Articles tagged with Deep Learning
Researchers at Chung-Ang University developed a novel GAN model, PMF-GAN, to address stability and efficiency issues. The model utilizes kernel functions and histogram transformations to improve the generator's ability to produce diverse outputs, reducing mode collapse and gradient vanishing.
A new study uses deep learning to infer the frequency of atmospheric blocking events over the past 1,000 years, shedding light on their potential impact under climate change. The model was trained using historical data and large ensembles of climate model simulations.
Researchers used deep learning to correlate citizen science data with remote sensing images, predicting plant distributions down to scales of a few square meters. The AI model, Deepbiosphere, outperformed previous methods in accuracy and showed potential for global monitoring of vegetation change.
A new study published in Scientific Reports reveals the importance of foot movement in early infant development and interaction. By using machine and deep learning techniques, researchers found that AI can accurately classify five-second clips of 3D infant movements, with foot movements showing the highest accuracy rates.
A team developed an AI system to analyze label-free photoacoustic histological images of human liver cancer tissues, achieving 98% accuracy in distinguishing between cancerous and non-cancerous cells. The integration of PAH with AI reduces tissue biopsy time and enhances reliability.
Researchers have developed an AI technology that can analyze mammary tissue biopsies to identify signs of damaged cells, a key indicator of breast cancer risk. The study found the AI was far better at predicting risk than current clinical benchmarks, offering improved treatment options for women.
A team of OU scientists, led by Nathan Snook, will use deep learning techniques to analyze numerical simulations of tornadoes. The goal is to improve tornado forecasting by identifying key factors that influence their formation.
Researchers from the University of Toronto's Rotman School of Management found that campaign size, social capital, and reward options are top factors in success. Machine learning identified a sweet spot for campaign duration and reward options, with success plateauing after 50 options.
A Concordia-led team developed a framework that enables crowdsourced deep reinforcement learning as a service, using blockchain technology. This allows smaller organizations to access complex AI tasks previously out of reach, reducing costs and risk.
Researchers introduced a novel illumination beam design based on deep learning, eliminating the need for sophisticated optics tools. The approach enhances image quality by optimizing both the deep learning network and the illumination beam simultaneously.
A novel deep learning model, DS-ViT-ESA, was developed to predict lithium battery lifespan with high accuracy using only a small amount of charging cycle data. The model achieved low prediction errors even when tested on unseen charging strategies, demonstrating its zero-shot generalization capability.
Researchers leverage deep learning networks to recover and enhance compromised metrics in biophotonic image data. This approach improves imaging speed and quality, allowing for high-fidelity all-in-focus images and efficient reconstruction with reduced data acquisition.
A breakthrough technology allows for touchless infrared imaging to monitor changes in pupil size and gaze direction behind closed eyes. This innovation can help identify wakefulness, awareness, and pain in sleep, anesthesia, and intensive care, enabling more accurate clinical decision-making.
A novel approach to overcome limitations of traditional methods, NeuPh uses local conditional neural fields to reconstruct high-resolution phase information from low-resolution measurements. It provides robust resolution enhancement and outperforms existing models in accuracy.