This paper compares different data processing strategies for NNs for prediction of Boolean function complexity (BFC).
We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes.
By comparing data points, the energy-based processing approach (green data points in Fig. 6; every other point is plotted) may be the most suitable to generate the representative curve.
The focus on Hadoop MapReduce is justified by the suitability of its functionalities as to handling urban data as well as to its advantages associated with load balancing, cost effectiveness, flexibility, and processing power compared to other data processing platforms.
One reason for this state of affairs is the fact that simulating cellular models is computationally expensive, when compared to the data processing tasks.
The SST, in conjunction with the regular robust multiple-array average normalization method (SST-RMA, the dark blue bars), was able to improve the fold-change in the HTA2.0 arrays, and provided a 2-5x greater fold-change estimates overall, as compared to conventional data processing method (regular RMA, the light blue bars).
Geoffrey Crew, an astronomer at MIT who helped coordinate the data processing, compared the effort to discerning a concerto played at a piano that is missing several keys.
Empirical experiments confirmed the ability of this approach to improve the accuracy of fingerprint identification queries when compared to the static data processing architecture of current systems.
In WSN, data transmission consumes more energy compared to sensing and data processing [5].
Nevertheless, we can show that the property of the neighboring similarity is beneficial for efficient data processing compared with conventional methods (Section 3).
