Many real-life datasets suffer from class imbalance, where one or more classes are under-represented in the dataset, resulting reduced classifier performance, with expected decline quality of procedures depending on classification results, such as financial losses to businesses inferior product quality. Improving accuracy by handling imbalance will positively impact accuracy. In this study, we ...

In this paper we propose a generalization of multiscale finite element methods (MsFEM) to nonlinear problems. We study the convergence of the proposed method for nonlinear elliptic equations and propose an oversampling technique. Numerical examples demonstrate that the oversampling technique greatly reduces the error. The application of MsFEM to porous media flows is considered. Finally, we des...

This paper presents the performance of a classifier built using the stackingC algorithm in nine different data sets. Each data set is generated using a sampling technique applied on the original imbalanced data set. Five new sampling techniques are proposed in this paper (i.e., SMOTERandRep, Lax Random Oversampling, Lax Random Undersampling, Combined-Lax Random Oversampling Undersampling, and C...

<span>The first year of an engineering student was important to take proper academic planning. All subjects in the were essential for basis. Student performance prediction helped academics improve their better. Students checked by themselves. If they aware that are low, then could make some improvement better performance. This research focused on combining oversampling minority class data...

This paper discusses the possibility of a high-sensitivity digital signal detection technique using frame oversampling and a CMOS image sensor with non-destructive high-speed intermediate high-speed readout mode. Simulation results show that the frame oversampling is very effective to reduce output amplifier noise and quantization noise of ADC.