International Journal of Computing Science and Mathematics (107 papers in press)

Regular Issues

• DETECTION OF BRAIN TUMOR BY USING MOMENTS AND TRANSFORMS ON SEGMENTED MAGNETIC RESONANCE BRAIN IMAGES  
  by RAHUL UPNEJA, AJAY PRASHAR 
  Abstract: Brain tumor occurs when abnormal cells appear within the brain. Primary tumor starts with abnormal growth of brain cells whereas Secondary (Metastatic) tumor initiates as cancer in other parts of the body and spread to the brain through blood stream. In this paper, we propose a novel approach to detect tumor in Magnetic Resonance (MR) brain images. The proposed method uses Improved Incremental Self Organize Mapping (I2SOM) to segment the brain image and to calculate asymmetry Zernike Moments (ZMs), Pseudo-Zernike Moments (PZMs) and Orthogonal Fourier Mellin Moments (OFMMs) are used. It generates global and geometric feature set of an image and it omits the limitation of previous method of taking only one tissue under consideration while calculating asymmetry. The effectiveness of the proposed method is analyzed by doing experiments on 30 MR brain images with tumor and 30 normal MR brain images. It is observed that tumor detection is successfully realized for 30 MR brain images with tumor.
  Keywords: Tumor detection; Zernike Moments; Pseudo-Zernike Moments; Orthogonal Fourier Mellin Moments; Polar Harmonic Transforms; Segmentation.
  
  
• An 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.
  
  
• 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.
  
  
• 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.
  
  
• An Efficient Resource Deployment Method for Steam-based Stochastic Demands in Distributed Cloud Platforms  
  by Yang Liu, Wei Wei 
  Abstract: It has been a consensus that deploying geographically dispersed stream-based online services into distributed cloud platforms has gained exceptional advantages. Globally visiting services make user requests characterized with dramatic fluctuation, which introduces stochastic demands for various resources. In order to maximize satisfied user requests and guarantee Quality-of-Service under given expense budget, efficient resource deployment becomes the key to this problem. We propose a stochastic demand oriented resource deployment method with more profits and less time complexity. Experiments using simulated and realistic data indicate that proposed method can outperform existing algorithms by increasing the weighted summation of satisfied demands up to 37%, fit for all scenarios with heterogeneous distributed cloud resources.
  Keywords: Resource Deployment; Differential Evolution; Stochastic demand; Heterogeneous clouds.
  
  
• Asymmetric Convolution with Densely Connected Networks  
  by Liejun Wang, Huanglu Wen, Jiwei Qin 
  Abstract: Convolutional neural networks are vital to some computer vision tasks, and the densely connected network is a creative architecture among them. In densely connected network, most convolution layer tends to have a much larger number of input channels than output channels, making itself to a funnel shape. We replace the 3x3 convolution in the densely connected network with two continuous asymmetric convolutions to make the DenseNet famliy more diverse. We also proposed a model in which two continuous asymmetric convolutions each outputs half of the output channels and concatenate them as the final output of these layers. Compared with the original densely connected network, our models achieve similar performance on CIFAR-10/100 dataset with fewer parameters and less computational cost.
  Keywords: Densely Connected Network; Asymmetric convolution; Concatenation.
  
  
• Research on Travel Network Structure Based on Normalized Laplacian Spectrum  
  by Yang Sun, Hanhan Deng, Ling Zhao, Sumin Liu, Zhenshi Zhang, Ronghua Du, Zhixiang Hou 
  Abstract: To study the residents Trip-Acitivity Chain and Patterns, we start from the residents Trip-Acitivity network. The normalized Laplacian spectrum from the local metric features described the topological structure information of network with the statistical analysis method of complex network.The statistical analysis of complex networks failed to show the global information of the network and described the network structure more completely, intuitively and conveniently. The normalized Laplacian spectrum is used to portray the subway network, the airline network and the macro-laws of the network from residents travel. In this paper, We analysis other networks and classify networks qualitatively. The results proved that the normalized Laplacian spectra is an efficient tool for analyzing macro-structural or micro-structural features of geographic networks.
  Keywords: Travel network structure; Normalized Laplacian spectrum; Resident travel chain; Global topology.
  
  
• Dynamic Multi-Swarm Pigeon-Inspired Optimization  
  by Yichao Tang, Bo Wei, Xuewen Xia 
  Abstract: Pigeon-inspired optimization (PIO) has shown favourable performance on global optimization problems. However, it lacks the part of individual experience, which makes it prone to premature convergence when solving multimodal problems. Moreover, the landmark operator model in PIO may cause the population size to decrease too quickly, which is harmful for exploration. To overcome the shortcomings, a dynamic multi-swarm pigeon-inspired optimization (DMS-PIO) is proposed in this research. In PIO, the entire population is divided into multiple swarms. During the evolutionary process, the size of each swarm can be dynamically adjusted, and the multiple swarms can be randomly regrouped. Relying on the dynamic adjustment of swarms sized, exploration and exploitation are balanced in the initial evolutionary stage and last stage. Furthermore, the randomly regrouping schedule is used to keep the population diversity. To enhance the comprehensive performance of PIO, the map and compass operator and the landmark operator in it are conducted alternately in each generation. Experimental results between DMS-PIO and other five PIO algorithms demonstrate that our proposed DMS-PIO can avoid the premature convergence problem when solving multimodal problems, and yields more effective performance in complex continuous optimization problems.
  Keywords: pigeon-inspired optimization; dynamical swarm sized; randomly regrouping schedule; continuous optimization problems.
  
