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Figure 3 Total number of reconfigurable mobile devices with and without the application of the prediction framework in the load balancing system for three different simulation scenarios.
To this purpose, the application of the prediction to a set of "filtered" data leads to a more representative view of the load conditions, smoothing the resources measurements.
Figure 4 Total number of autonomous mobile devices with and without the application of the prediction framework in the load balancing system.
At the end of this section we present an interesting application of the prediction system for generating models with confidential structures that can be exported and shared without risk of disclosing sensitive information.
As seen in Figures 3 and 4 depict the simulation results for the reconfigurable and autonomous devices, respectively; the application of the prediction system in the load-balancing framework results to dropping less mobile devices requests than that of the initial load-balancing framework.
Table 2 presents the results of the application of the prediction module to the whole dataset.
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However, the clinical utility of this application for the prediction of type 2 diabetes needs to be tested and validated.
In this paper the development of a computer application for the prediction of an ice bank system operation is presented.
However, the unique structure of biological tissues makes its application to the prediction of aneurysm rupture not as straightforward as one may hope.
Figure 5 shows the results of many applications of the prediction algorithm to the test experimental data with different thresholds.
In panel A, we show the results of many applications of the prediction algorithm to the test experimental data, with different thresholds.
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