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International Journal of Computing Science and Mathematics

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 International Journal of Computing Science and Mathematics (97 papers in press) Regular Issues Effective Test Data Generation Using Probabilistic Networks   by Farid Feyzi, Saeed Parsa Abstract: This paper presents a novel test data generation method called Bayes-TDG. It is based on principles of Bayesian Networks (BNs) and provides the possibility of making inference from probabilistic data in the model to increase the Prime Path Coverage Ratio (PPCR) for a given Program Under Test (PUT). In this regard, a new program structure-based probabilistic network, TDG-NET, is proposed that is capable of modeling the conditional dependencies among the program Basic Blocks (BBs) in one hand and conditional dependencies of the transitions between its BBs and input parameters on the other hand. To achieve failure-detection effectiveness, we propose a path selection strategy that works based on the predicted outcome of generated test cases. So, we mitigate the need for a human oracle, and the generated test suite could be directly used in fault localization. Several experiments are conducted to evaluate the performance of Bayes-TDG. The results reveal that the method is promising and the generated test suite could be quite effective. Keywords: Software Testing; Bayesian Net; Test Data Generation; Adaptive Random Testing; Fault Detection. Optimal dynamic pricing for non-instantaneous deteriorating items dependent on price and time demand   by Lisha Wang Abstract: This paper establishes a dynamic pricing model for non-instantaneous deteriorating products to maximize the companies' profit. The demand rate depends on time as well as the sales price. The optimal dynamic price strategy, optimal sale period and the maximal total profit are derived to solve the problem by applying Pontryagin's maximum principle. Meanwhile, uniform pricing and two-part pricing models are introduced to compare with the dynamic pricing model. Finally, numerical example are carried out to investigate that the dynamic pricing was better than the other two static pricing strategies. Moreover, some managerial conclusions and appropriate measures for decision makers have been obtained by discussing the sensitiveness of the main parameters. Keywords: Price and time dependent demand; Non-instantaneous deteriorating products; Dynamic pricing; Pontryagin's maximum principle. A new simultaneous extension method for B-spline curves blending with G2-continuity   by Hongying Yu, Xuegeng Lyu Abstract: Curve blending is an extremely common problem in CAD systems. The current blending methods were looking for a third curve to join curves and some of the methods needed to distinguish the transition curve were C- or S-shaped. In this paper, we study a simultaneous extension method to blend curves with G2-continuity. The method simultaneously extends the two curves at one of their endpoints and makes them intersect at a common joint under geometric constraints. The basic concept of B-spline curves and its extension theory is presented firstly. Then we propose the blending algorithm of simultaneous extension. This method does not need to prejudge the shape of transition curves, which is, without considering the placement of two original curves, and reduces the number of blending joints from two to one. Four curve blending examples are presented to verify the validity of the new method. Keywords: curves blending; simultaneous extension method; B-spline curve; G2-continuity. DETECTION OF BRAIN TUMOR BY USING MOMENTS AND TRANSFORMS ON SEGMENTED MAGNETIC RESONANCE BRAIN IMAGES   by RAHUL UPNEJA, AJAY PRASHAR Abstract: Brain tumor occurs when abnormal cells appear within the brain. Primary tumor starts with abnormal growth of brain cells whereas Secondary (Metastatic) tumor initiates as cancer in other parts of the body and spread to the brain through blood stream. In this paper, we propose a novel approach to detect tumor in Magnetic Resonance (MR) brain images. The proposed method uses Improved Incremental Self Organize Mapping (I2SOM) to segment the brain image and to calculate asymmetry Zernike Moments (ZMs), Pseudo-Zernike Moments (PZMs) and Orthogonal Fourier Mellin Moments (OFMMs) are used. It generates global and geometric feature set of an image and it omits the limitation of previous method of taking only one tissue under consideration while calculating asymmetry. The effectiveness of the proposed method is analyzed by doing experiments on 30 MR brain images with tumor and 30 normal MR brain images. It is observed that tumor detection is successfully realized for 30 MR brain images with tumor. Keywords: Tumor detection; Zernike Moments; Pseudo-Zernike Moments; Orthogonal Fourier Mellin Moments; Polar Harmonic Transforms; Segmentation. AN EFFICIENT FIFTH-ORDER ITERATIVE SCHEME FOR SOLVING A SYSTEM OF NONLINEAR EQUATIONS AND PDE   by A. Singh Abstract: This article, introduces an efficient fifth-order iterative technique for solving systems of nonlinear equations. The order of convergence of the proposed method has been verified by the computational order of convergence.Some numerical examples are employed to show the superiority of the proposed iterative method. The computational efficiency index has also been illustrated and analyzed. The application of proposed scheme for solving nonlinear PDE has also been discussed here. Keywords: Nonlinear equation; nonlinear systems; order of convergence; partial differential equation; flops-like efficiency index. A multi-criteria adaptive sequential sampling method for radial basis function   by Haiyang Hu, Zhansi Jiang, Yanxue Wang, Shuilong He Abstract: A Multi-criteria adaptive sequential sampling method is proposed for radial basis function metamodel and a new global approximation method is developed in this paper. In this new sampling method, objective, curvature and distance are considered as sampling criteria. With the three criteria, it guarantees that the entire domain will be covered by samples, and more sampling points will be gathered in the peak and valley regions, which is useful for enhance accuracy and efficiency of approximation model. Intensive testing shows that the efficiency of method and accuracy of metamodel are satisfactory by this new global approximation method. Keywords: Multi-criteria Adaptive sequential sampling; Global approximation; Metamodel; Radial basis function. An improved flower pollination algorithm for solving nonlinear system of equations   by Mohamed Abdel-Basset, Shereen Zaki, Abd El-Nasser H. Zaied, Yongquan Zhou Abstract: It is difficult to solve a system of nonlinear equations, especially for higher-order nonlinear equations when we do not have an efficient and reliable algorithm, even though much work has been done in this area. Newton's method and its improved form are widely used at present, but their convergence and performance characteristics can be highly sensitive to the initial guess of the solution, and the methods fail if the initial guess of the solution is inopportune. It is difficult to select a good initial guess for most systems of nonlinear equations. For this reason, it is necessary to find an efficient algorithm for systems of nonlinear equations. Metaheuristic optimization algorithms have been proposed by many researchers to solve systems of nonlinear equations. The flower pollination algorithm (FPA) is a novel metaheuristic optimization algorithm with quick convergence, but its population diversity and convergence precision can be limited in some applications. To enhance its exploitation and exploration abilities, in this paper, an elite opposition-based flower pollination algorithm (EFPA) has been applied for solving systems of nonlinear equations. The results show that the proposed algorithm is robust, has high convergence rate and precision, and can give satisfactory solutions of nonlinear equations. Keywords: Flower pollination algorithm; Meta-heuristics; elite opposition; Optimization; Nonlinear Equations. Computational Fluid Dynamics (CFD) simulation for the prediction of the venturi scrubber performance using Finite Volume Method   by Atta- Ullah, Muhammad Bilal Khan Niazi, Muhammad Ahsan, Majid Ali Abstract: The toxicity and severity of particulates and toxic gasses resulting from industrial activities on human health and environment is a major concern worldwide. Venturi scrubber is widely employed to abate the pollutant concentration because of their high removal efficiency. For an accurate and efficient design of venturi scrubber, the complex fluid dynamic behavior inside the venturi scrubber needs to be understood. The present Multiphase Euler-Lagrange CFD study successfully provides a computational model to predict pressure drop and collection efficiency by employing the commercial CFD package FLUENT. Throat gas velocities of 50, 70 and 100 m/s are simulated. Dust particles TiO2 having a diameter of 1 Keywords: Euler-Lagrangian method; venturi scrubber; CFD; pressure drop. Approximate Solution of Fractional Differential Equations using Shannon Wavelet Operational Matrix Method   by Javid Iqbal, Rustam Abass, Puneet Kumar Abstract: Many physical problems are frequently governed by fractional differential equations and obtaining the solution of these equations have been the subject of lot of investigations in recent years. The aim of this paper is to propose a novel and effective method based on Shannon wavelet operational matrices of fractional-order integration. The theory of Shannon wavelets and its properties are first presented. Block Pulse functions and collocation method are employed to derive a general procedure in constructing these operational matrices. The main peculiarity of the proposed technique is that it condenses the given problem into a system of algebraic equations that can be easily solved by MATLAB package. Furthermore, designed scheme is applied to numerical examples to analyse its applicability, reliability and effectiveness. Keywords: Shannon wavelets; Operational matrix method; Fractional differential equation; Numerical simulation; MATLAB. Hybrid Whale Optimization and -hill Climbing Algorithm for Continuous Optimization Problems   by Bilal Abed-alguni, Ahmad F. Klaib Abstract: The whale optimization algorithm (WOA) is an efficient optimization algorithm inspired by the bubble-net hunting strategy of humpback whale. As any optimization algorithm, WOA may prematurely converge to suboptimal solutions. This paper introduces a new hybrid WOA algorithm (WOABHC) that efficiently combines the WOA algorithm with the β-hill climbing algorithm (BHC) to control the diversity of the search space. The β-hill climbing algorithm is called at each iteration of WOABHC based on the probability function used in simulated annealing to reduce the number of computations required to achieve a good solution. WOABHC was tested and compared to well-known optimization algorithms using 25 standard benchmark functions. The experimental results confirm the efficiency of the proposed method in improving the accuracy of the results compared to WOA and other well-known optimization algorithms. Keywords: Whale Optimization; Beta-hill Climbing Search; Simulated Annealing; Optimization; Metaheuristic. Applications of the dynamic system and differential equations to Taiwan mortality   by Yong-Shiuan Lee, Meng-Rong Li, Jengnan Tzeng, Tsung-Jui Chiang-Lin Abstract: Modelling mortality is an important part of demographic researches. Since most developed countries have experienced rapid declines in mortality rates and population aging lately, it requires a more accurate mortality model to characterise and explain the phenomenon. Rather than stochastic models, the approach of the dynamic system and differential equations which is popular in natural sciences is applied in this study. The proposed model emphasises the mean reversion of the mortality where the mean stands for a hypothetical minimum rate. The model also depicts the speed of the convergence toward the minimum as the logistic curve. The empirical study shows that the model possesses reasonable characterisation and forecasts of Taiwan male and female age-specific mortalities. Subject to the algorithm the errors suggest that the model is comparatively better than Lee-Carter model, the benchmark model, for the ages from 15 to 70. Modelling the coefficients and modifying the algorithm will be the future work to raise the forecasting ability of the model. Keywords: dynamic systems; differential equations; Taiwan; mortality; age-specific mortality; modelling; forecasting; demography; Lee-Carter model; mean reversion; Newton’s law of cooling; logistic growth. Improvement and Simulation of cost risk assessment model for intelligent building engineering   by Fang Yu Abstract: In view of the drawbacks of traditional self-similarity regression model for intelligent building engineering cost risk assessment such as various confounding factors and low prediction accuracy, a risk assessment model for intelligent building is established in order to reduce engineering cost and improve engineering quality and to realize risk cost forecast for intelligent building engineering cost. A novel risk assessment model for intelligent building engineering cost based on Markov model and adaptive equilibrium cooperative game is proposed. Firstly, constrained parameter model for building engineering cost risk assessment is constructed, and Markov model is adopted for engineering cost risk assessment objective function building. Secondly, the cost and quality of engineering cost are compared for balance cooperative game. Recursive analysis method is used for the adaptive optimization of engineering costs risk cost to achieve the associated fusion processing for engineering cost risk parameter value; Finally, fuzzy directive clustering method is used to achieve building engineering cost risk assessment and forecasting. Simulation