Mobile devices are playing an important role in our daily lives. Nowadays, mobile devices are not only phones to call and text, but they are also smart devices that enable users to do almost any task that could be done on a regular PC. At the heart of the design of smartphones, there lies the processor to which almost all the development in the smartphone arena is attributed. Recently, ARM processors are among the most prominent processors used in mobile devices, smartphones, and embedded systems. This paper conducts an experimental comparative study of ARM 64-bit processors in terms of performance and their effect on power consumption, CPU temperature, and battery temperature. We use a number of well-known benchmarks to evaluate those characteristics of three smartphones, namely, Snapdragon 778G+, Exynos 1280 and HiSilicon Kirin 980. Those smartphones are all equipped with ARM 64-bit processors. Our results reveal that none of the three-selected smartphones was the best in all characteristics; each has superiority amongst others in certain characteristics and is dominated by others in other characteristics.

Travelling Salesman Problem (TSP) is one of the most popular NP-complete problems for the researches in the field of computer science which focused on optimization. TSP goal is to find the minimum path between cities with a condition of each city must to visit exactly once by the salesman. Grey Wolf Optimizer (GWO) is a new swarm intelligent optimization mechanism where it success in solving many optimization problems. In this paper, a parallel version of GWO for solving the TSP problem on a Hypercube Interconnection Network is presented. The algorithm has been compared to the alternative algorithms. Algorithms have been evaluated analytically and by simulations in terms of execution time, optimal cost, parallel runtime, speedup and efficiency. The algorithms are tested on a number of benchmark problems and found parallel Gray wolf algorithm is promising in terms of speed-up, efficiency and quality of solution in comparison with the alternative algorithms.

In this paper, we introduce a novel approach to address the dynamic prediction of customer activity in electronic payment transactions for individual clients. Our approach is founded on customer online payment transaction records from registered UK-based online retailers between 01/12/2009 and 09/12/2011. These retailers primarily specialize in unique gift items for various occasions, catering to a wide range of clients, including wholesalers. We used classification analysis based on the correlation coefficient to measure and describe a customer's electronic payment capability based on the quality of products they purchase. Furthermore, we trained multi-layered models (linear model, deep learning, random forest, and support vector machines (SVM)) to capture the dynamics of e-bank transaction reinforcement for retail customers using machine learning. Real transaction data from a UK online retailer was employed in our study. The experimental results consistently demonstrated the effectiveness of our proposed strategy.