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design optimization » bayesian optimization (Expand Search)
based optimization » whale optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
binary base » binary mask (Expand Search), ciliary base (Expand Search), binary image (Expand Search)
base based » case based (Expand Search), blame based (Expand Search), made based (Expand Search)
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121
Details of 23 basic benchmark functions.
Published 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. …”
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122
Related researches.
Published 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. …”
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123
S1 Dataset -
Published 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. …”
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124
Pseudo Code of RBMO.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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125
P-value on CEC-2017(Dim = 30).
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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126
Memory storage behavior.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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127
Elite search behavior.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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128
Description of the datasets.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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129
S and V shaped transfer functions.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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130
S- and V-Type transfer function diagrams.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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131
Collaborative hunting behavior.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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132
Friedman average rank sum test results.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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133
IRBMO vs. variant comparison adaptation data.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”
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134
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135
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136
ROC curve for binary classification.
Published 2024“…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. …”
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137
Confusion matrix for binary classification.
Published 2024“…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. …”
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138
Bayesian sequential design for sensitivity experiments with hybrid responses
Published 2023“…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”
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139
Flowchart scheme of the ML-based model.
Published 2024“…<b>F)</b> Feature extraction using three different steps: <b>Fi)</b> Color moments in different orders based on color distribution. <b>Fii)</b> Texture information using local binary patterns. …”
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140
A new fast filtering algorithm for a 3D point cloud based on RGB-D information
Published 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. …”