A Purdue University digital forestry team has created a computational tool to obtain and analyze urban tree inventories on ...

AI outputs vary because confidence varies. Corroboration and entity optimization turn inconsistent AI visibility into consistent presence.

Traditional safety protocols weren’t designed for self-improving systems, which raises important questions about validation, ...

Predictive analytics is transforming the insurance industry, enhancing efficiency and risk management. ✓ Learn more and subscribe.

VeritasChain releases CPP with LiDAR/ToF depth analysis. VeraSnap delivers world's first consumer LiDAR-based screen ...

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 ...

AI agents help businesses stop guessing — linking predictions to actions so teams can move from “what might happen” to ...

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, ...

Read more about AI-driven learning analytics struggle to deliver measurable gains in higher education on Devdiscourse ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning ...