Impact of Artificial Intelligence on Society

✍ Scribed by Sumit Tripathi (editor), Joanna Rosak-Szyrocka (editor)

• Publisher: Chapman and Hall/CRC
• Year: 2024
• Tongue: English
• Leaves: 199
• Edition: 1
• Category: Library

✦ Synopsis

The book presents a comprehensive and interdisciplinary exploration of the impact of AI on various sectors of society to foster a greater understanding of the opportunities and challenges presented by this transformative technology. It explores the impact AI has had on varied sectors of society, including healthcare, education, the workplace, and the economy. It provides a holistic view of this fast-growing technology by critical study of the possible benefits and drawbacks linked with the application of AI in many industries. The book also examines the ethical, social, and economic implications of AI and the potential risks and challenges associated with its use.

• Focuses on the future influence of AI, providing insights into how it could disrupt several industries and change the way we live, work, and connect with one another
• Explores how AI can be used to tackle global issues such as climate change, food security, and public health concerns
• Offers case studies and specific examples of how artificial intelligence is being employed in many industries, covering both successes and failures
• Investigates cutting-edge technology breakthroughs in AI and how they can be used to improve efficiency, productivity, and performance across multiple industries
• Understands the limitations and potential biases of artificial intelligence, as well as the significance of human monitoring and accountability

