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 data, covering three cases: Circular-Circular (CCBART), Circular-Linear (CLBART), and Linear-Circular (LCBART). The repository includes implementations of all proposed methods and their benchmarks (projected random forests and linear models), simulation study scripts for all three cases, and real data analysis scripts applied to wind direction forecasting data from London and Galicia, and human motor resonance data.
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…
High-quality data is essential for building reliable machine learning models. Raw datasets often contain missing values, outliers, duplicates, inconsistent formats, and unstructured categorical variables. These issues reduce model accuracy and lead to biased predictions. This pap…
## 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…
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…
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…
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…