The Lancet

When to and when not to use machine learning in risk prediction models

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...
Digital Trends

You can now use Gemini’s Thinking model without worrying about Pro model limits

Google has quietly reworked Gemini‘s usage limits, splitting the shared pool and boosting the individual caps for the Thinking and Pro models. At launch, both models had the same daily quota, meaning ...
Visual Studio Magazine

Tsetlin Machine Binary Classification Using C#

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
IEEE

Machine Learning Techniques to Improve the Cognitive Workload Classification Using Multimodal Sensors’ Data

Abstract: Using machine learning applied to multimodal physiological data allows the classification of cognitive workload (low, moderate, or high load) during task performance. However, current ...
IEEE

Flower Classification using Deep Learning models

Abstract: Deep learning techniques are used widespread for image recognition and classification problems. Gradually, deep learning architectures have modified to comprise more layers and become more ...
Frontiers

Migraine triggers, phases, and classification using machine learning models

In many countries, patients with headache disorders such as migraine remain under-recognized and under-diagnosed. Patients affected by these disorders are often unaware of the seriousness of their ...
PNAS

Adaptable microplastic classification using similarity learning on µFTIR spectra collected from µFTIR focal plane array imaging

Microplastics have been found to be highly pervasive in the environment, driving concerns for health, environment, and ecology. Analytical methods that can accurately identify microplastics are ...
Frontiers

A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA)

Physical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. Early and accurate detection of frailty is essential for timely ...