International Journal of Computing Science and Mathematics (96 papers in press)

Regular Issues

• Modelling user pictures with Hierarchical Dirichlet Process of P2P lending market  
  by Danyang Li, Yongquan Liang, An Liu 
  Abstract: The emergence of Peer-to-Peer (P2P) lending has drawn a lot of attention. The enormous data generated from this billions level market bring us lots of challenges and opportunities. One interesting question of modelling this data is that can we discover the hidden pattern of users characteristics from it? Currently, few works have been made to this area. In this article, we try to build a Bayesian probabilistic model to discover the latent user pictures. Especially, we build a user picture model via Hierarchical Dirichlet Process from data of one of the biggest market, Lending Club. The discovered user picture is interpretable and can be evaluated from many perspectives. To demonstrate the usage of user picture, we also proposed a method to predict loan status. The experimental results show our approach outperformed the comparison methods
  Keywords: P2P lending; User pictures; Hierarchical Dirichlet Process; Prediction.
  
  
• Applying Refined Descriptive Sampling on the vibrating string model  
  by Megdouda OURBIH-TARI, Sofia GUEBLI, Abdelouhab ALOUI 
  Abstract: Monte Carlo methods (MC) and Refined Descriptive Sampling (RDS) are sampling methods that can be used to produce input values for estimation of expectation of function of output variables. This paper gives an application of RDS method in a two-dimensional problem of a vibrating string. An empirical comparison of theserntwo methods demonstrating the effectiveness of RDS on MC is performed by using the variance as a statistical criterion since both methods are unbiased.
  Keywords: Sampling method; Variance; Monte Carlo; Expectation.
  DOI: 10.1504/IJCSM.2017.10019650
   
  
• An image hole inpainting algorithm with improved FMM for mobile devices  
  by Huiqin Wang, Dong Fang, Congcong Wang, Jianqiu Jin 
  Abstract: During the warping operation with DIBR, the hole will be generated through the synthesis of 3D image. For mobile device, it has lower performance so that the hole inpainting can not be completed in real-time. In order to overcome this shortcoming, we propose an image hole inpainting algorithm with improved FMM for mobile devices. The algorithm fills the hole after 3D image warping by DIBR. First, we mark the color image and the corresponding holes. Then, we preserve the original image foreground edge information by the improved expanded core of traditional FMM. Finally, we implement the algorithm on mobile devices. Experimental results show that the improved FMM proposed in this paper is better than the traditional FMM.
  Keywords: Mobile device; Inpainting; DIBR; FMM.
  
  
• New Concepts of Domination Sets in Vague Graphs with Applications  
  by Hossein Rashmanlou, Yahya Talebi 
  Abstract: A vague graph is a generalized structure of a fuzzy graph that gives more precision, flexibility, and compatibility to a system when compared with systems that are designed using fuzzy graphs, which is introduced by Ramakrishna cite{12}. Domination in graphs has many applications to several fields. Domination arises in facility location problems, where the number of facilities (e.g., hospitals, fire stations) is fixed and one attempts to minimize the distance that a person needs to travel to get to the closest facility. Concepts from domination setrnalso appear in problems involving finding sets of representatives in monitoring communication orrnelectrical networks, and in land surveyor must stand in order to take height measurements for anrnentire region. Hence, in this paper, double domination of vague graphs is introduced and some basic theorems are proved.rnAn interesting result on $gamma_{dd}(G)$ using some known parameter of $G$ is obtained. Finally, some applications of domination in vague graph are given.
  Keywords: Vague graph; double domination set; cut node; fuzzy set.
  
  
• Fourth Order Computational Method for Two Parameters Singularly Perturbed Boundary Value Problem using Non Polynomial Cubic Spline  
  by Kolloju Phaneendra, G. Mahesh 
  Abstract: In this paper, we proposed a fourth order finite difference scheme using non polynomial cubic spline for the solution of two parameters singularly perturbed two-point boundary value problem having dual boundary layer on a uniform mesh. In this method, the first order derivatives in the non polynomial cubic spline finite difference scheme are replaced by the higher order finite differences to get the discretization equation for the problem. The discretization equation is solved by the tridiagonal solver discrete invariant imbedding. The proposed method is analyzed for convergence and a fourth order rate of convergence is proved. The numerical results are compared with exact solutions and the outcomes of other existing numerical methods.
  Keywords: Two parameters singularly perturbed two point boundary value problem; Dual boundary layer; Characteristic equation; Non polynomial cubic spline.
  
