Search alternatives:
quality optimization » policy optimization (Expand Search), whale optimization (Expand Search), path optimization (Expand Search)
methods optimization » lead optimization (Expand Search), model optimization (Expand Search), path optimization (Expand Search)
based quality » care quality (Expand Search), seed quality (Expand Search), bone quality (Expand Search)
based methods » mixed methods (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
lens » less (Expand Search)
quality optimization » policy optimization (Expand Search), whale optimization (Expand Search), path optimization (Expand Search)
methods optimization » lead optimization (Expand Search), model optimization (Expand Search), path optimization (Expand Search)
based quality » care quality (Expand Search), seed quality (Expand Search), bone quality (Expand Search)
based methods » mixed methods (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
lens » less (Expand Search)
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QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm
Published 2020“…Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”
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ROC curve for binary classification.
Published 2024“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”
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Confusion matrix for binary classification.
Published 2024“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”
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Minimizing the Optical Illusion of Nanoparticles in Single Cells Using Four-Dimensional Cuboid Multiangle Illumination-Based Light-Sheet Super-Resolution Imaging
Published 2022“…Additionally, a 4D multiangle illumination-based algorithm was created to select the optimal illumination angle by combining three-dimensional super-resolution imaging with multiangle observation, even in the presence of obstacles. …”
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Minimizing the Optical Illusion of Nanoparticles in Single Cells Using Four-Dimensional Cuboid Multiangle Illumination-Based Light-Sheet Super-Resolution Imaging
Published 2022“…Additionally, a 4D multiangle illumination-based algorithm was created to select the optimal illumination angle by combining three-dimensional super-resolution imaging with multiangle observation, even in the presence of obstacles. …”
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Datasets and their properties.
Published 2023“…However, the underlying deficiency of the single binary optimizer is transferred to the quality of the features selected. …”
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Parameter settings.
Published 2023“…However, the underlying deficiency of the single binary optimizer is transferred to the quality of the features selected. …”
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Example of simulated calcium imaging dataset.
Published 2024“…The parameters for each operation such as the kernel size, sigma and footprint size were optimized. We then validated the utility of the algorithm with simulated data and freely moving nociception experiments using the lensless devices. …”
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Probability density of each bin of accuracy.
Published 2024“…The parameters for each operation such as the kernel size, sigma and footprint size were optimized. We then validated the utility of the algorithm with simulated data and freely moving nociception experiments using the lensless devices. …”
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Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity
Published 2019“…We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. …”
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SHAP bar plot.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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Sample screening flowchart.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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Descriptive statistics for variables.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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SHAP summary plot.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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ROC curves for the test set of four models.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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Display of the web prediction interface.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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Image4_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF
Published 2022“…<p>We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. …”
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Image1_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF
Published 2022“…<p>We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. …”