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Showing 1 - 12 results of 12 for search '(( primary ct model optimization algorithm ) OR ( binary mapk driven optimization algorithm ))', query time: 0.41s Refine Results
  1. 1
    Pseudocode of artificial dragonfly algorithm.

    Pseudocode of artificial dragonfly algorithm. by Ghassan Ahmed Ali (17041488)

    Published 2023
    “…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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  2. 2
    The Hopfield artificial neural network algorithm.

    The Hopfield artificial neural network algorithm. by Ghassan Ahmed Ali (17041488)

    Published 2023
    “…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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  3. 3
    Training error and accuracy for all HNN models.

    Training error and accuracy for all HNN models. by Ghassan Ahmed Ali (17041488)

    Published 2023
    “…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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  4. 4
    RMSE performance of various HNN-EB<i>k</i>SAT models.

    RMSE performance of various HNN-EB<i>k</i>SAT models. by Ghassan Ahmed Ali (17041488)

    Published 2023
    “…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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  5. 5
    <i>MAPE performance of various</i> HNN-EB<i>k</i>SAT models.

    <i>MAPE performance of various</i> HNN-EB<i>k</i>SAT models. by Ghassan Ahmed Ali (17041488)

    Published 2023
    “…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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  6. 6
    G<i>m</i>R performance of various HNN-EB<i>k</i>SAT models.

    G<i>m</i>R performance of various HNN-EB<i>k</i>SAT models. by Ghassan Ahmed Ali (17041488)

    Published 2023
    “…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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  7. 7
    Flow diagram of Wan Abdullah method for HNN.

    Flow diagram of Wan Abdullah method for HNN. by Ghassan Ahmed Ali (17041488)

    Published 2023
    “…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …”
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  8. 8
    Study design.

    Study design. by Maxence Coulombe (17921106)

    Published 2024
    “…At the delivery stage, all patients will receive both a Providence-type brace optimized by the semi-automatic algorithm leveraging a patient-specific FEM (Test) and a conventional Providence-type brace (Control), both designed using CAD/CAM methods. …”
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  9. 9
    Table_1_Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model.XLSX

    Table_1_Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model.X... by Fang Tian (360682)

    Published 2025
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  10. 10
    Data_Sheet_1_Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19.docx

    Data_Sheet_1_Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19.docx by Xiao-Hui Wang (1762348)

    Published 2021
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  11. 11
    Data_Sheet_1_A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer.docx

    Data_Sheet_1_A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer.docx by Tingting Ma (228102)

    Published 2024
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  12. 12
    Table_1_Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy.docx

    Table_1_Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy.docx by Xiaohan Wang (691917)

    Published 2024
    “…Feature selection was facilitated using the Extreme Gradient Boosting (XGBoost) algorithm, with model validation conducted via fivefold cross-validation. …”
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