Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …

Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-
based models can produce feedback from physiological data such as …

Prediction of indoor airborne pollutant concentrations can enable a smart indoor air quality
control strategy that potentially reduces building energy use and improves occupant comfort …

In this paper, we introduce DetailCLIP: A Detail-Oriented CLIP to address the limitations of
contrastive learning-based vision-language models, particularly CLIP, in handling detail …

In this paper, a numerical approach is proposed based on least squares support vector
regression for solving Volterra integral equations of the first and second kind. The proposed …

Cataloging the complex behaviors of dynamical systems can be challenging, even when
they are well-described by a simple mechanistic model. If such a system is of limited …

Deep learning is a crucial point of valuable intelligence resources to deal with complicated
mathematical problems. The effectiveness of deep learning in solving differential equations …

Time-varying fluid flows are ubiquitous in modern engineering and in the life sciences.
Particularly challenging is the characterization of unsteady aerodynamic forces and …

The reaction-diffusion equation is one of the cornerstones equations in applied science and
engineering. In the present study, a deep neural network has been trained in order to predict …