Phys.org

Using geometry and physics to explain feature learning in deep neural networks

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...
Frontiers

Intelligent emotion recognition for drivers using model-level multimodal fusion

Unstable emotions are considered to be an important factor contributing to traffic accidents. The probability of accidents can be reduced if emotional anomalies of drivers can be quickly identified ...
Smithsonian Magazine

Showcasing Digital Storytelling as a Tool for Active and Deep Learning

After almost a decade of hands-on workshops and ongoing research, educators share the power of digital storytelling as an approach that makes learning more personal, emotional, and impactful Antonia ...
IEEE

Speech Emotion Recognition Using Deep Learning

Abstract: This study explores the application of deep learning techniques in recognizing emotional states from spoken language. Specifically, we employ Convolutional Neural Networks (CNNs) and the ...
C&EN

Neural Network-Based Molecular Dynamics Simulation of Water Assisted by Active Learning

School of Information, Guizhou University of Finance and Economics, University City of Huaxi District, Guiyang, Guizhou 550025, PR China Guizhou Provincial Key Laboratory of Computational ...
Frontiers

An improved multi-input deep convolutional neural network for automatic emotion recognition

Current decoding algorithms based on a one-dimensional (1D) convolutional neural network (CNN) have shown effectiveness in the automatic recognition of emotional tasks using physiological signals.
IEEE

Efficient Arabic Emotion Recognition Using Deep Neural Networks

Abstract: Emotion recognition from speech signal based on deep learning is an active research area. Convolutional neural networks (CNNs) may be the dominant method in this area. In this paper, we ...