  
• An Eco-epidemiological Model for Newcastle Disease in Central Zone of Tanzania  
  by Alfred Hugo, Oluwole Daniel Makinde, Santosh Kumar 
  Abstract: Newcastle disease is a contagious bird disease which affects main domestic and wild avian species. A deterministic compartmental model for Newcastle disease (ND) is developed and analysed using ordinary differential equation theory. The uncertainties of model parameters were therefore examined using Markov Chain Monte Carlo (MCMC) simulations for the data of chicken death cases due to Newcastle disease from five districts in two regions in Tanzania. The parameter distribution was tested using MCMC convergence diagnostics. The graphical diagnostic test for MCMC used include Trace plots or time series plot, two-dimensional parameter plots and autocorrelation function plots. Hence, model parameters were successfully estimated for numerical simulations and the results of simulations were presented.
  Keywords: Eco-epidemiology; Newcastle Disease; Parameter estimation.
  
  
• Modified Bessel Series Solution of the Single Server Queueing Model with Feedback  
  by Chandra Shekhar, Amit Kumar, Shreekant Varshney 
  Abstract: In multi-access systems, scheduling mechanism often requires a proper feedback policy. In this paper, the direct and simple transient solution technique for the state of the system in a single server/processor Markovian queueing model with feedback is presented using modified Bessel function of the second kind. This technique appears to economize in algebra. The expression for a time-dependent measure of effectiveness such as an expected number of the customers in the system is also derived. We demonstrate how fast the state probabilities tend to their equilibrium limits when it exists. The sensitivity of the state of the system and expected number of the customers in the system has been also analyzed and the results are depicted in the tables and graphs.
  Keywords: Single server; Feedback policy; Modified Bessel function of the second kind; Transient solution; Poisson queues.
  
  
• A uniformly convergent numerical scheme for singularly perturbed differential equation with integral boundary condition arising in neural network  
  by Deepti Shakti, Jugal Mohapatra 
  Abstract: This article deals with a singularly perturbed quasilinear boundary value problem with integral boundary condition which arises in neural network. The problem is discretized by using an upwind finite difference scheme on a nonuniform mesh obtained via equidistribution of a monitor function. We prove that the method is first order convergent in the discrete maximum norm independent of perturbation parameter. The parameter uniform convergence is confirmed by numerical computations.
  Keywords: Singular perturbation; Upwind scheme; adaptive grid; integral boundary condition; Boundary layer.
  
  
• Nonsmooth Multiobjective Fractional Programming Problem Involving Higher order Functions  
  by Pallavi Kharbanda, Divya Agarwal 
  Abstract: In this paper, a new generalized class of higher order $(F,\alpha,\rho,d)$-V-type I function is introduced for a nonsmooth multiobjective fractional programming problem involving support functions. The newly defined class extends several known classes in the literature has been justified through a non-trivial example. In the framework of new concept, we determine conditions under which a fractional function becomes higher order $(F,\alpha,\rho,d)$-V-type I function and do some computational work to substantiate the analysis. Further, we establish Karush-Kuhn-Tucker type sufficient optimality conditions and derive various duality results for higher order Mond-Weir type and Schaible type dual programs.
  Keywords: Multiobjective programming; $(F,\alpha,\rho,d)$-V-type I function; Fractional programming; Non-linear Programming; Efficient solution.
  
  
• New exact solutions to nonlinear diffusion equation that occurs in image processing  
  by Rafaa Chouder, Benhamidouche Noureddine 
  Abstract: In this paper, we would like to seek new exact solutions to nonlinear diffusion equation that occurs in image processing. This equation is called degenerate parabolic equation. The solutions which we seek are called "travelling profiles solutions". For that, we have used the "travelling profiles method" in order to find, explicitly, new exact solutions to this equation under some conditions. An interesting particular case has been discussed, this case coincides with particular solutions called "intermediate asymptotic solutions" used to study the contour enhancement in image processing.
  Keywords: Nonlinear degenerate parabolic equations - Travelling profiles solutions - Exact solutions.
  
  
• QUADRATIC NUMERICAL TREATMENT FOR SINGULAR INTEGRAL EQUATIONS WITH LOGARITHMIC KERNEL  
  by Mostefa Nadir, Bachir Gagui 
  Abstract: The goal of this paper is to present a direct method for an approximative solution of a weakly singular integral equations (W.S.I.E) with logarithmic kernel on a piecewise smooth integration path using a modified quadratic spline approximation, we also show that this approximation gives an e
  Keywords: Weakly singular integral; Quadratic interpolation; Holder space and Holder condition.
  
