Nowadays, customer churn has been reflected as one of the main concerns in the processes of the telecom sector, as it affects the revenue directly. Telecom companies are looking to design novel methods to identify the potential customer to churn. Hence, it requires suitable systems to overcome the growing churn challenge. Recently, integrating different clustering and classification models to develop hybrid learners (ensembles) has gained wide acceptance. Ensembles are getting better approval in the domain of big data since they have supposedly achieved excellent predictions as compared to single classifiers. Therefore, in this study, we propose a customer churn prediction (CCP) based on ensemble system fully incorporating clustering and classification learning techniques. The proposed churn prediction model uses an ensemble of clustering and classification algorithms to improve CCP model performance. Initially, few clustering algorithms such as k-means, k-medoids, and Random are employed to test churn prediction datasets. Next, to enhance the results hybridization technique is applied using different ensemble algorithms to evaluate the performance of the proposed system. Above mentioned clustering algorithms integrated with different classifiers including Gradient Boosted Tree (GBT), Decision Tree (DT), Random Forest (RF), Deep Learning (DL), and Naive Bayes (NB) are evaluated on two standard telecom datasets which were acquired from Orange and Cell2Cell. The experimental result reveals that compared to the bagging ensemble technique, the stacking-based hybrid model (k-medoids-GBT-DT-DL) achieve the top accuracies of 96%, and 93.6% on the Orange and Cell2Cell dataset, respectively. The proposed method outperforms conventional state-of-the-art churn prediction algorithms.
General formulations of the temporal averaged pulse intensity for optical pulses propagating through either non-Kolmogorov or Kolmogorov turbulence are deduced under the strong fluctuation conditions and the narrow-band assumption. Based on these formulations, an analytical formula for the turbulence-induced temporal half-width of spherical-wave Gaussian (SWG) pulses is derived, and the single-point, two-frequency mutual coherence function (MCF) of collimated Gaussian-beam waves in atmospheric turbulence is formulated analytically, by which the temporal averaged pulse intensity of collimated space-time Gaussian (CSTG) pulses can be calculated numerically. Calculation results show that the temporal broadening of both SWG and CSTG pulses in atmospheric turbulence depends heavily on the general spectral index of the spatial power spectrum of refractive-index fluctuations, and the temporal broadening of SWG pulses can be used to approximate that of CSTG pulses on the axis with the same turbulence parameters and propagation distances. It is also illustrated by numerical calculations that the variation in the turbulence-induced temporal half-width of CSTG pulses with the radial distance is really tiny.
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