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Finally, we tested our methodology on real dataset.
In this and the following subsections, we demonstrate our methodology on RF systems.
In our case we want to test our methodology on prior time periods.
We also study the effect of our methodology on actuarial applications.
We demonstrate the performance of our methodology on two labeled benchmark datasets with respect to robustness, efficiency, and scalability.
Finally, we apply our methodology on real enterprise data, showing its effectiveness and efficiency in supporting role engineering.
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Our methodology focuses on the integration of economic components of landscape management based on contingent valuation method (CVM) and net-value change (NVC).
At the core, our methodology relies on SDR [1].
Our methodology focuses on career-minded, "never-marrieds" under the age of 35.
Our methodology relies on an experimental design with two rounds of decision making and multiple triads of agents.
Our methodology relies on a calibration approach that provides a relation between the distribution of App users and the crowd density.
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