An Explainable Machine Learning Model and Bedside Nomogram Support Hemodialysis Decision-Making in Lithium Poisoning

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

# 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

Enhancing Cardiovascular Disease Diagnosis through Data-Driven Feature Analysis and Cross-Validated Machine Learning Models

Abhilash Butola

Abstract - Cardiovascular diseases are a major global health problem, accounting for 17.9 million deaths per year and constituting 32 percent globally. According to the World Health Organization, the disease in people is due to an unhealthy diet,such as the intake of more junk fo…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

Comparative Analysis of Machine Learning Classification Algorithms and Hybrid Models for Student Performance Prediction

Ms. Pooja C. Soni, Dr. Hetal R. Modi, PC Negi

This study focuses on the analysis and comparison of machine learning classification algorithms and hybrid machine learning models for predicting student academic performance. Educational Data Mining techniques are used to extract meaningful insights from student datasets. Variou…Read more

Source Dependence and Cross-Publication Transportability of Machine-Learning Models in Extrusion Bioprinting

Mahdi Arabinour, Nasser Sotudeh, Nargis Sultani, Noël Ziebarth, Xiangyang Zhou, Lobat Tayebi

Journal-facing reproducibility repository containing code, locked configurations and validation splits, raw and processed datasets, consolidated model outputs, statistical analyses, tables, figure source data, and final figures for the associated article.