results show that the method can be used to evaluate the cost of intelligent building, which improves the accurate forecasting ability of engineering cost and reduces the cost of engineering risk. When the number of iterations is 50, the accuracy of the proposed method is 100%, which effectively realizes the balanced game of building quality and engineering cost, the overall accuracy is about 2% higher than the traditional method. Moreover, it improves the building quality and has good guiding significance in building engineering cost planning. Keywords: Construction; Engineering cost; Risk assessment; Prediction; Game; Markov model. Bayesian approach to smoothing parameter selection in spline estimate for regression curve   by Sonia Amroun, Lamia Djerroud, Smail Adjabi Abstract: Spline functions have proved to be very useful in statistics, in particular, to estimate the nonparametric regression. Many different smoothing parameter selectors for the smoothing spline are proposed in the literature such as cross-validation (CV), generalized cross-validation (GCV). In this article, we propose the Bayesian approach to estimate the smoothing parameter and the variance of the Gaussian error model in the context of the nonparametric regression. We use the Markov chain Monte Carlo (MCMC) method to compute the estimators given by the proposed Bayesian approach. The performance of the Bayesian approach is compared with the classical generalized cross-validation method through simulation and real data. Keywords: Nonparametric regression; Smoothing spline; Bayesian approach; Smoothing parameter. Smart grid planning method based on multi-objective particle swarm optimization algorithm   by Jianguang Zhang Abstract: Smart grid refers to a modern electric energy supply system to tackle a lot of problems in grid management, such as, resource shortage, environment pollution, and so on. In this paper, we propose a novel smart grid planning method using multi-objective particle swarm optimization algorithm. The goal of smart grid plan is to calculate the minimum investment and annual operating costs, when we obtain the planning level of load distribution, substation capacity and power supply area to satisfy the load requirement and optimized substation location. Afterwards, we propose a multi-objective particle swarm optimization algorithm which integrates the estimation of distribution algorithm. Furthermore, the propose approach divides the particle population into a lot of sub-populations and then build probability models for each population. Finally, experimental results demonstrate that the proposed method can effectively arrange new substation, which is able to make up for deficiencies of current existing substations. Keywords: Smart grid planning; Multi-objective optimization; Particle swarm optimization; Estimation of distribution algorithm. Motor synchronization control for multistage hot die forging press feed manipulator based on BP-PID controller   by Lin Hong, Yu Sun, Chunping Cao Abstract: The rapid development of the automotive industry provides a great opportunity for the development of the forging industry. The multistage precision hot die forging is widely used across the world. In order to improve production efficiency, reduce labor costs, and realize the production automation, a multistage hot die forging press feed manipulator is designed in this paper. The mechanical structure is designed firstly. Then the multi-motor synchronization control problem is also studied. Based on the adjacent cross-coupling control structure, the BP neural network-PID control algorithm is proposed. Simulation and experiment results show that the BP-PID controller has a high control accuracy, a fast convergence speed, and can achieve multi-motor synchronous control effectively. The manipulator designed in this paper has a high practical application value. Keywords: Multi-station feed manipulator; synchronization control; adjacent cross-coupling control; BP-PID. Effect on flow characteristics of blood in overlapping stenosed artery considering the axial variation of viscosity using Power-Law non-Newtonian fluid model   by Kanika Gujral, S.P. Singh Abstract: In the present investigation, a mathematical model is developed to study the effect of non-Newtonian behavior of blood on overlapping stenosed artery considering Power- Law fluid model. The constitutive equations of the model are solved analytically with the help of given boundary conditions to get different expressions for flow characteristics such as flow rate, flow resistance and wall shear stress considering no-slip condition at the wall and viscosity variation as axial. The flow of blood is taken to be steady, incompressible, laminar and one-dimensional. In this paper, we have concluded that the flow rate decreases while flow resistance and wall shear stress increases with the increase in stenosis size and also the comparison of these flow characteristics has been done for linear and quadratic variation of viscosity. Keywords: Flow rate; Power- Law fluid model; Overlapping stenosis; Flow resistance; Variable viscosity; Shear stress. Approximate solution of a fifth order ordinary differential equations with block method   by Saumya Ranjan Jena, Guesh Simretab Gebremedhin Abstract: In this paper an eighth step block method has been developed to obtain the approximate solution of an initial value problem involving fifth order ordinary differential equations. The derivation of the eight step block method is performed by collocation and interpolation approaches. The efficiency of an eighth step block method is illustrated by four numerical examples and comparison of the new method has been made with ODE45, LMM and analytical solutions. Stability and convergence analysis are discussed. The method is useful for solving fifth order ODE arising in various physical problems. Keywords: Block method; Initial value problems; Taylor series; Stability. Particle Swarm Optimization by Adding Gaussian Disturbance Item Guided by Hybrid Narrow Center   by Hui Sun, Zhicheng Deng, Jia Zhao, Haihua Xie Abstract: This study proposed the optimized PSO algorithm after the addition of Gaussian disturbance guided by hybrid narrow center. By combining narrow center and improved narrow center particles, the hybrid narrow center can be constructed. In the updating formula of particle velocity, Gaussian disturbance item controlled by hybrid narrow center is used for replacing self-cognition item. Owing to the guidance of hybrid narrow center, the convergence is accelerated, while the introduction of Gaussian disturbance can prevent the particles from falling into local optimum. The combination of hybrid narrow center and Gaussian disturbance can effectively avoid premature convergence and greatly increase convergence rate. Keywords: particle swarm optimization; narrow center; Gaussian disturbance; self-cognition. An Efficient Resource Deployment Method for Steam-based Stochastic Demands in Distributed Cloud Platforms   by Yang Liu, Wei Wei Abstract: It has been a consensus that deploying geographically dispersed stream-based online services into distributed cloud platforms has gained exceptional advantages. Globally visiting services make user requests characterized with dramatic fluctuation, which introduces stochastic demands for various resources. In order to maximize satisfied user requests and guarantee Quality-of-Service under given expense budget, efficient resource deployment becomes the key to this problem. We propose a stochastic demand oriented resource deployment method with more profits and less time complexity. Experiments using simulated and realistic data indicate that proposed method can outperform existing algorithms by increasing the weighted summation of satisfied demands up to 37%, fit for all scenarios with heterogeneous distributed cloud resources. Keywords: Resource Deployment; Differential Evolution; Stochastic demand; Heterogeneous clouds. Asymmetric Convolution with Densely Connected Networks   by Liejun Wang, Huanglu Wen, Jiwei Qin Abstract: Convolutional neural networks are vital to some computer vision tasks, and the densely connected network is a creative architecture among them. In densely connected network, most convolution layer tends to have a much larger number of input channels than output channels, making itself to a funnel shape. We replace the 3x3 convolution in the densely connected network with two continuous asymmetric convolutions to make the DenseNet famliy more diverse. We also proposed a model in which two continuous asymmetric convolutions each outputs half of the output channels and concatenate them as the final output of these layers. Compared with the original densely connected network, our models achieve similar performance on CIFAR-10/100 dataset with fewer parameters and less computational cost. Keywords: Densely Connected Network; Asymmetric convolution; Concatenation. Research on Travel Network Structure Based on Normalized Laplacian Spectrum   by Yang Sun, Hanhan Deng, Ling Zhao, Sumin Liu, Zhenshi Zhang, Ronghua Du, Zhixiang Hou Abstract: To study the residents Trip-Acitivity Chain and Patterns, we start from the residents Trip-Acitivity network. The normalized Laplacian spectrum from the local metric features described the topological structure information of network with the statistical analysis method of complex network.The statistical analysis of complex networks failed to show the global information of the network and described the network structure more completely, intuitively and conveniently. The normalized Laplacian spectrum is used to portray the subway network, the airline network and the macro-laws of the network from residents travel. In this paper, We analysis other networks and classify networks qualitatively. The results proved that the normalized Laplacian spectra is an efficient tool for analyzing macro-structural or micro-structural features of geographic networks. Keywords: Travel network structure; Normalized Laplacian spectrum; Resident travel chain; Global topology. Dynamic Multi-Swarm Pigeon-Inspired Optimization   by Yichao Tang, Bo Wei, Xuewen Xia Abstract: Pigeon-inspired optimization (PIO) has shown favourable performance on global optimization problems. However, it lacks the part of individual experience, which makes it prone to premature convergence when solving multimodal problems. Moreover, the landmark operator model in PIO may cause the population size to decrease too quickly, which is harmful for exploration. To overcome the shortcomings, a dynamic multi-swarm pigeon-inspired optimization (DMS-PIO) is proposed in this research. In PIO, the entire population is divided into multiple swarms. During the evolutionary process, the size of each swarm can be dynamically adjusted, and the multiple swarms can be randomly regrouped. Relying on the dynamic adjustment of swarms sized, exploration and exploitation are balanced in the initial evolutionary stage and last stage. Furthermore, the randomly regrouping schedule is used to keep the population diversity. To enhance the comprehensive performance of PIO, the map and compass operator and the landmark operator in it are conducted alternately in each generation. Experimental results between DMS-PIO and other five PIO algorithms demonstrate that our proposed DMS-PIO can avoid the premature convergence problem when solving multimodal problems, and yields more effective performance in complex continuous optimization problems. Keywords: pigeon-inspired optimization; dynamical swarm sized; randomly regrouping schedule; continuous optimization problems. Technology Enterprise Value Assessment Based on BP Neural Network   by Xiangtian Xie Abstract: Proposed a technology enterprise value assessment model based on BP neural network, considering that the technology enterprise has the characteristics of asset-light and high growth, whose value is difficult to evaluate. The model not only includes the non-financial indicators with intellectual property and the financial indicators with performance, but also has the advantages of artificial intelligence. Through analyzing the model, it can be seen that increasing the intellectual property indicators can reduce the assessment deviation and the traingda is optimal in the negative gradient learning functions. Keywords: technology enterprise; value assessment; BP neural network. Numerical Simulation of the Black-Scholes Equation using the SPH method.   