بدائل البحث:
feature optimization » resource optimization (توسيع البحث), feature elimination (توسيع البحث), structure optimization (توسيع البحث)
case optimization » based optimization (توسيع البحث), phase optimization (توسيع البحث), dose optimization (توسيع البحث)
task feature » based feature (توسيع البحث), each feature (توسيع البحث), a feature (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
binary task » binary mask (توسيع البحث)
based case » base case (توسيع البحث), based cancer (توسيع البحث)
feature optimization » resource optimization (توسيع البحث), feature elimination (توسيع البحث), structure optimization (توسيع البحث)
case optimization » based optimization (توسيع البحث), phase optimization (توسيع البحث), dose optimization (توسيع البحث)
task feature » based feature (توسيع البحث), each feature (توسيع البحث), a feature (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
binary task » binary mask (توسيع البحث)
based case » base case (توسيع البحث), based cancer (توسيع البحث)
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S and V shaped transfer functions.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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S- and V-Type transfer function diagrams.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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Collaborative hunting behavior.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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24
Friedman average rank sum test results.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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25
IRBMO vs. variant comparison adaptation data.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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Sample image for illustration.
منشور في 2024"…<div><p>Feature description is a critical task in Augmented Reality Tracking. …"
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31
Comparison analysis of computation time.
منشور في 2024"…<div><p>Feature description is a critical task in Augmented Reality Tracking. …"
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Process flow diagram of CBFD.
منشور في 2024"…<div><p>Feature description is a critical task in Augmented Reality Tracking. …"
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33
Precision recall curve.
منشور في 2024"…<div><p>Feature description is a critical task in Augmented Reality Tracking. …"
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34
Quadratic polynomial in 2D image plane.
منشور في 2024"…<div><p>Feature description is a critical task in Augmented Reality Tracking. …"
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38
Analysis and design of algorithms for the manufacturing process of integrated circuits
منشور في 2023"…From this, we propose: (i) a new ILP model, and (ii) a new solution representation, which, unlike the reference work, guarantees that feasible solutions are obtained throughout the generation of new individuals. Based on this new representation, we proposed and evaluated other approximate methods, including a greedy algorithm and a genetic algorithm that improve the state-of-the-art results for test cases usually used in the literature. …"
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Summary of LITNET-2020 dataset.
منشور في 2023"…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …"
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SHAP analysis for LITNET-2020 dataset.
منشور في 2023"…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …"