We examine a prototypical biomedical data generation process to illustrate factors that may negatively impact the C&R of the data throughout different stages of the process.
This is a prototypical big data problem.
Due to the novelty of the plant and its prototypical character, reliability data introduced in the numerical simulation underwent an accurate screening process among the current available databases, sometimes requiring the expert judgment assessment.
We designed and developed a prototypical system, which supports data warehousing of web log data, extraction of data mining models and simulation of the web caching algorithms.
We just started to apply the proposed clustering approach to identify prototypical components within neuronal data helping to understand the structure of neuronal information.
Initially, the set of prototypical implemented algorithms (including data classification mechanisms and statistical distribution analysis) were applied using a default configuration.
To demonstrate F l u χ feasibility, usefulness, and efficiency, the assessment of F l u χ was centered on a realistic prototypical example of intensive data processing, addressing the evaluation of air quality, pollution and health risks, for a city based on sensory data, gathered asynchronously, from thousands of sensors.
The proposed clustering procedure corroborates this finding, but, additionally, quantifies the results and answers the question, whether there are additional prototypical movements within the data set.
We introduce Iterative Regularized Kernel Regression (IRKR), an iterative nonlinear feature selection method combined with a Lasso-regularized version of the original MLKR formulation that improves on the state-of-the-art results on several AU databases, ranging from prototypical to natural and wild data.
For contextual analysis, histograms of the kernel data for 12 prototypical days were drawn to highlight variation in infection rates.
