يعرض 21 - 40 نتائج من 72 نتيجة بحث عن '(( binary based swarm optimization algorithm ) OR ( binary based from optimization algorithm ))', وقت الاستعلام: 0.51s تنقيح النتائج
  1. 21

    Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization حسب Sandeep Jagonda Patil (22048337)

    منشور في 2025
    "…To tackle these challenges, this paper proposes the Blockchain Based Trusted Distributed Routing Scheme for MANET using Latent Encoder Coupled Generative Adversarial Network Optimized with Binary Emperor Penguin Optimizer (LEGAN-BEPO-BCMANET). …"
  2. 22

    A* Path-Finding Algorithm to Determine Cell Connections حسب Max Weng (22327159)

    منشور في 2025
    "…Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. The A* algorithm then evaluated connectivity by minimizing Euclidean distance and heuristic cost between cells. …"
  3. 23

    Datasets and their properties. حسب Olaide N. Oyelade (14047002)

    منشور في 2023
    "…To address this, we proposed a novel hybrid binary optimization capable of effectively selecting features from increasingly high-dimensional datasets. …"
  4. 24

    Parameter settings. حسب Olaide N. Oyelade (14047002)

    منشور في 2023
    "…To address this, we proposed a novel hybrid binary optimization capable of effectively selecting features from increasingly high-dimensional datasets. …"
  5. 25

    Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF حسب Maya Khatun (7437011)

    منشور في 2019
    "…This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. …"
  6. 26

    <i>hi</i>PRS algorithm process flow. حسب Michela C. Massi (14599915)

    منشور في 2023
    "…From this dataset we can compute the MI between each interaction and the outcome and <b>(D)</b> obtain a ranked list (<i>I</i><sub><i>δ</i></sub>) based on this metric. …"
  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. …"
  13. 33

    A new fast filtering algorithm for a 3D point cloud based on RGB-D information حسب Chaochuan Jia (7256237)

    منشور في 2019
    "…This method aligns the color image to the depth image, and the color mapping image is converted to an HSV image. Then, the optimal segmentation threshold of the V image that is calculated by using the Otsu algorithm is applied to segment the color mapping image into a binary image, which is used to extract the valid point cloud from the original point cloud with outliers. …"
  14. 34

    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment حسب Jianfang Cao (1881379)

    منشور في 2019
    "…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …"
  15. 35

    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.…"
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  17. 37

    the functioning of BRPSO. حسب Hossein Jarrahi (22530251)

    منشور في 2025
    "…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …"
  18. 38

    Characteristic of 6- and 10-story SMRF [99,98]. حسب Hossein Jarrahi (22530251)

    منشور في 2025
    "…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …"
  19. 39

    The RFD’s behavior mechanism (2002). حسب Hossein Jarrahi (22530251)

    منشور في 2025
    "…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …"
  20. 40