Sentence examples for multiple data combinations from inspiring English sources

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In this study, we combine vehicular traffic theory, M/G/1 queuing analysis, and Markov chain analysis in order to investigate the transition between non-saturation and saturation regimes for an IEEE 802.11p-based network which is composed of mobile nodes with multiple data combinations and multiple data classes per combination.

Similar(59)

Integrated approaches utilising multiple data types in combination with prior biological knowledge may help the separation of functionally important findings from noise and data set-specific biases.

However, the utility of automated biosurveillance will be expanded with the availability of better population coverage and more specific data, the use of multiple data types in combination, and improved detection algorithms, such as those proposed here.

We have used multiple data sources and a combination of search methods to locate divergent Tasmanian devil immune genes.

To assess the impact of the intervention, the study is utilising multiple data sources using a combination of qualitative and quantitative methods.

AUC scores for single and all combinations of multiple data sources are summarized in an ascending order in Table 1, and their corresponding data fusion results are shown and discussed in the following sections.

When analysing several feature combinations from multiple data points, care must be taken not to introduce errors in statistical analysis by overfitting of data.

Mashup, a combination of multiple data formats or sources, such as maps, music, photographs, video, and animations, into one digital file.

When it comes to the business model, in addition to the device for consumers, the company has an API and can give you air quality forecasts based on past readings, a combination of multiple data points and machine learning.

We then characterized their signature similarity to the 337 DEGs by the combination of multiple data sources, including gene ontology, functional annotation, protein-protein interaction, gene expression, literature mining and sequence data.

These have made useful suggestions, such as greater sensitivity with graduated combination of multiple data sources [ 19] and looking at the impact of interventions not solely the risk [ 8].

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