يعرض 81 - 100 نتائج من 123 نتيجة بحث عن '(( binary image based optimization algorithm ) OR ( binary data process optimization algorithm ))', وقت الاستعلام: 0.63s تنقيح النتائج
  1. 81

    Testing results for classifying AD, MCI and NC. حسب Nicodemus Songose Awarayi (18414494)

    منشور في 2024
    "…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …"
  2. 82

    Summary of existing CNN models. حسب Nicodemus Songose Awarayi (18414494)

    منشور في 2024
    "…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …"
  3. 83

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

    منشور في 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. …"
  4. 84
  5. 85

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

    منشور في 2021
    "…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …"
  6. 86

    Fig 1 - حسب Jakub Stoklosa (3315042)

    منشور في 2023
  7. 87

    Summary of literature review. حسب Muhammad Ayyaz Sheikh (18610943)

    منشور في 2024
    "…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"
  8. 88

    Topic description. حسب Muhammad Ayyaz Sheikh (18610943)

    منشور في 2024
    "…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"
  9. 89

    Notations along with their descriptions. حسب Muhammad Ayyaz Sheikh (18610943)

    منشور في 2024
    "…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"
  10. 90

    Detail of the topics extracted from DUC2002. حسب Muhammad Ayyaz Sheikh (18610943)

    منشور في 2024
    "…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"
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  14. 94

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

    منشور في 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. 95

    Sample image for illustration. حسب Indhumathi S. (19173013)

    منشور في 2024
    "…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"
  16. 96

    GSE96058 information. حسب Sepideh Zununi Vahed (9861298)

    منشور في 2024
    "…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …"
  17. 97

    The performance of classifiers. حسب Sepideh Zununi Vahed (9861298)

    منشور في 2024
    "…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …"
  18. 98

    Confusion matrix. حسب Yixian Wen (12201388)

    منشور في 2025
    "…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …"
  19. 99

    Parameter settings. حسب Yixian Wen (12201388)

    منشور في 2025
    "…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …"
  20. 100

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

    منشور في 2024
    "…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.<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.…"