  
• Lanczos-type Algorithms with Embedded Interpolation and Extrapolation Models for Solving Large Scale Systems of Linear Equations  
  by Wali Khan 
  Abstract: The new approach to combating instability in Lanczos-type algorithms for large scale problems is proposed. It is a modification of so-called embedded interpolation and extrapolation model in Lanczos-type algorithms (EIEMLA), which enables us to interpolate the sequence of vector solutions generated by a Lanczos-type algorithm entirely, without re-arranging the position of the entries of the vector solutions. The numerical results show that the new approach performs more effectively than EIEMLA. In fact, we extend this new approach on the use of a restarting framework to obtain the convergence of Lanczos algorithms accurately. This kind of restarting challenges other existing restarting strategies in Lanczos-type algorithms.
  Keywords: Numerical Analysis; Interpolation; Extrapolation; Lanczos Algorithms; EIEMLA; Modified EIEMLA; Restarting Strategy.
  
  
• Finite Element Simulation of Prevention Thermal Cracking in Mass Concrete  
  by Juncai Xu, Qingwen Ren, Zhenzhong Shen, Song Yang, Xin Xie, Zhengyu Yang 
  Abstract: Mass concrete structures play a very important role in civil engineering. The cracking of concrete is regarded as one of the biggest engineering problems. Therefore, it is very necessary for the cracking of mass concrete to do the control analysis. Some factors should be considered in mass concrete crack control analysis, mainly including the heat releases model of concrete, the mechanical model to the concrete, the process of temperature control in the pipe model. Differential evolution algorithm and equivalent algorithm are adopted to solve the coefficient of adiabatic temperature and cool water effect. In the paper, stress field calculation, back analysis calculations, and cooling pipe processing create secondary development based on the ABAQUS software platform. The second development of the code is used to reasonably solve the problem with one actual aqueduct in hydraulic engineering.
  Keywords: Mass concrete; differential evolution algorithm; equivalent algorithm; crack control.
  
  
• Computational Study of Drug Delivery in Tumorous Human Airways  
  by Vivek Kumar Srivastav, Akshoy Ranjan Paul, Anuj Jain 
  Abstract: There is increasing interest in the research of direct drug delivery in respiratory tract because of its attractiveness to produce greater therapeutic benefit for the treatment of pulmonary diseases and systemic diseases. In the present study, a three dimensional human airway geometric model was constructed from computer tomography (CT) scan images. A tumor was artificially created in the trachea of the airway model for the CFD simulation of the aerosol-particles transport in the airways and its deposition on the tumor. Low Reynolds Number (LRN) k-omega model was used to model turbulence flow behavior and Discrete Phase Model (DPM) was applied to simulate aerosol-particle transport. The CFD simulation was carried out for three air inhalation flow rates: 20 L/min (normal breathing), 40 L/min (moderate breathing) and 60 L/min (high breathing), and three particle sizes of 1, 5 and 10 m to determine the effect of these parameters on the deposition efficiency of the particles on the tumor. The air flow patterns show that the more flow disturbance occurs at the downstream of the tumor as compared to upstream. The results show that the maximum aerosol deposition on the tumor occurs at 60 L/min inhalation rate for 5 to 10 micron aerosol-particles size. The findings will be useful to maximize therapeutic benefit of respiratory drug delivery.
  Keywords: Human Airway model; Tumorous trachea; Aerosol-Particle Deposition; Computational Fluid Dynamics (CFD); Drug delivery.
  
  
• Modelling and Analysis of TCP Congestion Control Mechanisms Using Stochastic Reward Nets  
  by Osama Younes 
  Abstract: Modelling of congestion control mechanisms of Transmission Control Protocol (TCP) helps to obtain parametric results that help to better understand the TCP behaviour under different operating conditions. Several analytical models were proposed to analyse the behaviour of TCP congestion control mechanisms. However, most of these models were designed for a few TCP sessions with non-persistent connections. A few analytical studies were presented for modelling persistent TCP connections. Nevertheless, these studies have many limiting assumptions and are not scalable. In this work, a stochastic reward nets model is introduced for the TCP Reno with persistent TCP connections, which share two bottleneck links in a wide area network. A microscopic approach was used for constructing the proposed model that captures most features of congestion control mechanisms used by the TCP Reno, and interactions between different TCP sessions. The proposed model relaxed several limiting assumptions adopted by other related models introduced in the literature. To validate the proposed model, analytical results are extensively compared with simulation results.
  Keywords: performance modelling; Petri Nets; congestion control; TCP performance; TCP Reno.
  
  
• Finding equitable risk routes for hazmat shipments  
  by Huo Chai, Ruichun He, Changxi Ma, Cunjie Dai 
  Abstract: This paper develops a model to analyse hazmat shipments routing in the context of hazmat transportation between specified origin-destination (OD) pair. A novel aspect of this model is the consideration of risk equity using standard deviation, an established computation to assess equity. To solve the model, a two-phase method is developed, in which the multi-objective shortest path algorithm is used to obtain the alternative Pareto-optimal paths set, and get the routes using estimation of distribution algorithm after paths choice. We then present a test problem of hazmat shipment with consideration of risk equity and discuss computational results.
  Keywords: hazmat shipments; vehicle routing problem; risk equity; transportation network; multi-objective shortest path; estimation of distribution algorithm.
  