  
• Technology Enterprise Value Assessment Based on BP Neural Network  
  by Xiangtian Xie 
  Abstract: Proposed a technology enterprise value assessment model based on BP neural network, considering that the technology enterprise has the characteristics of asset-light and high growth, whose value is difficult to evaluate. The model not only includes the non-financial indicators with intellectual property and the financial indicators with performance, but also has the advantages of artificial intelligence. Through analyzing the model, it can be seen that increasing the intellectual property indicators can reduce the assessment deviation and the traingda is optimal in the negative gradient learning functions.
  Keywords: technology enterprise; value assessment; BP neural network.
  
  
• Numerical Simulation of the Black-Scholes Equation using the SPH method.  
  by Abdelmjid Qadi El Idrissi, Boujemaa Achchab, Abdellahi Cheikh Maloum 
  Abstract: In this paper we present a numerical method for solving the European options (Call and Put) using the Black-Scholes model. The numerical method considered is based on the SPH method. SPH is one of the most popular and efficient numerical schemes used in the approximation of partial differential equations particularly in fluid dynamic. Before applying SPH method, the Black-Scholes equation needs to be written into the heat equation. With this form, the numerical resolution of the Black-Scholes equation is further simplified and ensures the stability of the scheme. Numerical experiments were performed for different financial parameters. We investigate the accuracy of the numerical method proposed by given some comparisons between analytical and numerical computation.
  Keywords: Black Scholes equation; European option; SPH Method.
  
  
• Portfolio Optimization with Cardinality Constraint based on Expected Shortfall  
  by Ezra Putranda Setiawan, Dedi Rosadi 
  Abstract: Abstract: Assets diversification is a well-known strategy to reduce the investment risk and become a mathematical problem since Markowitzs work in 1952. In this paper, we investigated the portfolio selection method using Expected Shortfall (ES), which also known as Expected Tail Loss (ETL) or Conditional Value-at-Risk (CVaR), as a risk measure. A cardinality constraint was added to the model in order to help the investor choose k from n available assets into the portfolio, where k is higher than the lower bound L and smaller than the upper bound U. To solve this complex portfolio optimization problem, we use the genetic algorithm method with binary chromosomes and obtain the optimal weight using exact method. A numerical case-study is provided using several stocks in Indonesia Stock Market.
  Keywords: genetic algorithm; portfolio; cardinality constraint; expected shortfall.
  
  
• Optimization and Application Research of Ant Colony Algorithm in Vehicle Routing Problem  
  by Lede Niu, Liran Xiong 
  Abstract: In this paper, an improved ant colony algorithm based on ant system is proposed in order to solve vehicle routing problem. When choosing the path, the 2-opt method is used to explore the reasonable selection of the parameters of the algorithm for vehicle routing problem taking the path savings among customers as heuristic information. the performance of ant colony algorithm is affected by the information heuristic factor α, expectation heuristic factor β and pheromone volatile factor ρ. The method breaks through empirically setting the ant colony algorithm parameter values. By calculation, the optimal parameters of the ant colony algorithm in solving the vehicle routing problem are: α∈[1.0,1.7],β∈[4.5, 8.5],ρ∈[0.5,0.6]. At last, an exploration is established to find the optimal solution by combining three parameters and the ant colony algorithm will have a better effect in the actual optimization problem.
  Keywords: Ant colony algorithm; vehicle routing problem; parameter selection.
  
  
• Multidimensional Portfolio Risk Measurement: A Mixed Copula Approach  
  by Wenli Cai, Na Liu, Yuxuan Wu, Xiangdong Liu 
  Abstract: It is an increasingly challenging task to explore the risk measurement for multidimensional portfolios with nonlinear correlative assets. A risk measurement scheme based on the mixed copula theory is proposed in this paper, where the mixed copula is constructed by the linear combination of three single Archimedean copulas, embodying greater flexibility than single copula in connecting different types of marginal distributions. In the scenario, ARMA-EGARCH model with t innovation is employed to fit marginal distributions, and the parameter values of the mixed copulas is inferred by maximum likelihood estimation (MLE) method, and interior point algorithm is used to calculate the extreme values of the MLE and VaR and CVaR, corresponding to the optimal portfolio with the minimum risk. Finally, an empirical study on five international stock market indexes in Europe is performed to verify the feasibility and effectiveness of the scheme.
  Keywords: ARMA-EGARCH; CVaR; Mixed Copula; Risk Measurement.
  
  
• Joint Estimation of battery state-of-charge based on the Genetic Algorithm - Adaptive Unscented Kalman Filter  
  by Zhixiang Hou, Jiqiang Hou 
  Abstract: In order to effectively improve the accuracy of SOC estimation and overcome the problems that the conventional Kalman filter algorithm relies too much on an accurate battery model and the system noise must obey the white Gaussian noise distribution, a joint estimation method of battery state-of-charge based on genetic algorithm - adaptive unscented Kalman filter (GA-AUKF) is proposed in this paper. Firstly, in order to accurately simulate the working mechanism of a battery and express the relationship between the main parameters concerning the battery, on-line identification of model parameters is performed in this paper through the forgetting factor recursive least-squares (FFRLS) algorithm based on second-order equivalent circuit model. Secondly, in order to weaken the effect of system noise and measurement noise on the accuracy of SOC estimation, the genetic algorithm is adopted to optimize and update the adaptive unscented Kalman filter noise matrix. Finally, FFRLS is combined with GA-AUKF for SOC estimation. Experimental results show that the method proposed in this paper is obviously better than the AUKF algorithm and others in estimation accuracy, and it can effectively reduce the effect of filter noise covariance and improve the estimation accuracy with an estimation error less than 1%.
  Keywords: Lithium battery; state-of-charge; battery model; unscented Kalman filter; joint estimation.
  
