Recent Trends in Artificial Intelligence Towards a Smart World: Applications in Industries and Sectors (Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications)

This book compiles artificial intelligence (AI) applications in new communication technologies such as the cognitive radio networks, internet of things (IoT), internet of drones (IoD), internet of vehicles (IoV), and autonomous underwater vehicles (AUV), which are expected to increase the amount of data traffic. Recognizing that AI is revolutionizing industries with its applications and helping us solve complex problems with ease, the book tackles a variety of industries and sectors such as agriculture, logistics, infrastructure, manufacturing, education, disaster management, transport, surveillance, and more. Contributions included in the book are useful for students, engineers (disciplines like telecommunication, mechanical and computer science, etc.), teachers, people studying and working for strategic, tactical and operational management. It is also useful for data scientists and anyone else who wants to have an insight into the impact of artificial intelligence on various industries. Due to its focus on healthcare and agriculture, the horizon of the book is enhanced to include healthcare industry personnels and agriculture sector. Additionally, it provides guidance for government personnel who are working towards system upgradation for managing dynamic traffic demands. Preface Contents Editors and Contributors 1 AI-Driven Cognitive Radio Networks for Transforming Industries and Sectors Towards a Smart World 1.1 Introduction 1.2 Integration of AI in CRNs 1.3 The Potential for AI-Driven CRNs to Revolutionize a Variety of Industries 1.4 Cognitive Radio Networks and Artificial Intelligence 1.4.1 Cognitive Radio Networks: An Overview 1.4.2 Artificial Intelligence: An Overview 1.5 Artificial Intelligence in Cognitive Radio Networks 1.5.1 Artificial Intelligence Methodologies to Cognitive Radios Networks 1.5.2 Machine Learning Approaches to Cognitive Radio Networks 1.6 Applications of CRNs and AI-Driven CRNs in Sectors of Smart World 1.6.1 Application of the Cognitive Radio Networks 1.6.2 Application of the AI-Driven CRNs 1.7 Challenges and Solutions of AI-Driven CRNs 1.8 Future Trends AI-Driven CRNs and Anticipated Impacts on Industries and Sectors 1.9 Conclusion References 2 Beyond the Code: Bridging Ethical and Practical Gaps in Data Privacy for AI-Enhanced Healthcare Systems 2.1 Introduction 2.2 The Pivotal Role of Patient Data 2.3 Background 2.4 Types of Healthcare Data 2.5 Evolution to Digital Health Records 2.6 Challenges in AI for Healthcare Systems 2.6.1 Data Privacy and Security 2.6.2 Bias and Fairness Ethical 2.6.3 Reliability and Accountability 2.7 AI in Diagnostic Imaging: Transforming Radiology with Deep Learning 2.7.1 Introduction to AI in Diagnostic Imaging 2.7.2 The Role of AI in Radiology 2.7.3 Advanced Applications of AI in Radiology 2.7.4 Integration of AI in Radiological Workflows 2.7.5 Addressing Challenges in AI-Driven Radiology 2.8 Case Study: AI-Powered Detection of Pulmonary Diseases 2.9 Introduction to AI in Patient Monitoring 2.10 Case Study: AI for Enhanced Patient Monitoring and Care 2.11 Introduction to AI in Drug Discovery 2.11.1 AI’s Impact on the Drug Discovery Process 2.11.2 The Role of AI in Personalized Medicine 2.11.3 Overcoming Challenges in Drug Discovery with AI 2.11.4 AI Collaborations in Pharma 2.12 Case Study: Accelerating Alzheimer's Drug Discovery 2.13 Ethical Debates and Perspectives in AI-Enhanced Healthcare 2.14 AI's Broad Impact Across Healthcare Sectors References 3 Smart Healthcare: Paradigm Shift in Industry 5.0 Using AI 3.1 Introduction 3.2 What Is Smart Healthcare? 