The telecommunication industry faces fierce competition to retain customers, and therefore requires an efficient churn prediction model to monitor the customer’s churn. Enormous size, high dimensionality and imbalanced nature of telecommunication datasets are main hurdles in attaining the desired performance for churn prediction. In this study, we investigate the significance of a Particle Swar...

Compressed sensing is a processing method that significantly reduces the number of measurements needed to accurately resolve signals in many fields of science and engineering. We develop a two-dimensional variant of compressed sensing for multidimensional spectroscopy and apply it to experimental data. For the model system of atomic rubidium vapor, we find that compressed sensing provides an or...

The advent of the digital economy and Industry 4.0 enables financial organizations to adapt their processes mitigate risks losses associated with fraud. Machine learning algorithms facilitate effective predictive models for fraud detection 4.0. This study aims identify an efficient stable model platforms be adapted By leveraging a real credit card transaction dataset, this proposes compares fiv...

Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction students’ academic achievement) based on predictive models. However, class imbalance problem educational datasets could hamper accuracy models as many these are designed assumption that predicted balanced. Although previous studies proposed several methods to...

We present \surface frequency" representations, which describe how Cartesian positions on a surface vary with the intrinsic position along the surface. These representations are constructed using optimized coordinate values, individual sample weights, and an iterative transform algorithm. We then use surface frequency to monitor a deformable surface model and increase the number of samples when...

A new, infinite series representation for the error function is developed. It is especially suitable for computing erfc(x) for large x. For instance, for any x 4, the error function can be evaluated with a relative error less than 10 10 by using only eight terms. Similarly, the error function can be evaluated with a relative error less than 8 10 7 for any x 2 using just six terms. An analytical...

An imbalanced class on a dataset is common classification problem. The effect of using datasets can cause decrease in the performance classifier. Resampling one solutions to this This study used 100 from 3 websites: UCI Machine Learning, Kaggle, and OpenML. Each will go through processing stages: resampling process, significance testing process between evaluation values combination classifier p...

The problem of long-distance imaging through time-varying scattering media, such as the atmosphere, is encountered in many science fields. Recent studies have demonstrated that random atmospheric variability can be considered a spatial light modulator compressed sensing imaging. However, quality reconstructed image needs to further improved. In this paper, we propose distributed cumulative synt...

PURPOSE To develop a fast three-dimensional (3D) k-space encoding method based on spiral projection imaging (SPI) with an interleaved golden-angle approach and to validate this novel sequence on small animal models. METHODS A disk-like trajectory, in which each disk contained spirals, was developed. The 3D encoding was performed by tilting the disks with a golden angle. The sharpness was firs...