A Radio mean labeling of a connected graph is an injective function from the vertex set, , to the set of natural numbers such that for any two distinct vertices and of , ⌈ ⌉. The radio mean number of , , is the maximum number assigned to any vertex of. The radio mean number of , , is the minimum value of , taken over all radio mean labeling of. This work has three contributions. The first one is proving two theorems which find the radio mean number for cycles and paths. The second contribution is proposing an approximate algorithm which finds an upper bound for radio mean number of a given graph. The third contribution is that we introduce a novel integer linear programing formulation for the radio mean problem. Finally, the experimental results analysis and statistical test proved that the Integer Linear Programming Model overcame the proposed approximate algorithm according to CPU time only. On the other hand, both the Integer Linear Programming Model and the proposed approximate algorithm had the same upper bound of the radio mean number of G.

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The development of biometric applications, such as facial recognition (FR), has recently become important in smart cities. Many scientists and engineers around the world have focused on establishing increasingly robust and accurate algorithms and methods for these types of systems and their applications in everyday life. FR is developing technology with multiple real-time applications. The goal of this paper is to develop a complete FR system using transfer learning in fog computing and cloud computing. The developed system uses deep convolutional neural networks (DCNN) because of the dominant representation; there are some conditions including occlusions, expressions, illuminations, and pose, which can affect the deep FR performance. DCNN is used to extract relevant facial features. These features allow us to compare faces between them in an efficient way. The system can be trained to recognize a set of people and to learn via an online method, by integrating the new people it processes and improving its predictions on the ones it already has. The proposed recognition method was tested with different three standard machine learning algorithms (Decision Tree (DT), K Nearest Neighbor(KNN), Support Vector Machine (SVM)). The proposed system has been evaluated using three datasets of face images (SDUMLA-HMT, 113, and CASIA) via performance metrics of accuracy, precision, sensitivity, specificity, and time. The experimental results show that the proposed method achieves superiority over other algorithms according to all parameters. The suggested algorithm results in higher accuracy (99.06%), higher precision (99.12%), higher recall (99.07%), and higher specificity (99.10%) than the comparison algorithms.

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In this paper, we propose a novel blended algorithm that has the advantages of the trisection method and the false position method. Numerical results indicate that the proposed algorithm outperforms the secant, the trisection, the Newton–Raphson, the bisection and the regula falsi methods, as well as the hybrid of the last two methods proposed by Sabharwal, with regard to the number of iterations and the average running time.

Groundwater sustainability is the development and use of groundwater resources to meet current and future beneficial uses without causing unacceptable environmental or socioeconomic consequences. This study is the first time to apply the hybrid optimization technique for solving of managing underground water aquifers, the confined steady flow problems, where a hybrid water cycle - particle swarm optimization WCA-PSO is proposed. In particular, we introduce a novel hybrid algorithm using water cycle algorithm (WCA) and particle swarm Optimization (PSO). The performance of the novel hybrid algorithm WCA-PSO is evaluated to solve 10 benchmark problems chosen from literature. The simulation results and comparison with pure WCA and PSO algorithms confirm the effectiveness of the proposed algorithm WCA-PSO for solving various benchmark optimization functions. Finally, we solve the problem of managing underground water aquifers by WCA, PSO and the hybrid optimization WCA-PSO. The experimental results analysis and statistical tests prove that the hybrid algorithm WCA-PSO overcomes the pure algorithms.

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The neutrosophic primal simplex algorithm starts from a neutrosophic basic feasible solution. If there is no such a solution, we cannot apply the neutrosophic primal simplex method for solving the neutrosophic linear programming problem. In this chapter, the authors propose a neutrosophic two-phase method involving neutrosophic artificial variables to obtain an initial neutrosophic basic feasible solution to a slightly modified set of constraints. Then the neutrosophic primal simplex method is used to eliminate the neutrosophic artificial variables and to solve the original problem.