Multimodal affective computing : technologies and applications in learning environments / Ramón Zatarain Cabada, Héctor Manuel Cárdenas López, Hugo Jair Escalante.
Cabada, Ramón Zatarain.; López, Héctor Manuel Cárdenas.; Escalante, Hugo Jair.
2023
LB1028.3 .C33 2023
Available Online

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Title Multimodal affective computing : technologies and applications in learning environments / Ramón Zatarain Cabada, Héctor Manuel Cárdenas López, Hugo Jair Escalante.
Author Cabada, Ramón Zatarain.
ISBN 9783031325427 (electronic bk.)
3031325427 (electronic bk.)
3031325419
9783031325410
DOI https://doi.org/10.1007/978-3-031-32542-7
Imprint Cham : Springer, 2023.
Language English
Description 1 online resource (211 p.)
Other Standard Identifiers 10.1007/978-3-031-32542-7 doi
Call Number LB1028.3 .C33 2023
Dewey Decimal Classification 371.334
Summary This book explores AI methodologies for the implementation of affective states in intelligent learning environments. Divided into four parts, Multimodal Affective Computing: Technologies and Applications in Learning Environments begins with an overview of Affective Computing and Intelligent Learning Environments, from their fundamentals and essential theoretical support up to their fusion and some successful practical applications. The basic concepts of Affective Computing, Machine Learning, and Pattern Recognition in Affective Computing, and Affective Learning Environments are presented in a comprehensive and easy-to-read manner. In the second part, a review on the emerging field of Sentiment Analysis for Learning Environments is introduced, including a systematic descriptive tour through topics such as building resources for sentiment detection, methods for data representation, designing and testing the classification models, and model integration into a learning system. The methodologies corresponding to Multimodal Recognition of Learning-Oriented Emotions are presented in the third part of the book, where topics such as building resources for emotion detection, methods for data representation, multimodal recognition systems, and multimodal emotion recognition in learning environments are presented. The fourth and last part of the book is devoted to a wide application field of the combination of methodologies, such as Automatic Personality Recognition, dealing with issues such as building resources for personality recognition, methods for data representation, personality recognition models, and multimodal personality recognition for affective computing. This book can be very useful not only for beginners who are interested in affective computing and intelligent learning environments, but also for advanced and experts in the practice and developments of the field. It complies an end-to-end treatment on these subjects, especially with educational applications, making it easy for researchers and students to get on track with fundamentals, established methodologies, conventional evaluation protocols, and the latest progress on these subjects.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed July 12, 2023).
Added Author López, Héctor Manuel Cárdenas.
Escalante, Hugo Jair.
Available in Other Form Multimodal Affective Computing
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Part I: Fundamentals
Chapter 1. Affective Computing
Chapter 2. Machine learning and pattern recognition in affective computing
Chapter 3. Affective Learning Environments
Part II: Sentiment Analysis for Learning Environments
Chapter 4. Building resources for sentiment detection
Chapter 5. Methods for data representation
Chapter 6. Designing and testing the classification models
Chapter 7. Model integration to a learning system
Part III: Multimodal Recognition of Learning-Oriented Emotions
Chapter 8. Building Resources for Emotion Detection
Chapter 9. Methods for Data Representation
Chapter 10. Multimodal recognition systems
Chapter 11. Multimodal emotion recognition in learning environments
Part IV: Automatic Personality Recognition
Chapter 12. Building resources for personality recognition
Chapter 13. Methods for data representation
Chapter 14. Personality recognition models
Chapter 15. Multimodal personality recognition for affective computing.

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