ICT and Data Sciences
- Length: 283 pages
- Edition: B
- Language: English
- Publisher: CRC Pr I Llc
- Publication Date: 2022-05-12
- ISBN-10: 0367501147
- ISBN-13: 9780367501143
- Sales Rank: #0 (See Top 100 Books)
This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems.
ICT and Data Sciences brings together IoT and Machine Learning and provides the careful integration of both, along with many examples and case studies. It illustrates the merging of two technologies while presenting basic to high-level concepts covering different fields and domains such as the Hospitality and Tourism industry, Smart Clothing, Cyber Crime, Programming, Communications, Business Intelligence, all in the context of the Internet of Things.
The book is written for researchers and practitioners, working in Information Communication Technology and Computer Science.
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 Prosthetics Active 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
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