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International Journal of Computing Science and Mathematics

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 International Journal of Computing Science and Mathematics (103 papers in press)  Regular Issues  Group Acceptance Sampling Plans for Resubmitted Lots under Exponentiated Fr   by Srinivasa Rao Gadde Abstract: In quality control, we used to develop different types of sampling plans to ensure the quality of product lifetime. In this paper, we develop a group acceptance sampling plan (GASP) for lot resubmitting, to ensure the quality of product lifetime assuming that the product lifetime follows the exponentiated Fr Keywords: Resubmitted lot; group sampling plan; life test; producer’s risk; consumer’s risk;rnpercentile life.rn. Real-time online action detection and segmentation using Improved Efficient Linear Search   by Shiye Wang, Zhezhou Yu, Xiangchun Yu Abstract: More and more attention has been paid to linear-time online action detection and video segmentation, due to wide application in the fields of human-computer interaction, games and surveillance. In this paper we propose a new descriptor which can be adopted for action recognition, online action detection and segmentation. In addition, we propose the Improved Efficient Linear Search (Improved ELS) whose scheme is modified to solve the problem of the existence of many action classes maximum subarray sums exceeding their thresholds. Then we evaluated our approach on MSRC-12 and MSR-Action3D datasets. The results show that our descriptor achieves the state-of-the-art results on action recognition and the performance of the Improved ELS is much higher than that of the ELS. Keywords: linear-time; skeleton data; action recognition; action detection and segmentation; moving pose descriptor; Improved Efficient Linear Search. Parameter estimation for partially observed nonlinear stochastic system   by Chao Wei, Chaobing He Abstract: This paper is concerned with the parameter estimation problem for partially observed nonlinear stochastic system. The suboptimal estimation of the state is obtained by constructing the extended Kalman filtering equation. The likelihood function is provided based on state estimation equation. The strong consistency of the estimator is proved by applying maximal inequality for martingales, Borel-Cantelli lemma and uniform ergodic theorem. An example is provided to verify the effectiveness of the method. Keywords: nonlinear stochastic system; state estimation equation; parameter estimation; strong consistency.DOI: 10.1504/IJCSM.2017.10009084  Numerical solution of fuzzy differential equations using orthogonal polynomials   by Smita Tapaswini, Snehashish Chakraverty Abstract: Present paper proposed a new method to solve n-th order fuzzy differential equations using collocation type of method. In the solution procedure, Gram Schmidt orthogonalisation process is used with Legendre and Chebyshev polynomials. Known example problems are solved and compared with the exact results to illustrate the efficiency and reliability of the proposed method. Keywords: Fuzzy number; Triangular fuzzy number; Legendre polynomial; Chebyshev polynomial; n-th order fuzzy differential equations. A Spline Based Computational Technique Applicable for Solution of Boundary Value Problem Arising in Human Physiology   by Pankaj Srivastava Abstract: Nonpolynomial quintic spline functions based algorithms are used for computing an approximation to the non-linear two point second order singular boundary value problems arising in human physiology. After removing the singularity by L hospital rule, the resulting boundary value problem is then efficiently treated by employing nonpolynomial quintic spline for finding the numerical solution. Two examples have been included and comparison of the numerical results made with cubic extended B-spline method and finite difference method. Keywords: Nonpolynomial quintic spline; Nodal points; Singular boundary value problem; System of equations; Maximum absolute error. Bifurcation analysis of H   by Ilham Djellit, Wissame Selmani Abstract: The dynamic behaviour of a dynamical system, described by a planar map, is analytically and numerically explored. We examine analytical conditions for stability and bifurcation of the fixed points of the system and by using numerical methods, we compute bifurcation curves of fixed points and cycles with orders up to 5 under variation of three parameters, and compute all codimension 1 and codimension-2 bifurcations on the corresponding curves. These curves form stability boundaries of various types of cycles which emanate around codimension-2 bifurcation points. Mathematical underpinnings and numerical simulations confirm our results and contribute to reveal further complex dynamical behaviours. Keywords: Codimension-2 Bifurcations; Blumberg’s