3.3 Evolution of Industry 5.0 3.4 Industry 4.0 as a Precursor 3.5 Industry 5.0 3.5.1 Human-Centric Principles 3.5.2 Cobots and Collaborative Robotics 3.5.3 Immersive Technologies 3.5.4 Lights-out Manufacturing with a Human Touch 3.5.5 Edge Computing for Real-Time Decision-Making 3.5.6 The Role of Data 3.5.7 Connectivity Through 5G Technology 3.6 Cybersecurity in Industry 5.0 3.6.1 Workforce Dynamics and Organizational Structures 3.6.2 Societal Implications and Environmental Considerations 3.7 Impact of AI in Healthcare 3.8 Paradigm Shift Using AI in Industry 5.0 3.9 Comparative Study of Industry 4.0 and 5.0 in Healthcare 3.10 Advantages and Disadvantages of Smart Healthcare in Industry 5.0 3.10.1 Advantages of Smart Healthcare in Industry 5.0 3.10.2 Disadvantages of Smart Healthcare in Industry 5.0 3.11 Applications of Smart Healthcare in Industry 5.0 Using AI 3.12 Summary and Future Work References 4 Navigating the Digital Age: Smart Manufacturing and Logistics 4.1 Introduction 4.1.1 Significance of Smart Manufacturing and Logistics in Today's Industrial Landscape 4.2 Industry 4.0 4.2.1 The First Industrial Revolution (Industry 1.0—Late Eighteenth and Early Nineteenth Centuries) 4.2.2 Second Industrial Revolution (Industry 2.0—Late Nineteenth to Early Twentieth Centuries) 4.2.3 Third Industrial Revolution (Industry 3.0—Late Twentieth Century) 4.2.4 Fourth Industrial Revolution (Industry 4.0—Present) 4.3 Internet of Things (IoT) 4.4 Artificial Intelligence 4.5 Automation 4.6 Historical Context 4.6.1 Family Histology in Smart Logistics 4.7 Explore Fundamental Concepts and Principles of Smart Manufacturing and Logistics 4.8 Use of IOT in Smart Manufacturing and Logistics 4.8.1 RFID 4.8.2 WSN 4.8.3 Wireless Communication Technologies 4.9 Use of Cloud Computing in Smart Manufacturing and Logistics 4.9.1 Use of Big Data in Smart Manufacturing and Logistics 4.9.2 Use of Artificial Intelligence in Smart Manufacturing and Logistics 4.10 Key Technologies 4.11 Benefits of Smart Manufacturing and Logistics 4.12 Challenges and Concerns 4.13 Case Studies 4.14 Integration with Industry 4.0 4.15 Best Practices 4.16 Popular Algorithms 4.16.1 Machine Learning Algorithms 4.17 Conclusion References 5 Industrial Automation Through AI-Powered Intelligent Machines—Enabling Real-Time Decision-Making 5.1 Introduction 5.1.1 Definition of Industrial Automation 5.1.2 Overview of Artificial Intelligence in Industrial Automation 5.1.3 Significance of Combining AI and Industrial Automation 5.2 Foundations of Industrial Automation 5.2.1 Historical Evolution of Industrial Automation 5.2.2 Key Components and Technologies in Industrial Automation 5.3 Challenges Addressed by Industrial Automation 5.4 Intersection of AI and Industrial Automation 5.4.1 Integration of AI in Manufacturing Processes 5.4.2 AI Applications in Control Systems 5.4.3 Role of AI in Predictive Maintenance 5.5 Industrial Internet of Things (IIoT) 5.5.1 Overview of IIoT 5.5.2 Interconnected Systems in Industrial Automation 5.5.3 AI-Driven Decision-Making in IIoT 5.6 Data Analytics in Industrial Automation 5.6.1 Importance of Data in Automation 5.6.2 Role of Big Data Analytics 5.6.3 Real-Time Data Processing and Analysis 5.7 Machine Learning in Industrial Automation 5.7.1 Basics of Machine Learning 5.7.2 Applications of Machine Learning in Manufacturing 5.7.3 Adaptive Learning and Continuous Improvement 5.8 Robotics in Smart Manufacturing 5.8.1 Role of Robotics in Automation 5.8.2 AI-Enhanced Robotics 5.8.3 Human–Robot