The success of deep learning over the traditional machine learning techniques in handling
artificial intelligence application tasks such as image processing, computer vision, object …

In recent years, with the rapid development of machine learning, automatic emotion
recognition based on electroencephalogram (EEG) signals has received increasing …

This paper presents the results from the experimental application of smartwatch sensors to
predict occupants' thermal comfort under varying environmental conditions. The goal is to …

Exploration on emotions continues from past to present. Nowadays, with the rapid
advancement of intelligent technology, computer-aided emotion recognition using …

Multi-lead electrocardiogram (ECG) is a fundamental and reliable diagnostic tool for the
detection of heart arrhythmias. An increasing number of deep neural network models have …

Radiogenomic and radiotranscriptomic studies have the potential to pave the way for a
holistic decision support system built on genomics, transcriptomics, radiomics, deep features …

Human emotions analysis has been the focus of many studies, especially in the field of
Affective Computing, and is important for many applications, eg human-computer intelligent …

Affect modeling is viewed, traditionally, as the process of mapping measurable affect
manifestations from multiple modalities of user input to affect labels. That mapping is usually …

Human stress is intricately linked with mental processes such as decision making. Public
protection practitioners, including Law Enforcement Agents (LEAs), are forced to make …

This paper explores the influence of convolutional layer in deep multimodal fusion
(intermediate fusion) for the detection of subject-independent stress using physiological …