dynamics; Fold and flip bifurcation curves; Diffeomorphism; Embedding.DOI: 10.1504/IJCSM.2017.10007951  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. T*: A Weighted Double-heuristic Search Algorithm to Find the Shortest Path   by Mohammad Samadi Gharajeh Abstract: This paper proposes a weighted double-heuristic search algorithm to find the shortest path between two points. It can be used in numerous fields such as graph theory, game theory, and network. This algorithm, called T*, uses a weighted and heuristic function as f(x) = α × t(x) + β × h1(x) + γ × h2(x). It selects the path which minimises f(x) where x is a current node on the path, t(x) is cost of the path from start to x, h1(x) is a heuristic to estimate the cost from x to the straight line passing through start and target, and h2(x) is a heuristic to estimate cost of the cheapest path from x to target. Furthermore, α, β, and γ indicate effective weights of each sub-function on f(x). T* algorithm is compared to the Greedy and A* algorithms in terms of hit rate and the number of processed nodes. Comparison results show that the proposed algorithm has a high efficiency compared to the other algorithms. Keywords: Shortest Path; Search Algorithm; Weighted Strategy; Double-heuristic Function; Graph Theory. Analytical solutions to nonlinear problems by the generalized form of HAM : A note   by Anant Kant Shukla, Tumkur R. Ramamohan, Suripeddi Srinivas Abstract: The objective of this article is to obtain analytical solutions for a set of nonlinear problems by using Further Generalization of HAM\'\'. In comparison to the Homotopy analysis method (HAM) solutions, more accurate solutions are obtained by introducing an extra term in the frame of HAM. We consider a set of three nonlinear problems of which first two are governed by single nonlinear Ordinary differential equation (they are two cases of the forced Van der Pol Duffing oscillator) and third one is governed by a system of four coupled nonlinear Ordinary differential equations. A maximum reduction of approximately 25% in the square residual error is obtained by using the generalized form of HAM compared to the square residual error without the generalized form. Keywords: Further generalization of HAM; Homotopy analysis method; Square residual error.DOI: 10.1504/IJCSM.2017.10010878  Dynamic Navigation of Web Query Results Using B-tree and Improved Page Rank Algorithm   by Lakshmi Lingutla Abstract: Most of the web search queries submitted by users are short, uncertain and ambiguous. The size of World Wide Web is increasing, as millions of web pages are added to it every day. The information retrieval process is very complicated today as it depends on many factors like classification of a web query, number of phrases present in the query, number of in-links to the documents, the number of out-links from the document, vocabulary, changing nature of the meaning of words, and number of times each phrase appears in a document. Information retrieval process mainly involves two steps, retrieval of relevant documents for user queries and retrieved documents are sorted using efficient page rank algorithms. Most of the existing systems use static navigation of the web query and ranking. They mainly depend on the number of in links and out links of a web page due to which they produce more number of non-relevant documents. In this paper, we proposed dynamic navigation of web query using B-tree navigation method to retrieve relevant documents efficiently by reducing non-relevant documents and resulting documents are sorted by using an improved page rank algorithm. The main objective of this paper is to retrieve most appropriate results for the given query, by reducing time taken to retrieve web pages and reducing the number of non-relevant web pages. Keywords: Web query classification; Session log; Categorization factor; Dynamic navigation; Unique visit count; Distance. Mathematical Model of Childhood Diseases Outbreak with Optimal Control and Cost Effectiveness Strategy   by Kazeem Okosun, Oluwole Makinde Abstract: In this paper, we derive and analyze a deterministic model for the transmission of childhood disease perform optimal control analysis of the model. The model is found to exhibit multiple equilibria. However, a unique endemic equilibrium exists when there is no disease induced death. We also derive and analyze the necessary conditions for the optimal control of the disease. In addition, we investigate the cost-effectiveness of the controls to determine the most effective strategy to control childhood disease with minimum costs. Finally, we present the numerical solutions. Keywords: Childhood coverage; Sensitivity indices; diseases; Epidemiological model; Vaccination. 