Showing 1 - 20 results of 28 for search '(( binary values from optimization algorithm ) OR ( binary mask codon optimization algorithm ))', query time: 0.44s Refine Results
  1. 1
  2. 2
  3. 3

    Datasets and their properties. by Olaide N. Oyelade (14047002)

    Published 2023
    “…To address this, we proposed a novel hybrid binary optimization capable of effectively selecting features from increasingly high-dimensional datasets. …”
  4. 4

    Parameter settings. by Olaide N. Oyelade (14047002)

    Published 2023
    “…To address this, we proposed a novel hybrid binary optimization capable of effectively selecting features from increasingly high-dimensional datasets. …”
  5. 5
  6. 6
  7. 7

    Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes by Yu Y. (3096192)

    Published 2022
    “…Introduction: Increasingly, logistic regression methods for genetic association studies of binary phenotypes must be able to accommodate data sparsity, which arises from unbalanced case-control ratios and/or rare genetic variants. …”
  8. 8
  9. 9
  10. 10

    Analysis and design of algorithms for the manufacturing process of integrated circuits by Sonia Fleytas (16856403)

    Published 2023
    “…Additionally, the results obtained from our new ILP model indicate that our genetic algorithm results are very close to the optimal values.…”
  11. 11
  12. 12
  13. 13

    Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

    Published 2024
    “…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …”
  14. 14

    Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by Daniel Pérez Palau (11097348)

    Published 2024
    “…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”
  15. 15

    Image processing workflow. by Denis Tamiev (7404980)

    Published 2020
    “…These annotated training images were passed to the cCNN to determine optimal network weights (e). The output of the network (from image depicted in panel c) is a confidence value for each sub-class (A–artifact; I—X–single through ten cell cluster), here presented in a radar chart (F). …”
  16. 16

    Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

    Published 2024
    “…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”
  17. 17

    Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports by Olivier Q. Groot (9370461)

    Published 2020
    “…At the same threshold, the NLP algorithm had a positive predictive value of 0.97 and F1-score of 0.96.…”
  18. 18
  19. 19

    Flow diagram of the automatic animal detection and background reconstruction. by David Tadres (9120564)

    Published 2020
    “…(E) The threshold value is calculated based on the histogram: it is the mean of the image subtracted by 4 (optimal value defined by trial and error). …”
  20. 20

    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. …”