by Abdelmjid Qadi El Idrissi, Boujemaa Achchab, Abdellahi Cheikh Maloum Abstract: In this paper we present a numerical method for solving the European options (Call and Put) using the Black-Scholes model. The numerical method considered is based on the SPH method. SPH is one of the most popular and efficient numerical schemes used in the approximation of partial differential equations particularly in fluid dynamic. Before applying SPH method, the Black-Scholes equation needs to be written into the heat equation. With this form, the numerical resolution of the Black-Scholes equation is further simplified and ensures the stability of the scheme. Numerical experiments were performed for different financial parameters. We investigate the accuracy of the numerical method proposed by given some comparisons between analytical and numerical computation. Keywords: Black Scholes equation; European option; SPH Method. Portfolio Optimization with Cardinality Constraint based on Expected Shortfall   by Ezra Putranda Setiawan, Dedi Rosadi Abstract: Abstract: Assets diversification is a well-known strategy to reduce the investment risk and become a mathematical problem since Markowitzs work in 1952. In this paper, we investigated the portfolio selection method using Expected Shortfall (ES), which also known as Expected Tail Loss (ETL) or Conditional Value-at-Risk (CVaR), as a risk measure. A cardinality constraint was added to the model in order to help the investor choose k from n available assets into the portfolio, where k is higher than the lower bound L and smaller than the upper bound U. To solve this complex portfolio optimization problem, we use the genetic algorithm method with binary chromosomes and obtain the optimal weight using exact method. A numerical case-study is provided using several stocks in Indonesia Stock Market. Keywords: genetic algorithm; portfolio; cardinality constraint; expected shortfall. Optimization and Application Research of Ant Colony Algorithm in Vehicle Routing Problem   by Liran Xiong, Lede Niu Abstract: In this paper, an improved ant colony algorithm based on ant system is proposed in order to solve vehicle routing problem. When choosing the path, the 2-opt method is used to explore the reasonable selection of the parameters of the algorithm for vehicle routing problem taking the path savings among customers as heuristic information. the performance of ant colony algorithm is affected by the information heuristic factor α, expectation heuristic factor β and pheromone volatile factor ρ. The method breaks through empirically setting the ant colony algorithm parameter values. By calculation, the optimal parameters of the ant colony algorithm in solving the vehicle routing problem are: α∈[1.0,1.7],β∈[4.5, 8.5],ρ∈[0.5,0.6]. At last, an exploration is established to find the optimal solution by combining three parameters and the ant colony algorithm will have a better effect in the actual optimization problem. Keywords: Ant colony algorithm; vehicle routing problem; parameter selection. Multidimensional Portfolio Risk Measurement: A Mixed Copula Approach   by Wenli Cai, Na Liu, Yuxuan Wu, Xiangdong Liu Abstract: It is an increasingly challenging task to explore the risk measurement for multidimensional portfolios with nonlinear correlative assets. A risk measurement scheme based on the mixed copula theory is proposed in this paper, where the mixed copula is constructed by the linear combination of three single Archimedean copulas, embodying greater flexibility than single copula in connecting different types of marginal distributions. In the scenario, ARMA-EGARCH model with t innovation is employed to fit marginal distributions, and the parameter values of the mixed copulas is inferred by maximum likelihood estimation (MLE) method, and interior point algorithm is used to calculate the extreme values of the MLE and VaR and CVaR, corresponding to the optimal portfolio with the minimum risk. Finally, an empirical study on five international stock market indexes in Europe is performed to verify the feasibility and effectiveness of the scheme. Keywords: ARMA-EGARCH; CVaR; Mixed Copula; Risk Measurement. Joint Estimation of battery state-of-charge based on the Genetic Algorithm - Adaptive Unscented Kalman Filter   by Zhixiang Hou, Jiqiang Hou Abstract: In order to effectively improve the accuracy of SOC estimation and overcome the problems that the conventional Kalman filter algorithm relies too much on an accurate battery model and the system noise must obey the white Gaussian noise distribution, a joint estimation method of battery state-of-charge based on genetic algorithm - adaptive unscented Kalman filter (GA-AUKF) is proposed in this paper. Firstly, in order to accurately simulate the working mechanism of a battery and express the relationship between the main parameters concerning the battery, on-line identification of model parameters is performed in this paper through the forgetting factor recursive least-squares (FFRLS) algorithm based on second-order equivalent circuit model. Secondly, in order to weaken the effect of system noise and measurement noise on the accuracy of SOC estimation, the genetic algorithm is adopted to optimize and update the adaptive unscented Kalman filter noise matrix. Finally, FFRLS is combined with GA-AUKF for SOC estimation. Experimental results show that the method proposed in this paper is obviously better than the AUKF algorithm and others in estimation accuracy, and it can effectively reduce the effect of filter noise covariance and improve the estimation accuracy with an estimation error less than 1%. Keywords: Lithium battery; state-of-charge; battery model; unscented Kalman filter; joint estimation. An ant colony optimization based approach to solve time interval dependent travelling salesman problem under fuzziness   by Chiranjit Changdar, KOUSIK DHARA, RAJAT PAL, PRAVASH GIRI Abstract: In this study, we have explained a constrained travelling salesman problem (TSP) where total travelling cost must maintain a maximum level. The objective of this proposed TSP is to minimize the total travel time. In this proposed TSP we have considered a time interval dependent constraint as well. There is a time interval in which a traveller must visit a predetermined set of cities (city-set). Here, a city-set consists of a set of cities and the time interval is a time slot in his/her total time to complete the tour. The problem is solved in fuzzy random environment. The travel cost, time, and total travelling cost limit are considered as fuzzy random in nature. The proposed TSP is solved by an Ant Colony Optimization (ACO) based approach. The basic ACO algorithm is improved by adopting a filtering operation. Finally, experimental results are given to illustrate the proposed approach; the computed results obtained are also highly encouraging. Keywords: Travelling Salesman Problem; Ant Colony Optimization; City-set; Filtering Operation; Fuzzy Random Number. An optimal class of fourth-order multiple-root finders of Chebyshev-Halley type and their basins of attraction   by Raj Bala, Munish Kansal, Vinay Kanwar Abstract: In this paper, we propose a family of optimal fourth-order of Chebyshev-Halley type methods free from second-order derivative for finding the multiple roots. The new methods are tested and compared with other well- known methods on the number of academical test functions. For quantitative comparison, we have also computed total number of convergent points and convergent percentages, average number of iterations per convergent points and CPU time (in seconds) along with the basins of attraction on number of test problems to recommend the best optimal fourth-order method. We also consider a concrete variety of real life problems such as the trajectory of an electron in the air gap between two parallel plates, Van der Waals equation which explains the behaviour of a real gas by introducing in the ideal gas equation, in order to check the applicability and effectiveness of our proposed methods. Keywords: Nonlinear equations; Chebyshev-Halley type methods; Multipleroots; Efficiency index; Optimal order of convergence; Basins of attraction. Anti-Synchronization of Nonidentical Fractional Order Hyperchaotic Systems   by Abedel-Kareem Alomari, Mohammad Al-Jamal, Nedal Tahat Abstract: This paper deals with the anti-synchronization of fractional order hyperchaotic systems. In particular, we employ the active control method to achieve complete anti-synchronization between fractional order Lorenz and Chen hyperchaotic systems. Based on stability theorems in the fractional calculus, analysis of stability is performed for the proposed method. Numerical simulations demonstrate the feasibility of the proposed algorithm. Keywords: active control; anti-synchronization; fractional hyperchaotic systems. Study on the hydrodynamic behavior of journal bearing with herringbone grooved sleeve considering cavitation   by He Qiang Abstract: This work proposes the development of a hydrodynamic model for journal bearing with herringbone grooved sleeve considering cavitation. The equations of motion for this system are obtained by CFD method and the effects related to hydrodynamics, friction and lubrication. The simulation model used in this paper is based on the multiphase flow model, considering the effects of cavitation on the pressure distribution of oil-film. The proposed model is further verified by the results of published experiment data. Further analysis reveals that the eccentricity ratio, rotational speed, groove depth and number of groove have a great influence on the hydrodynamic behavior of herringbone grooved journal bearing. Therefore, it is necessary to develop a more realistic simulation model of journal bearing with herringbone grooved sleeve that provide a reference for the journal bearing design. Keywords: journal bearing; herringbone grooved; hydrodynamic analysis; CFD； multiphase flow model. Combination of Neural Network Model for Enterprise Accounting Information Quality Assessment   by Yajing Hao Abstract: In order to evaluate the quality of accounting information, this paper tries to find an effective evaluation method and construct a reasonable and scientific evaluation model, and puts forward an evaluation method of enterprise accounting information quality combined with neural network model. By evaluating the quantity and quality of enterprise accounting information, the dimension of evaluation matrix is determined, and the evaluation matrix is generated in real time. On this basis, a neural network model is constructed to improve the accuracy and adaptability of enterprise accounting information evaluation. Finally, the simulation results show that the applicability and superiority of the model can provide reference for the evaluation of enterprise accounting information quality. Keywords: Neural network model; enterprise accounting; information quality; assessment matrix. Bifurcation and Stability of a dynamical system with threshold prey harvesting   by Imane AGMOUR, Meriem BENTOUNSI, Naceur ACHTAICH, Youssef EL FOUTAYENI Abstract: In this study, a predator-prey interaction model with Holling type II functional response is studied. As the continuous threshold prey harvesting is introduced, the proposed model displays a dynamics in the predator-prey plane. The main purpose is to show how the stability properties of some coexistence equilibria could be directly affected by harvesting. First, the positivity and boundedness of solutions of this model are provided and then the coexistence and stability of equilibrium points and bioeconomic equilibria are discussed. The local bifurcation solutions for different parameters of the model are obtained via bifurcation theory. Finally, some numerical simulations are given to demonstrate the results. Keywords: Predator-prey interaction model; Holling type IIrnfunctional response; Stability; Bioeconomic equilibria; Bifurcations. Toward a hybrid machine learning approach for extracting and clustering learners behaviours in adaptive educational system   by Ouafae EL AISSAOUI, Yasser EL ALAMI EL MADANI, Lahcen OUGHDIR, Ahmed DAKKAK, Youssouf El Allioui Abstract: The student model is the core component of an adaptive E-learning system, since it provides a structured presentation of the learner's characteristics that must be taken into account while recommending learning materials. Among those learner's characteristics, there is the learning style which refers to the preferred way in which an individual learns best. The traditional method detecting learning styles (using questionnaires) has notable drawbacks. Firstly, filling in a question-naire is a boring task that consumes a lot of time. Secondly, producing inaccurate results because students aren't always aware of their own learning preferences. Thus in this paper we have proposed an ap-proach to identify learning styles automatically, based on Felder and Silverman learning style model (FSLSM) and using web usage mining and machine learning techniques. The first step of the web usage min-ing process is used to reprocess the data extracted from the log file of the E-learning environment and capture the learners' sequences. The captured learners' sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning systems log file, and in order to evaluate the performance of the used classifier, the confu-sion matrix method was used. The obtained results demonstrate that our approach yields excellent results. Keywords: Unsupervised Algorithm; Supervised Algorithm; K-Means; Naïve Bayes; Adaptive E-Learning Systems; Felder-Silverman Learning Style Model. Analysis of bulk queue with unreliable service station, second optional repair, N-policy multiple vacation, loss and immediate feedback in production system   by Ayyappan Govindan, Nirmala Marimuthu Abstract: In this paper an unreliable server bulk service queue with second optional repair, multiple vacation under N-policy, customer's feedback and impatience are considered. After finishing a service, the server avails a multiple vacation under N-policy only when the queue length is less than `a'. Server vacation models are very much useful for a queueing system in which server needs to utilize his idle time for a different purpose. The server is subject to breakdown during the service and requires second optional repair for restoration. After repair completion, the server will start its remaining service to the batch of customers whose service was interrupted. Further, it is assumed that the arriving units may balk (loss) from the system when the server is on vacation and immediate feedback service was provided to the dissatisfied customers. The queue size distribution at a random and departure epoch has been derived using the supplementary variable technique. Finally, the stability condition, some performance measures, particular cases and numerical results of the proposed model are obtained. Keywords: General bulk service; Unreliable server; Loss; Immediate feedback; Second optional repair; Multiple vacation under N-policy. A parameter robust computational method for a weakly coupled system of singularly perturbed convection-diffusion boundary value problem with discontinuous source terms   by Mahabub Basha Pathan, Shanthi Vembu Abstract: In this paper, a parameter robust computational method using an iterative procedure for a weakly coupled system of singularly perturbed convection-diffusion boundary value problem with discontinuous source terms subject to Dirichlet boundary conditions is presented on uniform mesh. A difference scheme based