Mean Annual Habitat Quality and Its Driving Variables in China (1990–2018) by ChenXi Zhu (21374876)

“…</p><p dir="ltr">(HQ: Habitat Quality; CZ: Climate Zone; FFI: Forest Fragmentation Index; GPP: Gross Primary Productivity; Light: Nighttime Lights; PRE: Mean Annual Precipitation Sum; ASP: Aspect; RAD: Solar Radiation; SLOPE: Slope; TEMP: Mean Annual Temperature; SM: Soil Moisture)</p><p dir="ltr"><br>A Python script used for modeling habitat quality, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), and implementation of four machine learning models to predict habitat quality.…”

IGD-cyberbullying-detection-AI by Bryan James (19921044)

“…[<a href="https://doi.org/10.6084/m9.figshare.27266961" rel="nofollow" target="_blank">https://doi.org/10.6084/m9.figshare.27266961</a>]</p><h2>Table of Contents</h2><ul><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#overview" target="_blank">Overview</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#requirements" target="_blank">Requirements</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#datasets" target="_blank">Datasets</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#installation" target="_blank">Installation</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#running-the-code" target="_blank">Running the Code</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#expected-results" target="_blank">Expected Results</a></li></ul><h2>Overview</h2><p dir="ltr">This repository provides the code for predicting mental health outcomes associated with Internet Gaming Disorder (IGD) and Cyberbullying using machine learning and deep learning models. Models like Logistic Regression, Random Forest, Ensemble Models, CNNs, and LSTMs are implemented to detect patterns from behavioral data.…”

MSc Personalised Medicine at Ulster University by Steven Watterson (100045)

“…This includes the economic models that underpin big pharma as well the importance of entrepreneurship and small medium enterprises in driving forward healthcare innovation.…”

Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service) by U.S. Forest Service (17476914)

“…Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. …”

Microscopic Detection and Quantification of Microplastic Particles in Environmental Water Samples by Derek Lam (11944213)

“…Image processing algorithms, implemented in Python using adaptive thresholding techniques, were applied to segment particles from the background. …”

Comprehensive Fluid and Gravitational Dynamics Script for General Symbolic Navier-Stokes Calculations and Validation by Stylianos Touloumidis (19938747)

“…It provides a flexible foundation on which theoretical assumptions can be validated, and practical calculations performed. Implemented in Python with symbolic calculations, the script facilitates in-depth analysis of complex flow patterns and makes advanced mathematical computations more accessible. …”

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 by Andrew Rogers (17623239)

“…</p><h2>Software and Spatial Resolution</h2><p dir="ltr">The VRE siting model is implemented using Python and relies heavily on ArcGIS for comprehensive spatial data handling and analysis.…”

Code by Baoqiang Chen (21099509)

“…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”