  
• An ant colony optimization based approach to solve time interval dependent travelling salesman problem under fuzziness  
  by Chiranjit Changdar, KOUSIK DHARA, RAJAT PAL, PRAVASH GIRI 
  Abstract: In this study, we have explained a constrained travelling salesman problem (TSP) where total travelling cost must maintain a maximum level. The objective of this proposed TSP is to minimize the total travel time. In this proposed TSP we have considered a time interval dependent constraint as well. There is a time interval in which a traveller must visit a predetermined set of cities (city-set). Here, a city-set consists of a set of cities and the time interval is a time slot in his/her total time to complete the tour. The problem is solved in fuzzy random environment. The travel cost, time, and total travelling cost limit are considered as fuzzy random in nature. The proposed TSP is solved by an Ant Colony Optimization (ACO) based approach. The basic ACO algorithm is improved by adopting a filtering operation. Finally, experimental results are given to illustrate the proposed approach; the computed results obtained are also highly encouraging.
  Keywords: Travelling Salesman Problem; Ant Colony Optimization; City-set; Filtering Operation; Fuzzy Random Number.
  
  
• An optimal class of fourth-order multiple-root finders of Chebyshev-Halley type and their basins of attraction  
  by Raj Bala, Munish Kansal, Vinay Kanwar 
  Abstract: In this paper, we propose a family of optimal fourth-order of Chebyshev-Halley type methods free from second-order derivative for finding the multiple roots. The new methods are tested and compared with other well- known methods on the number of academical test functions. For quantitative comparison, we have also computed total number of convergent points and convergent percentages, average number of iterations per convergent points and CPU time (in seconds) along with the basins of attraction on number of test problems to recommend the best optimal fourth-order method. We also consider a concrete variety of real life problems such as the trajectory of an electron in the air gap between two parallel plates, Van der Waals equation which explains the behaviour of a real gas by introducing in the ideal gas equation, in order to check the applicability and effectiveness of our proposed methods.
  Keywords: Nonlinear equations; Chebyshev-Halley type methods; Multipleroots; Efficiency index; Optimal order of convergence; Basins of attraction.
  
  
• Anti-Synchronization of Nonidentical Fractional Order Hyperchaotic Systems  
  by Abedel-Kareem Alomari, Mohammad Al-Jamal, Nedal Tahat 
  Abstract: This paper deals with the anti-synchronization of fractional order hyperchaotic systems. In particular, we employ the active control method to achieve complete anti-synchronization between fractional order Lorenz and Chen hyperchaotic systems. Based on stability theorems in the fractional calculus, analysis of stability is performed for the proposed method. Numerical simulations demonstrate the feasibility of the proposed algorithm.
  Keywords: active control; anti-synchronization; fractional hyperchaotic systems.
  
  
• Study on the hydrodynamic behavior of journal bearing with herringbone grooved sleeve considering cavitation  
  by He Qiang 
  Abstract: This work proposes the development of a hydrodynamic model for journal bearing with herringbone grooved sleeve considering cavitation. The equations of motion for this system are obtained by CFD method and the effects related to hydrodynamics, friction and lubrication. The simulation model used in this paper is based on the multiphase flow model, considering the effects of cavitation on the pressure distribution of oil-film. The proposed model is further verified by the results of published experiment data. Further analysis reveals that the eccentricity ratio, rotational speed, groove depth and number of groove have a great influence on the hydrodynamic behavior of herringbone grooved journal bearing. Therefore, it is necessary to develop a more realistic simulation model of journal bearing with herringbone grooved sleeve that provide a reference for the journal bearing design.
  Keywords: journal bearing; herringbone grooved; hydrodynamic analysis; CFD； multiphase flow model.
  
  
• Combination of Neural Network Model for Enterprise Accounting Information Quality Assessment  
  by Yajing Hao 
  Abstract: In order to evaluate the quality of accounting information, this paper tries to find an effective evaluation method and construct a reasonable and scientific evaluation model, and puts forward an evaluation method of enterprise accounting information quality combined with neural network model. By evaluating the quantity and quality of enterprise accounting information, the dimension of evaluation matrix is determined, and the evaluation matrix is generated in real time. On this basis, a neural network model is constructed to improve the accuracy and adaptability of enterprise accounting information evaluation. Finally, the simulation results show that the applicability and superiority of the model can provide reference for the evaluation of enterprise accounting information quality.
  Keywords: Neural network model; enterprise accounting; information quality; assessment matrix.
  
  
• Bifurcation and Stability of a dynamical system with threshold prey harvesting  
  by Imane AGMOUR, Meriem BENTOUNSI, Naceur ACHTAICH, Youssef EL FOUTAYENI 
  Abstract: In this study, a predator-prey interaction model with Holling type II functional response is studied. As the continuous threshold prey harvesting is introduced, the proposed model displays a dynamics in the predator-prey plane. The main purpose is to show how the stability properties of some coexistence equilibria could be directly affected by harvesting. First, the positivity and boundedness of solutions of this model are provided and then the coexistence and stability of equilibrium points and bioeconomic equilibria are discussed. The local bifurcation solutions for different parameters of the model are obtained via bifurcation theory. Finally, some numerical simulations are given to demonstrate the results.
  Keywords: Predator-prey interaction model; Holling type IIrnfunctional response; Stability; Bioeconomic equilibria; Bifurcations.
  
