Percentage of PNC Utilizations. حسب Daniel Niguse Mamo (16813898)

"…The study employs machine learning techniques to analyse secondary data from the 2016 Ethiopian Demographic and Health Survey. It aims to predict postnatal care utilization and identify key predictors via Python software, applying fifteen machine-learning algorithms to a sample of 7,193 women. …"

Percentage of Missing Data from PNC Dataset. حسب Daniel Niguse Mamo (16813898)

"…The study employs machine learning techniques to analyse secondary data from the 2016 Ethiopian Demographic and Health Survey. It aims to predict postnatal care utilization and identify key predictors via Python software, applying fifteen machine-learning algorithms to a sample of 7,193 women. …"

Top 10 features influencing PNC utilization. حسب Daniel Niguse Mamo (16813898)

"…The study employs machine learning techniques to analyse secondary data from the 2016 Ethiopian Demographic and Health Survey. It aims to predict postnatal care utilization and identify key predictors via Python software, applying fifteen machine-learning algorithms to a sample of 7,193 women. …"

Relationship between sunlight measures and daily activity. حسب Oleg Kovtun (1618660)

Data Sheet 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx حسب Shi Qiu (425335)

"…Nomogram construction, ROC analysis, and DCA evaluation were performed to assess model performance. Statistical analyses were conducted using Python and R, with significance set at p < 0.05.…"

Data Sheet 1_Feasibility of predicting next-day fatigue levels using heart rate variability and activity-sleep metrics in people with post-COVID fatigue.csv حسب Nana Yaw Aboagye (22637360)

"…Predicted and observed fatigue scores were strongly correlated for both models (XGBoost: r = 0.89 ± 0.02; Random Forest: r = 0.86 ± 0.01). …"

Integrated experimental and techno-economic modeling of renewable natural gas production from prairie biomass حسب Elmin Rahic (21394722)

"…Response variables included biogas and biomethane yields, in addition to numerous digestate physico-chemical characteristics. Statistical models were developed from the experimental data to predict these responses and were subsequently incorporated into a techno-economic model developed in Python using BioSTEAM. …"

Data Sheet 1_Machine learning-driven prediction of intratumoral tertiary lymphoid structures in hepatocellular carcinoma using contrast-enhanced CT imaging and integrated clinical... حسب Jun Wu (4002)

"…Multivariate analysis identified the albumin-bilirubin (ALBI) score as an independent predictor of intratumoral TLSs expression. The combined model demonstrated the highest predictive accuracy, with AUCs of 0.947 in the training set and 0.909 in the validation set, outperforming both the clinical (AUC: 0.709 training, 0.714 validation) and radiomics (AUC: 0.935 training, 0.890 validation) models.…"

Environmental Census: Modeling Synthetic Biology Ecological Risk with Metagenomic Enzymatic Data and High-Performance Computing حسب John Docter (22772100)

Bayesian Neural Network-Based Ground Motion Model for Horizontal and V/H spectral ordinate with Epistemic Uncertainty for the European Region حسب jyothi yedulla (18319545)

"…<p dir="ltr">The Python code provides the computation of 100 predictions using the proposed BNN model for the given input parameters which includes Moment magnitude (M_w), Joyner-Boore distance (R_JB), Shear wave velocity (V_s30), focal depth (d) and focal mechanism.…"

Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis حسب Alan Glanz (22109698)

"…</p><h2><b>Included Files</b></h2><h3><b>1. </b><code><strong>GenosophusV2.py</strong></code></h3><p dir="ltr">Executable Python implementation of the Genosophus Engine.…"

Yokoyama et al., Supplementary Data for "Prediction of Li-ion Conductivity in Ca and Si co-doped LiZr₂(PO₄)₃ Using a Denoising Autoencoder for Expe... حسب Yumika Yokoyama (20174705)

Image 1_Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.tif حسب Xiaobing Li (291454)

"…Objective<p>To develop a differential diagnostic prediction model for distinguishing large opacities in pneumoconiosis from peripheral lung cancer based on CT radiomics.…"

Image 2_Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.tif حسب Xiaobing Li (291454)

"…Objective<p>To develop a differential diagnostic prediction model for distinguishing large opacities in pneumoconiosis from peripheral lung cancer based on CT radiomics.…"

Collaborative Research: Framework: Improving the Understanding and Representation of Atmospheric Gravity Waves using High-Resolution Observations and Machine Learning حسب Joan Alexander (17047170)

"…Establishing a framework for implementing and testing ML-based parameterizations in atmospheric models. Focusing first on idealized atmospheric modeling systems, we will tackle challenges associated with coupling interactively an ML-based data-driven scheme and a climate model (e.g., numerical instabilities, linking Python and Fortran). …"

Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds حسب Gloria Castañeda-Valencia (20758502)

"…The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. …"

PepENS حسب Abel Chandra (16854753)

"…To the best of our knowledge, this is the first time representations learnt using attention mechanism in transformers are transformed into images to utilize the potential of Convolutional Neural Networks in protein-peptide prediction. The spatial relationships formed between the features by DeepInsight to produce images and the feature extraction from those images by the Convolutional Neural Network play a key role in the performance of the ensemble model. …"

Missing Value Imputation in Relational Data Using Variational Inference حسب Simon Fontaine (7046618)

"…Additional results, implementation details, a Python implementation, and the code reproducing the results are available online. …"

LGF v11 Final Archive: EWL, GMMI, LGF-EWI Metrics and Language Spacetime Specification (Dec 2025) حسب heojeongbeom heo (22544705)

"…This was confirmed by the final $\mathbf{\Psi}$ Operator simulation: the Python code returned <b>$\text{nan}$</b><b> (Not a Number)</b> due to the runaway divergence of the Language Potential ($\mathbf{\Phi_L}$), serving as the <b>computational proof of the </b><b>$\mathbf{T=0}$</b><b> structural failure.…"

Harmonic Shell Structures in MaDCoWS2 Clusters: Empirical Validation of Oscillatory Field Genesis (OFG) حسب Drippy Stone (21218843)

"…Analyzing 22,971 galaxy clusters from MaDCoWS2 DR2, we demonstrate: - **Harmonic shell spacings** (λ = 2π/|∇Φ·∇Θ|) in high-z clusters (z ≥ 1.5) - **Phase-locked redshift distributions** via Lomb-Scargle periodograms - **Predictive templates** for LSST void lensing (Δκ ≥ 0.5) and SPHEREx spectral harmonics Includes: - Cluster spacing histograms (`Cluster_Spacing_Histogram.csv`) - Redshift-phase coherence data (`theta_oscillation_peaks.csv`) - High-z cluster catalog (`High-Redshift_Cluster_z.csv`) - Full analysis code (Python/Jupyter) </p><p><br></p><p><br></p><p dir="ltr">Related Publications**: - Gonzalez et al. (2024) MaDCoWS2 DR2 Catalog (ApJ, 967, 123) - OFG Theoretical Framework (J.D.S. 2025, DeepSeek-verified) - **Tools Required**: Python 3.10+, NumPy, Pandas, Astropy - **Experiment Type**: Observational/Cosmological Simulation </p>…"