Deep Learning is a subset of machine learning that uses artificial neural networks with multiple layers (hence "deep") to model and understand complex patterns in datasets. It's particularly effective for tasks like image and speech recognition, natural language processing, and translation, and it's the technology behind many advanced AI systems.
Researchers introduce a speech emotion recognition (SER) system that accurately predicts a speaker's emotional state using audio signals. By employing convolutional neural networks (CNN) and Mel-frequency cepstral coefficients (MFCC) for feature extraction, the proposed system outperforms existing approaches, showcasing its potential in various applications such as human-computer interaction and emotion-aware technologies.
Researchers have developed the PETAL sensor patch, a paper-like wearable device that incorporates five colorimetric sensors for comprehensive wound monitoring. With the aid of artificial intelligence and deep learning algorithms, the patch accurately classifies wound healing status, providing early warning for timely intervention and enhancing wound care management.
Engineers at Rice University and the University of Maryland have developed NeuWS, a full-motion video technology capable of seeing through scattering media like fog, smoke, and even body tissues. The technology uses a combination of neural networks and complex wavefront shaping techniques to rapidly measure and correct for light scattering, overcoming a significant challenge in optical imaging.
Large-scale multi-label dataset, Incidents1M, is introduced for incident detection and image-filtering experiments on social media. It enables timely understanding of natural disaster progression and aftermath using automated methods.
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