talk-data.com

An AI-driven system provides real-time insulin dosing suggestions based on continuous glucose data, aiming to enhance insulin management by eliminating personal biases and improving accuracy.

Reinforcement learning is used to optimize design parameters for reinforced concrete beams, aiming to minimize material costs while ensuring structural safety. The agent will also configure beam layouts for given floorplans.

This tool analyzes news and opinion content to identify tactics like sensationalism and manipulation, promoting a critical approach to media consumption and awareness of content designed to elicit strong emotional responses.

This project improves inventory tracking, traditionally reliant on barcode scanners or RFID readers, by addressing their limitations like manual scanning and line-of-sight requirements.

This project presents the use of large multimodal Models to enhance accessibility and independence for blind or individuals with low vision. We will explore different use cases to assist performing daily tasks.

Using the PSG-Audio dataset, this project employs a convolutional neural network to classify sleep apnea events based on ambient audio recordings, allowing for at-home analysis using smartphone microphones.

An AI network uses CNNs and a diffusion model with U-Net architecture to generate new drum patterns from a base pattern. The customizable dataset allows for diverse and innovative drum patterns.

This project develops a predictive model to identify dangerous city areas using police records and satellite data. Advanced AI techniques like semantic segmentation, UNet, and ImageNet will analyze historical crime data and satellite imagery to highlight high-risk zones.

This project identifies gentrifying neighborhoods in Atlanta and predicts whether they will experience inclusive growth or displacement. The goal is to guide neighborhood change to promote inclusive growth and mitigate displacement.

This project explores how Equivariant Graph Neural Networks (EGNNs) enhance predictive accuracy in point cloud data analysis. The results aim to advance the processing of unstructured 3D data for autonomous vehicle perception and forecasting.