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

ADR1D-EWS: Causal Machine-Learning Early Warning for Reactive Contaminant Transport

Gerardo Tinoco-Guerrero, Francisco J. Domínguez-Mota, José A. Guzmán-Torres

ADR1D-EWS is a reproducible machine-learning system for predicting whether contaminant concentration at a protected downstream sensor in the ADR1D benchmark will reach 0.01 mg/L within one hour. The system uses 52 causal features derived only from upstream observations available…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

Harmonized soil-erosion database and machine-learning erodibility predictor for overtopping dam-breach forecasting

Hongning Lu

It provides (1) a harmonized multi-device soil-erosion database — 1,146 specimen records from EFA, SETD, JET, HET and related devices (1,013 with critical shear stress and 972 with erodibility coefficient), 186 raw erosion-rate-versus-shear-stress curves with power-law fits, and…Read more

Financial data source preparation and analysis as the initial stage of stochastic time series modeling by machine learning techniques

Yurchenko Yuriy, Oleksandr Zakovorotnyi

Proceedings of the Scientific Conference "The 13th International Scientific and Practical Online Conference of Young Scientists and Students ‘Contemporary Problems of Automation and Control’".The conference talk presents a structured approach to preparing and analyzing financial…Read more

Also available via: European Organization for Nuclear Research

Interpretable machine-learning risk stratification at diagnosis for 3-year mortality in de novo metastatic prostate cancer (SEER): reproducibility code

Xin Wang, Guanglei Yao, Wei Ding

This archive contains the analysis code, the predictor dictionary, and the retrained primary model objects underlying the manuscript "Interpretable machine-learning risk stratification at the time of diagnosis for 3-year mortality in de novo metastatic prostate cancer: developmen…Read more

# 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