Search alternatives:
algorithm both » algorithm blood (Expand Search), algorithm b (Expand Search), algorithm etc (Expand Search)
model function » novel function (Expand Search), model fusion (Expand Search), model reaction (Expand Search)
both function » body function (Expand Search), growth function (Expand Search), beach function (Expand Search)
flow function » from function (Expand Search), low functional (Expand Search), loss function (Expand Search)
algorithm both » algorithm blood (Expand Search), algorithm b (Expand Search), algorithm etc (Expand Search)
model function » novel function (Expand Search), model fusion (Expand Search), model reaction (Expand Search)
both function » body function (Expand Search), growth function (Expand Search), beach function (Expand Search)
flow function » from function (Expand Search), low functional (Expand Search), loss function (Expand Search)
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Machine learning algorithm parameters.
Published 2023“…Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). …”
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The summarized flow of the methodology.
Published 2023“…Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). …”
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Pair Potentials as Machine Learning Features
Published 2020“…M. J. Chem. Inf. Model. 2019, 59, 1919−1929]. Here, as an example of combining ML methods with traditional potential functions, we followed the same work flow to combine the RF models with force field potential functions from Amber. …”
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Pair Potentials as Machine Learning Features
Published 2020“…M. J. Chem. Inf. Model. 2019, 59, 1919−1929]. Here, as an example of combining ML methods with traditional potential functions, we followed the same work flow to combine the RF models with force field potential functions from Amber. …”
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Pair Potentials as Machine Learning Features
Published 2020“…M. J. Chem. Inf. Model. 2019, 59, 1919−1929]. Here, as an example of combining ML methods with traditional potential functions, we followed the same work flow to combine the RF models with force field potential functions from Amber. …”
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River basin of the Malwathu River in Sri Lanka.
Published 2023“…Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). …”
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General two-input ANFIS structure.
Published 2023“…Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). …”
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Deep Learning-Based Multi-Scale Bubble Detection and Feature Analysis
Published 2025“…The spatial and channel reconstruction convolution (SCConv) module is integrated in the neck network of the model to reduce feature redundancy in both the channel and spatial dimensions. …”
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Optimal parameters for the single process model.
Published 2023“…However, a comprehensive understanding of the underlying control algorithms is still outstanding. In fish it is often assumed that the OMR, by reducing average optic flow across the retina, serves to stabilize position with respect to the ground. …”
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Combining urban scaling and polycentricity to explain socio-economic status of urban regions
Published 2019“…The urban scaling concept is constructed from a theoretical perspective, but functional relationships between urban centres are not taken into account in scaling models. …”
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