ICT and Data Sciences

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Chapter 1: Impact and Analysis of Machine Learning and IoT Application in People Analytics
1.1 Introduction to People Analytics
1.2 Purpose and Motivation of Machine Learning and Internet of Things (IoT) in People Analytics
1.2.1 Machine Learning and People Analytics
1.2.2 Internet of Things and People Analytics
1.3 Challenges of Implementing Machine Learning and Internet of Things in People Analytics
1.4 Research Design and Methodology
1.4.1 Data Sources
1.4.2 Screening
1.4.3 Data Analysis
1.4.4 Descriptive Analysis of Literature
1.5 Results through Thematic Analysis of Literature
1.5.1 Role of Machine Learning in People Analytics
1.5.2 Role of Internet of Things (IoT) in People Analytics
1.6 Practical Implications
1.7 Conclusion
References
Chapter 2: Augmented Reality in Online Shopping
2.1 Introduction
2.2 Literature Review
2.3 Methodology
2.3.1 Comparison between AR and VR
2.3.2 Advantages and Disadvantages
2.3.3 Research Objective
2.3.4 Conceptual Models
2.3.4.1 Virtual Fitting Room (VFR) Application
2.4 Conclusion
References
Chapter 3: Internet of Things (IoT): Their Ethics and Privacy Concerns
3.1 Introduction
3.2 Methodology
3.3 Internet of Things (IoT)
3.4 Definition of Ethics and Privacy
3.4.1 Definition of Ethical Issues
3.4.2 Definition of Privacy Concerns
3.5 Impact of IoTs on Individuals
3.5.1 Monitoring Individuals
3.5.2 Monitoring Individual’s Vehicle Usage
3.5.3 Monitoring the Company an Individual Keeps
3.5.4 Monitoring User Finances and Business Interests (or Nature of Work)
3.6 Monitoring User Preference for Technologies
3.7 Monitoring User’s State-of-Health and Well-Being
3.8 Monitoring User’s Personal Behavior in an Open Environment and Their Security
3.9 Monitoring the Society a User Is In
3.10 Monitoring a User’s Visit Patterns to Offices and Places
3.11 Impact of IoTs on Society
3.11.1 Monitoring Production of Various Agricultural Crops and Their Prices
3.11.2 Monitoring Availability of Beds and Doctors
3.11.3 Monitoring the Buying Patterns of Users
3.11.4 Monitoring Users’ Preferences and Societal Patterns
3.11.5 Monitoring Mass Movement of Entities (e.g., Diseases, Vehicles, and People)
3.11.6 Monitoring and Managing the Security and Well-being of Society
3.11.7 Monitoring Security Profiles of Organizations, Systems, and Nations
3.11.8 Monitoring Business Practices and Business Patterns
3.11.9 Monitoring Company Trade Secrets
3.12 Ethical Aspects and Privacy Concerns of IoTs on Individuals and Society
3.12.1 Programmability of Software of IoTs
3.12.2 Embedded Algorithms in IoTs
3.12.3 Use of IoT Embedded Household Appliances
3.12.4 Monitoring Medical Conditions
3.13 Active ProstheticsActive prosthetics are available to replace a variety of body parts (e.g., [ 33, 34 ]). These IoT-enabled parts are being currently used in a small scale. However, when an intruder manipulates these parts, it can lead to dangerou
3.13.1 Inequality of Access to Data of Value
3.13.2 Public Attitudes, Opinions, and Behavior
3.13.3 Monitoring Workplace
3.13.4 Exploiting Consumption Data by Individuals and Neighborhoods
3.14 Future Scope
3.15 Conclusions on the Panopticon Impacts of IoT
Notes
References
Chapter 4: Artificial Intelligence and Deep Learning are Changing the Healthcare Industry