  
• Generalized Interval-valued Intuitionistic Fuzzy Entropy with Some Similarity Measures  
  by Pratiksha Tiwari, Priti Gupta 
  Abstract: Interval-valued intuitionistic fuzzy environment is appropriate for most of the practical scenarios involving uncertainty, vagueness and insufficient information such as pattern recognition, medical diagnoses, decision making etc. Entropy, similarity, distance, inclusion and cross entropy measures are few methods used for measuring uncertainty and classifying interval-valued intuitionistic fuzzy sets. This paper presents generalized entropy measure for interval-valued intuitionistic fuzzy sets and relation is established that can be used to define generalized similarity measures using the proposed entropy measure. Further, the proposed entropy measure is compared with some existing measure of entropy with the help of an illustrative example and lastly we demonstrated how the proposed measure can be used in decision making.
  Keywords: interval-valued intuitionistic fuzzy sets; generalized entropy measure; generalized similarity measure.
  
  
• Robust and Minimum Spanning Tree in Fuzzy Environment  
  by Arindam Dey, Tandra Pal, Sahanur Mondal 
  Abstract: This paper proposes an algorithm to find the fuzzy minimum spanning tree (FMST) of an undirected weighted fuzzy graph, in which mixed fuzzy numbers, either triangular or trapezoidal, are used to represent the lengths/costs of the arcs. In the proposed algorithm, we incorporate the uncertainty in Kruskal's algorithm for MST using fuzzy number as arc length. The concept of possibility programming is used to compare between the fuzzy number (i.e., costs of arcs) and addition operation of fuzzy numbers is used to find the cost of the spanning tree. We also investigate the robust version of the FMST problem. We define two measures for robustness of an FMST: absolute robustness and relative robustness. We characterize the fuzzy worst case scenarios for a given fuzzy spanning tree for both the measures. The corresponding fuzzy robust spanning trees are respectively defined as absolute robust fuzzy spanning tree (ARFST) and relative robust fuzzy spanning tree (RRFST). We extend Kruskal's algorithm to compute the ARFST and RRFST in fuzzy environment. An example of fuzzy graph is used to illustrate the effectiveness of the proposed methods.
  Keywords: possibility programming; robust spanning tree; Kruskal's algorithm; minimum spanning tree; triangular fuzzy number; trapezoidal fuzzy number; fuzzy graph; fuzzy minimum spanning tree; absolute fuzzy robust spanning tree; relative fuzzy robust spanning tree.
  DOI: 10.1504/IJCSM.2017.10008767
   
  
• A study on spectral methods for linear and nonlinear fractional differential equations  
  by Mahmoud Behroozifar, Farkhondeh Ahmadpour 
  Abstract: In this paper, a computational method based on the spectral methods with shifted Jacobi polynomials is applied for the numerical solution of the linear and nonlinear multi-order fractional differential equations (FDEs). Fractional derivative is described in the Caputo sense. Operational matrix of fractional differential of shifted Jacobi polynomials is stated. This matrix together with the tau method and collocation method are utilized to reduce the linear and nonlinear fractional differential equations to a system of algebraic equations, respectively. The purpose of this paper is to make a comparison between this simple method and other existing methods to show the performance and preciseness of the presented method. Due to this, we used this technique for some illustrative numerical tests which the results demonstrate the validity and efficiency of the method.
  Keywords: Fractional-order differential equation; Operational matrix; Jacobi polynomials; Spectral method; Caputo derivative.
  
  
• Modelling DAX by applying parabola approximation method  
  by Meng-Rong Li, Daniel Wei-Chung Miao, Tsung-Jui Chiang-Lin, Young-Shiuan Lee 
  Abstract: Existing studies indicate that nonlinear phenomenon occurs in the movement of stock prices (or returns) but few models provide adequate explanations. We apply Parabola Approximation as an inclusion of nonlinear explanatory variable to model German DAX (Deutscher Aktien IndeX) closing prices during 2 Jan.2006 to 12 Jun. 2013. The empirical result shows accurate fits which means the model applied characterizes DAX appropriately. As a result, the coefficients of the model meaningfully determine the movement of DAX. After examining the coefficients, unusual changes of the coefficients as a sign of approaching fluctuations in DAX prices display right before the announcement of bankruptcy of Lehman Brothers. In this way, we provide an instrument to detect the prompt structural changes and risks of the financial market.
  Keywords: ordinary differential equation; nonlinear dynamic system; parabola approximation; stock index; DAX; Deutscher Aktienindex; financial crisis.
  