on cubic spline in tension with fitting factor is considered outside the point of discontinuity whereas the centered finite difference scheme with the average of the source terms is used at the point of discontinuity. Numerical results are provided to demonstrate the efficiency of the proposed method with parameter-uniform convergence and also confirm the results obtained by this method are better than the existing methods. Keywords: Singular perturbation problem; Weakly coupled system; Convection-Diffusion; Cubic spline in tension; Discontinuous source term; Parameter-uniform. Regression Testing of Object-Oriented Systems Using UML State Machine Diagram and Sequence Diagram   by Namita Panda, Arup Abhinna Acharya, Durga Prasad Mohapatra Abstract: In this paper, we have modelled the software requirements using UML state machine diagram and UML sequence diagram. The different features of both the diagrams are combined and an intermediate graph i.e. State Sequence Graph (SSG) is generated. The graph is traversed to generate the test scenarios by identifying the linear independent paths present in SSG. The test scenarios generated are now capable of detecting message dependency fault as well as state change faults. The affected nodes, due to different changes in the past versions of the applications are identified and stored for further analysis. Whenever, a new version of the software is developed, and it is under regression testing, then test scenario prioritization is carried out by finding the frequent pattern from the stored modification history of the previous versions. Along with the frequent pattern, different other factors like number of message passing, number of state changes etc. also contribute in prioritizing the test cases. The proposed approach is applied to an online shopping cart application for validation. The proposed approach is also applied on other case studies and the results are recorded. The approach of combining different UML diagrams is found to be very efficient when evaluated using prioritization metric and compared with other related work. Keywords: Regression Testing; Test Case Prioritization; State machine diagram; Sequence Diagram; Test Scenarios; UML. Numerical solution of Fredholm integral equations of the first kind with singular logarithmic kernel and singular unknown function via monic Chebyshev polynomials   by E.S. Shoukralla, M. Kamel, M.A. Markos Abstract: This paper provides a new method for the numerical solution of Fredholm integral equation of the first kind whose unknown function is singular at the end-points of the integration domain and has a weakly singular logarithmic kernel. The method is based on monic Chebyshev polynomials approximation. The singular behaviour of the unknown function is isolated by replacing it with a product of two functions; the first is a well-behaved unknown function, while the second is a badly-behaved known function. Furthermore, the singularity of the kernel is treated by creating two asymptotic expressions. This method, in addition to its simplicity, has a very important advantage, namely its ability to compute the functional values of the unknown function at the end-points of the integration domain, whereas the exact solution and other methods failed to find these values. It turns out from the two illustrated examples that the presented method significantly simplifies the computations, saves time, and ensures a superior accuracy of the solution. Keywords: Monic Chebyshev polynomials; weakly singular; Fredholm integral equations; first kind; potential-type equations. MGWHD-SVM: Maximum Weighted Heteroscedastic Migration Learning Algorithm   by Min Zhang, Liangguang Mo Abstract: Maximum mean discrepancy (MMD) is a global measure of the distribution differences between domains at present, as a standard for effectively measuring the distribution differences between source and destination domains, however, MMD has some shortcomings in measuring the local structure and distribution differences between fields. This paper proposes a new measure: Maximum local weighted heteroscedasticity discrepancy (MLWHD), this measure not only fully considers the local structure and distribution differences among fields, but also shows good adaptability to the exception points and noise, further, MLWHD was used to determine the maximum global weighted heteroscedasticity discrepancy (MGWHD), and MGWHD was embedded into the training of Support Vector Machine (SVM). Finally, the test shows that the MGWHD method has better robustness. Keywords: Maximum local weighted heteroscedasticity discrepancy; support vector machine; migration learning. NRS-CSO: Neighborhood Rough Set-based Cat Swarm Optimization Algorithms   by Zi-hao Leng, Jian-cong Fan Abstract: Cat Swarm Optimization (CSO) is a typical evolutionary method inspired by the cats in the nature for solving optimization problem. After CSO was first proposed, it has been improved and applied in different fields, the series of CSO algorithms has been verified that they have better performance compared to many other swarm optimization algorithms, such as the Particle Swarm Optimization (PSO) algorithm. In this research, we propose an improved CSO named as Neighborhood Rough Set-based Cat Swarm Optimization (abbreviated as NRS-CSO). This algorithm uses neighborhood rough set theory to obtain two adaptive coefficients, and then these coefficients are used to modify the CSO algorithm. Experimental results show that the proposed algorithm obtains better performance than the standard CSO, which can spend less time to converge and the iteration times are less too. Keywords: Cat Swarm Optimization; Neighborhood Rough Set; Computational Intelligence; Function Optimization. Approximate Analytical Modeling of Fuzzy Reaction-Diffusion Equation   by Sarmad Altaie, Azizan Saaban, Ali Jameel Abstract: In this work, we developed an approximate analytical method based on the Optimal Homotopy Asymptotic Method (OHAM) to solve fuzzy partial differential equations (FPDE). The method has been applied to Fuzzy Reaction-Diffusion Equation with initial condition. By means of illustrative examples, we demonstrated the accuracy, efficiency, and flexibility of the proposed method. Keywords: Fuzzy Partial Differential Equations; Fuzzy Reaction-Diffusion Equation; Approximate-Analytical methods; Optimal Homotopy Asymptotic Method. Inclined magnetic field, thermal radiation and Hall current effects on Natural convection flow between vertical parallel plates   by Kaladhar K, Madhusudhan Reddy K, Srinivasacharya D Abstract: This paper investigates the impact of thermal radiation and an inclined magnetic field on free convection flow through a vertical channel. In addition to this Hall current effect has been taken into consideration. Spectral Quasilinearization Method (SQLM) has been utilized to solve the dimensionless governing equations; those were obtained by using similarity transformations from the system of governing partial differential equations. Influence of all the pertaining flow parameters of this study on all the dimensionless profiles were calculated and presented through graphs. Also the nature of the physical parameters were calculated and presented in table form. This study clearly exhibits that the inclined magnetic field influences the fluid flow remarkably. Keywords: Inclined magnetic field; Thermal radiation; Natural convection; Hall effect: SQLM. 3D ANISOTROPIC TRANSIENT HEAT CONDUCTION BY THE LOCAL POINT INTERPOLATION BOUNDARY ELEMENT METHOD   by Gael Pierson, Richard Kouitat Njiwa Abstract: The boundary elements method (BEM) is an effective numerical method for the solution of a large class of problems including heat conduction in isotropic media. The main appealing of this pure-BEM (reduction of the problem dimension by one) is tarnished to some extend when a fundamental solution to the governing differential equations does not exist. This is usually the case for anisotropic and nonlinear problems. Another attractive numerical approach due to its ease of implementation is the local point interpolation method applied to the strong form differential equations. The accuracy of this meshless method deteriorates in the presence of Neumann type boundary conditions. The main appealing of the BEM can be maintained by a judicious coupling of the pure-BEM with the local point interpolation method. The resulting approach, named the LPI-BEM, is shown to be effective for the solution of transient isotopic and anisotropic heat conduction. Keywords: BEM; local point interpolation; anisotropy; transient heat conduction. E-Bayesian Estimation for Burr-X Distribution Based on Type-II Hybrid Censoring Scheme   by Abdalla Rabie, Junping Li Abstract: In this paper, Burr-X distribution with Type-II hybrid censoring data is considered. The E-Bayesian estimation (the expectation of the Bayesian estimate) and the corresponding Bayesian and maximum likelihood estimation methods are studied for the distribution parameter and the reliability function. The Bayesian and the E-Bayesian estimates are obtained under LINEX and squared error loss functions. By applying the Markov chain Monte Carlo techniques, the Bayesian and the E-Bayesian estimates are obtained. Also, confidence intervals of maximum likelihood estimates and credible intervals of Bayesian and E-Bayesian estimates are constructed. Furthermore, a numerical example of real-life data is provided for the purpose of illustration. Finally, a comparison among the E-Bayesian, the Bayesian and the maximum likelihood methods is presented. Keywords: Bayesian estimation; E-Bayesian estimation; maximum likelihood estimation; Hybrid censoring scheme; Confidence and Credible intervals; MCMC method. Aircraft Pushback Slot Allocation Bi-level Programming Model based on Congestion Pricing   by Li-hua Liu, Minglei Song, Xue-jiao Wang, Ming-hui Wang Abstract: In order to alleviate the congestion of airport surface, aircraft pushback slot congestion pricing is explored. First, the feasibility of aircraft pushback slot pricing is analyzed. Then the Stackelberg game model for aircraft pushback slot allocation is established by combining the congestion pricing with non-cooperation game. By using bi-level programming theory, this game model is further extended to a bi-level programming model. Considering the discretization of this model, an improved artificial fish algorithm is also designed by improving the fish school behavior and optimum solution selection criteria. Finally, an example analysis is performed on Xinzheng International Airport. By comparing the simulation result of pushback slot congestion pricing with other strategies, the advantages of the developed model and algorithm are emphasized. Keywords: AFSA; aircraft pushback slot; Congestion Pricing; Stackelberg Game. The use of unclear conclusion in the tasks of forecasting of the durability of corrosive constructions   by Larysa Korotka Abstract: The borders of each cluster are considered as the carrier of a term set, and the center of the corresponding cluster is considered as its core. It is assumed that the choice of the type of membership function remains according to the expert in the subject area. The necessary and sufficient volumes of the fuzzy rule base, as well as term sets of linguistic variables are considered. When using Mamdani fuzzy inference and the centroid method, it is possible to obtain the parameters of numerical procedures for the task of forecasting the durability of corrosive structures. As an alternative to neural networks in solving approximation tasks, it is proposed to use a generalized algorithm based on the theory of fuzzy sets. The results of numerical experiments make it possible to assert that a fuzzy clusteriser allows to determine rational parameters of numerical procedures for the class of tasks. Keywords: clustering; fuzzy knowledge base; fuzzy inference; forecasting of durability; corroding structures. Analysis of Subway Users Behavior Based on the Latent Class Regression   by Jianrong Liu Abstract: Since that there is difference of satisfaction and willing-to-travel-by-subway among different subway users, a task one should take into account of is how to classify subway users effectively, and analyze factors affect the satisfaction and willing-to-travel-by-subway of different subgroups of subway users, respectively. Based on subway users satisfaction of the subway and their traveling behavior, this paper classifies subway users with the latent class regression model. This result shows that subway users should be classified into three subgroups: the neutral passengers, the satisfied passengers, and the loyal passengers, also it is found that private car ownership, accessibility of the subway station, price evaluation and speed evaluation have a great influence on the classification. Based on the classification, this paper regresses the overall satisfaction of the subway, the recommendation of the subway, and the frequency of traveling by subway on the factors, respectively. And it is found that there are non-negligible differences of factors parameters on the three subgroups. Keywords: urban traffic; subway; classification; latent class regression; regression analysis. The influence of double diffusive gradient boundary condition on Micropolar nano fluid flow through stretching surface with a higher order chemical reaction   by G. Nagaraju Abstract: The higher order chemical reaction of a magneto micropolar nanofluid flow through a stretching sheet under the effect of Brownian motion and thermophoresis is investigated by applying the convective boundary condition on temperature and mixed boundary condition on concentration. The leading partial differential equations were transformed into ordinary equations using a similarity transformation and solved numerically using Matlab bvp4c solver. The presence of the nanoparticles has a most remarkable effect on the heat flow improvement of micropolar nanofluid. The results are depicted graphically for different flow governing