4.1 Introduction
4.2 Deep Learning in Healthcare
4.2.1 What Is Deep Learning?
4.2.2 How Deep Learning Works?
4.2.3 Convolutional Neural Network (CNN)
4.3 The Case for Glaucoma
4.3.1 AI and Glaucoma
4.3.2 Glaucoma Misdiagnosis
4.4 Future Scope
4.4.1 AI for Diagnostics
4.4.2 AI for Patient Management
4.4.3 AI for Drug Discovery
4.4.4 AI-based Advanced Applications
4.5 Conclusion
References
Chapter 5: Application of Disruptive Technology in Food Trackability
5.1 Introduction to Trackability System
5.1.1 System of Trackability and Its Integral Components
5.1.2 Trackability Systems’ Main Drivers
5.1.3 Trackability and Systematic Methods
5.1.4 Trackability and Chronology of Ownership (COO)
5.1.5 Transparency and Trackability
5.2 Food Sector and Use of Blockchain Technology
5.2.1 Major Contributors to Blockchain Technology
5.2.1.1 Blockchain as a Service (BaaS)
5.2.1.2 Blockchain Applications Offered by Amazon AWS
5.2.1.3 Blockchain Workbench Offered by Microsoft Azure
5.2.1.4 IBM BlueMix
5.2.1.5 Blockchain First Limited
5.2.1.6 A Large Number of Development Platforms
5.2.1.7 Vertically Integrated Solutions
5.2.2 Overlays and APIs (Application Programming Interface)
5.2.3 Brief Status on Blockchain Technology Applications as Prevalent in Food Industry
5.3 Comparative Analysis of Performance of Conventional vs. Blockchain-based Trackability Systems
5.3.1 Appropriateness of Database
5.3.2 Records Assessment and Authenticity
5.3.3 Susceptibility, Veracity, and Pellucidity
5.3.4 Confidentiality
5.3.5 Assurance
5.3.6 Velocity and Efficacy
5.3.7 Robustness
5.3.8 Interoperability
5.4 Practical and Economic Implementation Issues in Blockchain-based Systems
5.4.1 Food Product Supply Chains in Practice
5.4.2 Usefulness of Recorded Data in a Blockchain-Supported System
5.5 Inference and Recommendation
References
Chapter 6: Analyzing Cyber Security Breaches
6.1 Introduction
6.1.1 Types of Cyber Security Breaches
6.2 Related Facts
6.3 Case Study for Cyber-Attack
6.3.1 Process Implementation
6.4 Conclusion and Future Scope
References
Chapter 7: Industrial Internet of Things (IoT) and Cyber Manufacturing Systems: Industry 4.0 Implementation and Impact on Business Strategy and Value Chain
7.1 Introduction
7.2 The Evolution of Industry 4.0
7.3 Enablers of Industry 4.0
7.4 Conceptual Framework for Industry 4.0
7.5 Drivers of Industry 4.0
7.5.1 Flexibility
7.5.2 Remote Monitoring
7.5.3 Mass Customization
7.5.4 Proactive Maintenance
7.5.5 Optimized Decision-Making and Visibility
7.5.6 Connected Supply Chain
7.5.7 New Planning Methods
7.5.8 Creating Values from Big Data Collected
7.5.9 Creating New Services
7.6 Conclusions
7.6.1 Limitations of the Study
7.6.2 Future Scope
References
Chapter 8: Artificial Intelligence-Based Hiring in Data Science Driven Management Context
8.1 Introduction
8.2 Artificial Intelligence (AI) and Hiring
8.3 About the Study
8.3.1 Objectives
8.3.2 Methodology
8.3.2.1 Delphi Method to Identify the Benefits and Apprehensions
8.3.2.2 AHP Method: Benefits and Apprehensions
8.4 Results and Discussion
8.4.1 Factors Influencing the AI-based Hiring Adoption Decision
8.4.2 Benefits and Apprehensions
8.4.2.1 Weightage of the Benefits of AI-based Hiring
8.4.2.2 Weightage of the Apprehensions Associated with AI-based Hiring
8.5 Conclusion and Future Implications
Acknowledgments
References
Chapter 9: New Patterns in Cyber Crime with the Confluence of IoT and Machine Learning
