Emotion recognition systems (ERS) have become a popular research field to contribute to
human-machine interaction in different areas. Different kinds of applications on ERS can …

Electroencephalogram (EEG) signals in recognizing emotions have several advantages.
Still, the success of this study, however, is strongly influenced by: i) the distribution of the …

Due to a large number of potential applications, a good deal of effort has been recently
made toward creating machine learning models that can recognize evoked emotions from …

Emotion recognition has increased the potential of affective computing by getting an instant
feedback from users and thereby, have a better understanding of their behavior …

In recent years, deep learning has been increasingly used in the field of multimodal emotion
recognition in conjunction with electroencephalogram. Considering the complexity of …

In the recycling industry, the use of deep spectral convolutional networks for the purpose of
material classification and composition estimation is still limited, despite the great …

Emotion recognition plays an important role in the field of human–computer interaction
(HCI). An electroencephalogram (EEG) is widely used to estimate human emotion owing to …

With the development of sensing technologies and machine learning, techniques that can
identify emotions and inner states of a human through physiological signals, known as …

The purpose of this study is to utilize machine learning techniques to detect any damages
that occurred in power transmission towers. In the first step, various machine learning …

Studies have investigated electroencephalogram (EEG)-based emotion recognition using
hand-crafted EEG features (eg, differential entropy) or the annotated emotion categories …