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. The new exact analytical solution and numerical simulation of (3 + 1)-dimensional time fractional KZK equation   by Lanfang Zhang, Juanjuan Ji, Julang Jiang, Chaolong Zhang Abstract: The KZK parabolic nonlinear wave equation is one of the most widely employed nonlinear models for propagation of 3D diffraction sound beams in dissipative media. In this paper, the exact analytical solutions of (3+1)-dimensional time fractional KZK equation have been constructed in the sense of modified Riemann-Liouville derivative and the (G'/G)-expansion method , the simplest equation and the fractional complex transform. As a result, some new exact analytical solutions are obtained, and the effects of diffraction, attenuation and nonlinearity are researched deeply using the obtained exact analytical solutions. Keywords: (3+1)-dimensional time fractional KZK equation; diffraction; attenuation; nonlinearity; fractional complex transform; numerical simulation. 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. Using kernel based collocation methods to solve a delay partial differential equation with application to finance.   by Mojtaba Moradipour, Hossein Azari Abstract: We consider a delay partial differential equation arising in a jump diffusion model of option pricing. Under the mean--reverting jump--diffusion model, the price of options on electricity satisfies a second order partial differential equation. In this paper, we use positive definite kernels to discretize the PDE in spatial direction and achieve a linear system of first order differential equation with respect to time. We impose homogeneous boundary conditions of the PDE by using a manipulated version of kernels called recursive kernels''. The proposed methods are fast and accurate with low computational complexity. No integrations are necessary and the time dependent system of differential equations can be solve analytically. Illustrative example is included to demonstrate the validity and applicability of the new techniques. Keywords: Positive definite kernels; collocation methods; mesh free methods; jump diffusion models; option pricing.DOI: 10.1504/IJCSM.2017.10015433  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. A novel Computation Method for 2D Deformation of Fish Scale based on SURF and N-R Optimization   by Guihua Li, Pengxiang Ge Abstract: Fish scales were unique structural materials that served as a form of natural armor and affected the mechanical properties which had important applications in bionics. Digital Image Correlation (DIC) method was used to determine the mechanical properties, but it took a long time to calculate the uniaxial tensile deformation. In this investigation a DIC optimization algorithm method based on Speeded-Up Robust Features (SURF) and Newton-Raphson (N-R) was conducted on specimens prepared from the scales. First, the SURF algorithm was used to detect the matched points and their coordinate values in the digital images before and after deformation. Then, the initial displacement of the interest point was estimated from the affine transformation fitted to the matched feature points inside the subset area. Last, the Zero-mean Normalized Sum of Squared Differences (ZNSSD) metric function was optimized by the N-R iterative method, and the optimized displacement value of the interest points would be gained. The numerical translation experiments and simulation results showed that this method improved the search speed and the measurement accuracy effectively. So the deformation of fish scales for axial tension would be calculated by this method. Keywords: Tensile deformation; DIC; SURF algorithm; N-R Algorithm; Fish scale. 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. Respondents View of Novel Framework for Data Protection in Social Networking Sites: An Analysis   by Shilpi Sharma Abstract: The era of social networking technologies has been met with mixed reactions by every user around the world. This study explored the novel framework for data protection at users level in social networking site. The study followed a descriptive research design wherein a questionnaire was used as the main research tool. The data collected was analyzed using SPSS 19. Data was gathered from 300 users and analyzed in accordance with the objectives of the study. As we know that concerns rose about the disclosure of personal information on social network sites, users continue to disclose huge quantity of personal information. They find that reading privacy policy is time consuming and changes made can result into improper settings. The analysis of the results concludes that the novel framework satisfies the requirements and the needs to secure user data in a platform i.e. social media. The members of website have appreciated the implementation that lay towards graphical authentication, watermarking feature, encryption technique, approval of friendship request prior to approval and consent of service provider while processing or sharing information for data protection in social networking sites. Keywords: Social Networking Sites; Information Disclosure; Privacy Setting; Authentication; Significance. 