material parameters. Keywords: Micropolar nanofluid; linearly stretched sheet; heat and mass transfer; mixed concentration condition; convective boundary condition. Cuckoo search with dual-subpopulation and information-sharing strategy   by Jun Xi, Liming Zheng Abstract: Cuckoo search (CS) algorithm is simple and powerful in dealing with the global optimization problem. However, how to strike a good balance between exploration and exploitation in CS is still an open question. The paper proposes a modified CS with dual-subpopulation and information-sharing strategy (DSIS_CS). In DSIS_CS, the population is divided into two subpopulations which are assigned different update task. Then, random solutions are selected from the dissimilar subpopulations in order to avoid the results from easily falling into the local optima. In addition, the DSIS strategy can be incorporated into other state-of-the-art CS variants to improve their optimization performance. Extensive experiments on 28 functions chosen from CEC 2013 have been carried out. The results suggest that the DSIS strategy helps both the CS and its variants to achieve a better trade-off between exploration and exploitation. Keywords: cuckoo search algorithm; swarm intelligence; global optimization; dual-subpopulation strategy. Homotopy Analysis Method Approximate Solution For Fuzzy Pantograph Equation   by Akram Hatim, Ali Jameel, Nidal Anakira, Abdel Kareem Alomari, Azizan Saaban Abstract: This paper investigates the powerful method namely the Homotopy Analysis Method (HAM), to solve the fuzzy pantograph equation (FPE) in approximate analytic form. HAM yields a convergent infinite series solution to the solution of FPE without the need to reduce the FPE to the first order system or compare it with the exact solution, and this is one of the advantages of this method. For a better approximate solution, the HAM uses a convergence control parameter from the convergence region of the infinite series solution. HAM solution of FPE is obtained by reformulate crisp standard approximation via the properties of the fuzzy set theory. Keywords: Fuzzy Set Theory; Fuzzy Differential Equations; Fuzzy Pantograph Equation; Homotopy Analysis Method. Exponentially-fitted Pseudo Runge-Kutta Method   by Shruti Tiwari, Ram Pandey Abstract: This article is devoted to the development of embedded pseudo Runge-Kutta method of order three (EPRK3) and exponentially-fitted embedded pseudo Runge-Kutta method of order three (ef-EPRK3). The motivation behind this development is two folded, the first one is to minimize the cost of computation of existing Runge-Kutta (RK) method and the other one is to make RK method compatible to solve the initial value problem (IVP) having periodic solutions. In this paper, first we derive a family of explicit embedded pseudo Runge-Kutta method (EPRKM) of order three and next, we have fitted PRKM exponentially and developed ef-EPRKM of exponential order two. Where, we assume that ef-EPRKM exactly integrates two exponential functions $\exp (\pm\omega x)$, with unknown frequency $\omega$. The proposed methods are applied to two IVPs of order two. The novelty of ef-EPRK over EPRKM are shown via the numerical examples. Further from Table 1, it is observed that EPRK and ef-EPRK methods are less expensive than the existing RK methods. These methods expend less computation cost in the form of function evaluations per step. In Table 2 and Table 3, a comparison of norms of end point errors is made between EPRK3 method, ef-EPRK3 method, Berghe's exponentially fitted RK method and Simos's exponentially fitted RK method from which it is quite evident that errors by ef-EPRK3 are smallest. This ensures the superiority of ef-EPRK. The local truncation error (LTE) for ef-EPRKM is also computed and by the expression of LTE, the value of unknown frequency $\omega$ in $\exp (\pm\omega x)$ is calculated. Keywords: Runge-Kutta method; Pseudo Runge-Kutta method;rnExponentially tting; Truncation error; Oscillatory initial value problem. Jump OpVaR on Option Liquidity   by ALIREZA BAHIRAIE Abstract: Impact of operational risk on the option pricing through thernextension of Mitras model with Mertons jump diffusion model is assessed.rnA partial integral differential equation (PIDe) is derived and the impactrnof parameters of Mertons model on operational risk and option value byrnoperational Value-at-Risk measure, which is derived by Mitra, is studied.rnThe option values in the presence of operational risk on S&P500 indexrnare computed. The result shows that most operational risks occur aroundrnat-the-money options. Keywords: Option pricing; Operational risk ; OpVaR; Operational Value atrnRisk; Hedging,. Image encryption using anti-synchronization and Bogdanov transformation map   by Obaida M. Al-Hazaimeh, Mohammad F. Al-Jamal, Abedel-Karrem Alomari, Mohammed J. Bawaneh, Nedal Tahat Abstract: A new image encryption algorithm based on anti-synchronization of Chen system and BOGDANOV map is presented in this paper. To illustrate the performance and robustness of the new algorithm, some of security analyses against different cryptanalytic attacks are presented. The security analyses found that, the new algorithm can be applied to provide a suitable application through insecure networks (i.e. internet). As well as, the anti-synchronization Chen system and BOGDANOV transformation map not just limited to digital image encryption, but can be applied directly in several other applications such as real-time application (i.e. video encryption). Keywords: Image encryption; BOGDANOV chaotic map; Crypto-systems; Anti-synchronization; Chen system. Two combined methods for the global solution of implicit semilinear differential equations with the use of spectral projectors and Taylor expansions   by Maria Filipkovska Abstract: Two combined numerical methods for solving implicit semilinear differential equations are obtained and their convergence is proved. The comparative analysis of these methods is carried out and conclusions about the effectiveness of their application in various situations are made. In comparison with other known methods, the obtained methods require weaker restrictions for the nonlinear part of the equation. Also, the obtained methods enable to compute approximate solutions of the equations on any given time interval and, therefore, enable to carry out the numerical analysis of global dynamics of the corresponding mathematical models. The examples demonstrating the capabilities of the developed methods are provided. To construct the methods we use the spectral projectors, Taylor expansions and finite differences. Since the used spectral projectors can be easily computed, to apply the methods it is not necessary to carry out additional analytical transformations. Keywords: implicit differential equation; differential-algebraic equation; combined method; regular pencil; spectral projector; global dynamics.DOI: 10.1504/IJCSM.2019.10025236  Method for Measuring and Evaluating the Difficulty Data of Aerobics Complete sets of Movements in University based on Multiple Regression   by Lijuan Guo Abstract: Aiming at the unreasonable selection of the evaluation indicator for the difficulty data of aerobics complete sets of movements in universities, which leads to the lower satisfaction of aerobics coaches and referees, a method for evaluating the difficulty of aerobics complete sets of movements in universities based on multiple regression is put forward. In this method, SPSS13.2 software is used to evaluate the body shape index and physical quality index that affect the specific technical level of aerobics athletes in universities. The final indexes are obtained by correlation analysis and cluster analysis, and the evaluation model is established by multiple regression method to evaluate the difficulty data of aerobics complete sets of movements in universities. The experimental results show that the evaluation indicator for difficult movements of aerobics in universities selected and set by the research method is reasonable, and the passing rate of the athletes participating in the evaluation is higher, which is consistent with the actual situation. Keywords: Aerobics in university; Complete sets of movements; Difficulty data; Evaluation; Multiple regression;. A novel six-dimensional hyperchaotic system with a self-Excited attractors and its chaos synchronization   by Saad AL-Azzawi, Ahmed Sedeeq Abstract: A few researches are available in the aspect of high dimensional nonlinear dynamical systems. This paper presents a novel 6-D continuous real variable hyperchaotic system with self-excited attractors, consists of 17-terms and various characteristics which include equilibria and their stability, Lyapunov exponents, chaos synchronization. Firstly, a novel model with linear feedback controller is proposed. The error dynamics for complete control strategy is found. Three different suitable and effective controllers are designed to stabilize this error by using nonlinear control and based on two main tools: Lyapunov stability theory and the linearization method. Finally, comparison between the two tools was done. The proposed controller are effective and convenient to achieve chaos synchronization of the new systems. Moreover. numerical simulations were carried out by using MATLAB to validate all the synchronization phenomena derived in this paper. Keywords: Chaos synchronization; novel 6-D dynamical systems; self-excited attractors; Lyapunov stability theory; nonlinear control. Effects of combination of therapies on chronic hepatitis B virus through resolution of an optimal control problem using comparative of direct method and Pontryagin's maximum principle   by Jean Marie Ntaganda Abstract: This paper aims at using direct approach and Pontryagin's maximum principle to solve a hepatitis B virus dynamics optimal control problem. The controls consist of combination therapy of two treatments. The numerical implementation is done using Matlab packages. Comparative results from these two methods show they are so close in optimal trajectories of determinant variables. Furthermore, both numerical methods are in good agreement with experimental data. In particular, combination of two treatments controls Hepatitis B virus to ensure healthy. Keywords: Hepatitis B virus; Optimal control; Treatment; Direct method; Pontryagin's maximum principle; Numerical simulation. Public Key and Leveled Attributes Access Policy Oriented Fully Homomorphic Encryption Scheme   by Biksham Vankudoth, Vasumathi Devara Abstract: Fully Homomorphic Encryption (FHE) has been considered as significant transition of cryptography, a difficult aim which could answer the worlds obstacles of security and trust. FHE is also considered among the golden solutions for achieving security in cloud computation, MPC, data banks etc. FHE was conceptualized in 1978, but it took approximately three decades to invent the first credible construction of FHE. The research in this area erupted after Craig Gentry's construction of FHE in 2009. Considerable developments have been made in getting more practical and efficient solutions. In this paper, we have gathered the state of art information in this field through surveying various schemes. We proposed detailed construction of our fully homomorphic system. The security and correctness analysis for the proposed procedure along with the experimental results are also given in the paper. Keywords: Fully Homomorphic Encryption; Lattice based encryption; Secure outsourcing; Learning with errors; Attribute based encryption; Privacy.DOI: 10.1504/IJCSM.2019.10027458  Production Simulation of Tight Oil Reservoirs with Coupled Mathematical Model   by Xijun Ke, Jiaqi Li, Dali Guo, Shuanghan Luo, Shouchang Xie Abstract: This paper constructed a coupled mathematical model with two flow directions (matrix inside fracture network) to analyze the rate transient behaviors in oil reservoirs. Firstly, we formulate the 1-D flow solutions for each region, and then couple them by imposing flux and pressure continuity across the boundaries between regions. Secondly, we solve the model with the linear flow model and using Laplace transform and Stehfest algorithm comprehensively. Thirdly, the corresponding algorithm is designed and the code is written for calculation. Finally, the model solution is verified with one flow direction model thoroughly. Keywords: Multi-linear flow model; Coupled Mathematical Model; Laplace transform; Stehfest algorithm ; Two flow directions model. Homotopy solution for MHD mixed convective unsteady flow of a Powell-Eyring fluid in a vertical porous space with oscillating wall temperature   by Ramamoorthy Muthuraj, R.K. Selvi Abstract: In this paper, unsteady MHD flow of Powell-Eyring fluid in a vertical channel with diffusion thermo and thermal diffusion effects in the presence of heat source have been studied. The governing nonlinear, coupled partial differential equations are reduced to ODEs using oscillating parameter and then solved by homotopy analysis method (HAM). The influence of emerging parameters on heat and mass transfer characteristics of the flow are studied. Also, variations on physical quantities such as the skin friction coefficient, heat and mass transfer coefficient with different values of pertinent parameters are presented in tables. It is found that the velocity of a Non-Newtonian fluid is found to decrease in comparison with Newtonian fluid (i.e., absence of material parameters). It is also observed that thermal parameters, Schmidt number and Soret number are lead to promote the temperature of the fluid significantly. Increasing magnetic parameter tends to produce oscillating behaviour on volume flow rate whereas uniform depreciation can be noticed with increasing Schmidt number. Keywords: MHD; Unsteady flow; Dufour effect; Soret effect; HAM; Vertical Channel. A rapid optimization method of TSPs based on water centripetal motion   by Change Lv, Shuo Liu Abstract: Traveling salesman problem (TSP) solutions are commonly used in