In this paper an attempt is made to show that the performance of daily river flow forecasting is improved when data-preprocessing techniques are used with computational intelligence methods.
The tests show that the inferential engine has good predictive capabilities, which could be improved when more data become available.
When used in the simpler product-specific manner, the efficiency of IO models can be improved when primary data are collected.
We previously investigated whether the prediction of distant metastasis in breast cancer patients could be improved when considering microarray data besides clinical data [ 29].
We expect the sensitivity value of the hub classifier to be improved when more annotation data become available for the four species in the training sets.
The study also demonstrated that the precision under UAVHYB can be improved when including additional field data from other inventories to enhance the model.
Indeed, our recent studies have shown that the predictivity of QSAR models for in vivo toxicity can be improved when in vitro testing data, treated as biological descriptors of chemicals, are combined with traditional chemical descriptors (Zhu et al. 2008, 2009b).
In this article, we show that the scheduling of scientific workflows can be improved when both task scheduling and the data file assignment problems are treated together.
The predictions are improved when taking data on the iron status into account.
The model was improved when endocrine data (GHmax profile, IGF-I and leptin) collected before starting GH treatment were included.
We further studied whether prediction performance is improved when the HAc data are derived from the same cell condition from which the TF binding data (used for evaluating performance) are derived.
