Data-driven health prognostic is attracting more and more attention to machinery prognostic
and health management. It enables machinery to realize predictive maintenance and rarely …

Recent studies found that using machine learning for social applications can lead to
injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes. To …

At present, the method of emotion recognition based on Electroencephalogram (EEG)
signals has received extensive attention. EEG signals have the characteristics of non-linear …

Remote sensing image scene classification aims to automatically assign semantic labels for
remote sensing images. Recently, to overcome the distribution discrepancy of training data …

Understanding traffic scene images taken from vehicle mounted cameras is important for
high-level tasks, such as advanced driver assistance systems and autonomous driving. It is …

Many incurable diseases prevalent across global societies stem from various influences,
including lifestyle choices, economic conditions, social factors, and genetics. Research …

Unsupervised domain adaptation is used to effectively learn a classifier for data of the
unlabeled target domain by utilizing the data of the source domain with sufficient labels but …

Abstract Machine learning-based human activity recognition (HAR) is important as the
means of human–computer interaction to empower the existing systems in many areas, such …

In this paper, we propose a new theoretical analysis of unsupervised domain adaptation that
relates notions of large margin separation, adversarial learning and optimal transport. This …

Sepsis is a deadly condition affecting many patients in the hospital. There have been many
efforts to build models that predict the onset of sepsis, but these models tend to perform …