  
• Toward a hybrid machine learning approach for extracting and clustering learners behaviours in adaptive educational system  
  by Ouafae EL AISSAOUI, Yasser EL ALAMI EL MADANI, Lahcen OUGHDIR, Ahmed DAKKAK, Youssouf El Allioui 
  Abstract: The student model is the core component of an adaptive E-learning system, since it provides a structured presentation of the learner's characteristics that must be taken into account while recommending learning materials. Among those learner's characteristics, there is the learning style which refers to the preferred way in which an individual learns best. The traditional method detecting learning styles (using questionnaires) has notable drawbacks. Firstly, filling in a question-naire is a boring task that consumes a lot of time. Secondly, producing inaccurate results because students aren't always aware of their own learning preferences. Thus in this paper we have proposed an ap-proach to identify learning styles automatically, based on Felder and Silverman learning style model (FSLSM) and using web usage mining and machine learning techniques. The first step of the web usage min-ing process is used to reprocess the data extracted from the log file of the E-learning environment and capture the learners' sequences. The captured learners' sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning systems log file, and in order to evaluate the performance of the used classifier, the confu-sion matrix method was used. The obtained results demonstrate that our approach yields excellent results.
  Keywords: Unsupervised Algorithm; Supervised Algorithm; K-Means; Naïve Bayes; Adaptive E-Learning Systems; Felder-Silverman Learning Style Model.
  
  
• Analysis of bulk queue with unreliable service station, second optional repair, N-policy multiple vacation, loss and immediate feedback in production system  
  by Ayyappan Govindan, Nirmala Marimuthu 
  Abstract: In this paper an unreliable server bulk service queue with second optional repair, multiple vacation under N-policy, customer's feedback and impatience are considered. After finishing a service, the server avails a multiple vacation under N-policy only when the queue length is less than `a'. Server vacation models are very much useful for a queueing system in which server needs to utilize his idle time for a different purpose. The server is subject to breakdown during the service and requires second optional repair for restoration. After repair completion, the server will start its remaining service to the batch of customers whose service was interrupted. Further, it is assumed that the arriving units may balk (loss) from the system when the server is on vacation and immediate feedback service was provided to the dissatisfied customers. The queue size distribution at a random and departure epoch has been derived using the supplementary variable technique. Finally, the stability condition, some performance measures, particular cases and numerical results of the proposed model are obtained.
  Keywords: General bulk service; Unreliable server; Loss; Immediate feedback; Second optional repair; Multiple vacation under N-policy.
  
  
• A parameter robust computational method for a weakly coupled system of singularly perturbed convection-diffusion boundary value problem with discontinuous source terms  
  by Mahabub Basha Pathan, Shanthi Vembu 
  Abstract: In this paper, a parameter robust computational method using an iterative procedure for a weakly coupled system of singularly perturbed convection-diffusion boundary value problem with discontinuous source terms subject to Dirichlet boundary conditions is presented on uniform mesh. A difference scheme based on cubic spline in tension with fitting factor is considered outside the point of discontinuity whereas the centered finite difference scheme with the average of the source terms is used at the point of discontinuity. Numerical results are provided to demonstrate the efficiency of the proposed method with parameter-uniform convergence and also confirm the results obtained by this method are better than the existing methods.
  Keywords: Singular perturbation problem; Weakly coupled system; Convection-Diffusion; Cubic spline in tension; Discontinuous source term; Parameter-uniform.
  
  
• Regression Testing of Object-Oriented Systems Using UML State Machine Diagram and Sequence Diagram  
  by Namita Panda, Arup Abhinna Acharya, Durga Prasad Mohapatra 
  Abstract: In this paper, we have modelled the software requirements using UML state machine diagram and UML sequence diagram. The different features of both the diagrams are combined and an intermediate graph i.e. State Sequence Graph (SSG) is generated. The graph is traversed to generate the test scenarios by identifying the linear independent paths present in SSG. The test scenarios generated are now capable of detecting message dependency fault as well as state change faults. The affected nodes, due to different changes in the past versions of the applications are identified and stored for further analysis. Whenever, a new version of the software is developed, and it is under regression testing, then test scenario prioritization is carried out by finding the frequent pattern from the stored modification history of the previous versions. Along with the frequent pattern, different other factors like number of message passing, number of state changes etc. also contribute in prioritizing the test cases. The proposed approach is applied to an online shopping cart application for validation. The proposed approach is also applied on other case studies and the results are recorded. The approach of combining different UML diagrams is found to be very efficient when evaluated using prioritization metric and compared with other related work.
  Keywords: Regression Testing; Test Case Prioritization; State machine diagram; Sequence Diagram; Test Scenarios; UML.
  
  
• Numerical solution of Fredholm integral equations of the first kind with singular logarithmic kernel and singular unknown function via monic Chebyshev polynomials  
  by E.S. Shoukralla, M. Kamel, M.A. Markos 
  Abstract: This paper provides a new method for the numerical solution of Fredholm integral equation of the first kind whose unknown function is singular at the end-points of the integration domain and has a weakly singular logarithmic kernel. The method is based on monic Chebyshev polynomials approximation. The singular behaviour of the unknown function is isolated by replacing it with a product of two functions; the first is a well-behaved unknown function, while the second is a badly-behaved known function. Furthermore, the singularity of the kernel is treated by creating two asymptotic expressions. This method, in addition to its simplicity, has a very important advantage, namely its ability to compute the functional values of the unknown function at the end-points of the integration domain, whereas the exact solution and other methods failed to find these values. It turns out from the two illustrated examples that the presented method significantly simplifies the computations, saves time, and ensures a superior accuracy of the solution.
  Keywords: Monic Chebyshev polynomials; weakly singular; Fredholm integral equations; first kind; potential-type equations.
  