9.1 Introduction
9.2 Background
9.3 Objectives
9.4 Cyber Crimes Associated with IoT Devices
9.4.1 Denial of Service (DDoS) Attack
9.4.1.1 How Does a DDoS Attack Operate?
9.4.1.2 Forms of DDoS Attacks
9.4.2 Botnets
9.4.2.1 How Does a Botnet Attack Work?
9.4.2.2 Types of Botnet Attacks
9.4.3 Identity Theft
9.4.3.1 How Does Identity Theft Work?
9.4.3.2 Types of Identity Theft
9.4.4 Social Engineering
9.4.4.1 How Does Social Engineering Work?
9.4.4.2 Types of Social Engineering
9.4.5 Man-in-the-Middle (MITM) Concept
9.4.5.1 How Does the MITM Attack Work?
9.4.5.2 Kinds of Man-in-the-Middle Attacks
9.5 Security Challenges of IoT Devices
9.6 Security Threats, Attacks, and Weaknesses
9.6.1 Weaknesses
9.6.2 Exposure
9.6.3 Threats
9.6.4 Attacks
9.7 Security and Privacy Objectives for IoT
9.7.1 Confidentiality
9.7.2 Integrity
9.7.3 Authentication and Authorization
9.7.4 Availability
9.7.5 Accountability
9.7.6 Auditing
9.7.7 Privacy Goals
9.7.7.1 IoT’s Main Privacy Goals
9.8 IoT Security Solutions Based on Machine Learning (ML)
9.9 Used Machine Learning (ML) Algorithms
9.10 Machine Learning Techniques
9.10.1 Supervised Learning
9.10.2 Unsupervised Learning
9.10.3 Reinforcement Learning
9.10.4 ML-based IoT Security Methods
9.11 Conclusion
References
Chapter 10: A Review for Cyber Security Challenges on Big Data Using Machine Learning Techniques
10.1 Introduction
10.2 Systematic Review
10.3 Searching Strategies for Groundwork Studies
10.3.1 Information Collection
10.4 Results
10.5 Conclusion
10.6 Future Scope
References
Chapter 11: Research Agenda for Use of Machine Learning and Internet of Things in “People Analytics”
11.1 Introduction
11.2 Literature Review
11.2.1 Definition of Terms (People Analytics, ML, and IoT)
11.2.1.1 People Analytics
11.2.1.2 Workforce Analytics
11.2.1.3 Internet of Things (IoT)
11.2.1.4 Big Data
11.2.1.5 Machine Learning (ML)
11.2.2 History of Peoples Analytics
11.2.2.1 People Analytics – Human-Driven Human Resources (HR) (Solely Human-based Analysis)
11.2.2.2 People Analytics – Data-Driven HR (Technology-based Analysis)
11.2.3 Types of People Analytics
11.2.3.1 Descriptive Analytics
11.2.3.2 Predictive Analytics
11.2.3.3 Prescriptive Analytics
11.2.3.4 Diagnostic Analytics
11.2.4 People Analytics Case Studies, Opportunities and Challenges of People Analytics
11.2.4.1 Advantages of People Analytics
11.2.4.2 Disadvantages of People Analytics
11.2.5 The Process of People Analytics
11.2.5.1 Readiness of the Organization
11.2.5.2 Stakeholders Buy-In
11.2.5.3 Defining the Roadmap
11.2.6 Use of IoT, Big Data, and Machine Learning in People Analytics
11.2.6.1 IoT in People Analytics, Opportunities, and Challenges
11.2.6.2 Big Data in People Analytics, Opportunities, and Challenges
11.2.6.3 Machine Learning in People Analytics, Opportunities, and Challenges
11.2.7 Theoretical and Conceptual Frameworks on People Analytics
11.2.7.1 Frameworks for Use of ML and IoT
11.2.7.2 Security Risks and Breach of Privacy to Optimize Future Opportunities of ML and IoT in People Analytics
11.3 Methodology
11.4 Results and Analysis
11.4.1 IoT and ML Uses, Challenges and Strategies to Enhance People Analytics
11.4.2 Specific IoT and ML Technologies Implemented in People Analytics
11.4.3 Benefits and Risks Associated with IoT and ML Application in PA
11.4.4 Discussion of Findings
11.5 Conclusion
References
Chapter 12: IoT-Integrated Photovoltaic System for Improved System Performance
12.1 Introduction
12.2 IoT-Based Structure for Photovoltaic Systems
12.3 IoT-Based Photovoltaic Systems with Artificial Intelligence
12.3.1 Parameters Identification of Solar Cells Model
12.3.2 PV System Sizing
12.3.3 PV System Control
12.3.3.1 Sun Tracking
12.3.3.2 Inverter Control
12.3.4 Maximum Power Point Tracking (MPPT)
12.3.5 Irradiance Forecasting and PV Output Power Estimation
12.3.6 Fault Diagnosis of Photovoltaic Systems
12.4 Conclusions
References
Chapter 13: Metaheuristic Optimization in Routing Protocol for Cluster-Based Wireless Sensor Networks and Wireless Ad-Hoc Networks
13.1 Introduction
13.2 Metaheuristic Algorithms
13.2.1 Ant Colony Optimization (ACO)
13.2.2 Particle Swarm Optimization (PSO)
13.2.3 Firefly Algorithm
13.3 Basics of Wireless Sensor Networks (WSN) and Wireless Ad-Hoc Networks
