chapter 10 Big Data and Its Application in Smart Education during the COVID-19 Pandemic Situation
	10.1 Introduction
	10.2 Effect of COVID-19 on the Education Sector
	10.3 Conduction of Online Lectures
	10.4 Learning Management System (LMS)
	10.5 Virtual Laboratories
	10.6 The Organization of Webinars During the COVID-19 Situation
	10.7 Organization of E-conferences
	10.8 Conclusion
	References
Chapter 11 Role of IoT, Machine Learning, and Big Data in Smart Building
	11.1 Introduction
	11.2 Literature Survey
	11.3 Contribution of the Work
	11.4 Layers of Smart Building Implementation
	11.5 IoT-Based Sensors
	11.6 Control Devices and Actuators
	11.7 Feature Set
	11.8 Role of IoT in Data Collection
		11.8.1 Type and Size of Data
		11.8.2 Data Preprocessing
			11.8.2.1 Feature Extraction
			11.8.2.2 Feature Selection
	11.9 Machine Learning Algorithms
		11.9.1 Support Vector Machine
		11.9.2 Artificial Neural Networks
		11.9.3 Decision Trees
		11.9.4 Unsupervised Learning
		11.9.5 Reinforcement Learning
	11.10 Available ML Platform and Tools for SB Design
	11.11 Research Challenges
	11.12 Conclusion
	References
Chapter 12 Design of Futuristic Trolley System with Comparative Analysis of Previous Models
	12.1 Introduction
		12.1.1 Applications of the Autonomous Trolley System
		12.1.2 Challenges
		12.1.3 Motivation
	12.2 Related Work
	12.3 Methodology
		12.3.1 System Architecture
		12.3.2 Trolley Architecture
	12.4 Conclusion
	References
Chapter 13 Big Data for Smart Health
	13.1 Introduction
		13.1.1 What is Big Data?
		13.1.2 Types of Big Data
			13.1.2.1 Structured
			13.1.2.2 Unstructured
			13.1.2.3 Semi-structured
		13.1.3 Internet of Thing
	13.2 Challenges
		13.2.1 Data Protection and Privacy
			13.2.1.1 Incorporation: Different Gadgets and Conventions
		13.2.2 Information Over-Burden and Exactness
			13.2.2.1 Cost
	13.3 Framework Analysis
	13.4 Big Data Analytics in Health Informatics
		13.4.1 The Frameworks Available for the Analysis of Healthcare Data
		13.4.2 The Six Key Features of the Architecture
			13.4.2.1 Strategy of Big Data
			13.4.2.2 Big Data Architecture
			13.4.2.3 Big Data Algorithms
			13.4.2.4 Big Data Processes
			13.4.2.5 Big Data Functions
			13.4.2.6 Artificial Intelligence
	13.5 Impact of Big Data on the Healthcare System
		13.5.1 Data Science of Healthcare Data Analytics
	13.6 Applications of IoT in Healthcare with Big Data
	13.7 Conclusion
	References
Index