  
• MGWHD-SVM: Maximum Weighted Heteroscedastic Migration Learning Algorithm  
  by Min Zhang, Liangguang Mo 
  Abstract: Maximum mean discrepancy (MMD) is a global measure of the distribution differences between domains at present, as a standard for effectively measuring the distribution differences between source and destination domains, however, MMD has some shortcomings in measuring the local structure and distribution differences between fields. This paper proposes a new measure: Maximum local weighted heteroscedasticity discrepancy (MLWHD), this measure not only fully considers the local structure and distribution differences among fields, but also shows good adaptability to the exception points and noise, further, MLWHD was used to determine the maximum global weighted heteroscedasticity discrepancy (MGWHD), and MGWHD was embedded into the training of Support Vector Machine (SVM). Finally, the test shows that the MGWHD method has better robustness.
  Keywords: Maximum local weighted heteroscedasticity discrepancy; support vector machine; migration learning.
  
  
• NRS-CSO: Neighborhood Rough Set-based Cat Swarm Optimization Algorithms  
  by Zi-hao Leng, Jian-cong Fan 
  Abstract: Cat Swarm Optimization (CSO) is a typical evolutionary method inspired by the cats in the nature for solving optimization problem. After CSO was first proposed, it has been improved and applied in different fields, the series of CSO algorithms has been verified that they have better performance compared to many other swarm optimization algorithms, such as the Particle Swarm Optimization (PSO) algorithm. In this research, we propose an improved CSO named as Neighborhood Rough Set-based Cat Swarm Optimization (abbreviated as NRS-CSO). This algorithm uses neighborhood rough set theory to obtain two adaptive coefficients, and then these coefficients are used to modify the CSO algorithm. Experimental results show that the proposed algorithm obtains better performance than the standard CSO, which can spend less time to converge and the iteration times are less too.
  Keywords: Cat Swarm Optimization; Neighborhood Rough Set; Computational Intelligence; Function Optimization.
  
  
• Approximate Analytical Modeling of Fuzzy Reaction-Diffusion Equation  
  by Sarmad Altaie, Azizan Saaban, Ali Jameel 
  Abstract: In this work, we developed an approximate analytical method based on the Optimal Homotopy Asymptotic Method (OHAM) to solve fuzzy partial differential equations (FPDE). The method has been applied to Fuzzy Reaction-Diffusion Equation with initial condition. By means of illustrative examples, we demonstrated the accuracy, efficiency, and flexibility of the proposed method.
  Keywords: Fuzzy Partial Differential Equations; Fuzzy Reaction-Diffusion Equation; Approximate-Analytical methods; Optimal Homotopy Asymptotic Method.
  
  
• Inclined magnetic field, thermal radiation and Hall current effects on Natural convection flow between vertical parallel plates  
  by Kaladhar K, Madhusudhan Reddy K, Srinivasacharya D 
  Abstract: This paper investigates the impact of thermal radiation and an inclined magnetic field on free convection flow through a vertical channel. In addition to this Hall current effect has been taken into consideration. Spectral Quasilinearization Method (SQLM) has been utilized to solve the dimensionless governing equations; those were obtained by using similarity transformations from the system of governing partial differential equations. Influence of all the pertaining flow parameters of this study on all the dimensionless profiles were calculated and presented through graphs. Also the nature of the physical parameters were calculated and presented in table form. This study clearly exhibits that the inclined magnetic field influences the fluid flow remarkably.
  Keywords: Inclined magnetic field; Thermal radiation; Natural convection; Hall effect: SQLM.
  
  
• 3D ANISOTROPIC TRANSIENT HEAT CONDUCTION BY THE LOCAL POINT INTERPOLATION BOUNDARY ELEMENT METHOD  
  by Gael Pierson, Richard Kouitat Njiwa 
  Abstract: The boundary elements method (BEM) is an effective numerical method for the solution of a large class of problems including heat conduction in isotropic media. The main appealing of this pure-BEM (reduction of the problem dimension by one) is tarnished to some extend when a fundamental solution to the governing differential equations does not exist. This is usually the case for anisotropic and nonlinear problems. Another attractive numerical approach due to its ease of implementation is the local point interpolation method applied to the strong form differential equations. The accuracy of this meshless method deteriorates in the presence of Neumann type boundary conditions. The main appealing of the BEM can be maintained by a judicious coupling of the pure-BEM with the local point interpolation method. The resulting approach, named the LPI-BEM, is shown to be effective for the solution of transient isotopic and anisotropic heat conduction.
  Keywords: BEM; local point interpolation; anisotropy; transient heat conduction.
  
  
• E-Bayesian Estimation for Burr-X Distribution Based on Type-II Hybrid Censoring Scheme  
  by Abdalla Rabie, Junping Li 
  Abstract: In this paper, Burr-X distribution with Type-II hybrid censoring data is considered. The E-Bayesian estimation (the expectation of the Bayesian estimate) and the corresponding Bayesian and maximum likelihood estimation methods are studied for the distribution parameter and the reliability function. The Bayesian and the E-Bayesian estimates are obtained under LINEX and squared error loss functions. By applying the Markov chain Monte Carlo techniques, the Bayesian and the E-Bayesian estimates are obtained. Also, confidence intervals of maximum likelihood estimates and credible intervals of Bayesian and E-Bayesian estimates are constructed. Furthermore, a numerical example of real-life data is provided for the purpose of illustration. Finally, a comparison among the E-Bayesian, the Bayesian and the maximum likelihood methods is presented.
  Keywords: Bayesian estimation; E-Bayesian estimation; maximum likelihood estimation; Hybrid censoring scheme; Confidence and Credible intervals; MCMC method.
  
