In recent years, emotion recognition has received increasing attention as it plays an
essential role in human-computer interaction systems. This paper proposes a four-class …

This study explores a novel approach to emotion recognition through machine learning,
addressing the limitations of previous methods. While deep learning has shown promise in …

The purpose of this study is to improve human emotional classification accuracy using a
convolution neural networks (CNN) model and to suggest an overall method to classify …

This research contributed to the development of advanced feature selection model,
hyperparameter optimization and temporal multimodal deep learning model to improve the …

In this paper, we propose a multimodal approach to emotion recognition using physiological
signals by showing how these signals can be combined and used to accurately identify a …

Abstract Background and Objective Using deep-learning methodologies to analyze
multimodal physiological signals becomes increasingly attractive for recognizing human …

Physiological signal analysis has gained a lot of interest in recent years and has been used
in a variety of fields including emotion recognition, activity recognition, and health …

Emotion recognition has become increasingly utilized in the medical, advertising, and
military domains. Recognizing the cues of emotion from human behaviors or physiological …

Physiological signals are the most reliable form of signals for emotion recognition, as they
cannot be controlled deliberately by the subject. Existing review papers on emotion …

The human body as an entire structure of a person contains physiological and physical
reactions that connect with emotions. Emotions play a crucial role in our day to day activities …