Showing 61 - 64 results of 64 for search '(( primary data step optimization algorithm ) OR ( binary b process optimization algorithm ))', query time: 0.43s Refine Results
  1. 61

    Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS by Hui Tang (226667)

    Published 2019
    “…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”
  2. 62

    Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

    Published 2019
    “…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”
  3. 63

    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

    Published 2025
    “…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”
  4. 64

    An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

    Published 2025
    “…Cloud Workflows}, author={AlbumForge Research Team}, year={2025}, publisher={Figshare}, doi={10.6084/m9.figshare.XXXXXXX}, url={https://figshare.com/articles/dataset/XXXXXXX} } Contributing and Data Governance Issue Reporting Technical issues, data quality concerns, or methodological questions should be reported via GitHub Issues with the following template: **Issue Type**: [Bug Report / Data Quality / Methodology Question] **Hardware Configuration**: [Specify if applicable] **Dataset Version**: [e.g., v1.0.0] **Description**: [Detailed description of the issue] **Reproducibility**: [Steps to reproduce if applicable] **Expected Behavior**: [What should happen] **Actual Behavior**: [What actually happens] Data Update Protocol Dataset versioning follows semantic versioning (SemVer) principles: Major version (X.0.0): Incompatible schema changes Minor version (0.X.0): Backward-compatible feature additions Patch version (0.0.X): Backward-compatible bug fixes Technical Support and Community For advanced technical discussions, algorithmic improvements, or collaborative research opportunities, please contact: Primary Maintainer: research@albumforge.com Technical Issues: github.com/albumforge/ecological-benchmark/issues Methodology Discussions: [Academic collaboration portal] Industry Partnerships: partnerships@albumforge.com Acknowledgments: This research was conducted using computational resources provided by AlbumForge (https://albumforge.com) under the Green Computing Initiative. …”