many areas. We provide an innovative and optimal solution method of TSPs based on water flow centripetal motion, and it different from other algorithms. In the case of 34 cities in China, the center hole is computed with a pentagon which formed by the outermost 5 cities. Place a string to enclose all rods in a circle after water fills the tank, and start to pump water at a steady rate. And a uniform and equal centripetal force is generated and the string will shrink uniformly until connect each of rods as well as water runs out. The string is the best route that connects the 34 cities. The method's optimal result is 15 759.75 km, which is 32.55 km shorter than the current optimal solution. The method eliminates the complex procedure, takes less time, and is highly feasible and reliable. Keywords: transportation economy; traveling salesman problem; optimal route; test solution; test devices. McEliece Cryptosystem: Simulation and Security Vulnerabilities   by RAKESH KUMAR, SWAMY NAIDU ALLU, AJEET SINGH, APPALA NAIDU TENTU Abstract: While symmetric key cryptosystems utilize single\r\nprivate key at both ends for encryption and decryption purposes\r\nand this mentioned scenario gives advantages of better speed-\r\nup as compare to public key cryptosystems. Still, security is a\r\nchallenge while performing compatibly efficient key distribution\r\nand secure private data transfer among entities in an untrusted\r\nenvironment. McEliece cryptosystem, designed in 1978, is a public\r\nkey based cryptosystem whose security is based on some unknown\r\nnatured error-correcting codes. This paper gives state-of-the-\r\nart detailed overview on this cryptosystem, its component-wise\r\nalgorithmic description and implementation. Various attacks on\r\nMcEliece cryptosystem are discussed separately. The experimental results employing Goppa codes are also presented in the\r\npaper where the simulations are performed on various Extension\r\ndegrees. Based on the simulations performed, we concluded the\r\nresults along with the various issues faced while implementation. Keywords: Public key; Substitution matrix; Generator matrix; Permutation matrix; Hamming code; Irreducible polynomial. A Bi-objective Optimization Approach for the Critical Chain Project Scheduling Problem   by Wuliang Peng, Jiali Lin Abstract: Since time and cost are two important issues in real-life project scheduling applications, the optimization problems about project make-span and cost have been extensively studied in project management academic field over the last few decades. This study addresses the bi-objective critical chain project scheduling problem aiming at minimizing both project make-span and cost. Taking account in the characteristics of the critical chain method, we formulate the conceptual model considering project make-span and cost under both the resource-constraints and precedence relations. In the model, the promised delivery time is used as project make-span, and the discounted cost is used to measure project cost. To solve the problem, an evolutionary algorithm based on Greedy Randomized Adaptive Search Procedure (GRASP) is proposed to search for the non-dominated solutions of the problem. Several neighborhood search methods are investigated and compared with each other regarding four multi-objective performance measures: Optimal ratio, Hyper-volume indicator, Epsilon metric and Average distance. The computational tests have indicated the algorithm with variable neighborhood appears to be superior to others. Keywords: critical chain method; multi-objective optimization; greedy randomized adaptive search procedure;project scheduling. Cosine kernel based Density peaks clustering algorithm   by Wang Jiayuan, Lv Li, Wu Runxiu, Fan Tanghuai, Lee Ivan Abstract: Density peaks clustering (DPC) determines the density peaks according to density-distance, and local density computation significantly impacts the clustering performance of the DPC algorithm. Following this lead, a revised DPC algorithm based on cosine kernel is proposed and examined in this paper. The cosine kernel function uses local information of data sets to define the local density, which not only finds the position difference of different samples within the cutoff distance, but also balances the influence of center points and boundary points of clusters on local density of samples. Theoretical analysis and experimental verification are included to demonstrate the proposed algorithms improvement in clustering performance and computational time over the DPC algorithm. Keywords: density peaks; clustering; local density; cosine kernel function. Numerical study of entropy generation analysis for non-Newtonian MHD flow of blood in a porous channel with partial slip   by MAMATA PARIDA, SUDARSAN PADHY Abstract: In the present work, a numerical investigation of magnetohydrodynamic flow of blood through an arterial segment is carried out by taking into account the slip velocity at the wall of the artery. A mathematical model of the problem is presented by considering blood as a non-Newtonian fluid obeying third grade fluid model and the artery is chosen to be a porous channel. A uniform magnetic field is applied in the transverse direction of the flow. The governing momentum equation is discretized by finite element method and the obtained system of non-linear algebraic equations is solved by damped Newton's method. A quantitative analysis is made through numerical computations and graphical presentation of velocity and temperature for different pertinent parameters. In addition, the second law analysis for the physiological fluid is examined and the influence of important parameters on the entropy generation rate and irreversibility ratio are discussed. Our analysis indicates that the flow rate and irreversibility ratio increases with increase in third grade fluid parameter, whereas the magnetic parameter produces reverse effect on these profiles. Keywords: Entropy generation; Slip effects; Hartmann number; Third-grade fluid. Fabrication and Characterization of Nanostructure Step Height Sample   by Fenghua Xu, Xiaotong Wu, Chunlong Zou, Shenghuai Wang Abstract: Step height calibration sample is a high-precision height (depth) standard transfer material for correcting the Z-axis characteristic parameter values of surface structure measuring instruments such as AFM, SEM and white light interferometer. It has the characteristics of accurate value transfer and small size structure. According to the traceability value of VLSI step height standard, step structure calibration samples with nominal height of 8nm, 18nm and 44nm were fabricated by ICP etching and lithography, and the height and surface roughness of the step structures are evaluated by AFM. The experimental results show that the standard deviation of the step height calculated by the algorithm is 0.820nm, 0.770nm, 1.786nm, respectively. The upper surface roughness of the step structure was the surface roughness of the SiO2 film. The roughness of the bottom surface was related to the surface quality of SiO2 film, ICP etching process parameters. Keywords: Step height; calibration sample; ICP. A modification of nonlinear feedback controller   by Maysoon Aziz, Saad AL-Azzawi Abstract: This paper derives new results for the modify of nonlinear feedback controller. The stability results are established using the Lyapunov's second method as a main tool. Since the finding characteristic equations are not required for the modify method, the nonlinear feedback control strategy with this modification is very effective and convenient to realize the chaos control for dynamical systems. Furthermore, modify method is used to overcome some problem in nonlinear feedback controller, which helps to achieve the convergence of dynamics system easily. This proposed method has certain significances for reducing the effort and complexity of controller implementation. Finally, numerical simulations are given to illustrate and verify the results. Keywords: Chaos control; nonlinear feedback control; nonlinear dynamical system; Lyapunov's second method. A new QPSO based hybrid algorithm for bound-constrained optimization problem and its application in engineering design problems   by NIRMAL KUMAR, AVIJIT DUARY, SANAT KUMAR MAHATO, ASOKE KUMAR BHUNIA Abstract: The aim of this paper is to introduce a new hybrid algorithm for bound constrained optimization problem combining Quantum behaved Particle Swarm Optimization (QPSO) and binary tournamenting technique. Depending on the different options of binary tournamenting process six diverse forms of hybrid algorithm are introduced. Then the efficiency and performance of these hybrid algorithms are investigated through six well known benchmark bound constrained optimization problems. Computational results are compared graphically as well as numerically. Finally, this algorithm is utilized to solve the engineering design problem and results are compared with the recent algorithm available in the literature. Keywords: PSO; QPSO; Adaptive QPSO; Gaussian QPSO; Tournamenting; Hybrid algorithm; Engineering design problem. Linear Programming Method For Buckling Critical Load Of Foundation Piles In Nonlinear Foundation   by Weizhe Li, Ping Lou Abstract: Linear programming method is presented for calculation of the Buckling critical load of the pile in nonlinear soils. MATLAB programming for buckling stability analysis of pile in nonlinear soils is self-provided. Cases analysis of buckling stability analysis for both test piles and model piles in nonlinear soils are done, and conclusions are derived as follows:1); the key parameters ? and V in the transient buckling stability equation has extremely remarkable linear relationship with each other; 2) the buckling critical load of the pile will decrease with the increase of horizontal load at the top of the pile; 3) critical buckling load value of the pile in nonlinear soils is much smaller than that of the pile by m-method, and it is advised that nonlinear effects of soils should be carefully considered in application for critical buckling analysis of pile in practice. Keywords: nonlinear soils; critical buckling analysis; transient buckling stability equation; fitting curve method ;critical buckling load; pile. Research On Robot Optimal Path Planning Method Based On Improved Ant Colony Algorithm   by Hui Tian Abstract: In order to overcome the problem of poor convergence and obstacle avoidance when traditional methods are used to plan the optimal path of robot, a new optimal path planning method based on improved ant colony algorithm is proposed. Firstly, the odometer model, ultrasonic sensor model and robot motion model are constructed to obtain the environmental information and robot motion state information. Then, according to the adaptive transformation heuristic function of the target point and the principle of wolf swarm assignment, the pheromone is refreshed. On this basis, the core parameters of the improved ant colony algorithm are optimized by particle swarm algorithm, so as to complete the optimal path planning of the robot. The experimental results show that the overall mean value of collision avoidance of the proposed method is 0.97, and the planning performance is significantly better than that of similar planning methods, with considerable application value. Keywords: Improved ant colony algorithm; Robot; Optimal path; Planning; Particle swarm optimization. A covering method for continuous global optimization   by Ziadi Raouf Abstract: In this paper we improve the reducing transformation method for solving a large class of global optimization problems. The reducing transformation method allows us to transform a multidimensional problem into a one-dimensional one of the same type, and then use the one-dimensional Evtushenko algorithm to obtain the global minimum. To accelerate the corresponding mixed algorithm (Reuducing transformation - Evtushenko), we have incorporated the Hook-Jeeves algorithm to explore promising regions. Our approach is suitable for solving a large class of global optimization problems on a rectangle of $mathbb{R}^n$ where the objective function is only continuous. This method converges in a finite number of iterations to the global minimum within a prescribed accuracy $delta>0$. Numerical experiments are achieved on some typical test problems and a comparison with well known methods is carried out to show the performance of our algorithm. Keywords: Global optimizationrn Reducing transformation methodrn Evtushenko's algorithmrn Hooke-Jeeves algorithm. Comprehensive Algebraic Proof that the Metric Time is Discrete: Strong Mathematical Challenge against Einsteins Continuum Spacetime Hypothesis of GR   by Yohannes Yebabe Tesfay Abstract: In modern physics and cosmology, it is widely accepted that the general theory of relativity successfully predicts spacetime, gravity and the large scale structure of our universe. However, the core foundation and all the calculations of the general theory of relativity are based on the postulation of spacetime continuum. In this article, the author proposed to test that whether the metric time is continuous or discreet. Using the energy-time uncertainty relationship and the theory of abstract algebra, the author introduces theorem of quantum mechanical theory of time and gives a complete proof that the metric time is quantized and has the smallest value. The proof of the theorem is, therefore, a strong challenge the continuum spacetime hypothesis of the general theory of relativity and gives strong evidence to the scheme of quantum gravity. Keywords: abstract algebra; general relativity; metric time; quantization; and uncertainty principle. A fast ADI algorithm for nonlinear Poisson equation in heterogeneous dielectric media   by Wufeng Tian Abstract: A nonlinear Poisson equation has been introduced to model nonlinear and nonlocal hyperpolarization effects in electrostatic solute-solvent interaction for biomolecular solvation analysis. Due to a strong nonlinearity associated