Frontiers

Disease classification via interpretable machine learning based on multi-center routine coagulation test

This study aims to establish an interpretable disease classification model via machine learning and identify key features related to the disease to assist clinical disease diagnosis based on a ...
IEEE

Tamilnews Inspector: A Multilingual Fake News Detection Framework Using Sentence Embeddings and Ensemble Learning

Abstract: TamilNews Inspector is a multilingual fake news detection framework developed to tackle the growing dissemination of fake news in linguistically diverse digital landscapes, explicitly ...
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 ...
Scientific Research Publishing

Diabetic Retinopathy Severity Classification Using Data Fusion and Ensemble Transfer Learning ()

Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image ...
Visual Studio Magazine

Binary Classification Using New PyTorch Best Practices, Part 2: Training, Accuracy, Predictions

Dr. James McCaffrey of Microsoft Research explains how to train a network, compute its accuracy, use it to make predictions and save it for use by other programs. This is the second of two articles ...
Scientific Research Publishing

Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs ()

In the last decade, a few valuable types of research have been conducted to discriminate fractured zones from non-fractured ones. In this paper, petrophysical and image logs of eight wells were ...