Search alternatives:
field optimization » lead optimization (Expand Search), guided optimization (Expand Search), linear optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
laboratory based » laboratory values (Expand Search), laboratory data (Expand Search), laboratory tests (Expand Search)
based field » pulsed field (Expand Search)
binary mask » binary image (Expand Search)
field optimization » lead optimization (Expand Search), guided optimization (Expand Search), linear optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
laboratory based » laboratory values (Expand Search), laboratory data (Expand Search), laboratory tests (Expand Search)
based field » pulsed field (Expand Search)
binary mask » binary image (Expand Search)
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Rapid Prediction of Chemical Ecotoxicity Through Genetic Algorithm Optimized Neural Network Models
Published 2020“…Evaluating potentially hazardous effects of chemicals on ecosystems has always been an important research topic traditionally studied using laboratory or field experiments. Experiment-based ecotoxicity test results are only available for a limited number of chemicals due to the extensive experimental effort and cost. …”
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Identification of <i>Bacillus</i> and <i>Yersinia</i> species and hoax agents by protein profiling using microfluidic capillary electrophoresis with peak detection algorithms
Published 2021“…Parameters assessed included variation within and between Experion™ Pro260 chips and the ability to discriminate between samples over time intervals, between operators and between field and laboratory analyses.</p> <p>Classification with optimal Boolean logic paths reported no misclassification with an accuracy of 100% for <i>B. anthracis</i> Sterne strain, <i>B. thuringiensis</i> (powder and culture-based), <i>B. cereus</i> and plain wheat flour. …”
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Location of study area and sampling sizes.
Published 2023“…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …”
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S1 Data set -
Published 2023“…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …”
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The flowchart of this research.
Published 2023“…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …”
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SOM modeling results using characteristic bands.
Published 2023“…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …”
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Key variables selected by CARS of raw spectra.
Published 2023“…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …”
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SOM modeling results using full spectral bands.
Published 2023“…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …”
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Diversity and specificity of lipid patterns in basal soil food web resources
Published 2019“…Lipid profiles from microorganisms and algae were assessed from laboratory cultures, whereas plant tissue was derived from an arable field. …”