  
• Pyramidal method of extrapolation for short time series  
  by Yuriy Turbal, Andriy Bomba, Anastasiia Sokh, Olena Radoveniuk, Mariana Turbal 
  Abstract: The paper proposed a new method of short time series extrapolation, which can be used for predicting of economic, environmental and geophysical parameters. This method is based on the property of the rows of modified finite differences that the best cubic approximation is in the range of convexity. Numerical results show significant advantages of the proposed method in comparison with approaches to extrapolate, based on the use of polynomials, including Newtons extrapolation.
  Keywords: time series; extrapolation; forecasting; Newton - Gregory backward difference formula; finite differences; short time series.
  
  
• Analysis of Legendre Polynomial Kernel in Support Vector Machines  
  by DJELLOUL Naima, AMIR Abdessamad 
  Abstract: For several types of machines learning problems, Support Vector Machine is a method of choice. The kernel functions are a basic ingredient in Support Vector Machine theory. Kernels based on the concepts of orthogonal polynomials gave great satisfaction in practice. In this paper we identify the Reproducing Kernel Hilbert Space of Legendre polynomial kernel which allows us to understand its ability to extract more discriminative features. We also show that without being a universal kernel, Legendre kernel possesses the same separation properties. The Legendre, Gaussian and polynomial kernel performance has been first evaluated on two dimensional illustrative examples in order to give a graphical comparison, then on real world data sets from UCI repository. For non linearly separable data, Legendre kernel always gives satisfaction regarding classification accuracy and reduction in the number of support vectors.
  Keywords: Support Vector Machine; Kernel trick; Reproducing Kernel Hilbert Space; Orthogonal Polynomials; Tensor Product.
  
  
• Fuzzy reliability evaluation of Linear m-Consecutive Weighted-k-out-of-r-from-n: F systems  
  by Seema Negi, S.B. Singh 
  Abstract: This paper deals with the introduction and study of fuzzy reliability measures of a linear m-consecutive weighted-k-out-of-r-from-n: F system. The paper provides an algorithim for the evaluation of fuzzy reliability of the proposed system based on application of universal generating function and fuzzy exponential distribution. It is assumed in the study that failure rate follows generalized symmetric trapezoidal fuzzy number in fuzzy exponential distribution. Further, generalized symmetric trapezoidal fuzzy number and its arithmetic operations are defined. Fuzzy mean time to failure and Brinbaum system fuzzy reliability importance is also calculated. A numerical example is also presented to illustrate the proposed approach.
  Keywords: System fuzzy reliability; universal generating function; linear m-consecutive k-out-of-r-from-n: F systems; linear m-consecutive weighted-k-out-of-r-from-n: F systems; Fuzzy mean time to failure; Brinbaum fuzzy reliability importance.
  
  
• Equilibrium customers strategies in the Markovian working vacation queue with setup times  
  by Huining Wang, Xiuli Xu, Shuo Wang 
  Abstract: In this paper, we research the customers equilibrium behaviour in the single server Markovian queue with setup times and working vacation. In such an M/M/1 queueing system, the arriving customers decision is whether to enter the system or balk based on the reward-cost structure, which includes their desire for service and their unwillingness to wait. We separately discuss the fully observable and fully unobservable cases. For each of case, we acquire the related equilibrium balking strategies of customers and the expected social benefits per time unit. Finally, we obtain some numerical examples to illustrate the effect of several parameters on the equilibrium and optimal strategy.
  Keywords: M/M/1 queue; Equilibrium strategies; Setup times; Working vacations; Social benefits.
  
  
• Khmer-Chinese Bilingual LDA Topic Model Based on Dictionary  
  by Xiaohui Liu, Xin Yan 
  Abstract: Multilingual probabilistic topic models have been widely used in topic mining area in multilingual documents, this paper proposes a method called KCB-LDA (Khmer-Chinese Bilingual Latent Dirichlet Allocation) based on Bilingual dictionary. With the bilingual attribute of entries in dictionary, this method first maps the words expressing same semantic meaning to the concept abstract layer, then group concepts into the same topic space. Finally, documents in different languages will share the same latent topics. The same topics can be represented in both Chinese and Khmer jointly when given a bilingual corpus by the introduction of the concept layer. The experimental results show that the topic mining effects of KCB-LDA model are much better than the LDA model.
  Keywords: Multilingual probabilistic topic models; Bilingual dictionary; KCB-LDA; Concept.
  
  
• Solving nonlinear system of second-order boundary value problems using a newly constructed scaling function  
  by Yanan Liu 
  Abstract: In this paper, a scaling function constructed by special filter coefficients is used for solving nonlinear system of second-order boundary value problems. The basis functions in interval originated from the newly constructed scaling function are directly used for function approximation. The Galerkin method and iteration approach are used for solution. Some numerical examples are presented to demonstrate the validity of the numerical technique. Numerical results prove that the new basis functions have good approximation ability and the present method is very efficient and highly accurate in solving nonlinear system of second-order boundary value problems.
  Keywords: filter coefficients; scaling functions; nonlinear system; Galerkin method; iteration.
  
