Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular ...

Ad fraud is no longer a fringe issue. It is a systemic threat to digital advertising, and its scale demands a technological ...

Researchers report that the integration of machine learning and Internet of Things (IoT) technologies is enabling a new generation of intelligent industrial environments capable of real-time ...

A new study suggests that lenders may get their strongest overall read on credit default risk by combining several machine learning models rather than relying on a single algorithm. The researchers ...

A machine learning-driven eNose detects ovarian cancer in blood plasma with 97 % sensitivity and specificity, offering a promising biomarker-agnostic approach.

Accurate assessment of soil salinity is critical for sustainable agriculture and food security, yet remains technically challenging at fine spatial scales.

A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.

Steven Weisberg, a researcher at The University of Texas at Arlington, put some of the most advanced artificial intelligence ...

AI-enhanced optical spectroscopy revolutionizes food quality monitoring with rapid, non-destructive analysis, ensuring safety and reducing waste in production.

A study published in The Journal of Engineering Research (TJER) at Sultan Qaboos University presents an advanced intrusion detection system (IDS) designed to improve the accuracy and efficiency of ...

ABSTRACT: This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates ...