In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

Research reveals that knowledge distillation significantly compensates for sensor drift in electronic noses, improving ...

The tech tool has been subject to criticism and controversy since being launched by the department five years ago.

Ayyoun is a staff writer who loves all things gaming and tech. His journey into the realm of gaming began with a PlayStation 1 but he chose PC as his platform of choice. With over 6 years of ...

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...

Learn how to choose the right cross-validation method for your machine learning projects. Compare techniques like k-fold, stratified, and leave-one-out cross-validation, and understand when to use ...

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

Abstract: Existing magnetic anomaly detection (MAD) methods are widely categorized into target-, noise-, and machine learning-based methods. This article first analyzes the commonalities and ...

1 School of Taxation and Public Administration, Shanghai Lixin University of Accounting and Finance, Shanghai, China. 2 School of Business, Computing and Social Sciences, University of Gloucestershire ...

The final, formatted version of the article will be published soon. Background): Diabetes Mellitus (DM) is a chronic metabolic disorder that poses a significant global health challenge, affecting ...