Search alternatives:
resource optimization » resource utilization (Expand Search), resource utilisation (Expand Search), resource limitations (Expand Search)
feature optimization » feature elimination (Expand Search), structure optimization (Expand Search), iterative optimization (Expand Search)
also resource » actor resource (Expand Search), value resource (Expand Search), low resource (Expand Search)
task feature » based feature (Expand Search), each feature (Expand Search), a feature (Expand Search)
binary task » binary mask (Expand Search)
resource optimization » resource utilization (Expand Search), resource utilisation (Expand Search), resource limitations (Expand Search)
feature optimization » feature elimination (Expand Search), structure optimization (Expand Search), iterative optimization (Expand Search)
also resource » actor resource (Expand Search), value resource (Expand Search), low resource (Expand Search)
task feature » based feature (Expand Search), each feature (Expand Search), a feature (Expand Search)
binary task » binary mask (Expand Search)
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21
IRBMO vs. variant comparison adaptation data.
Published 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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Dynamic resource allocation process.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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23
Supplementary file 1_Encodings of the weighted MAX k-CUT problem on qubit systems.pdf
Published 2025“…This study explores encoding methods for MAX k-CUT on qubit systems by utilizing quantum approximate optimization algorithms (QAOA) and addressing the challenge of encoding integer values on quantum devices with binary variables. …”
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Confusion matrix.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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26
Parameter settings.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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27
Event-driven data flow processing.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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28
Sample image for illustration.
Published 2024“…<div><p>Feature description is a critical task in Augmented Reality Tracking. …”
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29
Comparison analysis of computation time.
Published 2024“…<div><p>Feature description is a critical task in Augmented Reality Tracking. …”
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30
Process flow diagram of CBFD.
Published 2024“…<div><p>Feature description is a critical task in Augmented Reality Tracking. …”
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31
Precision recall curve.
Published 2024“…<div><p>Feature description is a critical task in Augmented Reality Tracking. …”
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32
Quadratic polynomial in 2D image plane.
Published 2024“…<div><p>Feature description is a critical task in Augmented Reality Tracking. …”
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33
Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP
Published 2022“…In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. …”
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34
Models and Dataset
Published 2025“…The algorithm does not rely on predefined control parameters like crossover or mutation rates, which makes it lightweight and easy to implement for various feature selection and optimization tasks.…”
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Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf
Published 2020“…In this way, for each binary problem, the CSP algorithm produces features to determine if the specific body part is engaged in the task or not. …”
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36
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”