  
• Local search based dynamically adapted Bat Algorithm in image enhancement domain  
  by Krishna Gopal Dhal, Sanjoy Das 
  Abstract: Bat algorithm (BA) is a new metaheuristic optimization algorithm, which has already proved its supreme performance on many optimization fields. However, it is possible to increase its efficiency when solving complex optimization problems. This study concentrates on improving the efficiency of BA by incorporating different types of local search strategies and novel self-adaption strategies of parameters such as loudness, pulse rate and frequency. Comparative analysis of three different proposed local search strategies has been performed to find the best one. The proposed modified BAs with local search strategies are employed to solve five popular image enhancement models. Experimental results prove that self-adaption of parameters enhances the capability of standard BA. But the addition of efficient local search technique with self-adaption increases the effectiveness of the standard BA to a great extent.
  Keywords: image enhancement; Bat Algorithm; Self-adaptive; local search; chaos.
  
  
• Motion Image Restoration Based on Sparse Representation and Guided Filter  
  by Hang Zuo, Liejun Wang 
  Abstract: When moving objects are present, current low-resolution blurring image reconstruction techniques with considerable noise do not perform well. This paper comes up with a new image reconstruction method based on K-SVD algorithm and guided filter technique. This method uses K-SVD to preprocess the image first, and apply canny boundary detector to obtain clear boundaries as prior model, thus we can estimate blurring kernel. Last, we apply guided filter to reconstruct our image. We do the second and third step iteration to obtain clear images. This paper uses simulated degeneration and actual low-resolution blurring image for experiments, and our result implies this method has good performance for reconstruction.
  Keywords: Image restoration; Motion blur; KSVD; Edge detection; Guided filter.
  
  
• Optimization of makespan of a flow shop problem using multi layer neural network  
  by Shailendra Giri, Harendra Kumar 
  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 minimize 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 minimize 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.
  
  
• Smart grid short term load estimation model based on BP neural network  
  by Jianqiang Shi, Chengchao Shi, Lei Han, Mengxi Xu 
  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 optimize 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; Fitness value.
  
  
• Image Reconstruction Based on Approximate Function and Modified Conjugate Gradient  
  by Ping GONG, Guohua Li, Jian Li 
  Abstract: In CS, L1 norm or TV norm is usually but individually used to solve the signal reconstruction problems. They have different advantages. L1 norm is used to control the reconstructed signals sparsity and the TV norm is used to constrain the reconstructed signals gradient variation and to preserve edge characteristics. The proposed approach combines the advantages of L1 norm and TV norm by combining L1 norm and TV norm to solve the image reconstruction problems. And the proposed approach reconstructs an image from the measured values by using the modified conjugate gradient algorithm for the purpose of improving the efficiency of image reconstruction. The objective function is constructed using the approximate function based on the L1 norm and TV norm. A sparse transformation is applied to the original image first. The random Gaussian matrix is then employed to perform a compressive measurement on the 2-D sparse signal. To reconstruct the image a regularized reconstruction model is established through the approximate norm that combines L1 norm and TV norm and the gradient of the approximate norm is computed. The image is finally reconstructed using the measured values and the modified conjugate gradient algorithm jointly. Experiments are conducted on images at different sampling rates and resolutions. The simulation results demonstrate the ability of the proposed method to reconstruct images more effectively and produce better results in terms of objective indicators such as PSNR and SSIM than classical methods.
  Keywords: compressive sensing; L1 norm; total variation; modified conjugate gradient algorithm; image reconstruction.
  
  
• A robust second order numerical method for a weakly coupled system of singularly perturbed reaction-diffusion problem with discontinuous source term  
  by Mahabub Basha Pathan, Shanthi Vembu 
  Abstract: In this paper, a fitted mesh numerical method on Shishkin mesh is proposed to solve a weakly coupled system of two singularly perturbed reaction-diffusion equations containing equal diffusion parameters with discontinuous source terms. This method uses the standard centered finite difference scheme constructed on piecewise-uniform Shishkin mesh with an iterative procedure. At the point of discontinuity, we consider the average of the source terms at the point of discontinuity. An error analysis is carried out and the method ensures that the parameter-uniform convergence of almost the second order. Numerical results are provided to confirm the theoretical results and compares well with the existing results.
  Keywords: Singular perturbation problem; Weakly coupled reaction-diffusion system; Fitted mesh method; Shishkin mesh; Discontinuous source term; Parameter-uniform.
  