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. Further results on the generalized hypergeometric matrix functions   by Mohamed Abdalla Abstract: In recent years, various results of the special matrix functions have been established in many papers. In the present paper, we have developed certain properties involving generalized hypergeometric matrix functions, such as, integral representations and reduction formulae. Also, open problems concerning the generalized hypergeometric matrix functions are stated. Keywords: The generalized hypergeometric matrix function; Integral form; Reduction formulae. 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. Arabic language and Knowledge reduction in Formal Contexts   by Issam Sahmoudi Abstract: Formal Concept Analysis (FCA) is a mathematical tool that offers conceptual data for knowledge representation, extraction and analysis with applications in different areas. One of the main problem of FCA is the computational cost due to the large number of formal concepts generated. The objective of this paper is to address this problem by reducing the knowledge in formal contexts, we propose to use a linguistic approach, and we apply this latter for Arabic language. Objective Performance Evaluations are conducted to assess the efficiency of our proposed method. Keywords: Formal Concept Analysis;Formal Contexts;Knowledge Reduction. 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. Inclusion properties of Hypergeometric functions in some class of analytic functions   by Satwanti Devi Abstract: The aim of the present paper is to determine the sufficient conditions on real parameters, so that the sequence formed by the coefficients of Hypergeometric functions are convex decreasing. Interesting consequences of the results are also provided, which establish the mapping of the geometrical properties of Hypergeometric functions onto the class of analytic functions defined by R. M. Ali et al. in 2012 cite{Abeer S*}. Keywords: Analytic function; Confluent hypergeometric function; Convolution operator; Gaussian hypergeometric function. 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 dificult 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. Impulsive control on a nonautonomous dispersal almost periodic competition system   by Liyan Pang Abstract: This paper gives some new sucient conditions for the uniform persistence, global asymptotical stability and almost periodic solution to a nonautonomous dispersal competition system with impulsive e ects. The main results of this paper extend and improve some corresponding results in recent years. And the method used in this paper provides a possible method to study the uniform persistence, global asymptotical stability and almost periodic solution of the models with impulsive perturbations in biological populations. Keywords: Uniform persistence; Dispersal competition system; Comparison theorem; Almost periodicity; Impulse. 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. Hydromagnetic chemically reacting and radiating unsteady mixed convection Blasius flow past surface flat in a porous medium   by Oluwole Daniel Makinde, Adetayo Samuel Eegunjobi, Onesmus Shuungula, S.N. Neossi-Nguetchue Abstract: We investigate numerically in this paper, the mutual effects of thermal radiation, magnetic field and buoyancy forces on mixed convection of an electrically conducting chemically reacting incompressible viscous fluid flow over a heated vertical flat surface embedded in a porous medium. Suitable governing equations are obtained and changed to a system of couple nonlinear ordinary differential equations using desirable transformations. Boundary valued problems are therefore solved numerically using the Runge-Kutta-Fehlberg integration procedure coupled with shooting method. The results of the dimensionless velocity, temperature and concentration are then used to compute the skin friction, Nusselt number and Sherwood number. The influences of some of the flow parameters on each of these results are put up graphically and analysed. Keywords: unsteady MHD; Blasius flow; mixed convection; porous medium; chemically reacting; thermal radiation.DOI: 10.1504/IJCSM.2018.10017531  Multi-objective classification based on NSGA-II   by Binping Zhao, Yu Xue, Bin Xu, Tinghuai Ma, Jingfa Liu Abstract: The fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II) is currently the most popular multi-objective evolutionary algorithm (MOEA). NSGA-II has been shown to work well for two-objective problems by attaining near-optimal diverse and uniformly distributed Pareto solutions. To use the powerful multi-objective optimisation performance of NSGA-II directly and conveniently, an