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process optimization » model optimization (Expand Search)
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2 based » _ based (Expand Search), 1 based (Expand Search), ai based (Expand Search)
process optimization » model optimization (Expand Search)
based optimization » whale optimization (Expand Search)
basic process » based process (Expand Search), basic protein (Expand Search)
binary basic » binary mask (Expand Search)
binary 2 » binary _ (Expand Search), binary b (Expand Search)
2 based » _ based (Expand Search), 1 based (Expand Search), ai based (Expand Search)
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101
Steps in the extraction of 14 coordinates from the CT slices for the curved MPR.
Published 2025“…Protruding paths are then eliminated using graph-based optimization algorithms, as demonstrated in f). …”
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102
Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction
Published 2020“…Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO<sub>2</sub> and IrO<sub>3</sub>. …”
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103
Sample image for illustration.
Published 2024“…The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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104
Comparison analysis of computation time.
Published 2024“…The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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105
Process flow diagram of CBFD.
Published 2024“…The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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106
Precision recall curve.
Published 2024“…The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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107
Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx
Published 2020“…In this predictor, we introduced a sequence-based feature algorithm consisting of two feature representations, (1) k-mer spectrum and (2) positional nucleotide binary vector, to capture the sequential characteristics of 5hmC sites. …”
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108
DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
Published 2024“…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2 = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …”
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109
Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
Published 2024“…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2 = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …”
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110
DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx
Published 2024“…The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. …”
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111
Seed mix selection model
Published 2022“…</p> <p> </p> <p>We applied the seed mix selection model using a binary genetic algorithm to select seed mixes (R package ‘GA’; Scrucca 2013; Scrucca 2017). …”
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112
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</p><p dir="ltr">These biological metrics were used to define a binary toxicity label: entries were classified as toxic (1) or non-toxic (0) based on thresholds from standardized guidelines (e.g., ISO 10993-5:2009) and literature consensus. …”