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Emotional AI and Human-AI Interactions in Social Networking

1st Edition - August 20, 2023

Authors: Muskan Garg, Deepika Koundal

eBook ISBN:

9 7 8 - 0 - 4 4 3 - 1 9 0 9 7 - 1

Emotional AI and Human-AI Interactions in Social Networking makes readers aware of recent progress in this integrated discipline. Filling the existing vacuum in research in… Read more

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Emotional AI and Human-AI Interactions in Social Networking makes readers aware of recent progress in this integrated discipline. Filling the existing vacuum in research in artificial intelligence with the application of social science, this book provides in-depth knowledge of human-AI interactions with social networking and increased use of the internet. Chapters integrating Emotional Artificial Intelligence, examining behavioral interventions, compassion, education, and healthcare, as well as social cognitive networking, including social brain networks, play a pivotal role in enhancing interdisciplinary studies in the field of social neuroscience and Emotional AI.

This volume is a must for those wanting to dive into this exciting field of social neuroscience AI.

Cover image
Title page
Table of Contents
Copyright
Contributors
Chapter One. Introduction to social neuroscience
1. Introduction
2. Applications or focus on the whole
3. In contrast to explain more about the application, opportunities
4. Impact on society
5. Application social neurosciences
6. Where we can open the consultancy center?
7. Possibilities
8. Methods for investigating neural and social processes
9. Challenges and obstacles
10. Future scope of social neurosciences
11. Role of social Networking
12. Virtual reality
13. Author's opinion and recommendations
14. Outside of the lab
15. Conclusions
Chapter Two. Social neuroscience: inferring mental states in social media
1. Introduction
2. Related work
3. New techniques for understanding emotions in social media
4. The power of multimodal data analysis in mental state inference
5. The role of natural language processing
6. Challenges and limitations
7. Applications of inferencing
8. The need for cultural and contextual awareness
9. A comprehensive review of mental state inference in social media: Current state and future directions
10. Conclusion and future Scope
Chapter Three. Detection of social mental disorder using convolution neural network
1. Introduction
2. The application of deep learning in personality analysis and work-based learning
3. Experiments employ personality drafts and data
4. Conclusions
Chapter Four. Social Neuroscience: AI for education
1. Introduction
2. Related work
3. Training and academic prerequisites
4. Educational neuroscience
5. Findings
6. Importance of social and educational neuroscience
7. Conclusion
Chapter Five. Emotional AI: Computationally intelligent devices for education
1. Introduction
2. Emotional intelligence
3. Artificial intelligence
4. Emotional artificial intelligence
5. Ethics of EAI in educational sector
6. Future of emotional artificial intelligence in education
7. Conclusion
Chapter Six. Emotional AI: Neuroethics and Socially aligned networks
1. Introduction
2. Research methods
3. Human–AI interfaces within socially aligned networks
4. Boundary issues
5. Conclusion
Chapter Seven. Emotion AI in healthcare: Application, challenges, and future directions
1. Introduction
2. Applications of emotion AI
3. Emotion AI and healthcare
4. Applications of EAI in healthcare
5. Case studies of EAI devices
6. Challenges and issues in EAI and healthcare
7. Conclusion
8. Future prospects
Chapter Eight. Machine learning model for teaching and emotional intelligence
1. Introduction
2. Related work
3. Cognitive behavior therapy evaluation
4. Experimental study
5. Discussion
6. Conclusion
Chapter Nine. Emotion AI: Cognitive behavioral therapy for teens having some mental health disorders
1. Introduction
2. Related work
3. Cognitive behavior therapy evaluation
4. Analysis of experimental results
5. Conclusion
Chapter Ten. Human AI: Ethics and broader impact for mental healthcare
1. Introduction
2. Status of mental health issues
3. AI in daily affairs
4. Application of AI in mental health services
5. Issues of employing AI ethically in the mental health care services
6. Challenges in using AI in the mental healthcare setting
7. Legal and ethical considerations in AI in mental healthcare services
8. Stakeholders involved in promoting AI-based mental healthcare services
9. Steps to ethics-based governance of AI in mental healthcare services
10. Current trends and status of human-centric AI-based mental health services
11. Discussion and future implications
12. Conclusion
Chapter Eleven. Human AI: Social network analysis
1. Introduction to social network analysis
2. Understanding networking and their partners
3. Background of social network analysis
4. Depicting SNA via graphs and graph theory
5. Benefits of graphical algorithms
6. Visualization of social networks
7. Centrality and composition
8. The foundations of network connectivity
9. Conclusion
Chapter Twelve. Human AI: Explainable and responsible models in computer vision
1. Introduction
2. Background literature
3. Ontology and explainable AI
4. Results and discussion
5. Facial emotion detection: application inferring human mental states
6. Conclusion
Chapter Thirteen. Human AI: Social robot decision-making using emotional AI and neuroscience
1. Introduction
2. Background
3. The proposed method
4. Results
5. Conclusion
Chapter Fourteen. Human AI: Neurodegenerative disorders and conceptualization of cognitive ability
1. Introduction
2. Literature review
3. Pathology
4. Anatomic distribution of neurodegeneration
5. Factors contributing to neurodegeneration
6. Protein misfolding and impaired protein clearance
7. Diagnosis
8. Future scope and prospects
9. Conclusion
Index

Muskan Garg

Dr. Muskan Garg is working as a postdoctoral research fellow at Mayo Clinic, Rochester, Minnesota. She was previously working as Postdoctoral research associate at University of Florida and as an assistant professor in Thapar institute of engineering and technology, Patiala. Her research focuses on the problems in natural language processing, information retrieval, and social media analysis. She received her Masters and PhD from Panjab University, India. Prior to TIET, she worked as an assistant professor in Amity school of engineering and technology at Amity University. Her focus is on research and development of cutting-edge NLP approaches to solving problems of national and international importance and on initiation and broadening a new program in natural language processing (including a new NLP course series). Her current research interests are causal inference, mental health on social media, event detection and sentiment analysis. She contributes as a reviewer in The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) and Expert Systems with Applications, and prestigious conferences such as ACL, ICWSM, and EACL.

Affiliations and expertise

Mayo Clinic, Rochester, MN, USA.

Deepika Koundal

Dr. Deepika Koundal is currently associated with the University of Petroleum and Energy Studies, Dehradun. She has 12 years of teaching and research experience at many reputed Universities of India. She received her Bachelor Degree in Computer Science and Engineering from Kurukshetra University, Kurukshetra, India and subsequently her Masters and Doctorate Degrees in Computer Science & Engineering from Panjab University, Chandigarh, India. Her Ph.D. thesis is focused on Automated delineation of thyroid nodules in Ultrasound Images. She is actively pursuing research in Medical Image Processing. She is the awardee of Research excellence award given by Chitkara University in 2019. She also received the recognition and honorary membership from Neutrosophic Science Association from University of Mexico for her outstanding publication. She has published more than 30 research articles in reputed SCI and Scopus indexed journals and conferences.

Affiliations and expertise

University of Petroleum and Energy Studies, Dehradun, India IEEE Senior Member, WIE