optimisation classification model is presented. In the optimisation classification model, a linear equation set is constructed according to classification problems. In this paper, we introduced NSGA-II to solve the optimisation classification model. Besides, eight different datasets have been chosen in experiments to test the performance of NSGA-II. The results show that NSGA-II is able to find much better spread of solutions and has high classification accuracy and robustness. Keywords: evolutionary classification algorithm; non-dominated sorting genetic algorithm-II; NSGA-II; multi-objective; optimisation.DOI: 10.1504/IJCSM.2018.10017540  Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction   by Yun Niu, Zeyu Tian, Maoqing Zhang, Xingjuan Cai, Jianwei Li Abstract: Two-support vector machine is a new prediction model for software defect. For this model, one multi-objective oriented cuckoo search is designed to optimise several objects simultaneously to improve the defect accuracy, and the ratio of dataset plays an important role to determine the number of big/small modules. In this paper, we provide one extension for the multi-objective oriented cuckoo search, so that it can also adaptive optimise this ratio. Simulation results show our modification achieves the best performance when compared with two other software defect prediction models. Keywords: software defect prediction; support vector machine; SVM; multi-objective oriented cuckoo search.DOI: 10.1504/IJCSM.2018.10017541  Variation of wind speed distribution characteristics across Indian sub-continent   by D.C. Kiplangat, G.V. Drisya, K. Asokan, K. Satheesh Kumar Abstract: Knowledge of wind speed and its characteristics is important for the optimising the production and transmission of wind power generated by wind mills. In this paper, we analyse the frequency component level distribution characteristics of wind speed variations at the locations across Indian sub-continent. The results show significant variation of the distribution behaviour among frequency components. We also analyse effect of location, seasonal variation and temporal position in the solar cycle. The skewness and kurtosis show clear scaling behaviour against latitude showing a decrease as location approaches equator. The impact of solar cycle is more predominant at the locations closer to the equator. The seasonal impact on distribution characteristics is also the evident and more prominent at the locations closer to the equator in solar maximum year. Keywords: Weibull distribution; wind speed; solar cycle; wavelet decomposition.DOI: 10.1504/IJCSM.2018.10017530  Ladle health monitoring system based on LabVIEW   by Wenjun Chang, Ying Sun, Gongfa Li, Guozhang Jiang, Jianyi Kong, Du Jiang, Honghai Liu Abstract: Ladle is the carrier of steel production, and it plays an important role in the production of steel. So it is very important to monitor the production status and the design of the fault diagnosis system in time, which will be beneficial to the improvement of the steel production efficiency. Writing data acquisition interface by using the LabVIEW software and realising the function of data acquisition based on sensor and data acquisition card. The database of the ladle monitoring system is designed, and the temperature data and the data of the stress and the volume of the ladle are collected by the ladle monitoring system. Based on the research of the monitoring system module, the system design of the fault diagnosis on the LabVIEW software platform is based on the signal of the working layer, the permanent layer and the shell. Keywords: ladle; labview; signal acquisition; monitor; fault diagnosis.DOI: 10.1504/IJCSM.2018.10017532  Pulmonary nodules computer-aided diagnosis based on feature integration and ABC-LVQ network   by Qing-Shan Zhao, Guo-Hua Ji, Yu-Lan Hu, Guo-Yan Meng Abstract: For the computer aided diagnosis of lung cancer, a malignancy identification method based on multi-feature integration and learning vector quantisation (LVQ) network optimised by artificial bee colony (ABC) is proposed in this work. Firstly, the traditional features and the hidden features learned by Sparse Autoencoder of nodules are respectively extracted, and then the canonical correlation analysis (CCA) is used for feature integration. For classification, the ABC algorithm is used to optimise the LVQ network to overcome its sensitivity to initial value. Finally, the integrated features of nodules are input into the optimised classifier and the diagnosis results are obtained. Experimental results on LIDC pulmonary nodule image datasets show that this method can effectively identify the malignancy of nodules, with the area under the receiver operating characteristic (ROC) curve (AUC) of 0.90, 0.83, 0.80, 0.80, 0.85 for nodules of malignancy 1-5 classification, respectively. Keywords: pulmonary