  
• Aircraft Pushback Slot Allocation Bi-level Programming Model based on Congestion Pricing  
  by Li-hua Liu, Minglei Song, Xue-jiao Wang, Ming-hui Wang 
  Abstract: In order to alleviate the congestion of airport surface, aircraft pushback slot congestion pricing is explored. First, the feasibility of aircraft pushback slot pricing is analyzed. Then the Stackelberg game model for aircraft pushback slot allocation is established by combining the congestion pricing with non-cooperation game. By using bi-level programming theory, this game model is further extended to a bi-level programming model. Considering the discretization of this model, an improved artificial fish algorithm is also designed by improving the fish school behavior and optimum solution selection criteria. Finally, an example analysis is performed on Xinzheng International Airport. By comparing the simulation result of pushback slot congestion pricing with other strategies, the advantages of the developed model and algorithm are emphasized.
  Keywords: AFSA; aircraft pushback slot; Congestion Pricing; Stackelberg Game.
  
  
• The use of unclear conclusion in the tasks of forecasting of the durability of corrosive constructions  
  by Larysa Korotka 
  Abstract: The borders of each cluster are considered as the carrier of a term set, and the center of the corresponding cluster is considered as its core. It is assumed that the choice of the type of membership function remains according to the expert in the subject area. The necessary and sufficient volumes of the fuzzy rule base, as well as term sets of linguistic variables are considered. When using Mamdani fuzzy inference and the centroid method, it is possible to obtain the parameters of numerical procedures for the task of forecasting the durability of corrosive structures. As an alternative to neural networks in solving approximation tasks, it is proposed to use a generalized algorithm based on the theory of fuzzy sets. The results of numerical experiments make it possible to assert that a fuzzy clusteriser allows to determine rational parameters of numerical procedures for the class of tasks.
  Keywords: clustering; fuzzy knowledge base; fuzzy inference; forecasting of durability; corroding structures.
  
  
• Analysis of Subway Users Behavior Based on the Latent Class Regression  
  by Jianrong Liu 
  Abstract: Since that there is difference of satisfaction and willing-to-travel-by-subway among different subway users, a task one should take into account of is how to classify subway users effectively, and analyze factors affect the satisfaction and willing-to-travel-by-subway of different subgroups of subway users, respectively. Based on subway users satisfaction of the subway and their traveling behavior, this paper classifies subway users with the latent class regression model. This result shows that subway users should be classified into three subgroups: the neutral passengers, the satisfied passengers, and the loyal passengers, also it is found that private car ownership, accessibility of the subway station, price evaluation and speed evaluation have a great influence on the classification. Based on the classification, this paper regresses the overall satisfaction of the subway, the recommendation of the subway, and the frequency of traveling by subway on the factors, respectively. And it is found that there are non-negligible differences of factors parameters on the three subgroups.
  Keywords: urban traffic; subway; classification; latent class regression; regression analysis.
  
  
• The influence of double diffusive gradient boundary condition on Micropolar nano fluid flow through stretching surface with a higher order chemical reaction  
  by G. Nagaraju 
  Abstract: The higher order chemical reaction of a magneto micropolar nanofluid flow through a stretching sheet under the effect of Brownian motion and thermophoresis is investigated by applying the convective boundary condition on temperature and mixed boundary condition on concentration. The leading partial differential equations were transformed into ordinary equations using a similarity transformation and solved numerically using Matlab bvp4c solver. The presence of the nanoparticles has a most remarkable effect on the heat flow improvement of micropolar nanofluid. The results are depicted graphically for different flow governing material parameters.
  Keywords: Micropolar nanofluid; linearly stretched sheet; heat and mass transfer; mixed concentration condition; convective boundary condition.
  
  
• Cuckoo search with dual-subpopulation and information-sharing strategy  
  by Jun Xi, Liming Zheng 
  Abstract: Cuckoo search (CS) algorithm is simple and powerful in dealing with the global optimization problem. However, how to strike a good balance between exploration and exploitation in CS is still an open question. The paper proposes a modified CS with dual-subpopulation and information-sharing strategy (DSIS_CS). In DSIS_CS, the population is divided into two subpopulations which are assigned different update task. Then, random solutions are selected from the dissimilar subpopulations in order to avoid the results from easily falling into the local optima. In addition, the DSIS strategy can be incorporated into other state-of-the-art CS variants to improve their optimization performance. Extensive experiments on 28 functions chosen from CEC 2013 have been carried out. The results suggest that the DSIS strategy helps both the CS and its variants to achieve a better trade-off between exploration and exploitation.
  Keywords: cuckoo search algorithm; swarm intelligence; global optimization; dual-subpopulation strategy.
  
  
• Homotopy Analysis Method Approximate Solution For Fuzzy Pantograph Equation  
  by Akram Hatim, Ali Jameel, Nidal Anakira, Abdel Kareem Alomari, Azizan Saaban 
  Abstract: This paper investigates the powerful method namely the Homotopy Analysis Method (HAM), to solve the fuzzy pantograph equation (FPE) in approximate analytic form. HAM yields a convergent infinite series solution to the solution of FPE without the need to reduce the FPE to the first order system or compare it with the exact solution, and this is one of the advantages of this method. For a better approximate solution, the HAM uses a convergence control parameter from the convergence region of the infinite series solution. HAM solution of FPE is obtained by reformulate crisp standard approximation via the properties of the fuzzy set theory.
  Keywords: Fuzzy Set Theory; Fuzzy Differential Equations; Fuzzy Pantograph Equation; Homotopy Analysis Method.
  
