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One of the core question for science and practice regarding utility is: what are the necessary epistemological preconditions that make predictions based on model-based data processing acceptable for human stakeholders?
Thus, the conceptual framework follows the typical steps of conducting a model-based ESS: collection and preparation of empirical data for the model-based data processing, assumption-making, model application and preparation of model outputs as well as deriving comprehensive conclusions and recommendations.
Artificial neural network technique based data driven models are developed to empirically describe the relationship between the processing conditions and the properties.
We give a general mathematical description of the problem and then create the appropriate objective optimization model based on genetic algorithms: take the data processing in a data center, for example, and assume that a data center has N optional clusters and M jobs to be executed.
A computerized statistical model with network algorithm was developed based on data processing and grouping.
A web service model for geophysical data manipulation, analysis and modeling based on a generalized data processing system was implemented.
The inbound data stream can be passed through data processing pipelines based on Cloud Dataflow via Cloud Pub/Sub.
FA, as a linear model based on second-order statistics, generally has difficulty processing real-world data which has non-Gaussian distributions.
Chemical processes > Data processing.
To support large-scale data processing, we design a multi-factor real-time monitoring fault tolerance (MRMFT) model based on a GPU cluster.
Data pre-processing was carried out with dChip Invariant Set Normalization and PM-only Model Based Expression summarization.
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