AI tool can detect missed Alzheimer's diagnoses while reducing disparities

Researchers at UCLA have developed an artificial intelligence tool that can use electronic health records to identify ...
Becker's Hospital Review

UCLA Health develops AI to detect undiagnosed Alzheimer’s cases

UCLA Health develops AI to detect Alzheimer's cases using EHR data, improving early identification across diverse ...
IEEE

Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach

Abstract: This research proposes a lightweight hybrid approach for anomaly detection in correlated IoT sensor data, combining PCA for fast monitoring and Autoencoders for deeper analysis. Validated on ...
IEEE

Black Hole Prediction in Backbone Networks: A Comprehensive and Type-Independent Forecasting Model

Abstract: Network backbone black holes(BH) pose significant challenges in the Internet by causing disruptions and data loss as routers silently drop packets without notification. These silent BH ...
techxplore

New training method helps AI models handle messy, varied medical image data

Hospitals do not always have the opportunity to collect data in tidy, uniform batches. A clinic may have a handful of carefully labeled images from one scanner while holding thousands of unlabeled ...
GitHub

autoencoder-anomaly-detection

This repo contains all my Deep Learning semester work, including implementations of FNNs, CNNs, autoencoders, CBOW, and transfer learning. I explored TensorFlow, Keras, PyTorch, and Theano while ...
The Conversation

How do ‘AI detection’ tools actually work? And are they effective?

T.J. Thomson receives funding from the Australian Research Council. He is an affiliate with the ARC Centre of Excellence for Automated Decision Making & Society. Aaron J. Snoswell receives research ...