Search alternatives:
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
used optimization » based optimization (Expand Search), led optimization (Expand Search), guided optimization (Expand Search)
primary data » primary care (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
used optimization » based optimization (Expand Search), led optimization (Expand Search), guided optimization (Expand Search)
primary data » primary care (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
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Features selected by optimization algorithms.
Published 2024“…After the image has been pre-processed, it is segmented using the Thresholding Level set approach. Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024Subjects: -
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FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024Subjects: -
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FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024Subjects: -
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FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024Subjects: -
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FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024Subjects: -
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FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
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FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024Subjects: -
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Zimbabwe model outputs on actual and optimized 2016 spending and impact by intervention.
Published 2021Subjects: -
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Short overview of the primary dataset.
Published 2023“…Furthermore, on (HDD Mono) the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80/20 (87.72%), 70/30 (89.41%), and 5 folds cross-validation (88.38%), and (HDD-Multi) in terms of 80/20 (93.64%), 70/30 (90.91%), and 5 folds cross-validation (88.20%). Whereas, primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80/20 (97.14%), 70/30 (96.19%), and 5 folds cross-validation (95.85%) in the primary data results, but the algorithm complexity (0.17 seconds) is not good. …”
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Eight commonly used benchmark functions.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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