13.3.1 Wireless Sensor Network
13.3.2 Mobile Ad-Hoc Network
13.3.3 Ad-Hoc On-Demand Distance Vector Routing Protocol
13.3.4 Clustering in WSN
13.4 Metaheuristic Algorithms in Wireless Sensor Networks
13.4.1 ACO Based Clustering in WSN
13.4.2 PSO-Based Clustering in WSN
13.5 Metaheuristic Algorithms in Wireless Ad-Hoc Networks
13.5.1 Constricted PSO for Cluster Formation in WSN
13.5.2 Lévy-Flight-Based ACO Routing Optimization in WSN
13.5.3 Algorithm Lévy Flight ACO for Routing
13.5.4 Experimental Setup
13.6 Hybridization Using Metaheuristic in a Wireless Ad-Hoc Network
13.6.1 ACO and FA Hybrid-Based AODV Routing Protocols in MANET
13.6.2 Algorithm for ACO and FA Hybrid-Based AODV
13.6.3 Experiment Set Up and Result for ACO and FA-Hybrid-Based AODV
13.6.3.1 Experimental Setup
13.7 Conclusion
References
Chapter 14: Artificial Intelligence: A Threat to Human Dignity
14.1 Introduction
14.2 Research Methodology
14.3 Background
14.4 Advantages of AI
14.5 Drawbacks of AI
14.6 Threat to Human Dignity
14.7 Impact on Jobs
14.8 Proposed Solution
14.9 Probable Future
14.10 Conclusion
14.10.1 Future Scope
References
Chapter 15: Device Programming for IoT: In Defense of Python as the Beginner’s Language of Choice for IoT Programming
15.1 Introduction
15.2 Literature Review
15.3 Problem/Gap/Issue
15.4 Approach
15.5 Programming Language Popularity for IoT Development
15.6 Competitive Advantages of C, C++, and Python for IoT Development
15.6.1 Language Overview
15.6.1.1 C Overview
15.6.1.2 C++ Overview
15.6.1.3 Python Overview
15.6.2 Pros of C/C++
15.6.3 Pros of Python
15.7 Limitations of C, C++, and Python for IoT Development
15.7.1 Cons of C/C++
15.7.2 Cons of Python
15.8 Community Support of C, C++, and Python for IoT Development
15.9 Conclusion
References
Chapter 16: Enhancing Real-Time Learning Experiences through Information Communication Technology, Augmented Reality, and Virtual Reality
16.1 Introduction
16.2 Motivation and Challenges
16.3 Research Objectives
16.3.1 Research Gap
16.4 Computer-Assisted Learning
16.4.1 Computer-Assisted Learning Techniques
16.4.2 Techniques Related to Computer-Assisted Learning (CAL) [ 10 ]
16.4.2.1 Visual Learning
16.4.2.2 Hearing Practice
16.4.2.3 Tests
16.4.2.4 Games
16.4.2.5 Internet Browsers
16.4.2.6 Online Courses
16.5 Challenges of Computer-Assisted Learning (CAL)
16.5.1 Aim and Scope of CAL
16.5.2 Pros of Computer-Assisted Learning (CAL)
16.5.3 Cons of Computer-Assisted Learning (CAL)
16.5.3.1 It Can Be Costly
16.5.3.2 It Can Be Challenging for Teachers to Perform
16.5.3.3 CAL Activities Don’t Always Fit the Teacher’s Goals
16.5.3.4 It Can Lead to Isolation Among Students
16.6 Augmented and Virtual Reality
16.7 Experiential Learning in Higher Education Defining
16.7.1 Toward Virtual Reality Experiential Learning
16.7.1.1 Head-Mounted Equip (Hardware)
16.7.1.2 Mobile Phones
16.7.1.3 Software
16.7.1.4 Experiential Learning
16.8 Evaluation
16.9 Conclusion
References
Chapter 17: Topic-Based Classification for Aggression Detection in a Social Network
17.1 Introduction
17.2 Related Work Done for Aggression Detection Classification in a Social Network
17.3 Prevention of Cyber Bullying
17.4 Conclusion
References
Chapter 18: Role of ICT in Online Education during COVID-19 Pandemic and beyond: Issues, Challenges, and Infrastructure
18.1 Introduction
18.2 Global Education Trends
18.3 Digital Transformation in Education Domain
18.3.1 Digital Transformation and University/School Campus
18.3.2 Understanding Technology’s Impact on Education during the Coronavirus Pandemic
18.3.3 Tech Leverages Devices and Data for a Connected Experience
18.4 IoT Technologies in Smart Campus
18.4.1 In-House Management System (IHMS)
18.4.2 Take a Break (TaB)
18.5 Online Education: Issues and Challenges
18.6 Challenges in Online Education
18.6.1 Issues Faced by Teachers and Students
18.6.1.1 Issues Faced by Teachers
18.6.1.2 Issues Faced by Students
18.7 Challenges of Conducting Online Exams
18.7.1 Recommendations/Solutions to Better the Examination Process
18.8 Cyber Security
18.9 Educational Applications and Security
18.9.1 Zoom
18.9.2 Microsoft Teams
18.9.3 Slack
18.9.4 Adobe Connect
18.9.5 Skype
18.10 Addressing Security Vulnerabilities
18.11 Conclusion
References
Index