with the heterogeneous dielectric media, this Poisson model is difficult to solve numerically, particularly for large protein systems. A new pseudo-transient continuation approach is proposed in this paper to efficiently and stably solve the nonlinear Poisson equation. A Douglas type alternating direction implicit (ADI) method is developed for solving the pseudo-time dependent Poisson equation. Different approximations to the dielectric profile in heterogeneous media are considered in the standard finite difference discretization. The proposed ADI scheme is validated by considering benchmark examples with exact solutions and by solvation analysis of real biomolecules with various sizes. Numerical results are in good agreement with the theoretical prediction, experimental measurements, and those obtained from the boundary value problem approach. Since the time stability of the proposed ADI scheme can be maintained even using very large time increments, it is efficient for electrostatic analysis involving hyperpolarization effects. Keywords: Nonlinear Poisson equation; Non-local dielectric media; Pseudo-transient continuation approach; Alternating direction implicit (ADI);Solvation free energy. Mapped Gegenbauer rational collocation method for a class of Fredholm integral equations on the whole line   by Azedine Rahmoune, Ahmed Guechi Abstract: Several real problems were modeled by integral equations defined on infinite intervals with underlying solutions decay to zero at infinity. The use of Hermite polynomials to approximate these functions are not suitable due to their wild behaviors at infinity. Therefore, it is useful to employ non-weighted orthogonal systems constructed from Jacobi polynomials. This paper aims at developing accurate collocation method to solve a class of linear Fredholm integral equations on the whole line using mapped Gegenbauer rational functions. The proposed approach uses a special accurate quadrature formula based on the mapped Gegenbauer-Gauss integration rule for approximating integrals appeared in the scheme. Moreover, the associated algorithm is easy to implement on any personal computer. Error analysis including convergence rates of the presented method are established. The accuracy and stability are also validated by two typical numerical examples. Keywords: Mapped Gegenbauer rational approximation; Fredholm integral equations; The whole line; Collocation method; Stability. Efficient graph-based algorithms for solving team formation problem   by Abdulla Qaddoumi, Youssef Harrath, Abdul Fattah Salman Abstract: This research investigates the pair formation problem described as forming pairs of people to achieve certain objectives. This problem is a subcategory of the well-known grouping problem that is classified as NP-complete. The necessity of pairing people in teams is frequent in many real-life fields such as education, social life, and production environments. To solve the problem, a mathematical formulation and a weighted graph-based representation are proposed. Pairing people is usually affected by psychological and productivity factors such as expertise and managers' opinion. These factors are summarized to produce quantitative scores representing the fitness relationship of each person towards others. Four algorithms are proposed to maximize the total weight of the formed pairs. The proposed algorithms are implemented and benchmarked using data instances of various sizes. The performance of the algorithms is evaluated against two proposed upper bounds. The results showed that the edge-based first algorithm outperforms other algorithms. Keywords: Pair Formation; Graph Theory; Team Efficiency; Productivity; Manufacturing. Research On The Optimal Route Selection Method Based On Improved Ant Colony Algorithm   by Xiaoying Wei Abstract: In order to overcome the problems of large pheromone gap and poor convergence in the existing tourism route selection methods, a new optimal tourism route selection method based on improved ant colony algorithm is proposed. This method analyzes the pheromone trajectory update process of ant colony algorithm. Based on the metropolis sampling criterion, it improves the probability of generating different solutions of generate function and reduces the probability of generating different solutions of generate function. Based on the volatile characteristics of pheromone, the shortest path for ant colony to return to nest is obtained, and the result of tourism route selection is optimized. The experimental results show that the proposed method can quickly converge to obtain the optimal solution with high user satisfaction, the highest satisfaction is 98.4%, which fully shows that the proposed method can complete the optimal tourism route planning. Keywords: Improved Ant Colony Algorithm; Pheromone; Generate Function; Metropolis Sampling Criteria; Best Route Selection. A gray wolf algorithm for feature and parameter selection of support vector classification   by Omar Qasim, Zakariya Algamal Abstract: In classification problems, there are many data that contain a large number of features, some of which are irrelevant and cause confusion for the classifiers. The support vector classification (SVC) method is one of the most common methods used in classification. Feature selection, together with the parameters setting of SVC, such as the kernel parameter and the penalty parameter, significantly affects the classification performance of the SVC. In this study, the gray wolf optimization algorithm (GWO) is proposed to improve feature selection and determine the optimal parameter values of SVC simultaneously. Based on several benchmark datasets for diseases, the experimental results show that the proposed method, FOGWO-SVC, is capable in selecting the best features with best parameters determination. Further, the comparative results demonstrate that the FOGWO-SVC is better or comparable than other competitor algorithms in terms of classification accuracy and feature reduction. Keywords: Feature selection; gray wolf optimization; parameter determination; support vector classification; metaheuristic algorithms. Reformulation of Bilevel Linear Fractional/Linear Programming Problem into a Mixed Integer Programming Problem Via Complementarity Problem   by Anuradha Sharma Abstract: The bilevel programming problem is a static version of the Stackelberg's leader follower game in which in which Stackelberg strategy is used by the higher level decision maker called the leader given the rational reaction of the lower level decision maker called the follower.The bilevel programming problem is a two level hierarachical optimization problem and is non-convex.This paper deals with finding links between the bilevel linear fractional/linear programming problem(BF/LP), the generalized linear fractinal complememtarity problem(GFCP) and mixed integer linear fractional programming problem (MIFP),The (BFLP) is reformulated as a(GFCP) which in turn is reformulated as an (MIFP),The method is supported with the help of a numerical example. Keywords: Bilevel Programming; Generalized complementarity problem; Mixed integer programming; Fractional programming. Short-Term Traffic Flow Prediction Model based on Deep Learning Regression algorithm   by Yang Zhang, Dong-rong Xin Abstract: In view of the problem that the short-term traffic flow prediction under the condition of unsteady traffic flow, such as low precision and over-reliance on large sample historical data, proposing a novel short-term traffic-flow prediction method based on deep learning support vector regression (DL-SVR). A framework of the DL-SVR is built with a restricted Boltzmann machine (RBM) visible inputting layer, which is connected with several intermediate operating networks, and a radial SVR output layer. In addition, a T mutation particle swarm optimization algorithm is proposed to select the important parameter in DL-SVR. Experimental results show that the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the proposed short-term traffic-flow prediction method are better than other classic algorithms, and the real time also can meet the needs of practical use. Keywords: deep learning; support vector regression; short-term traffic flow; artificial neural network. Image Error Correction Of Hockey Players' Step-By-Step Pull Shooting Based On Bayesian Classification   by Hongping Li Abstract: The target recognition accuracy of the traditional motion image error correction method is low, which leads to its poor application effect. In order to solve this problem, this paper proposes a new image error correction method based on Bayesian classification for hockey players' step-by-step pull shooting action.The SLIC method is used to distinguish the hockey players' step-by-step pull shooting action from the image background area, to obtain the proportion of the hockey players' step-by-step pull shooting action in the super pixel area. The hockey players' step-by-step pull shooting action with incomplete naive Bayesian classification model, and to correct the image error with interpolation method. The experimental results show that the accuracy of the method for the hockey players' step-by-step pull shooting action is higher than 98%, and the image quality is high after the error correction. Keywords: Bayes classification; Hockey players; Step-by-step pull shooting; Image error correction. Predicting the Amount of Files Required to Fix a Bug   by Ahmed Otoom, Maen Hammad, Sara Al-jdaeh, Sari Awwad, Sahar Idwan Abstract: This paper proposes a classifier that can predict the amount of files required to fix a bug. A newly incoming bug can be classified into one of the three classes (categories): Small, Medium, or Large depending on the amount of files required to fix that bug. For this purpose, 5800 bug reports are studied from three open source projects. The projects are: AspectJ, Tomcat, and SWT. Then, feature sets are extracted for each project separately. The feature sets represent the occurrences of keywords in the summary and description parts of the bug reports. Due to the high dimensionality of the feature vectors, we propose to apply the well-known method, principle component analysis (PCA). The resulting feature vectors are then fed to a number of popular machine learning algorithms. For an enhanced performance, we experiment with multiclass support vector machine quadratic MSVM2. It provides improvements of classification accuracy ranging from 2.3%-22.3% compared to other classifiers. Keywords: software maintenanc; machine learning; bug reports; effort prediction; MSVM2. Classifying Defective Software Projects based on Machine Learning and Complexity Metrics   by Mustafa Hammad Abstract: Software defects can lead to software failures or errors at any time. Therefore, software developers and engineers spend a lot of time and effort in order to find possible defects. This paper proposes an automatic approach to predict software defects based on machine learning algorithms. A set of complexity measures values are used to train the classifier. Three public datasets were used to evaluate the ability of mining complexity measures for different software projects to predict possible defects. Experimental results showed that it is possible to min software complexity to build a defect prediction model with a high accuracy rate. Keywords: software defects; defect prediction; software metrics; machine learning; complexity. A New Intelligent Method For Travel Path Recommendation Based On Improved Particle Swarm Optimization   by Si Han Abstract: In order to overcome the problem of increasing fluctuation of Intelligent Tourism data and fuzzy optimal solution, the improved particle swarm optimization algorithm is introduced to design Intelligent Tourism path recommendation method. The gray Markov model is used to predict the number of tourist attractions, and the scoring mechanism of tourist attractions is constructed based on multiple perspectives. The constraints are the distance estimation, number prediction, scoring and user preference identification of tourist attractions. The improved particle swarm optimization algorithm is used to find the optimal solution of recommendation and recommend the tourist path for users. The experimental results show that the average absolute error value of the proposed intelligent travel path recommendation method is 8.13, the inverse relationship between the accuracy and recall rate is clear, and it has better recommendation effect. Keywords: Improved particle swarm optimization; Intelligence; Travel path; Recommendation. Optimisation of makespan of a flow shop problem using multi layer neural network   by Harendra Kumar, Shailendra Giri Abstract: This paper presents an approach based on a multi layer neural network algorithm (MLNNA) to find a sequence of jobs for flow shop scheduling problems with the objective of minimise the makespan. The purpose of this paper is to develop an artificial intelligence and trained a neural network model for solving the flow shop scheduling problem which gives a best jobs sequence with the objective of minimise the makespan. The effectiveness of the proposed MLNNA method is compared with many problems selected from different papers. A large number of problems are solved with the present MLNNA model and it is found suitable and workable in all the cases. Keywords: artificial neural network; flow shop problem; scheduling; multi layer network; makespan; job sequencing.DOI: 10.1504/IJCSM.2020.10028046  Smart grid short-term load estimation model based on BP neural network   by Jianqiang Shi, Shi Chengchao, Han Lei, Xu Mengxi Abstract: As reasonable short-term load estimation system can provide reliable support for the operating, planning and designing of the smart grid, in this paper, we propose an effective smart grid short-term load estimation method. Different types of data are input to the BP neural network and then the output of BP neural network is represented as the load estimation results. Although BP neural