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Affective Artificial Intelligence
Fundamentals, Challenges, and Applications
N. Thillaiarasu (Edited by), N Thillaiarasu (Author), G. Revathy (Edited by), T. Saravanan (Edited by), V. Muthukumaran (Edited by), S. Balamurugan (Edited by)
9781394422722, Wiley
Hardback, published 29 June 2026
384 pages
22.9 x 15.2 x 2.4 cm, 0.648 kg
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: Audience AI and Machine Learning researchers, academics, and engineers working towards AI-driven emotion recognition, sentiment analysis, and affective computing applications.
Series Preface xvii Part I: Fundamentals of Affective AI 1 1 Emotion Recognition Techniques: Facial Expression Analysis, Speech Emotion Recognition, Physiological Signal Processing, and Multimodal Approaches 3 2 Natural Language Processing for Sentiment and Emotion Analysis – AI-Driven Methods for Detecting the Emotions in Text, Chatbots, and Social Media 25 3 Affective Computing in Edge-Based Healthcare Systems 45 Part II: Machine Learning in Affective AI 77 4 Deep Learning for Emotion Recognition – CNNs, RNNs, Transformers, and Self-Supervised Learning Techniques 79 5 Early Detection of Mental Health Disorders Using Affective AI on Social Media Textual Data 105 6 Multi-Modal Emotion Detection System: Fusion of Facial Voice and Physiological Data for Improved Emotion Understanding 129 7 Affective AI-Enhanced Ensemble Learning for Cardiac Disease Risk Prediction 153 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 9 Emotionally Aware Machine Learning for X-Ray Fracture Classification: A SHAP-Driven Approach 193 10 Understanding Affective AI: A Review of Emotion Detection Computation and Real-World Applications 211 11 Smart Attendance System Using Face Emotion Recognition and Power BI for Affective AI 225 Part IV: Real World Applications of Affective AI 243 12 The Role of Logic in Emotion Understanding: A Neurosymbolic Perspective 245 13 Transfer Learning for Affective Computing: Challenges and Opportunities 265 14 Explainability and Interpretability in Affective AI Methods for Making AI-Driven Emotion Recognition Transparent and Trustworthy 285 15 From EEG to Emotion: Spatio-Temporal Deep Learning for Real-Time Affective State Recognition 311 References 349
Preface xix
Acknowledgement xxi
Monish L., Shaila S.G., Ramesh Chundi, Sumana S.G., Shivamma D. and Manjula M.
Shaila S.G., Monish L., Sumana S.G. and Ramesh Chundi
Dhamarai Selvi K.V., G. Revathy, Raja Rajeswari Balaji and M. Shyamalagowri
Shivamma D., Shaila S.G., Monish L., Ramesh Chundi and Sumana S.G.
A. Sumathi, V. Rishikeswaran and P. Umamaheswari
Monish L., Shaila S.G., Ramesh Chundi, Sumana S.G. and Manjula M.
M. Sridevia, A. L. Sriramb, P. Veeraragavanc and P. Madhavasarmad
Shivamma D., Shaila S.G., Monish L., Ramesh Chundi and Sumana S.G.
M. Sridevi, S. Venkatesh, P. Veeraragavan and P. Madhavasarma
Umamaheswari P., Abiramasundari S. and Sumathi A.
S. Selvi, S. A. Dhanasekaran and B. Vinaykrishna
M. Thangavel, D. Ravikumar, S. Senthilvadivu and M. Santhosh Kumar
Madona B. Sahaai, D. R. Ashwin Kumar, A. Priyadharshini and S. Gokulraj
Farjana Farvin Sahapudeen and T. Vigneswari
S. Murali Mohan, B. Rupa Devi, R. Tharun, Arakonam Elen Kokila, Konkala Divya and M. Sudhakara
Index 353
Subject Areas: Computer science [UY]
