Combining microscopy and machine-learning techniques leads to faster, more precise analyses of critical coating materials ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

MLE-STAR (Machine Learning Engineering via Search and Targeted Refinement) is a state-of-the-art agent system developed by Google Cloud researchers to automate complex machine learning ML pipeline ...

Background and objective: The increasing global prevalence of diabetes has led to a surge in complications, significantly burdening healthcare systems and affecting patient quality of life. Early ...

In an era of rapidly growing multimedia data, the need for robust and efficient classification systems has become critical, specifically the identification of class names and poses or styles. This ...

A suite of ML models—Logistic Regression, Random Forest, KNN, SVM, Gaussian Naive Bayes—was used to predict patient readmission. (1) Rasoul Samani, School of Electrical and Computer Engineering, ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

Teachable Machine is a Google offering that allows anyone to experiment with the powerful possibilities that AI offers. This website uses Google machine learning smarts to allow people to play with ...

Caption:MIT researchers created a periodic table of machine learning that shows how more than 20 classical algorithms are connected. The new framework sheds light on how scientists could fuse ...