Companion dataset and pipeline: microstructure-aware machine learning prediction of hydrogen embrittlement susceptibility in steels

# Artificial Intelligence in Metallurgical Engineering: A Comprehensive Review of Applications, Challenges, and Future Direction

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Transformation in Metallurgical Engineering: From Microstructure Analysis to Smart Manufacturing and Sustainable Production"** ### Alternative Title 2 (Process-Focused)**"Machine Learning and Deep Learning…Read more

Reproducibility package for: Validation-Induced Optimism in Machine-Learning Prediction of Concrete Compressive Strength: An Open-Access Methodological Audit and Leakage-Controlled Multi-Dataset Benchmark

Prasanna Jibi Ghimire, Prerana Khatiwada

Integrating Waterborne GPR and Group-Aware Machine Learning to Resolve Lakebed Hydrostratigraphy and Potential Groundwater–Lake Connectivity in a Climate-Stressed Shallow Lake

Diaa Sheishah

# 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

Machine Learning for Predictive Modeling and Simulation of Urban Renewal Using Multi-Source Spatial Data

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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