Collaboration 5.9 Case Studies and Success Stories 5.9.1 Real-World Implementations 5.9.2 Demonstrating Benefits and Outcomes 5.10 Challenges and Considerations 5.10.1 Ethical and Privacy Concerns 5.10.2 Security in AI-Driven Industrial Automation 5.10.3 Workforce Adaptation and Training 5.11 Future Trends and Emerging Technologies 5.11.1 AI Innovations in Industrial Automation 5.11.2 Predictions for the Future of Smart Manufacturing 5.11.3 Potential Impacts on Global Industries 5.12 Conclusion References 6 Artificial Intelligence and Machine Learning Technologies in Internet of Vehicles 6.1 Introduction 6.2 What Is the Internet of Vehicles (IoV)? 6.3 What Is the Need of Internet of Vehicles (IoV)? 6.3.1 How IoV Is Different from IoT? 6.3.2 Vehicle Telematics 6.3.3 How Does IoV Technology Works? 6.4 IoV Architecture 6.5 Importance of IoV 6.6 IoV Networks 6.7 Benefits of IoV 6.7.1 What Is Needed to Maintain IoV? 6.8 IoV Participators 6.9 Resource Optimization in IoV 6.9.1 Techniques for Optimization 6.9.2 Optimization in IoV 6.10 Types of Communication and Challenges 6.11 IoV Security 6.12 IoV Applications 6.13 IoV Users 6.14 Future of IoV References 7 Connecting the Dots: Unveiling the Power of Sensors in IoT 7.1 Introduction 7.1.1 Overview of IoT’s Impact 7.1.2 Transforming Industries 7.1.3 Enhancing Daily Life 7.1.4 Enabling New Possibilities 7.1.5 Challenges and Considerations 7.2 Understanding Sensors 7.2.1 Decoding Sensors: Unveiling the Essence of Sensing 7.2.2 Importance of Sensors in Various Fields 7.2.3 An In-depth Exploration of Sensor Varieties 7.3 Sensor Integration in IoT 7.4 Sensor Network 7.4.1 Key Aspects of Sensor Network 7.4.2 Research Area and Advancements: 7.4.3 Application of Sensors 7.5 Advantages and Disadvantages of Sensor Network 7.6 Wireless Communication 7.6.1 Protocols and Standards 7.6.2 Wireless Communication Protocol 7.6.3 Internet Security Standards 7.6.4 IoT Protocols 7.6.5 Data Exchange and Representation Standards 7.6.6 Power Efficiency 7.6.7 Low-Power Microcontrollers 7.6.8 Duty Cycling and Sleep Modes 7.6.9 Data Compression and Aggregation 7.6.10 Optimized Communication Protocols 7.7 Sensor Fusion 7.7.1 Applications of Sensor Fusion 7.7.2 Types of Sensor Fusion 7.7.3 The Coign of Vantage of Sensor Fusion 7.7.4 Sensor Fusion Algorithms 7.8 Case Study: Smart Building Management with IoT Sensors 7.9 Conclusion References 8 A Complex Intersection and Sustainable Solution of Smart Cities and Smart Homes: Building a Connected Habitat 8.1 Introduction 8.2 Smart Cities 8.2.1 Understanding Smart Cities 8.2.2 Benefits of Smart Cities 8.2.3 Applications of IoT in Smart Cities 8.3 Digital Urbanization: IoT Solutions for Smart and Sustainable Cities 8.3.1 Traffic Control Mechanism Using IoT in Smart Cities 8.3.2 Effective Construction Site Monitoring Using AI 8.3.3 Smart Healthcare in Smart Cities (Emerging Trends, Application and Challenges) 8.3.4 Smart Waste Management System in Smart Cities—Smart Bin 8.3.5 Cybersecurity in Smart Cities 8.3.6 Smart City Green Monitoring System 8.3.7 Smart Agriculture and Farming 8.4 Implementation Challenges of IoT for Smart and Sustainable Cities 8.5 Smart Homes 8.5.1 Understanding Smart Homes 8.5.2 Key Components of Smart Homes 8.5.3 Benefits of Smart Home 8.6 IoT Devices and Privacy Concerns: Safeguarding the Connected Home 8.7 Complex Intersection of Smart Cities and Smart Homes 8.7.1 Interconnected Technologies: The Rise of the Internet of Things (IoT) 8.7.2 Data Sharing 8.7.3 Interoperability 8.7.4 Grid Optimization 8.7.5 Traffic and Transportation Management 8.7.6 Environmental Monitoring 8.8 Case Study 