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marked decrease » marked increase (Expand Search)
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game » same (Expand Search), name (Expand Search)
marked decrease » marked increase (Expand Search)
teer decrease » greater decrease (Expand Search)
game » same (Expand Search), name (Expand Search)
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K-means results.
Published 2025“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”
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Feature importance result of SHAP.
Published 2025“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”
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Definition of variables.
Published 2025“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”
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Result of random forest.
Published 2025“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”
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Tree cover limits occupancy of a declining game bird
Published 2025“…Probability of bobwhite occupancy decreased as canopy cover increased (β<em><sub>Tree</sub></em> = -0.74, 95% CrI: -1.29 – -0.28); occupancy was over 19 times higher when canopy cover was 44% versus the mean observed value of 80.8% (range: 38–96%). …”
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A novel RNN architecture to improve the precision of ship trajectory predictions
Published 2025“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. This research proposes a new RNN architecture that decreases the prediction error up to 50% for cargo vessels when compared to the OU model. …”