Interpretable Machine Learning Recovers Transferable Gamma-Ray Attenuation Laws from A Priori Material Descriptors: Code and Data

R Code for: Bayesian Additive Regression Trees for Circular Data: A Machine Learning Framework

Talal Kurdi, Saralees Nadarajah

This repository contains the R code used in the paper "Bayesian Additive Regression Trees for Circular Data: A Machine Learning Framework" by Talal Kurdi and Saralees Nadarajah. The code implements Bayesian Additive Regression Trees (BART) methods for regression with circular dat…Read more

Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.

Muhammad Ahsan Hayat, Jahangir Baig, Shayan Ahmed, Ahmed Faraz Ayubi

Early detection of disease is a cornerstone for improving patient outcomes, reducing costs, and enabling preventative interventions. Traditional predictive models often rely on a single type of data (e.g., imaging, clinical labs, or genomics). However, human health is inherently…Read more

Also available via: European Organization for Nuclear Research

Before the Model: Why Datasets and Data Representation Define What Machine Learning Can Learn

Jean Franck Loa Rojas

Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…Read more

Code and data for: Leakage-audited machine learning versus ETAS for earthquake forecasting in the Sea of Marmara

Basri Kerem Alhan, Kenessary Khabat

Code, processed data products, configuration, and results artifacts for "Machine learning versus ETAS for earthquake forecasting in the Sea of Marmara: a leakage-audited negative result and a closed-form scoring artifact" (Alhan & Khabat, submitted to Seismica). Version 1.2.0 acc…Read more

Data and code for: Frequent Mental Distress Across Texas Census Tracts: Social-Environmental Co-Exposure, Spatial Dependence, and Interpretable Machine Learning

kwadwo Frimpong

This repository contains the processed analytic dataset and analysis code supporting the study "Environmental Co-Exposure, Green Space, and Frequent Mental Distress in Texas Census Tracts: An Interpretable Machine Learning and Spatial Analysis." The dataset includes tract-level f…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