Machine Learning-Guided Selection for Cystatin C Testing

# Artificial Intelligence-Enabled Quantification of Cube and Goss Textures in Polycrystalline Materials: A Comprehensive Review of Machine Learning, Deep Learning, and EBSD-Based Characterization Approaches

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Analysis of Cube {100}<001> and Goss {110}<001> Textures: Machine Learning, Deep Learning, and Generative Models for Crystallographic Texture Quantification in Metallurgical Engineering"** ### Alternative T…Read more

RAQA-AutoML: An Integrated Solution for Automated Machine Learning

Abdullah Kaviani Rad

Overview AutoML-Lite is a powerful, user-friendly desktop application designed to democratize machine learning by automating the entire modeling pipeline. Built with Python and PyQt6, it provides a comprehensive GUI-based environment for data preprocessing, feature engineering, m…Read more

A Systematic Review of Machine Learning, Deep Learning, and Explainable AI Approaches for Cardiac Disease Prediction

Sunanda Budihal, Sheetalrani Kawale, Abhishek Angadi

The cardiovascular (Cardiac) disease (CVD) is another factor that causes death among the global population most, and this is the reason why there is a high necessity to implement proper, effective, and interpretive diagnostic systems. The usage of machine learning (ML), deep lear…Read more

Also available via: European Organization for Nuclear Research

Chemistry-stratified reliability audit of selective abstention for universal machine-learning interatomic potentials

Zeyu Fu

Version 0.3.0 provides the sanitized, hermetic code and frozen result records supporting the chemistry-stratified selective-abstention analysis. It includes the 32-UIP roster analysis, prototype-blocked confidence intervals, mechanism and sensitivity controls, manuscript sources,…Read more

Client Recognition Using Text-Dependent Voice Biometrics: A Machine Learning and Deep Learning Approach

Annual Methodological Archive Research Review

Financial Distress Prediction in Mature Markets: A Machine Learning Approach across G7 Economies

Varunn Kaushik

Abstract: This study examines the determinants and predictive accuracy of financial distress for seven mature market economies: Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. The aim is to assess whether distress can be predicted through a unifo…Read more