Showing 101 - 118 results of 118 for search '(( primary data step optimization algorithm ) OR ( binary a process optimization algorithm ))', query time: 0.39s Refine Results
  1. 101

    Image2_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

    Published 2021
    “…<p>Quantum annealing is a global optimization algorithm that uses the quantum tunneling effect to speed-up the search for an optimal solution. …”
  2. 102

    DataSheet1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.pdf by Sizhuo Yu (11429743)

    Published 2021
    “…<p>Quantum annealing is a global optimization algorithm that uses the quantum tunneling effect to speed-up the search for an optimal solution. …”
  3. 103
  4. 104

    PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

    Published 2023
    “…This process generated a ground-truth binary semantic segmentation mask and determined the bounding box coordinates (XYWH) for each cell. …”
  5. 105
  6. 106

    Table_4_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. …”
  7. 107

    Table_2_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. …”
  8. 108

    Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx 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. …”
  9. 109

    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. …”
  10. 110

    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. …”
  11. 111

    Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction by Raul A. Flores (2910539)

    Published 2020
    “…We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.…”
  12. 112

    Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx by Yuan Liu (88411)

    Published 2020
    “…In this predictor, we introduced a sequence-based feature algorithm consisting of two feature representations, (1) k-mer spectrum and (2) positional nucleotide binary vector, to capture the sequential characteristics of 5hmC sites. …”
  13. 113

    Seed mix selection model by Bethanne Bruninga-Socolar (10923639)

    Published 2022
    “…Classic genetic algorithms consider a population of chromosomes and apply principles of natural selection (selection, mutation, and crossover processes) to generate optimal solutions. …”
  14. 114

    DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

    Published 2024
    “…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2  = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …”
  15. 115

    Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

    Published 2024
    “…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2  = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …”
  16. 116

    Data_Sheet_1_The impact of family urban integration on migrant worker mental health in China.docx by Xiaotong Sun (6535064)

    Published 2024
    “…The results of this study lead the authors to recommend formulating a family-centered policy for migrant workers to reside in urban areas, optimizing the allocation of medical resources and public services, and improving family urban integration among migrant workers in order to avoid mental health problems in the process of urban integration.…”
  17. 117

    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. …”
  18. 118

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

    Published 2025
    “…</p><p dir="ltr">IC50 (µg/mL): The concentration at which 50% of cells are inhibited, used as a toxicity threshold.</p><p dir="ltr">These biological metrics were used to define a binary toxicity label: entries were classified as toxic (1) or non-toxic (0) based on thresholds from standardized guidelines (e.g., ISO 10993-5:2009) and literature consensus. …”