The book is intended for researchers, practitioners, policymakers, and students who are interested in understanding the nature and role of AI with regard to different sectors of society.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Authors
List of Contributors
Introduction
Chapter 1: Disruptive Innovation in Medical Image Segmentation: A Comparative Study of Traditional and AI-Based Approaches
1.1 Introduction to Segmentation
1.2 Classical Methods of Medical Image Segmentation
1.2.1 Methods That Rely on Gray-Level Features
1.2.1.1 Histogram-Based Method for Segmentation
1.2.2 Methods That Rely on Edge Detection for Segmentation
1.2.3 Methods That Rely on Region Characteristics for Segmentation
1.2.4 Split and Merge Algorithm
1.3 Advanced Deep Learning-Based Methods for Image Segmentation
1.3.1 U-Net
1.3.1.1 Advantages of U-Net over Traditional Image Segmentation Methods
1.3.2 Generative Adversarial Network
1.3.2.1 Advantages of GAN over Traditional Methods
1.4 Evaluation Metrics for Image Segmentation
1.5 Advanced AI (Deep Learning) as a Disruptive Technology in Medical Image Segmentation
References
Chapter 2: Applications of Transformer in Medical Imaging: A Review
2.1 Introduction
2.2 Transformers
2.2.1 Attention Mechanism
2.2.2 Attention Mechanism for Computer Vision
2.3 Architecture
2.3.1 Encoder
2.3.2 Decoder
2.4 Vision-Based Transformers
2.5 Applications of Transformer in Medical Image
2.5.1 Classification
2.5.1.1 Pure Transformers in Classification
2.5.1.2 Hybrid Transformer in Classification
2.5.2 Transformer for Segmentation
2.5.2.1 Hybrid Transformers
2.5.2.2 Transformer Location in U-Shaped Architecture
2.5.3 Image Synthesis
2.5.4 Detection
2.6 Discussion
2.7 Different Learning Scenarios for Transformers
2.7.1 Multi-Modal Learning
2.7.2 Weakly Supervised Learning
2.7.3 Self-Supervised Learning
2.8 Conclusions
References
Chapter 3: Future of Learning: Adaptive Learning Systems Based on Language Generative Models in Higher Education
3.1 Introduction: Educational Background
3.2 Adaptive Learning Systems in Higher Education
3.3 Foundations of Adaptive Learning: The Pedagogical Approach, the Role of Artificial Intelligence, Data Analytics, and Learning Analytics
3.4 Role of Technology in Shaping Education
3.5 Challenges of Implementing Adaptive Learning in Higher Education
3.6 Conclusion: Implications for Practice, Ethical Considerations in Adaptive Learning, and Future Directions for Research
Notes
References
Chapter 4: Artificial Intelligence (AI) Integration in Higher Education: Navigating Opportunities and Ethical Frontiers in Education with Advanced Technologies
4.1 Introduction
4.2 Foundations of AI Integration: Understanding AI’s Role in Higher Education
4.3 AI Tools and Implementation: Innovative AI Tools for Education Management and Its Application
4.4 Challenges and Ethical Considerations of using AI in HE
4.5 Mitigating Challenges: Strategies for Successful AI Integration in Higher Education
4.6 Future Directions and Opportunities: The Role of AI in Shaping Future Educational Landscapes
4.7 Conclusion
References
Chapter 5: The Future of Higher Education: Using AI in Universities to Improve Learning Outcomes and Operational Efficiency
5.1 Introduction
5.1.1 Background
5.1.2 Objectives of the Chapter
5.1.3 Scope and Significance
5.2 Literature Review
5.3 AI in Higher Studies: A Transformative Shift
5.3.1 Overview of AI Integration in Higher Education
5.3.2 Current Landscape and Adoption Trends
5.3.3 Motivation for AI Integration
5.4 Impact on Learning Outcomes
5.4.1 Personalized Learning Experiences
5.4.2 Enhancing Student Engagement
5.4.3 AI-Driven Analytics for Student Performance
5.5 Operational Efficiency in Higher Education
5.5.1 Automation of Administrative Tasks
5.5.2 Resource Allocation and Optimization
5.5.3 Intelligent Systems for Administrative Processes
5.6 Challenges and Considerations
5.6.1 Ethical Considerations in AI Adoption
5.6.2 Data Privacy Concerns
5.6.3 Evolving Role of Educators
5.7 Future Scope
5.7.1 AI’s Potential in Shaping Higher Education
5.7.2 Innovative Applications and Use Cases
5.7.3 Anticipated Developments in the Field
5.8 Conclusion
References
Chapter 6: AI in Academic Research: Advances, Opportunities, and Challenges
6.1 Introduction
6.2 The Evolution of AI in Academic Research
6.3 AI Technologies and Tools of AI in Various Academic Disciplines
6.4 Future Advances, Opportunities, and Challenges
6.4.1 Future Advances
6.4.1.1 OpenAI
6.4.1.2 ChatGPT
6.4.1.3 Scite.ai
6.4.1.4 Elicit
6.4.2 Opportunities
6.4.3 Challenges
6.4.3.1 Academic Dishonesty
6.4.3.2 Limited Use of Language
6.4.3.3 Prejudice
6.4.3.4 Lack of Confidence
6.4.3.5 Lack of Accountability and Transparency
6.5 Ethical and Responsible AI in Academia
6.6 Conclusion
References
Chapter 7: Transforming Education through AI-Enhanced Content Creation and Personalized Learning Experiences
