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The learning from imbalanced Big Data sets demonstrates an average 6 8% rise in the results of the proposed technique over benchmarking techniques.
An empirical study using a series of benchmark data sets demonstrates that the proposed method outperforms other state-of-the-art multi-label feature selection methods.
Empirical study on a wide range of data sets demonstrates that the proposed algorithm outperforms the state-of-the-art unsupervised algorithms in terms of accuracy.
An evaluation based on two different public trajectory data sets demonstrates that the proposed STRP algorithm achieves highly accurate position prediction.
The similarity of the viscosity reduction values within each pair of data seta sets demonstrates that oil viscosity reduction is much more dependent on temperature and solubility than it is dependent on whether the oil is live or dead.
A thorough empirical analysis of the prime subnetwork upon real geographical data sets demonstrates the approach is able to identify significantly more redundant constraints than previously proposed algorithms, especially in constraint networks with larger proportions of partial overlap relations.
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— enchanting young thespians, of all shapes, sizes, skin tones, and skill sets, demonstrating Alexander Technique posture in chairs along a wall.
Both data sets demonstrated a similar retention time, recovery, and MCT.
Synthetic and field data sets demonstrate effectiveness and practicality of the proposed method.
The experimental results on three hyperspectral data sets demonstrate the effectiveness of the proposed classification algorithm.
Experimental results obtained for popular medical data sets demonstrate the advantages of the proposed approach.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com