The paper identifies three major areas in which AI is now vital. These include financial market prediction, macroeconomic ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Their study presents a new combination of computational tools that merges the Chinese Pangolin Optimizer (CPO) with the ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting?

Objective To develop and validate a 10-year predictive model for cardiovascular and metabolic disease (CVMD) risk using ...

AI in Education, Machine Learning, Educational Research Methods, Causal Inference, Explainable AI Mgonja, T. (2025) Archaic Methods in a Data Rich World: Why Educational Research Must Embrace AI ...

Understand the metrics and methods that turn generative AI from a promising tool into a reliable, defensible, and essential ...

For decades, manufacturing research and d (R&D) has largely relied on a time-tested but costly model: trial and error. Scientists and engineers iterate through experiments, testing different material ...

Suzanne is a content marketer, writer, and fact-checker. She holds a Bachelor of Science in Finance degree from Bridgewater State University and helps develop content strategies. Regression analysis ...

Animal testing is costly, slow, and poorly predictive. ORIVA offers a human-relevant alternative with the potential to change that.

While Black Forest Labs previously launched with and made a name for itself on open source text-to-image models in its Flux ...