Computational Intelligence for Affective Computing: Modeling, Algorithms and Applications

Dear Colleagues,

Affective computing aims to enable intelligent systems to recognize, interpret, and respond to human emotions, playing a critical role in human-centered artificial intelligence. With the rapid development of computational intelligence techniques—including machine learning, deep learning, evolutionary computation, and large-scale foundation models—affective computing has experienced significant advances in modeling emotional states, learning affective representations, and deploying real-world applications. Meanwhile, the increasing availability of multimodal data such as speech, facial expressions, physiological signals, text, and video introduces both opportunities and challenges for robust and interpretable affective analysis.

This Special Issue focuses on the integration of computational intelligence methods with affective computing, emphasizing novel models, efficient algorithms, and practical applications. Topics of interest include, but are not limited to, affective modeling and representation learning, multimodal emotion recognition and fusion, explainable and trustworthy affective systems, affective video and speech analysis, emotion-aware large models, and embodied or interactive affective intelligence. Application domains span human–computer interaction, mental health assessment, education, social robotics, intelligent recommendation, and multimedia understanding.

The aim of this Special Issue is to provide a forum for researchers and practitioners to present cutting-edge theoretical developments, algorithmic innovations, and application-driven studies, thereby advancing the state of the art in computational intelligence for affective computing.

Dr. Jisheng Dang
Prof. Dr. Wenjie Wang
Dr. Bimei Wang
Guest Editors

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