Emotional Artificial Intelligence: Practical Challenges and Applications explores the intersection of artificial intelligence and human emotions, challenging the myth that AI will replace humans. Instead, it emphasizes how emotional AI can enhance collaboration, improving efficiency across fields like healthcare, transportation, and space exploration. The book explores foundational theories such as Goleman’s emotional model and Ekman’s emotion theory, discussing methodologies for understanding human emotions through natural language processing and the ethical implications of AI in decision-making. It showcases real-world applications, from virtual nurses to emotion-aware driving systems, illustrating how these technologies can improve daily interactions and outcomes.
This essential resource equips researchers, practitioners, and policymakers with practical insights for integrating emotional AI into diverse domains. By addressing the complexities of human emotion recognition and ethical frameworks, it provides a forward-looking perspective on enhancing collaboration between humans and AI, ultimately driving improved productivity and efficiency.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. About the editors - p. xiii 8. Chapter 1 Explainable AI in emotional artificial intelligence: Challenges and applications - p. 1 9. Chapter 2 Advancing emotional intelligence in artificial intelligence: Challenges and applications - p. 15 10. Chapter 3 Recent advancements and techniques in NLP and emotion-based decision making in AI - p. 33 11. Chapter 4 Building models for emotional AI: A case study on sentiment analysis using python - p. 61 12. Chapter 5 Fully automated vs. semi-automated transportation system: A comparative study on graph neural network - p. 81 13. Chapter 6 Why tend not to adopt? Adoption resistance of chatbots in tourism and hospitality in India - p. 97 14. Chapter 7 Accountability in emotional AI: Legal responsibilities and implications of misinterpreted emotions - p. 115 15. Chapter 8 Unleashing the potential of AI with human doctors: Role of patient diagnosis with ensemble models - p. 139 16. Chapter 9 GANs beyond Earth: The GAN revolution for imaging, segmentation and human space exploration - p. 159 17. Chapter 10 Translating neural activity into language: A machine learning approach for EEG-based thought-to-text conversion - p. 181 18. Chapter 11 Empowering clinical diagnosis with emotionally intelligent AI: A deep ensemble framework for skin cancer detection - p. 199 19. Chapter 12 Emotion-based decision-making in Artificial Intelligence: New developments and trends in Natural Language Processing - p. 225 20. Chapter 13 Case study on gesture-based emotion AI in blood pressure detection: Revolution in healthcare sectors - p. 249 21. Chapter 14 Ethical and legal standards, policies and regulations in emotional AI: Myths and realities - p. 265 22. Chapter 15 Emotional AI tutors a case study on adaptive teaching using facial expression recognition - p. 279 23. Chapter 16 Exporing emotions and sentiment analysis in AI: An evolutionary algorithm based perspectivel - p. 303 24. Chapter 17 Case study on emotion-aware driver assistance systems in Tesla car - p. 319 25. Index - p. 347
Emotional Artificial Intelligence: Practical Challenges and Applications explores the intersection of artificial intelligence and human emotions, challenging the myth that AI will replace humans. Instead, it emphasizes how emotional AI can enhance collaboration, improving efficiency across fields like healthcare, transportation, and space exploration. The book explores foundational theories such as Goleman’s emotional model and Ekman’s emotion theory, discussing methodologies for understanding human emotions through natural language processing and the ethical implications of AI in decision-making. It showcases real-world applications, from virtual nurses to emotion-aware driving systems, illustrating how these technologies can improve daily interactions and outcomes.
This essential resource equips researchers, practitioners, and policymakers with practical insights for integrating emotional AI into diverse domains. By addressing the complexities of human emotion recognition and ethical frameworks, it provides a forward-looking perspective on enhancing collaboration between humans and AI, ultimately driving improved productivity and efficiency.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. About the editors - p. xiii 8. Chapter 1 Explainable AI in emotional artificial intelligence: Challenges and applications - p. 1 9. Chapter 2 Advancing emotional intelligence in artificial intelligence: Challenges and applications - p. 15 10. Chapter 3 Recent advancements and techniques in NLP and emotion-based decision making in AI - p. 33 11. Chapter 4 Building models for emotional AI: A case study on sentiment analysis using python - p. 61 12. Chapter 5 Fully automated vs. semi-automated transportation system: A comparative study on graph neural network - p. 81 13. Chapter 6 Why tend not to adopt? Adoption resistance of chatbots in tourism and hospitality in India - p. 97 14. Chapter 7 Accountability in emotional AI: Legal responsibilities and implications of misinterpreted emotions - p. 115 15. Chapter 8 Unleashing the potential of AI with human doctors: Role of patient diagnosis with ensemble models - p. 139 16. Chapter 9 GANs beyond Earth: The GAN revolution for imaging, segmentation and human space exploration - p. 159 17. Chapter 10 Translating neural activity into language: A machine learning approach for EEG-based thought-to-text conversion - p. 181 18. Chapter 11 Empowering clinical diagnosis with emotionally intelligent AI: A deep ensemble framework for skin cancer detection - p. 199 19. Chapter 12 Emotion-based decision-making in Artificial Intelligence: New developments and trends in Natural Language Processing - p. 225 20. Chapter 13 Case study on gesture-based emotion AI in blood pressure detection: Revolution in healthcare sectors - p. 249 21. Chapter 14 Ethical and legal standards, policies and regulations in emotional AI: Myths and realities - p. 265 22. Chapter 15 Emotional AI tutors a case study on adaptive teaching using facial expression recognition - p. 279 23. Chapter 16 Exporing emotions and sentiment analysis in AI: An evolutionary algorithm based perspectivel - p. 303 24. Chapter 17 Case study on emotion-aware driver assistance systems in Tesla car - p. 319 25. Index - p. 347