network can approximate any nonlinear continuous function with the condition of a specific structure and suitable weights, it is very difficult to obtain the global minimum result. In order to obtain the global optimum solution in short-term load estimation, we exploit the genetic algorithm to optimise the weights and thresholds of the BP neural network, which is the main advantage of the proposed model. Finally, experimental results demonstrate that the proposed method can estimate short-term load of smart grid with higher accuracy and it can also clearly show the load requirement distribution in different time period. Keywords: smart grid; short-term load; BP neural network; genetic algorithm; model.DOI: 10.1504/IJCSM.2020.10028091  Multivariate generalised gamma kernel density estimators and application to non-negative data   by Lynda Harfouche, Nabil Zougab, Smail Adjabi Abstract: This paper proposes a classical multivariate generalised gamma (GG) kernel estimator for probability density function (pdf) estimation in the context of multivariate nonnegative data. Then, we show that the multiplicative bias correction (MBC) techniques can be applied for multivariate GG kernel density estimator as in Funke and Kawka (2015). Some properties (bias, variance and mean integrated squared error) of the corresponding estimators are also provided. The choice of the vector of bandwidths is investigated by adopting the popular cross-validation technique. Finally, the performances of the classical and MBC estimator based on the family of GG kernels are illustrated by a simulation study and real data. Keywords: asymmetric kernels; bandwidth; generalised gamma kernels; multiplicative bias correction; MBC; multivariate estimation density.DOI: 10.1504/IJCSM.2020.10028092  Quartic Padé approximation to the exponential function and a class of local analytical difference schemes   by Cheng-De Zheng, Yan Xiao Abstract: This paper investigates the quartic non-diagonal algebraic Hermite-Padé approximation to the exponential function. Explicit formulas and differential equations are obtained for the polynomial coefficients. An exact asymptotic expression is obtained for the error function. As an application, a class of local analytical difference schemes based on quartic Padé approximation for diffusion-convection equation with constant coefficients are proposed. A numerical example is provided to demonstrate the effectiveness of the theoretical results. Keywords: Padé-type approximant; quartic Hermite-Padé approximation; asymptotic formula; diffusion-convection equation; difference scheme; exponential function; difference scheme.DOI: 10.1504/IJCSM.2020.10028093  Computation of multi-choice multi-objective fuzzy probabilistic two stage programming problem   by Prabhat Kumar Rout, Sudarsan Nanda, Srikumar Acharya Abstract: The aim of the paper is to present a multi-choice multi-objective fuzzy probabilistic two stage programming problem and its solution methodology. The mathematical programming problem suggested here is difficult to solve directly. Therefore, three major steps are suggested to solve the proposed mathematical programming problem. In first step, fuzzy chance constraint is transformed to its equivalent chance constraint programming problem using α – cut technique. Chance constraint technique is used to obtain a crisp model of multi-choice multi-objective two-stage programming problem. In the second step, two stage programming problem is converted to its equivalent deterministic model. In next step, importance is given to handle multi-choice parameter using least square approximation technique. At the end of third step, a multi-objective mathematical programming is obtained. Finally ∈-constraint approach is used to solve the transformed multi-objective mathematical programming. Using existing methodology and software the final solution of the proposed model is obtained. The proposed method is implemented with a numerical example. Keywords: multi-objective; fuzzy probability; ∈-constraint; multi-choice programming; least square approximation.DOI: 10.1504/IJCSM.2020.10028094  A robust algorithm for solving nonlinear system of equations using trust-region and line-search techniques   by Muhammad Nomani Kabir Abstract: Newton's method is an attractive method for solving nonlinear system of equations because of its fast convergence property. However, Newton's method may fail if the Jacobian matrices are singular. Newton's method with trust-region can be used to avoid such problem. In this work, a new trust-region technique for Newton's method was formulated to solve the nonlinear system of equations. The search direction in this method is computed by a sequence of factorisations of the Jacobian matrix with modified structure using a Lagrange multiplier associated with trust-region constraint such that the final modified Jacobian turns out to be well-conditioned (regularised). An optimal Lagrange multiplier was deduced using the same idea of unconstrained optimisation to satisfy the trust-region constraint. Furthermore, Armijo line-search technique is integrated with the method in order to improve the step length. Numerical tests were conducted to investigate the performance of Newton's method integrated with trust-region and line-search techniques. Keywords: unconstrained optimisation; trust-region method; Armijo line search; nonlinear system of equations.DOI: 10.1504/IJCSM.2020.10028095  Hybrid adaptive random testing   by Esmaee Nikravan, Saeed Parsa Abstract: Adaptive random testing (ART) subsumes a family of random testing techniques with an effective improvement. It is based on the observation that failure causing inputs tend to be clustered together. Hence the ART methods spread test cases more evenly within the input domain to improve the fault-detection capability of random testing. There have been several implementations of ART based on different intuitions and principles with their own advantages and disadvantages. In the different variants of ART methods, the majority of them use a variety of distance calculations, with corresponding computational overhead. The newly methods try to decrease computational overhead while maintaining the performance through partitioning the input domain. We outline a new partitioning-based ART algorithm with a hybrid search method and demonstrate experimentally that it can further improve the performance, with considerably lower overhead than other ART algorithms. Keywords: software testing; random testing; adaptive random testing; ART; test data generation.DOI: 10.1504/IJCSM.2020.10028215  Computational analysis of magnetohydrodynamic mixed convection flow along vertical cylinder in the presence of aligned magnetic field   by Muhammad Ashraf, Amna Saif Abstract: The magnetohydrodynamic mixed convection boundary layer flow in the presence of aligned magnetic field along a vertical cylinder is considered. Particular attention is paid to investigate how the buoyancy effects of thermal diffusion combine with joule heating affect the two dimensional flow. Effects of the mixed convection parameter λ, magnetic force parameter S, magnetic Prandtl number Pm and Prandtl number Pr on the velocity, temperature distribution, magnetic flux, skin friction, rate of heat transfer and magnetic intensity are studied. Further, the conservation equations are approximated by using finite difference method with a second order central difference scheme for entire regime, and extended series solution at the surface of cylinder. The obtained results show that the skin friction, rate of heat transfer, magnetic intensity, magnetic flux and temperature distribution can be enhanced or reduced by proper choice of parameters involved in the convective fluid flow problem and presented graphically. The increase in magnetic force parameter S enhances the momentum boundary layer while the thermal boundary layer and magnetic flux at the surface of the cylinder is reduced. The obtained results are compared by both methods and found to be in good agreement. Keywords: mixed convection; aligned magnetic field; finite difference method; FDM; extended series solution; ESS.DOI: 10.1504/IJCSM.2017.10022310  A discrete packing model of granular material confined in a vertical column   by Qinghai Jiang, Kai Wu, Yu Sun, Xin Xie, Zhengyu Yang Abstract: In this paper, we analysed the transmission rules of interparticle forces between granular particles, based on which, we then proposed a discrete packing model to calculate the static pressure at the bottom of granular material confined in a vertical column. Our mechanical analysis and numerical simulation results indicate that the silo effect is caused by the frictional contacts between border particles and inner walls, the static pressure at the bottom depends on the external load initially, and then tends to a saturation pressure (Pn) in an exponential form. The saturation pressure is positive linear related to the container radius (R) with the same granular matter and stacking manner. The saturation pressure is directly proportional to the particle size (ra), and the increasing or decreasing characteristic depends on the frictional property of inner walls, the friction and stacking angle of grains. Finally, we compared the predictions of the aforementioned model with the experimental results from the literature, and we observed that good agreement is achieved. Keywords: granular material; discrete packing model; silo effect.DOI: 10.1504/IJCSM.2017.10013404  Approximate of solution of a fourth order ordinary differential equations via tenth step block method   by Guesh Simretab Gebremedhin, Saumya Ranjan Jena Abstract: This paper carries a different approach of collection and interpolation to develop a tenth block method for the numerical solution of linear or nonlinear ordinary differential equations of fourth order with initial conditions. The method has been implemented at the selected mesh points to generate a direct tenth block method through Taylor series. Some critical properties of this method such as zero stability, order of the method, and convergence have been analysed. Two numerical tests have taken to make a comparison of the approximate results with exact as well as results of other authors. Keywords: block method; collocation; interpolation; tenth-step; Taylor series; zero stability; convergence; absolute stability.DOI: 10.1504/IJCSM.2020.10028216  A genetic-fuzzy control method for regenerative braking in electric vehicle   by Zhiqiang Liu, Shan Lu, Rong-hua Du Abstract: In order to improve the recovery ratio of the regenerative braking energy in electric vehicles, the influence factors on braking energy feedback in electric vehicles were analysed. Then, a parallel braking force distribution model was established, and a fuzzy controller on braking force distribution was designed, in which the inputs were vehicle speed, braking strength, battery SOC, and output was regenerative braking ratio. On the other hand, the implementation of genetic algorithm in optimisation process was studied. Furthermore, the genetic algorithm was used to optimise the fuzzy control rules, and new fuzzy distribution rules of electro-hydraulic braking force were obtained. The experimental results showed that the recoverable energy ratio was increased by 2.7% with the comparison of the optimised distribution rules and the original rules. So, the genetic-fuzzy control method is effective for regenerative braking in electric vehicles. Keywords: electric vehicle; braking force distribution; fuzzy control; genetic algorithm.DOI: 10.1504/IJCSM.2020.10028218  Proportional-integral-derivative controller parameter optimisation based on improved glowworm swarm optimisation algorithm   by Xing Guo, Shi-Chao Yin, Yi-Wen Zhang, Wei Li Abstract: The proportional-integral-derivative (PID) controller parameters tuning, is seeking the optimal value in the space of three parameters to achieve the optimal control performance of the system. It is the core of contemporary feedback control system design. However, its easily falling into local optimum weakened its global search ability. To tackle this problem, this paper proposes an improved glowworm swarm optimisation algorithm, (D-AGSO) with the introduction of directed moving and adaptive step strategy. The simulation experimental results show that D-AGSO continuously adapts the tuning parameters, achieving lower fluctuations features, time settling and smaller steady state error, specially applied to the time delay in the case of inertia controlled system of industrial production. Keywords: glowworm swarm optimisation; GSO; directed moving; adaptive step strategy; proportional-integral-derivative; PID controller.DOI: 10.1504/IJCSM.2020.10028217  Novel approach in multilingual and mixed English-Arabic test collection   by Mohammed M. Ali, Mohammed M. Abu Shquier, Afag Slah Eldeen, Mohamed E. Zidan, Ra'ed M. Al-Khatib Abstract: Mixing languages together in text and in talking is a major feature in non-English languages in developing countries. This mixed grammar is also emerging in SMS, Facebook communication, searching the Web and any future attempts also may increase the footprint of such a mixed language knowledge base. Traditional information retrieval (IR) and cross-language information retrieval (CLIR) systems do not exploit this natural human tendency as the underlying assumption is that user query is always monolingual. Accordingly, the majority of the text collections are either monolingual or multilingual. This paper explores the trends of mixed-language querying and writing. It also shows how the corpus is validated statistically and how an Arabic lexicon can be extracted using co-occurrence statistics. Results showed that the distribution of frequencies of words in the corpus is very skewed the vocabulary growth is a good fit. The results of how to handle mixed queries are also summarised. Keywords: multilingual; monolingual; multilingualism characteristic; retrieval of documents.DOI: 10.1504/IJCSM.2020.10028219