Search alternatives:
using optimization » joint optimization (Expand Search), design optimization (Expand Search), step optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
primary data » primary care (Expand Search)
binary b » binary _ (Expand Search)
b model » _ model (Expand Search), a model (Expand Search), 2 model (Expand Search)
using optimization » joint optimization (Expand Search), design optimization (Expand Search), step optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
primary data » primary care (Expand Search)
binary b » binary _ (Expand Search)
b model » _ model (Expand Search), a model (Expand Search), 2 model (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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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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<i>hi</i>PRS algorithm process flow.
Published 2023“…<b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”
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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: