AI model that found 370 exoplanets now digs into TESS data

Scientists have discovered over 6,000 planets that orbit stars other than our sun, known as exoplanets. More than half of ...
EurekAlert!

AI-accelerated gas sensing: a first-principles–guided machine learning platform for black phosphorus sensors

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...
IEEE

Development and Validation of Feature-Based Machine Learning for ECG Artifact Detection and Classification

Abstract: ECG signals are vital for diagnosing cardiovascular diseases, but artifacts like power line interference, baseline wander, and motion artifacts hinder accurate interpretation. This study ...
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 = ...
Frontiers

Comparative analysis of machine learning techniques on the BraTS dataset for brain tumor classification

1 Information Statistics Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 School of Computer Science and Technology, Hubei Business ...
IEEE

HMM-Based Feature Extraction and Machine Learning Methods for Event Detection and Classification in Microgrids

Abstract: The increasing penetration of inverter-based distributed generation (DG) into power grids improves access to electricity and provides a significant possibility for decarbonization. However, ...
Frontiers

Advanced ECG feature extraction and SVM classification for predicting defibrillation success in OHCA

Out-of-hospital cardiac arrest (OHCA) represents a critical challenge for emergency medical services, with the necessity for rapid and accurate prediction of defibrillation outcomes to enhance patient ...
Scientific Research Publishing

An Evaluation of Machine Learning Models for Threat Classification in IoT Devices ()

1 Center for Cyberspace Studies, Nasarawa State University, Keffi, Nigeria. 2 Department of Computer Engineering, Nile University of Nigeria, Abuja, Nigeria. 3 Department of Public and International ...