8.8.1 Implementation Challenges and Considerations 8.8.2 Urban Planning and Residential Design 8.9 Conclusion References 9 Customized CNN-Based Condition Monitoring of Road Traffic for Intelligent Transportation Routing 9.1 Introduction 9.2 Literature Survey 9.2.1 Research Motivation 9.2.2 Problem Statement 9.2.3 Research Gaps 9.3 Proposed Methodology 9.3.1 Image Preprocessing 9.3.2 Helmet Detection 9.3.3 Vehicle Detection and Counting 9.4 Vehicle Classification 9.5 HMM Feature Extraction 9.6 HCNN Classifier 9.7 Advantages of Proposed System 9.8 Results and Discussion 9.8.1 Traffic Flow Monitoring Dataset 9.8.2 Prediction Results 9.8.3 Traffic Flow Analysis 9.9 Conclusion References 10 Remote Monitoring and Control Systems in Agriculture and Farming 10.1 Introduction 10.1.1 Overview of Remote Monitoring and Control Systems 10.2 Importance of Technology in Enhancing Agricultural Efficiency and Sustainability 10.3 Components of Remote Monitoring Systems 10.3.1 Sensor Networks 10.3.2 Communication Protocols 10.3.3 Data Acquisition and Processing Platforms 10.3.4 Communication Equipment and Gateways 10.4 Functions of Gateways in Agricultural Monitoring 10.4.1 Aggregating Data from Sensors and Devices 10.4.2 Translating Protocols to Ensure Interoperability 10.4.3 Providing Secure Communication Channels 10.4.4 Managing Connectivity Across Remote Farm Locations 10.5 Sensor Technologies for Remote Monitoring 10.5.1 Soil Moisture Sensors 10.5.2 Weather Stations Sensors 10.5.3 Crop Health Sensors 10.5.4 GPS Trackers 10.5.5 Remote Cameras 10.5.6 Water Quality Sensors 10.5.7 Drone Technology 10.5.8 Satellite Imagery 10.6 Data Analytics in Agricultural Remote Monitoring 10.6.1 Predictive Analytics 10.6.2 Machine Learning Algorithms 10.7 Applications of Remote Monitoring Systems in Agriculture 10.7.1 Soil Health Monitoring 10.7.2 Crop Health Monitoring 10.7.3 Environmental Parameter Monitoring 10.7.4 Automated Irrigation Systems 10.7.5 Precision Fertilization 10.8 Case Studies and Success Stories 10.9 Challenges and Barriers to Adoption 10.9.1 Technical and Operational Challenges 10.9.2 Social and Economic Challenges 10.10 Future Trends and Innovations References 11 Enhanced Deep Learning Model for Road Transportation Safety with Accident Detection and Traffic Flow Analysis 11.1 Introduction 11.2 Literature Survey 11.2.1 Research Motivation 11.2.2 Problem Statement 11.3 Applications 11.4 Proposed Methodology 11.4.1 Image Preprocessing 11.5 Proposed DLCNN 11.6 Advantages of Proposed TrafficNet 11.7 Results and Discussion 11.7.1 Traffic Condition Dataset 11.7.2 Prediction Results 11.8 Conclusion References 12 Unmanned Aerial Networks—UAVs and AI 12.1 Introduction to Unmanned Aerial Networks 12.2 Components of a UAN 12.3 Applications of UANs 12.4 Key Principles 12.5 Challenges and Considerations 12.6 Potential Future Uses of UANs 12.7 Brief History and Evolution of Drone Technology 12.8 Regulatory Framework 12.9 Different Names for UAVs 12.10 Types of UAVs 12.10.1 Fixed-Wing UAVs 12.10.2 Rotary-Wing UAVs: Versatile Hoverers 12.10.3 Hybrid Designs: Bridging the Divide 12.11 Size Spectrum: From Nano to Large-Scale 12.12 Defining AI in UAVs: An Overview 12.13 Historical Perspective: The Gradual Integration of AI into UAV Technology 12.13.1 AI-Powered Autonomy 12.14 The Evolution and Impact of AI on Unmanned Aerial Vehicles 12.15 Enhancing UAV Capabilities with AI 12.16 Applications Transformed by AI-Driven UAVs 12.17 Applications of AI-Powered UAVs 12.18 Challenges and Ethical Considerations in AI-Powered UAVs 12.19 Conclusion References 13 Internet of Drones 13.1 Introduction 13.1.1 Related