Affective Artificial Intelligence

Fundamentals, Challenges, and Applications
1. Auflage 2026
ISBN: 978-1-394-42272-2
Verlag: Wiley

Buch, Englisch, 384 Seiten

Bridge the final frontier between logic and empathy with this definitive guide to building AI systems that do not just process data, but truly comprehend, interpret, and anticipate human emotion.

As the role of AI continues to grow in our everyday lives, there is an increasing need to address the fundamental connections between AI and humans. Innovators across a range of industries are exploring the principles, technological foundations, and practical applications of AI systems that can comprehend, interpret, and respond to human emotions. This book is an essential guide to one of the most transformative frontiers of AI, where machines utilize human emotion in communicative contexts to bridge the chasm between people and technology. It explores the cutting-edge field of AI systems that go beyond traditional AI, which operates on logic and predefined algorithms, to systems that are attuned to human emotions, interactions, and psychological states. The volume highlights the significance of human-like emotional intelligence in AI and its potential to create more natural, empathetic, and intuitive human-AI interactions by delving into the technological underpinnings of affective AI, including emotion recognition techniques such as facial expression analysis, speech emotion recognition, and physiological signal processing and discusses how these systems capture and interpret emotional cues from users, allowing AI to not only react to stimuli but anticipate human needs, feelings, and intentions. Through expert insights and real-world case studies, readers gain an in-depth understanding of how affective AI is poised to redefine the relationship between humans and machines.

Readers will find the volume: - Introduces the emergence of affective artificial intelligence;
- Discusses different applications of affective artificial intelligence in various industries;
- Presents state-of-the-art transfer learning analysis techniques.

Audience

AI and Machine Learning researchers, academics, and engineers working towards AI-driven emotion recognition, sentiment analysis, and affective computing applications.

Autoren/Hrsg.

Thillaiarasu, N.

Revathy, G.

Saravanan, T.

Balamurugan, S.

Muthukumaran, V.

Fachgebiete

• Sozialwissenschaften Psychologie Allgemeine Psychologie Sozialpsychologie
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

Series Preface xvii
Preface xix
Acknowledgement xxi

Part I: Fundamentals of Affective AI 1

1 Emotion Recognition Techniques: Facial Expression Analysis, Speech Emotion Recognition, Physiological Signal Processing, and Multimodal Approaches 3
Monish L., Shaila S.G., Ramesh Chundi, Sumana S.G., Shivamma D. and Manjula M.

2 Natural Language Processing for Sentiment and Emotion Analysis – AI-Driven Methods for Detecting the Emotions in Text, Chatbots, and Social Media 25
Shaila S.G., Monish L., Sumana S.G. and Ramesh Chundi

3 Affective Computing in Edge-Based Healthcare Systems 45
Dhamarai Selvi K.V., G. Revathy, Raja Rajeswari Balaji and M. Shyamalagowri

Part II: Machine Learning in Affective AI 77

4 Deep Learning for Emotion Recognition – CNNs, RNNs, Transformers, and Self-Supervised Learning Techniques 79
Shivamma D., Shaila S.G., Monish L., Ramesh Chundi and Sumana S.G.

5 Early Detection of Mental Health Disorders Using Affective AI on Social Media Textual Data 105
A. Sumathi, V. Rishikeswaran and P. Umamaheswari

6 Multi-Modal Emotion Detection System: Fusion of Facial Voice and Physiological Data for Improved Emotion Understanding 129
Monish L., Shaila S.G., Ramesh Chundi, Sumana S.G. and Manjula M.

7 Affective AI-Enhanced Ensemble Learning for Cardiac Disease Risk Prediction 153
M. Sridevia, A. L. Sriramb, P. Veeraragavanc and P. Madhavasarmad

Part III: Applications Affective AI 175

8 Affective AI in Healthcare and Mental Health – AI-Driven Emotion Detection for Diagnosing Depression, Anxiety, and Autism Spectrum Disorders 177
Shivamma D., Shaila S.G., Monish L., Ramesh Chundi and Sumana S.G.

9 Emotionally Aware Machine Learning for X-Ray Fracture Classification: A SHAP-Driven Approach 193
M. Sridevi, S. Venkatesh, P. Veeraragavan and P. Madhavasarma

10 Understanding Affective AI: A Review of Emotion Detection Computation and Real-World Applications 211
Umamaheswari P., Abiramasundari S. and Sumathi A.

11 Smart Attendance System Using Face Emotion Recognition and Power BI for Affective AI 225
S. Selvi, S. A. Dhanasekaran and B. Vinaykrishna

Part IV: Real World Applications of Affective AI 243

12 The Role of Logic in Emotion Understanding: A Neurosymbolic Perspective 245
M. Thangavel, D. Ravikumar, S. Senthilvadivu and M. Santhosh Kumar

13 Transfer Learning for Affective Computing: Challenges and Opportunities 265
Madona B. Sahaai, D. R. Ashwin Kumar, A. Priyadharshini and S. Gokulraj

14 Explainability and Interpretability in Affective AI Methods for Making AI-Driven Emotion Recognition Transparent and Trustworthy 285
Farjana Farvin Sahapudeen and T. Vigneswari

15 From EEG to Emotion: Spatio-Temporal Deep Learning for Real-Time Affective State Recognition 311
S. Murali Mohan, B. Rupa Devi, R. Tharun, Arakonam Elen Kokila, Konkala Divya and M. Sudhakara

References 349
Index 353

Über Autor(innen)

N. Thillaiarasu, PhD is an Associate Professor in the School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India with more than 12 years of teaching experience. He has more than 70 publications to his credit. His areas of interest include cloud computing, security, IoT, and machine learning.

G. Revathy, PhD is an Assistant Professor in the Department of Computer Science and Engineering in the Srinivasa Ramunjan Centre at SASTRA University, Kumbakonam, Tamil Nadu, India with more than 15 years of experience. She has published more than 35 international journal articles, ten books, and eight book chapters. Her research focuses on machine learning, computer vision, wireless mesh networks, Internet of Things and blockchain.

T. Saravanan, PhD is an Assistant Professor at GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India with more than ten years of experience. He has published many research papers in international journals and conferences, book chapters, and Indian patents. His research interests include computer networks, fuzzy logic, and wireless sensor networks.

V. Muthukumaran, PhD is an Assistant Professor in the Department of Mathematics at the SRM Institute of Science and Technology, Kattankulathur Campus, Tamil Nadu, India. He has 12 chapters, 12 patents, and has presented 25 papers in international conferences. His current research interests include machine learning, data science, blockchain, data mining, and algebraic cryptography.

S. Balamurugan, PhD is the Director of Research at the Indian Technological Research and Consulting Firm. With 20 years of researching various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies and startups. He has published 75 books, 300 papers in international journals, and conferences, and 300 patents.