Frontiers

Robust automated preclinical fMRI preprocessing via a multi-stage dilated convolutional Swin Transformer affine registration

Introduction: Accurate preprocessing of functional magnetic resonance imaging (fMRI) data is crucial for effective analysis in preclinical studies. Key steps such as denoising, skull-stripping, and ...
Frontiers

Concentration monitoring and dose optimization for infliximab in Crohn’s disease patients: a machine learning-based covariate ensemble model

Background: Trough concentration of Infliximab (IFX) was associated with its efficacy and toxicity. However, traditional therapeutic drug monitoring often results in suboptimal outcomes because dose ...
GitHub

Customer Churn Analysis & Prediction

A comprehensive machine learning solution for predicting customer churn in telecommunications companies, built with modern DevOps practices and cloud infrastructure. customer-churn-analysis-prediction ...
GitHub

EEG Motor Imagery Classification

The goal of this project is to classify EEG signals recorded during motor imagery tasks, i.e. when a subject imagines moving a limb (left or right hand). By decoding these imagined movements from ...
InfoQ

Training Data Preprocessing for Text-to-Video Models

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...
IEEE

Enhancing Obesity Prediction: Evaluating the Role of Data Preprocessing in Machine Learning Models

Abstract: The preprocessing of data serves as a fundamental requirement to improve machine learning model execution specifically when used in medical prediction systems. Testing multiple machine ...