Table of Contents:
  • Section 1: Socio-cognitive computing. Chapter 1. Cognitive computing ; Chapter 2. Use of socio-cognitive and affective computing to teach emotions to autistic children ; Chapter 3. An emotion-aware e-learning system based on psychophysiology ; Chapter 4. A predictive model emotion recognition on deep learning and shallow learning techniques using EEG signal ; Chapter 5. Enhanced BiLSTM model for EEG emotional data analysis ; Chapter 6. Harnessing the IoT-based activity trackers and sensors for cognitive assistance in COVID-19
  • Section 2. Affective computing. Chapter 7. Feasibility and necessity of affective computing in emotion sensing of drivers for improved road safety ; Chapter 8: A comprehensive overview of exercises for reducing stress among students in engineering institutions ; Chapter 9. Behavioral diagnosis of children utilizing support vector machine for early disorder detection ; Chapter 10. An analysis on multimodal framework for silent speech recognition ; Chapter 11. Voice-based image captioning system for assisting visually impaired people using neural networks ; Chapter 12. Statistical hypothesization and predictive modeling of reactions to COVID-19-induced remote work.