7.1 Introduction
7.1.1 Accessibility and Scalability
7.1.2 Adapting to Diverse Learning Styles
7.2 AI in Content Creation: Revolutionizing Educational Resources
7.2.1 Enabling Multilingual and Inclusive Learning
7.2.2 Enabling Augmented Reality and Virtual Reality
7.3 Personalized Learning: Tailoring Education for Every Learner
7.3.1 A Paradigm Shift in Education
7.3.2 Optimizing Retention and Understanding
7.3.3 Predictive Modeling for Early Intervention
7.4 The Symbiosis of AI and Pedagogy
7.4.1 AI-Enabled Professional Development
7.4.2 VR: Immersive Simulations and Virtual Laboratories
7.5 Ethical Considerations and Responsible Implementation
7.5.1 Ensuring Inclusivity and Accessibility in AI-Driven Learning
7.5.2 Universal Design for Learning (UDL)
7.5.3 Case Studies: Implementing AI in Education
7.5.3.1 Real-World Transformations: AI in Diverse Educational Settings
7.5.4 K-12 Schools: Personalized Learning at Scale
7.5.5 Higher Education: Enriching Learning Experiences
7.5.6 Corporate Training: Upskilling the Workforce
7.6 Future Trends and Emerging Technologies
7.7 Challenges and Overcoming Barriers
7.7.1 Navigating the AI Integration Maze: Strategies for Success
7.8 Empowering Educators: Training and Professional Development
7.8.1 The Educator’s Role in AI-Enhanced Education
7.8.2 Empowering Educators through Collaboration and Resources
7.9 Conclusion—Envisioning the Future of Education with AI
7.9.1 Enhanced Teaching and Learning
7.9.2 Ethical Considerations and Inclusivity
7.9.3 Continuous Learning and Collaboration
References
Chapter 8: Exploring the Interplay of Educational Social Media Usage, Procrastination, and Subjective Well-being in the Context of Industry 5.0 Education
8.1 Introduction
8.1.1 Industry 5.0 and Social Media Usages
8.1.2 Psychological Parameters
8.1.3 Social Networking Sites Dependency
8.1.4 Social Networking Sites Addiction
8.1.5 Impact of SNS Use on Subjective Well-being
8.1.6 Challenges of Smartphone and Social Media Integration
8.1.7 Relationship between Facebook Use and Life Happiness
8.1.8 Procrastination and Its Link to Subjective Well-being
8.2 Review of Literature
8.2.1 Need for the Study in Industry 5.0
8.2.2 Significance of the Study in Industry 5.0
8.3 Methodology
8.3.1 Objectives of the Study
8.3.2 Hypotheses
8.3.3 Operational Definitions of the Variables
8.3.4 Variables
8.3.5 Inclusion Criteria
8.3.6 Exclusion Criteria
8.3.7 Research Design
8.3.8 Tools of Assessment
8.3.9 Procedure of Administration
8.3.10 Ethical Consideration
8.4 Results
8.4.1 Findings in the Context of Industry 5.0: Unravelling the Nexus of Procrastination, Social Networking Usage, and Subjective Well-being
8.4.2 Descriptive Analysis
8.4.3 Correlational Analyses
8.4.4 Procrastination and Social Networking Usage
8.4.5 Social Networking Usage and Subjective Well-being
8.4.6 Procrastination and Subjective Well-being
8.5 Discussions
8.5.1 Analysis in the Context of Industry 5.0: Decoding the Dynamics of Procrastination, Social Networking Usage, and Subjective Well-being
8.5.2 Descriptive Overview
8.5.3 Correlational Insights
8.5.4 Implications for Industry 5.0
8.6 Limitations and Future Directions in Industry 5.0
References
Chapter 9: Advancements and Challenges in Fraudulent Message Detection
9.1 Introduction
9.2 Literature Survey
9.3 Procedure
9.4 Algorithms
9.4.1 KNN Algorithm
9.4.2 Naive Bayes Algorithms
9.4.3 Support Vector Machines
9.4.4 Suggested Method
9.5 Performance Analysis
9.6 Conclusion
References
Chapter 10: Artificial Intelligence of Things (AIoT)-Based Telehealth System Using Healthy Pi
10.1 Introduction: Background and Driving Forces
10.2 System Architecture
10.3 The Architecture of the Smart Healthcare System
10.4 Implementation and Classification of the Smart Healthcare System
10.5 Design Methodology
10.5.1 Hardware Components
10.5.2 Software Components
10.6 Result and Discussion
10.7 Conclusion and Future Work
References
Chapter 11: Artificial Intelligence for Social Good, Disaster Relief, Poverty Alleviation, and Environmental Sustainability: A New Era of Innovative Solution and Global Impact
11.1 Introduction: Background and Driving Forces
11.2 Research Question
11.3 Research Objectives
11.4 Conceptual Framework
11.5 Research Methodology
11.6 Discussion
11.7 Conclusion
11.8 Implications of the Study
Annexure 11.1
References
Chapter 12: Digital Innovations for Increasing Financial Inclusion: CBDC, Cryptocurrency, Embedded Finance, Artificial Intelligence, WaaS, Fintech, BigTech, and DeFi
12.1 Introduction
12.2 Literature Review
12.3 Some Recent Development
12.3.1 Role of Wallet-as-a-Service
12.3.2 Role of Embedded Finance
12.3.3 Role of Artificial Intelligence and Robotics
12.3.4 Role of Central Bank Digital Currency
12.3.5 Role of BigTech
12.3.6 Role of Fintech
12.3.7 Role of Cryptocurrencies
12.3.8 Role of DeFi
12.4 Conclusion
References
Index