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In controlling biological diseases, it is often more potent to use a combination of agents than using individual ones. However, the number of possible combinations increases exponentially with the number of agents and their concentrations. It is prohibitive to search for effective agent combinations by trial and error as biological systems are complex and their responses to agents are often a slow...
The scale of modern networks grows exponentially, challenging our ability of efficiently managing large-scale network systems. Building an efficient monitoring system for such a large-scale network to report problems is difficult because of the scale of the network. Conventional approaches based on a fixed period polling strategy cannot fast adapt to the change of network and fail to discover abnormal...
Due to the non-liner, poor selectivity and cross-sensitivity of the combustible gas in the sewer, an analysis prediction model of the combustible gas in the sewer has been established based on the PSO-SVR machine, the model has introduced a new particle swarm algorithm to support the vector regression machine so that it can optimize the important parameters, realizing the automatic determination of...
Because knowing information about the currently running workload and the thermal status of the processor is of importance for more adequate planning and allocating resources in microprocessor environments, we propose in this paper using support vector regression (SVR) to predict future processor thermal status as well as the currently running workload. We build two generalized SVR models trained with...
Prediction of village electrical load is very important to manage village electrical load efficiently. Support vector regression (SVR) is a new learning algorithm based on statistical learning theory, which has a good time-series forecasting ability. As the choice of the best parameters of support vector regression is an important problem for support vector regression, and this problem will directly...
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