By transforming movement into data, Timothy Dunn is reshaping how scientists can study behavior and the brain.

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

Satellite-based hydrology, particularly leveraging the GRACE and GRACE-FO missions, has revolutionized our understanding of ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.

Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

According to @godofprompt, the Ensemble Reasoning pattern in AI involves generating multiple solutions using deductive, inductive, and analogical reasoning, then synthesizing these paths to produce a ...

ABSTRACT: Accurate land use/land cover (LULC) classification remains a persistent challenge in rapidly urbanising regions especially, in the Global South, where cloud cover, seasonal variability, and ...