يعرض 1 - 15 نتائج من 15 نتيجة بحث عن '(( binary mask model optimization algorithm ) OR ( history data driven optimization algorithm ))', وقت الاستعلام: 1.04s تنقيح النتائج
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    A* Path-Finding Algorithm to Determine Cell Connections حسب Max Weng (22327159)

    منشور في 2025
    "…</p><p dir="ltr">Astrocytes were dissociated from E18 mouse cortical tissue, and image data were processed using a Cellpose 2.0 model to mask nuclei. Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. …"
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    List of data tables. حسب Mukhtar Ijaiya (18935122)

    منشور في 2025
    "…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …"
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    Flow chart of data source inclusion. حسب Mukhtar Ijaiya (18935122)

    منشور في 2025
    "…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …"
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    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. …"
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    Predictive model-building process. حسب Mukhtar Ijaiya (18935122)

    منشور في 2025
    "…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …"
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    Comparison of models performance metrics. حسب Mukhtar Ijaiya (18935122)

    منشور في 2025
    "…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …"
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    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). …"
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    Image 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.jpeg حسب Zhenze Zhang (22011422)

    منشور في 2025
    "…Predictors included demographics, comorbidities, laboratory parameters, vital signs, and disease severity scores. Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …"
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    Table 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.docx حسب Zhenze Zhang (22011422)

    منشور في 2025
    "…Predictors included demographics, comorbidities, laboratory parameters, vital signs, and disease severity scores. Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …"
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    Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants حسب Ahmed M. Alaa (5029781)

    منشور في 2019
    "…Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. …"
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