Search alternatives:
process optimization » model optimization (Expand Search)
based optimization » whale optimization (Expand Search)
ann based » cnn based (Expand Search)
b process » _ process (Expand Search), a process (Expand Search)
binary b » binary _ (Expand Search)
Showing 21 - 40 results of 46 for search '(( binary ann based optimization algorithm ) OR ( binary b process optimization algorithm ))', query time: 1.07s Refine Results
  1. 21
  2. 22
  3. 23
  4. 24
  5. 25
  6. 26
  7. 27
  8. 28
  9. 29
  10. 30
  11. 31
  12. 32
  13. 33

    Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

    Published 2021
    “…P <0.05 was considered statistically significant. (B). The MCDM algorithm-Stage 2. Feature Ranking, this stage is the process of using the TOPSIS method to rank features. …”
  14. 34

    Table_1_bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.docx by Yupeng Li (507508)

    Published 2023
    “…In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.…”
  15. 35
  16. 36
  17. 37
  18. 38
  19. 39

    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). …”
  20. 40

    Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

    Published 2025
    “…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”