Multimodal Affective Computing
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Multimodal Affective Computing
Author: Ramón Zatarain Cabada
language: en
Publisher: Springer Nature
Release Date: 2023-06-26
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.
Multimodal Affective Computing: Affective Information Representation, Modelling, and Analysis
Author: Gyanendra K. Verma
language: en
Publisher: Bentham Science Publishers
Release Date: 2023-03-21
Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimodal Affective Computing offers readers a concise overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches in applied affective computing systems and social signal processing. It covers affective facial expression and recognition, affective body expression and recognition, affective speech processing, affective text, and dialogue processing, recognizing affect using physiological measures, computational models of emotion and theoretical foundations, and affective sound and music processing. This book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered.The book is an informative resource for academicians, professionals, researchers, and students at engineering and medical institutions working in the areas of applied affective computing, sentiment analysis, and emotion recognition.
Intelligent Multidimensional Data and Image Processing
As the most natural and convenient means of conveying or transmitting information, images play a vital role in our daily lives. Image processing is now of paramount importance in the computer vision research community, and proper processing of two-dimensional (2D) real-life images plays a key role in many real-life applications as well as commercial developments. Intelligent Multidimensional Data and Image Processing is a vital research publication that contains an in-depth exploration of image processing techniques used in various applications, including how to handle noise removal, object segmentation, object extraction, and the determination of the nearest object classification and its associated confidence level. Featuring coverage on a broad range of topics such as object detection, machine vision, and image conversion, this book provides critical research for scientists, computer engineers, professionals, researchers, and academicians seeking current research on solutions for new challenges in 2D and 3D image processing.