  
• Exponentially-fitted Pseudo Runge-Kutta Method  
  by Shruti Tiwari, Ram Pandey 
  Abstract: This article is devoted to the development of embedded pseudo Runge-Kutta method of order three (EPRK3) and exponentially-fitted embedded pseudo Runge-Kutta method of order three (ef-EPRK3). The motivation behind this development is two folded, the first one is to minimize the cost of computation of existing Runge-Kutta (RK) method and the other one is to make RK method compatible to solve the initial value problem (IVP) having periodic solutions. In this paper, first we derive a family of explicit embedded pseudo Runge-Kutta method (EPRKM) of order three and next, we have fitted PRKM exponentially and developed ef-EPRKM of exponential order two. Where, we assume that ef-EPRKM exactly integrates two exponential functions $\exp (\pm\omega x)$, with unknown frequency $\omega$. The proposed methods are applied to two IVPs of order two. The novelty of ef-EPRK over EPRKM are shown via the numerical examples. Further from Table 1, it is observed that EPRK and ef-EPRK methods are less expensive than the existing RK methods. These methods expend less computation cost in the form of function evaluations per step. In Table 2 and Table 3, a comparison of norms of end point errors is made between EPRK3 method, ef-EPRK3 method, Berghe's exponentially fitted RK method and Simos's exponentially fitted RK method from which it is quite evident that errors by ef-EPRK3 are smallest. This ensures the superiority of ef-EPRK. The local truncation error (LTE) for ef-EPRKM is also computed and by the expression of LTE, the value of unknown frequency $\omega$ in $\exp (\pm\omega x)$ is calculated.
  Keywords: Runge-Kutta method; Pseudo Runge-Kutta method;rnExponentially tting; Truncation error; Oscillatory initial value problem.
  
  
• Jump OpVaR on Option Liquidity  
  by ALIREZA BAHIRAIE 
  Abstract: Impact of operational risk on the option pricing through thernextension of Mitras model with Mertons jump diffusion model is assessed.rnA partial integral differential equation (PIDe) is derived and the impactrnof parameters of Mertons model on operational risk and option value byrnoperational Value-at-Risk measure, which is derived by Mitra, is studied.rnThe option values in the presence of operational risk on S&P500 indexrnare computed. The result shows that most operational risks occur aroundrnat-the-money options.
  Keywords: Option pricing; Operational risk ; OpVaR; Operational Value atrnRisk; Hedging,.
  
  
• Image encryption using anti-synchronization and Bogdanov transformation map  
  by Obaida M. Al-Hazaimeh, Mohammad F. Al-Jamal, Abedel-Karrem Alomari, Mohammed J. Bawaneh, Nedal Tahat 
  Abstract: A new image encryption algorithm based on anti-synchronization of Chen system and BOGDANOV map is presented in this paper. To illustrate the performance and robustness of the new algorithm, some of security analyses against different cryptanalytic attacks are presented. The security analyses found that, the new algorithm can be applied to provide a suitable application through insecure networks (i.e. internet). As well as, the anti-synchronization Chen system and BOGDANOV transformation map not just limited to digital image encryption, but can be applied directly in several other applications such as real-time application (i.e. video encryption).
  Keywords: Image encryption; BOGDANOV chaotic map; Crypto-systems; Anti-synchronization; Chen system.
  
  
• Two combined methods for the global solution of implicit semilinear differential equations with the use of spectral projectors and Taylor expansions  
  by Maria Filipkovska 
  Abstract: Two combined numerical methods for solving implicit semilinear differential equations are obtained and their convergence is proved. The comparative analysis of these methods is carried out and conclusions about the effectiveness of their application in various situations are made. In comparison with other known methods, the obtained methods require weaker restrictions for the nonlinear part of the equation. Also, the obtained methods enable to compute approximate solutions of the equations on any given time interval and, therefore, enable to carry out the numerical analysis of global dynamics of the corresponding mathematical models. The examples demonstrating the capabilities of the developed methods are provided. To construct the methods we use the spectral projectors, Taylor expansions and finite differences. Since the used spectral projectors can be easily computed, to apply the methods it is not necessary to carry out additional analytical transformations.
  Keywords: implicit differential equation; differential-algebraic equation; combined method; regular pencil; spectral projector; global dynamics.
  DOI: 10.1504/IJCSM.2019.10025236
   
  
• Method for Measuring and Evaluating the Difficulty Data of Aerobics Complete sets of Movements in University based on Multiple Regression  
  by Lijuan Guo 
  Abstract: Aiming at the unreasonable selection of the evaluation indicator for the difficulty data of aerobics complete sets of movements in universities, which leads to the lower satisfaction of aerobics coaches and referees, a method for evaluating the difficulty of aerobics complete sets of movements in universities based on multiple regression is put forward. In this method, SPSS13.2 software is used to evaluate the body shape index and physical quality index that affect the specific technical level of aerobics athletes in universities. The final indexes are obtained by correlation analysis and cluster analysis, and the evaluation model is established by multiple regression method to evaluate the difficulty data of aerobics complete sets of movements in universities. The experimental results show that the evaluation indicator for difficult movements of aerobics in universities selected and set by the research method is reasonable, and the passing rate of the athletes participating in the evaluation is higher, which is consistent with the actual situation.
  Keywords: Aerobics in university; Complete sets of movements; Difficulty data; Evaluation; Multiple regression;.
  
