بدائل البحث:
codon optimization » wolf optimization (توسيع البحث)
used optimization » based optimization (توسيع البحث), led optimization (توسيع البحث), guided optimization (توسيع البحث)
binary primate » binary image (توسيع البحث)
primate codon » primate model (توسيع البحث)
primary data » primary care (توسيع البحث)
codon optimization » wolf optimization (توسيع البحث)
used optimization » based optimization (توسيع البحث), led optimization (توسيع البحث), guided optimization (توسيع البحث)
binary primate » binary image (توسيع البحث)
primate codon » primate model (توسيع البحث)
primary data » primary care (توسيع البحث)
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Features selected by optimization algorithms.
منشور في 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
منشور في 2024الموضوعات: -
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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
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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
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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
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Zimbabwe model outputs on actual and optimized 2016 spending and impact by intervention.
منشور في 2021الموضوعات: -
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Short overview of the primary dataset.
منشور في 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.
منشور في 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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