Spatiotemporal distribution of leptospirosis in Thailand (2007–2022). by Pikkanet Suttirat (13567965)

<b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b> by Marly G F Costa (19812192)

“…<p dir="ltr"><b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b></p><p dir="ltr">The code was developed in the Google Collaboratory environment, using Python version 3.7.13, with TensorFlow 2.8.2. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…<p dir="ltr">Objective<br><br>To evaluate the predictive ability of the "habitat" variable, in isolation, to determine mushroom toxicity (edible or poisonous) using a Support Vector Machine (SVM) classification model, investigating whether this single feature is sufficient to build a robust and reliable classifier. …”

Data for: An effective methodology to quantify cooling demand in the UK housing stock by Lloyd Corcoran (19094369)

af3cli: Streamlining AlphaFold3 Input Preparation by Philipp Döpner (21028454)

“…With the release of AlphaFold3, modeling capabilities have expanded beyond protein structure prediction to embrace the inherent complexity of biomolecular systems, including nucleic acids, ions, small molecules, and their interactions. …”

List of abbreviations. by Jingru Dong (14076094)

“…A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. …”

Heat map of the correlation of features. by Jingru Dong (14076094)

“…A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. …”

Experimental environment configuration table. by Jingru Dong (14076094)

“…A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. …”

Base learner parameters. by Jingru Dong (14076094)

“…A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. …”

Data inclusion and exclusion process. by Jingru Dong (14076094)

“…A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. …”

S1 Data - by Jingru Dong (14076094)

“…A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. …”

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

SingleFrag by Maribel Pérez Ribera (19868316)

“…</li><li><ol><li><b>ANN</b>: Artificial Neural Networks</li><li><b>GNN</b>: Graph Neural Networks</li><li><b>COM</b>: Networks that combine the predictive power of ANN and GNN</li></ol></li><li><b>Mol2vecModel</b>: Contains a Mol2vec model trained to obtain a 300-dimensional vector from molecule SMILES.…”

The perceived wealth and physical disorder scores prediction dataset for urban China by Yanji Zhang (22363905)

“…Based on the perception image annotation dataset labeled by Chinese urban planners (https://figshare.com/s/a942f102cd07f4a73515), these perception scores are predicted through model training and inference across urban China.…”

Processing parameters. by Hosamane Guruswamy Prashantha Kumar (21433265)

“…The predictive model results matched up with experimental data points within 5–8 percent ranges. …”

ML for anomalous diffusion model by Yuehua Zhao (8498052)

“…<p dir="ltr"><b>ML for anomalous diffusion model</b></p><p dir="ltr">Dapeng Wang</p><p>7.24.2025</p><p dir="ltr">This repository contains the necessary codes written in Python 3 to train the classifiers.…”

Illustration of model compartment links. by Yousef AbuHour (17536686)

“…Additionally, we analyze the reproduction number’s sensitivity and explore the proposed discrete system’s local and global stability. The model was simulated and analyzed using Python packages, providing practical solutions to improve cybersecurity in IoT networks. …”

Table 1_Magnetic resonance imaging-based deep learning for predicting subtypes of glioma.docx by Zhen Yang (107412)

“…The receiver operating characteristic curve (ROC), area under the curve (AUC) of the ROC were generated in the jupyter notebook tool using python language to evaluate the accuracy of the models in classification and comparing the predictive value of different MRI sequences.…”

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…</p><p dir="ltr">All data are stored in GeoTIFF (.tif) format and can be accessed and processed using ArcGIS, ENVI, R, and Python. Each GeoTIFF file contains grid-based predictions of habitat suitability, with values ranging from 0 to 1. …”

DRLiPS: A Novel Method for Prediction of Druggable RNA-Small Molecule Binding Pockets Using Machine Learning by Sowmya Ramaswamy Krishnan (20867198)