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We discuss conceptual and algorithmic considerations for selecting and fusing information from disparate sensors.
In this study, we propose a novel conceptual and algorithmic approach by elevating existing fuzzy models to granular fuzzy models in the form of fuzzy sets.
However, designing a real-world actor framework that subsumes full scalability, strong reliability, and high resource efficiency requires many conceptual and algorithmic additives to the original model.
The highly diversified conceptual and algorithmic landscape of Granular Computing calls for the formation of sound fundamentals of the discipline, which cut across the diversity of formal frameworks (fuzzy sets, sets, rough sets) in which information granules are formed and processed.
MFS, NP and PP initiated the work, while all authors contributed to the conceptual and algorithmic choices.
Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations.
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The purpose of this study was to compare student performance on two proctored quizzes and one practice test taken in paper-based or computer-based testing mode using an equal number of algorithmic, conceptual, and definition questions to examine the effect of testing mode when students take multiple tests in class.
The percent of conceptual questions ranged from 64to85%5%, recall ranged from 4 to 24%, and algorithmic from 0 to 11%.
Although the idea of information granulation has been shown to be an essential concept and algorithmic pursuit in time series prediction, there is still an acute need for developing a sound conceptual framework for time series prediction so that information granulation can capture the essence of collections of data better, including average and trend information.
It is also a blow at the idea of artificial intelligence and algorithmic censorship.
In contrast, Mark Fell is a multi-disciplinary artist-musician with a penchant for high-wired electronica and algorithmic structures.
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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