  
• Stationary distribution and ergodicity of a stochastic single-species model under regime switching in a polluted environment  
  by Yu Zhao, Changsheng Zhai 
  Abstract: The long-term statistical rule is one of the important questions for stochastic pollution-population dynamicalmodels, thus itwould beworth looking for the stationary distribution as an indicator in analyzing the effects of toxicant and noises on the variation of population in evolution process. In present paper,we investigate a stochastic single-species model under regime switching in a polluted environment. By use of the ergodic of Markov chain and constructing Lyapunov function, the sufficient conditions for the positive recurrence and ergodic property are established, which imply the existence of stationary distribution of the model. Moreover, the mean and variance of marginal stationary distribution are estimated. Our analysis indicates that the colored noise and toxicant may play an important role in determining the shape of stationary distribution and its statistics characteristics. Finally, numerical simulations are carried out to support our theoretical results.
  Keywords: Environmental pollution; Regime switching diffusion;rnPositive recurrence; Ergodic property; Statistics characteristics.
  
  
• Multivariate generalized gamma kernel density estimators and application to nonnegative data  
  by Lynda Harfouche, Nabil Zougab, Smail Adjabi 
  Abstract: This paper proposes a classical multivariate generalized 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. Some properties (bias, variance and mean integrated squared error) of the corresponding estimators are also provided. The choice of the vecto of bandwidths is investigated by adopting the popular cross-validation technique. Finally, the performances of the classical and MBC estimator based on the family f GG kernels are illustrated by a simulation study and real data.
  Keywords: Asymmetric kernels; Bandwidth; Generalized gamma kernels; Generalized gamma distributions; Multiplicative bias correction; Multivariate estimation density.
  
  
• An Easy-to-use Computer Program for Standardisation Methods of Population Morbidity Data  
  by Suan Mei Ong, Wan Nor Arifin, Najib Majdi Yaacob, Nyi Nyi Naing 
  Abstract: Standardisation is an essential procedure to eliminate the effect of confounding when comparisons between populations are carried out, where a standard population is used as a reference. There are two methods of standardisation, i.e. direct and indirect standardisation. Standardisation is commonly used in epidemiology studies especially when the morbidity or/and mortality rates of a disease are studied. A computer program (StdAn) which aims to simplify the process of standardisation of population morbidity data was developed with Microsoft Visual Studio 2010 Express software, using C++/CLI (C++ on Common Language Infrastructure) as the programming language. StdAn program provides a graphically easy-to-use interface for the implementation of standardisation analysis. It is simple, practical and easy to interpret.
  Keywords: standardisation analysis; direct standardisation; indirect standardisation; standardised morbidity rate; computer program.
  
  
• Quartic Pad'e Approximation to the Exponential Function and a Class of Local Analytical Difference Schemes  
  by Cheng-De Zheng 
  Abstract: This paper investigates the quartic nondiagonal algebraic Hermite-Pad'e 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 Pad'e approximation for diffusion-convection equation with constant coefficients are proposed. A numerical example is provided to demonstrate the effectiveness of the theoretical results.
  Keywords: Pad'e-type approximant; Quartic Hermite-Pad'e approximation; Asymptotic formula; Diffusion-convection equation; Difference scheme.
  
  
• Hybrid Adaptive Random Testing  
  by Esmaeel 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; Test Data Generation.
  
  
• Approximate 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 analyzed. 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.
  
  
• Computation of Multi-Choice Multi-Objective Fuzzy Probabilistic Two Stage Programming Problem  
  by Prabhat Rout, Sudarsan Nanda, Srikumar Acharya 
  Abstract: The aim of the paper is to present a multi-choice multi-objective fuzzy proba-bilistic two-stage programming problem and its solution methodology. The math-ematical programming problem suggested here is dificult 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, ϵ-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; \epsilon-constraint; Multi-choice pro-rngramming; Least square approximation.
  
  
• 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 analyzed 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
   
  
• 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 factorizations 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 (regularized). An optimal Lagrange multiplier was deduced using the same idea of unconstrained optimization 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 Optimization; Trust Region Method; Armijo Line Search; Nonlinear System of Equations.
  
  
• 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.
  
  
• Proportional-Integral-Derivative Controller Parameter Optimization Based on Improved Glowworm Swarm Optimization Algorithm  
  by Xing Guo, Shichao Yin 
  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 Optimization 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 Optimization; Directed Moving; Adaptive Step Strategy; PID Controller.
  
  
• Computational analysis of magnetohydrodynamic mixed convection flow along vertical cylinder in the presence of aligned magnetic field  
  by Ashraf Muhammad, 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.
  
  
• A genetic-fuzzy control method for regenerative braking in electric vehicle  
  by Zhiqiang Liu, Shan Lu, Ronghua 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 analyzed. 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 optimization process was studied. Furthermore, the genetic algorithm was used to optimize 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 optimized 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.
  
