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processing algorithm » modeling algorithm (Expand Search), routing algorithm (Expand Search), tracking algorithm (Expand Search)
data processing » image processing (Expand Search)
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element data » settlement data (Expand Search), relevant data (Expand Search), movement data (Expand Search)
sample using » samples using (Expand Search), cattle using (Expand Search)
processing algorithm » modeling algorithm (Expand Search), routing algorithm (Expand Search), tracking algorithm (Expand Search)
data processing » image processing (Expand Search)
using algorithm » using algorithms (Expand Search), routing algorithm (Expand Search), fusion algorithm (Expand Search)
data algorithm » data algorithms (Expand Search), update algorithm (Expand Search), atlas algorithm (Expand Search)
element data » settlement data (Expand Search), relevant data (Expand Search), movement data (Expand Search)
sample using » samples using (Expand Search), cattle using (Expand Search)
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FastICA algorithm and feature point selection.
Published 2025“…The sub-block selection algorithm sorts and filters sub-blocks based on the average pixel difference, reconstructing the input data to ensure accurate separation of melanin and hemoglobin. …”
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Improved random forest algorithm.
Published 2025“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”
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K-means++ clustering algorithm.
Published 2025“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”
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Ultrafast Hydrogen Detection System Using Vertical Thermal Conduction Structure and Neural Network Prediction Algorithm Based on Sensor Response Process
Published 2025“…Meanwhile, the model significantly enhanced the detection speed by enabling hydrogen concentration prediction using only the initial 40 data points (sampling frequency of 100 Hz) from the sensor response before the sensor completes the entire response process. …”
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Ultrafast Hydrogen Detection System Using Vertical Thermal Conduction Structure and Neural Network Prediction Algorithm Based on Sensor Response Process
Published 2025“…Meanwhile, the model significantly enhanced the detection speed by enabling hydrogen concentration prediction using only the initial 40 data points (sampling frequency of 100 Hz) from the sensor response before the sensor completes the entire response process. …”
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The CGO risk prediction process based on data augmentation and neuroevolution.
Published 2025Subjects: -
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bppMigration-algorithms-data.tgz
Published 2025“…<p dir="ltr">Population phylogenomics uses sampled genomes to jointly infer population genetic processes (ancestral and contemporary population sizes, historical gene flow) and a phylogenetic tree relating species or populations including species divergence times. …”