nodules; computer aided diagnosis; learning vector quantisation network; LVQ; artificial bee colony algorithm; ABC; feature integration.DOI: 10.1504/IJCSM.2018.10017537  Distance-based facility location problem for fuzzy demand with simultaneous opening of two facilities   by Ashish Sharma, Ashish Sharma, Anand Singh Jalal Abstract: In the real world there are so many businesses for which the major concern is the location of facility/store so that they can satisfy the demand in an efficient manner. Therefore the companies perform extensive survey for the finding the right location before the setup of facility/store. These surveys generate the probabilistic data. In the light of these real life aspects we developed a distance based on the facility location problem (FLP) for the fuzzy demand. The distance between the customer and the facility is incorporated in the form of constraints. Model is applied over different defuzzification methods and results are compared. Results are also obtained for the option of simultaneous opening of two locations. Results show that, sometimes the two facility opening option provides the better results as compared to one. Solution procedure is provided. Numerical example is presented in order to briefly explain the model using LINGO. Keywords: facility location problem; FLP; fuzzy demand; region search; distance-based FLP.DOI: 10.1504/IJCSM.2017.10011186  Research on the optimisation of flight landing scheduling with multi-runway   by Yunfang Peng, Qing Tang, Yajuan Han Abstract: Flight scheduling is the foundation and core of all the activities of airports and airlines. This paper analyses the problem of flight landing scheduling with multi-runway and establishes mathematical model. The aim is to determine the aircraft landing sequence and the landing time with the objective to minimise the landing time deviation cost. A new heuristic approach called cost decision algorithm (CDA) is present to solve the problem. This method selects the runway and decides the landing time by comparing the costs. With a series of experiments of different scale data, the mathematical model is solved by CDA and other algorithm. The computational results demonstrated the feasibility and superiority of the proposed algorithm by comparing with other algorithms in solutions and running time. Keywords: flight landing scheduling; multi-runway mathematical model; cost decision algorithm; CDA.DOI: 10.1504/IJCSM.2018.10017539  Energy consumption component analysis mathematical model of grinder energy unit   by Yan Zhou, Hua Zhang, Wei Yan, Feng Ma, Gongfa Li, Wenjun Chang Abstract: Aiming at the characteristics of grinding machine with many energy sources, this paper studied the energy consumption of grinding machine from the aspect of energy consumption, analysed the energy consumption of each energy consumption unit of grinding machine, based on the energy balance equations of each energy consumption unit of grinder, the mathematical model of energy consumption component analysis of energy consumption unit is established. The model can provide a new theoretical method for energy efficiency analysis and prediction in the process of grinding machine. At the same time, it builds multi-source information fusion between machine tool and workpiece to support the production process decision-making, which is good for the manufacturing industry to become intelligent and green and the direction of development. Keywords: grinding machine; multi energy source; energy efficiency; modelling and simulation.DOI: 10.1504/IJCSM.2018.10017533  Research on evaluation method of cigarette manufacturing process capability   by Jianhong Cao, Xu Kong, Qi Ji, Min Zhang Abstract: From the many varieties of cigarette production, production capacity matching, forward-looking and practical perspectives, use the 'expert scoring method' (part integration of the Delphi method) to quantify the evaluation leaf production, manufacture silk leaves, silk leaf expansion, stem silk production, mixing station and other typical blending process; according to the cigarette manufacturing process capability evaluation of technical standards, identify the characteristics and quality control parameters essential items of Hangzhou cigarette factory silk step is 21, use the analytic hierarchy process to analyse and determine the evaluation system weight, build the comprehensive evaluation model process of cigarette production capacity; based on MES system development process, the ability to rapidly evaluation system, so that the information platform for both fully formulated traditional processing and packet processing mode, the full process capability assessment of the production process continued optimisation and product quality steady improvement in guidance. Keywords: cigarette; process capability; evaluation; leaf production.DOI: 10.1504/IJCSM.2018.10017538