  
• 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.
  
  
• 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.
  
  
• Power Control of Wind Energy Conversion System under Multiple Operating Regimes with Deep Residual Recurrent Neural Network: Theory and Experiment  
  by Zhongli Shen, Yuguang Niu, Yi Zuo, Qiyue Xie, Zhishen Chen 
  Abstract: This paper makes a research for the speed control of wind turbine system under multiple operating regimes with deep residual recurrent neural network method is studied in this work. We aim at designing deep residual recurrent neural network robust controllers that guarantee the existence of the multiple regime system poles in some predefined zone and wind speed precise tracking. Moreover, the feedback gains which guarantee desired speed tracking performance are obtained by solving the Lyapunov stability functions. The results are applied to a directly driven wind energy conversion experiment systems, and the numerical experiment comparing with the existing results shows the satisfactory performance of the proposed method.
  Keywords: Power Control; Wind Turbine System; Deep residual recurrent neural network; Multiple Operating Regimes.
  
  
• Theoretical Analysis of the Magnetic Field and Eddy Current Within a Rectangular Giant Magnetostrictive Material Plate  
  by Huifeng Liu 
  Abstract: In this paper, we present the distribution functions of magnetic field intensity and eddy current intensity within a rectangular magnetostrictive material plate. Firstly, on the basis of Maxwells theory, the mathematical model for the magnetic field within the plate is established. Then, the governing equation for determining the magnetic field is solved by Fourier transform, an error in R.L. Stolls book (1983) is corrected. Furthermore, the function of eddy current intensity is deduced. Next, the expressions of the magnetic field and eddy current are given in the case of the external magnetic field and the plate makes an angle of . Lastly, taking rectangular giant magnetostrictive plate is parallel to external magnetic field as example, the influencing factors of the inner magnetic field and eddy current are unveiled: the skin effect is weakened with the increase of the exciting frequency and is strengthened with the increase of the relative permeability or conductivity; the higher the relative permeability or conductivity, the more significant the eddy current density increases.
  Keywords: giant magnetostrictive material; rectangular plate; magnetic field; eddy current; Maxwell’s equations; Fourier transform.
  
  
• 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.
  
  
• Research on Chinese Well-known E-commerce Enterprises Innovation Ability Based on Real Comment  
  by Changbing JIANG 
  Abstract: Based on the real comments from Jingdong Mall, Tmall and other e-commerce platforms as for the data resource, according to the analysis to the customers online shoping comments vocabularies, obtained 11 indicators which are constituted e-commerce platforms customer satisfaction index system and the satisfaction indicator value, using the Projection Pursuit Model (PPM) and Genetic Algorithms to make a further calculation to satisfaction indicators. The results show that: which has the greatest impact on e-commerce platforms overall satisfaction is the product reputation and popularity; Jingdong Mall has the best overall satisfaction among Jingdong Mall, Tmall, and Amazon this three e-commerce platforms; Meanwhile, each e-commerce platform has its own superiority and weakness.
  Keywords: Comment; Word frequency statistics; Satisfaction indicator; Projection Pursuit; Genetic Algorithms.
  
  
• 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.
  
  
• Collaborative filtering algorithm based on multi-factors  
  by Chonghuan Xu, Jie Wang, Jiangjun Yuan 
  Abstract: Recommender systems are widely used to provide e-commerce users appropriate items and have emerged in response to the problem of information overload. Collaborative filtering (CF) is one of the most successful recommender methods which recommends items to a given user based on the opinions of the similar users. However, the existing CF methods lack the consideration of factors such as time and geo-location. In this paper, we take into account many influencing factors including time and geo-location in the process of similarity computation. The simulation results on two real-world data sets show that our algorithm achieves superior performance to existing methods.
  Keywords: Recommender systems;Collaborative filtering;Multi-factors.
  
  
• 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.
  
  
• Research on web page classification method based on Newton's law of universal gravitation and HITS algorithm  
  by Zhongbao Liu, Jing Zhang 
  Abstract: Web page classification is the most important mining method in web mining. In recent years, numerous classifiers are proposed and used for web page classification. Though these classifiers perform well in practice, they do not take the link connections between web pages into consideration, and therefore, their classification efficiencies cannot be greatly improved. In view of this, we propose a web page classification method based on Newton's law of universal gravitation and hypertext-induced topic search (HITS) algorithm (WPCM), based on which, we constructs the web page classification system. We use PKU collection, containing 13,897 web pages and 11 categories, in our experiments. The comparative experiments with the traditional classifiers, such as SVM and KNN, demonstrate that the proposed system including the classifier WPCM provides acceptable classification accuracy.
  Keywords: Newton's law of universal gravitation; HITS algorithm; web page classification; Fisher criterion; manifold learning.
  
  
• 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.