Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the great instruments in customer retention process and inferring the future behavior of the customers. However, the performances of predictive models are greatly affected when the real-world data set is highly imbalanced. A data set is called imbalanced if the samples size from one class is very much smaller or larger than the other classes. The most commonly used technique is over/under sampling for handling the class-imbalance problem (CIP) in various domains. In this paper, we survey six well-known sampling techniques and compare the performances of these key techniques, i.e., mega-trend diffusion function (MTDF), synthetic minority oversampling technique, adaptive synthetic sampling approach, couples top-N reverse k-nearest neighbor, majority weighted minority oversampling technique, and immune centroids oversampling technique. Moreover, this paper also reveals the evaluation of four rules-generation algorithms (the learning from example module, version 2 (LEM2), covering, exhaustive, and genetic algorithms) using publicly available data sets. The empirical results demonstrate that the overall predictive performance of MTDF and rules-generation based on genetic algorithms performed the best as compared with the rest of the evaluated oversampling methods and rule-generation algorithms

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With the terrific growth of digital data and associated technologies, there is an emerging trend, where industries become rapidly digitized. These technologies are providing great opportunities to identify and resolve different problems. In particular, the telecommunication industry is facing a serious problem of customer churn relating to, the customers who are going to abandon their established relation with the business/network in the near future. This problem cannot only affect the rapid growth of the business but can also affect the revenues. Therefore, many customer churn prediction (CCP) models have been introduced but not yielding the desired performance in CCP. This is because there can be many factors, that contribute to customer churn which are still unexplored. In this paper, we focus on determining the effectiveness of the factors, i.e. lower and upper distance between the samples, are considered by the proposed model for the CCP. Further, we demonstrate a novel solution pertaining to the telecommunication sector showing the hidden factors considered for predicting the customer churn. Finally, we investigate the effects of both types of samples: those samples that are low distance and the upper distance (in terms of relevance) to the majority samples in given publicly available dataset. As a result of the study, we found that lower distance test set (LDT) samples have obtained best performance as compare to upper distance test set (UDT) samples in term of increased in the accuracy, f-measures, precision and recall when the uncertain sample size increases. Because the classification performance on upper distance samples remain almost the same when the size of samples increased in the test set.

Underwater wireless sensor networks are a newly emerging wireless technology in which small size sensors with limited energy and limited memory and bandwidth are deployed in deep sea water and various monitoring operations like tactical surveillance, environmental monitoring, and data collection are performed through these tiny sensors. Underwater wireless sensor networks are used for the exploration of underwater resources, oceanographic data collection, flood or disaster prevention, tactical surveillance systems, and unmanned underwater vehicles. Sensor nodes consist of a small memory, a central processing unit, and an antenna. Underwater networks are much different from terrestrial sensor networks as radio waves cannot be used in underwater wireless sensor networks. Acoustic channels are used for communication in deep sea water. Acoustic signals have many limitations, such as limited bandwidth, higher end-to-end delay, network path loss, higher propagation delay, and dynamic topology. Usually, these limitations result in higher energy consumption with a smaller number of packets delivered. The main aim nowadays is to operate sensor nodes having a smaller battery for a longer time in the network. This survey has discussed the state-of-the-art localization based and localization-free routing protocols. Routing associated issues in the area of underwater wireless sensor networks have also been discussed.

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