AI recommendation lists repeat less than 1% of the time: Study

AI recommendations vary widely, even when prompts are identical. New data shows intent is consistent, but position tracking is mostly noise.
BMJ Quality & Safety

Impact of COVID-19 on incidence and trends of adverse events among hospitalised patients in Calgary, Canada: a retrospective chart review study

Background While the incidence of hospital adverse events appeared to be declining before 2019, the COVID-19 pandemic may ...

AI helps find trees in a forest: Researchers achieve 3D forest reconstruction from remote sensing data

Existing algorithms can partially reconstruct the shape of a single tree from a clean point-cloud dataset acquired by laser-scanning technologies. Doing the same with forest data has proven far more ...
IEEE

Change Detection in Remote Sensing Optical Images Using a SPSA-Based Improved Random Forest Algorithm

Abstract: A precise change detection in the multi-temporal optical images is considered as a crucial task. Although a variety of machine learning-based change detection algorithms have been proposed ...
IEEE

Alpine Wetlands Information Extraction Using Optimized Multifeatures and Random Forest Algorithm

Abstract: Alpine wetlands are a special ecosystem that is extremely sensitive to global climate change. The unique geographical location and climatic conditions of the Yellow-River-Source National ...
Frontiers

Deep exploration and precise identification of key risk factors for diabetic peripheral neuropathy using the random forest algorithm

Background: Diabetic peripheral neuropathy (DPN) is a prevalent and highly disabling complication of diabetes mellitus, associated with markedly increased rates of disability and mortality. Timely ...