Search alternatives:
learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
image learning » maze learning (Expand Search), face learning (Expand Search), aware learning (Expand Search)
a feature » _ feature (Expand Search), _ features (Expand Search), each feature (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
image learning » maze learning (Expand Search), face learning (Expand Search), aware learning (Expand Search)
a feature » _ feature (Expand Search), _ features (Expand Search), each feature (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
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Classification performance after optimization.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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ANOVA test for optimization results.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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83
Wilcoxon test results for optimization.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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Classification baseline performance.
Published 2025“…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …”
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ANOVA test result.
Published 2025“…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …”
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96
Summary of literature review.
Published 2025“…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …”
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97
Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19.
Published 2021“…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …”
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QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm
Published 2020“…The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”
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Hyperparameters of the LSTM Model.
Published 2025“…This effectively balances exploration and exploitation, and addresses the early convergence problem of the original algorithms. To choose the most crucial characteristics of the dataset, the feature selection method employs the binary format of AD-PSO-Guided WOA. …”
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The AD-PSO-Guided WOA LSTM framework.
Published 2025“…This effectively balances exploration and exploitation, and addresses the early convergence problem of the original algorithms. To choose the most crucial characteristics of the dataset, the feature selection method employs the binary format of AD-PSO-Guided WOA. …”
