The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.

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In recent years, several emergent regions have become software development sourcing countries. This article investigates the possibilities of sub-Saharan Africa as a sourcing destination in the software field. To find out the reasons why sub-Saharan Africa countries, in general, and Nigeria, in particular, are not considered a destination for global software development projects, the authors interviewed a set of professionals from Europe and Africa. Results indicate that there are many disadvantages and difficulties impeding Nigeria from becoming a preferred sourcing destination, mainly the absence of a strong software industry and the concerns about legislative, fiscal, and commercial premises. On the other hand, it is observed that there are also relevant added values and competitive advantages in Nigeria (English-speaking country, same time zone, and cost); therefore, it can become a potential target for software development outsourcing in the medium and long terms.

Game-theoretic resource allocation algorithms are essential to managing the interference that Device-to-Device (D2D) and cellular transmissions could generate to each other in cellular networks since game-theoretic solutions are naturally autonomous and robust. In this paper, we present a survey on D2D communication in cellular networks with respect to the performance of the existing and accessible game-theoretic resource allocation algorithms published in 2013-2019. Each of the game-theoretic resource allocation algorithms with its properties such as utility, complexity, fairness, overhead cost, and convergence rate are reviewed and compared. The survey proved that gametheoretic solutions could be a viable strategy for practical implementation in 5G networks as each of the reviewed scheme attempts to optimize one or various essential performance metrics in the system. Finally, the paper recommends that serious efforts should be made by standardization bodies in incorporating game-theoretic strategy in D2D-enabled 5G networks while considering it as a road map for reliable and resource-efficient solutions in future cellular networks.

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