A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...

Synopsys stock analysis: wide moat, AI-driven chip complexity growth, and Ansys deal impact. Read more macro analysis here.

A new light-based imaging approach has produced an unprecedented chemical map of the Alzheimer’s brain. Rice University researchers have produced what they describe as the first full, label-free ...

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...

To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.

Dr. Michael Spaeder of the University of Virginia previews his upcoming HIMSS26 talk on using AI and machine learning to ...

Meta wants us to believe there’s a difference between addiction and ‘problematic use.’ The harm to kids suggests otherwise ...

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...

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., ...

Consequently, this study utilized machine learning with the XGBoost algorithm, LASSO regression, and univariate logistic regression for variable screening. The relationship between vitamin D and ...

Abstract: This paper aims to predict the diagnosis of Autism Spectrum Disorder (ASD) using the application of a few machine learning algorithms for comparison purposes to determine the performance of ...

Patent applications on artificial intelligence and machine learning have soared in recent years, yet legal guidance on the patentability of AI and machine learning algorithms remains scarce. The US ...