Work 13.2 Application Areas of IoD 13.3 Key Components of IoD Network 13.4 Challenges and Considerations 13.5 Future Trends and Opportunities 13.6 Industrial Evolution of the IoD 13.7 Conclusion References 14 Advancements in Traffic Sign Detection and Recognition for Adverse Image and Motion Artifacts in Transportation Systems 14.1 Introduction 14.2 Literature Survey 14.2.1 Research Motivation 14.2.2 Problem Statement 14.2.3 Applications 14.3 Proposed Methodology 14.3.1 Haze Removal Model Deployment 14.3.2 Deep Haze Removal 14.3.3 Traffic Sign Detection 14.4 Advantages 14.5 Results and Discussion 14.5.1 Subjective Performance 14.6 Conclusion References 15 Building Smart Campuses: Integrating AI in Higher Education 15.1 Introduction 15.2 Current State of Higher Education 15.3 The Necessity of Integrating New Technologies into Education 15.3.1 Information Accessibility 15.3.2 Connection Around the Globe 15.3.3 Personalized Learning 15.3.4 Engaging Learning Environments 15.3.5 Efficiency and Productivity 15.3.6 Preparation for the Digital Economy 15.3.7 Flexibility and Accessibility 15.3.8 Real-World Applications 15.3.9 Innovation in Teaching Methods 15.4 The Role of AI in Transforming Traditional Learning Environments 15.4.1 Physical Classrooms 15.4.2 Hard Book or Traditional Books 15.4.3 Content and Syllabus Design 15.4.4 Lecture Delivery Tools 15.4.5 Manual Attendance 15.5 The Foundations of Smart Campuses 15.5.1 Definition and Characteristics of Smart Campuses 15.5.2 Characteristics of Smart Campuses 15.6 The Importance of Integrating Technology into Higher Education 15.6.1 Tailor Ready Content 15.6.2 Fostering Collaboration and Communication 15.6.3 Administrative Efficiency and Cost Savings 15.6.4 Skill Development 15.6.5 Comfortable and Effective Learning 15.7 Overview of the Benefits of Creating Smart Campuses Using AI 15.7.1 Customized Educational Experiences 15.7.2 Improvised Campus Safety 15.7.3 Streamlined Administrative Processes 15.7.4 Sustainable and Efficient Campus Management 15.7.5 Enhanced Campus Engagement and Community Building 15.8 AI Technologies in Higher Education 15.8.1 Machine Learning in Adaptive Learning Systems 15.8.2 Natural Language Processing (NLP) for Personalized Education 15.8.3 Predictive Analytics for Student Success and Retention 15.8.4 Virtual Assistants and Chatbots for Student Support 15.9 Enhancing Teaching and Learning 15.9.1 AI-Powered Personalized Learning Experiences 15.9.2 Intelligent Tutoring Systems 15.9.3 Gamification and AI in Educational Content Development 15.10 Administrative Efficiency and Campus Management 15.10.1 AI for Streamlining Administrative Processes 15.10.2 Automated Student Enrollment and Registration 15.10.3 AI-Driven Campus Security and Surveillance 15.11 Optimizing Resource Allocation Through AI 15.12 Challenges and Considerations 15.12.1 Ethical Considerations in AI Implementation 15.12.2 Bias and Discrimination 15.12.3 Data Security and Privacy 15.12.4 Responsibility and Accountability 15.12.5 Explainability 15.13 Addressing Concerns About Job Displacement 15.13.1 Automation 15.13.2 Creation of New Tasks or Jobs 15.13.3 Legal and Privacy Implications 15.14 Case Studies and Future Trends 15.14.1 Implementation of AI-Based Smart Campuses in the Real World 15.14.2 Diverse Examples from Various Educational Institutions 15.15 Emerging Technologies in AI for Education 15.15.1 Predictions for the Future of Smart Campuses 15.15.2 The Role of AI in Shaping the Future of Higher Education 15.16 Implementation Strategies 15.16.1 Steps for Successfully Integrating AI into Higher Education 15.17 Conclusion References