يعرض 21 - 40 نتائج من 46 نتيجة بحث عن '(( binary image driven optimization algorithm ) OR ( binary based well optimization algorithm ))*', وقت الاستعلام: 0.58s تنقيح النتائج
  1. 21

    The flowchart of IMGO. حسب Ying Li (38224)

    منشور في 2024
    "…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"
  2. 22

    Comparison in terms of the selected features. حسب Ying Li (38224)

    منشور في 2024
    "…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"
  3. 23

    Iterative chart of control factor. حسب Ying Li (38224)

    منشور في 2024
    "…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"
  4. 24

    Details of 23 basic benchmark functions. حسب Ying Li (38224)

    منشور في 2024
    "…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"
  5. 25

    Related researches. حسب Ying Li (38224)

    منشور في 2024
    "…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"
  6. 26

    S1 Dataset - حسب Ying Li (38224)

    منشور في 2024
    "…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"
  7. 27

    SHAP bar plot. حسب Meng Cao (105914)

    منشور في 2025
    "…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
  8. 28

    Sample screening flowchart. حسب Meng Cao (105914)

    منشور في 2025
    "…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
  9. 29

    Descriptive statistics for variables. حسب Meng Cao (105914)

    منشور في 2025
    "…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
  10. 30

    SHAP summary plot. حسب Meng Cao (105914)

    منشور في 2025
    "…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
  11. 31

    ROC curves for the test set of four models. حسب Meng Cao (105914)

    منشور في 2025
    "…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
  12. 32

    Display of the web prediction interface. حسب Meng Cao (105914)

    منشور في 2025
    "…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
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    DataSheet_1_Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates.pdf حسب Marcel Dahms (9160118)

    منشور في 2022
    "…Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.…"
  19. 39

    Data_Sheet_1_Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer.pdf حسب Maliheh Aramon (6557906)

    منشور في 2019
    "…The Digital Annealer's algorithm is currently based on simulated annealing; however, it differs from it in its utilization of an efficient parallel-trial scheme and a dynamic escape mechanism. …"
  20. 40

    datasheet1_Graph Neural Networks for Maximum Constraint Satisfaction.pdf حسب Jan Tönshoff (10192709)

    منشور في 2021
    "…We introduce a graph neural network architecture for solving such optimization problems. The architecture is generic; it works for all binary constraint satisfaction problems. …"