This paper provides a comprehensive overview of affective computing systems for facial
expression recognition (FER) research in naturalistic contexts. The first section presents an …

In recent years, the rapid growth of online learning has highlighted the need for effective
methods to monitor and improve student experiences. Emotions play a crucial role in …

Facial expression recognition (FER) is a crucial task for human-computer interaction and a
multitude of multimedia applications that typically call for friendly, unobtrusive, ubiquitous …

Compared to other social media data, email data differs from it in various topic-specific ways,
including extensive replies, formal language, significant length disparities, high levels of …

In computer vision, the convolutional neural network (CNN) is a very popular model used for
emotion recognition. It has been successfully applied to detect various objects in digital …

In real-world dialog systems, the ability to understand the user's emotions and interact
anthropomorphically is of great significance. Emotion Recognition in Conversation (ERC) is …

Automatic signal classification is utilized in various medical and industrial applications,
particularly in schizophrenia (SZ) diagnosis, one of the most prevalent chronic neurological …

Emotion recognition using electroencephalogram signals has been widely studied in the last
decade, achieving artificial intelligence models that accurately classify primitive or primary …

Emotion recognition based on electroencephalography (EEG) has attracted significant
attention and achieved considerable advances in the fields of affective computing and …

Abstract Facial Expression Recognition (FER) suffers from data uncertainties caused by
ambiguous facial images and annotators' subjectiveness, resulting in excursive semantic …