This record contains the dataset and trained models supporting the manuscript entitled **"Development of a NOx Prediction Model for Marine Low-speed Engine"**. The dataset was constructed for NOx prediction under four representative load conditions of a marine low-speed engine: 25%, 50%, 75%, and 100% load. The original computational samples were obtained by combining simulation samples generated using a design of experiments (DOE) approach and representative experimental samples extracted from marine low-speed engine test-bench measurements. The Bayes Bootstrap method was applied to augment the simulation samples under each load condition. This record includes: - original computational datasets;- Bayes Bootstrap augmented datasets;- trained Back Propagation (BP) neural network models;- trained LightGBM models;- normalization files for the BP models;- example scripts for loading the models and performing NOx prediction.
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…
Code, cleaned matrices, model-comparison outputs, permutation-test outputs, figures, and manuscript artifacts for a comparative analysis of linear and neural-network liver-brain microRNA co-expression models in RRRM-1/RR-8 spaceflight-exposed mice.
The accelerating integration of solar photovoltaic (PV) systems into modern power grids has introduced unprecedented challenges in grid stability due to the stochastic nature of solar irradiance. Accurate short-term power forecasting is a critical operational requirement for ener…
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…
The CLAPE (Contextual Learner Attributes and Pupil Engagement) dataset is a validated multimodal educational dataset designed to support research on physiological student engagement using low-cost, non-invasive RGB webcam technology. The dataset integrates physiological pupil var…