  
• A novel six-dimensional hyperchaotic system with a self-Excited attractors and its chaos synchronization  
  by Saad AL-Azzawi, Ahmed Sedeeq 
  Abstract: A few researches are available in the aspect of high dimensional nonlinear dynamical systems. This paper presents a novel 6-D continuous real variable hyperchaotic system with self-excited attractors, consists of 17-terms and various characteristics which include equilibria and their stability, Lyapunov exponents, chaos synchronization. Firstly, a novel model with linear feedback controller is proposed. The error dynamics for complete control strategy is found. Three different suitable and effective controllers are designed to stabilize this error by using nonlinear control and based on two main tools: Lyapunov stability theory and the linearization method. Finally, comparison between the two tools was done. The proposed controller are effective and convenient to achieve chaos synchronization of the new systems. Moreover. numerical simulations were carried out by using MATLAB to validate all the synchronization phenomena derived in this paper.
  Keywords: Chaos synchronization; novel 6-D dynamical systems; self-excited attractors; Lyapunov stability theory; nonlinear control.
  
  
• Effects of combination of therapies on chronic hepatitis B virus through resolution of an optimal control problem using comparative of direct method and Pontryagin's maximum principle  
  by Jean Marie Ntaganda 
  Abstract: This paper aims at using direct approach and Pontryagin's maximum principle to solve a hepatitis B virus dynamics optimal control problem. The controls consist of combination therapy of two treatments. The numerical implementation is done using Matlab packages. Comparative results from these two methods show they are so close in optimal trajectories of determinant variables. Furthermore, both numerical methods are in good agreement with experimental data. In particular, combination of two treatments controls Hepatitis B virus to ensure healthy.
  Keywords: Hepatitis B virus; Optimal control; Treatment; Direct method; Pontryagin's maximum principle; Numerical simulation.
  
  
• Production Simulation of Tight Oil Reservoirs with Coupled Mathematical Model  
  by Xijun Ke, Jiaqi Li, Dali Guo, Shuanghan Luo, Shouchang Xie 
  Abstract: This paper constructed a coupled mathematical model with two flow directions (matrix inside fracture network) to analyze the rate transient behaviors in oil reservoirs. Firstly, we formulate the 1-D flow solutions for each region, and then couple them by imposing flux and pressure continuity across the boundaries between regions. Secondly, we solve the model with the linear flow model and using Laplace transform and Stehfest algorithm comprehensively. Thirdly, the corresponding algorithm is designed and the code is written for calculation. Finally, the model solution is verified with one flow direction model thoroughly.
  Keywords: Multi-linear flow model; Coupled Mathematical Model; Laplace transform; Stehfest algorithm ; Two flow directions model.
  
  
• A rapid optimization method of TSPs based on water centripetal motion  
  by Change Lv, Shuo Liu 
  Abstract: Traveling salesman problem (TSP) solutions are commonly used in many areas. We provide an innovative and optimal solution method of TSPs based on water flow centripetal motion, and it different from other algorithms. In the case of 34 cities in China, the center hole is computed with a pentagon which formed by the outermost 5 cities. Place a string to enclose all rods in a circle after water fills the tank, and start to pump water at a steady rate. And a uniform and equal centripetal force is generated and the string will shrink uniformly until connect each of rods as well as water runs out. The string is the best route that connects the 34 cities. The method's optimal result is 15 759.75 km, which is 32.55 km shorter than the current optimal solution. The method eliminates the complex procedure, takes less time, and is highly feasible and reliable.
  Keywords: transportation economy; traveling salesman problem; optimal route; test solution; test devices.
  
  
• A Bi-objective Optimization Approach for the Critical Chain Project Scheduling Problem  
  by Wuliang Peng, Jiali Lin 
  Abstract: Since time and cost are two important issues in real-life project scheduling applications, the optimization problems about project make-span and cost have been extensively studied in project management academic field over the last few decades. This study addresses the bi-objective critical chain project scheduling problem aiming at minimizing both project make-span and cost. Taking account in the characteristics of the critical chain method, we formulate the conceptual model considering project make-span and cost under both the resource-constraints and precedence relations. In the model, the promised delivery time is used as project make-span, and the discounted cost is used to measure project cost. To solve the problem, an evolutionary algorithm based on Greedy Randomized Adaptive Search Procedure (GRASP) is proposed to search for the non-dominated solutions of the problem. Several neighborhood search methods are investigated and compared with each other regarding four multi-objective performance measures: Optimal ratio, Hyper-volume indicator, Epsilon metric and Average distance. The computational tests have indicated the algorithm with variable neighborhood appears to be superior to others.
  Keywords: critical chain method; multi-objective optimization; greedy randomized adaptive search procedure;project scheduling.
  
  
• Numerical study of entropy generation analysis for non-Newtonian MHD flow of blood in a porous channel with partial slip  
  by MAMATA PARIDA, SUDARSAN PADHY 
  Abstract: In the present work, a numerical investigation of magnetohydrodynamic flow of blood through an arterial segment is carried out by taking into account the slip velocity at the wall of the artery. A mathematical model of the problem is presented by considering blood as a non-Newtonian fluid obeying third grade fluid model and the artery is chosen to be a porous channel. A uniform magnetic field is applied in the transverse direction of the flow. The governing momentum equation is discretized by finite element method and the obtained system of non-linear algebraic equations is solved by damped Newton's method. A quantitative analysis is made through numerical computations and graphical presentation of velocity and temperature for different pertinent parameters. In addition, the second law analysis for the physiological fluid is examined and the influence of important parameters on the entropy generation rate and irreversibility ratio are discussed. Our analysis indicates that the flow rate and irreversibility ratio increases with increase in third grade fluid parameter, whereas the magnetic parameter produces reverse effect on these profiles.
  Keywords: Entropy generation; Slip effects; Hartmann number; Third-grade fluid.
  
  
• A modification of nonlinear feedback controller  
  by Maysoon Aziz, Saad AL-Azzawi 
  Abstract: This paper derives new results for the modify of nonlinear feedback controller. The stability results are established using the Lyapunov's second method as a main tool. Since the finding characteristic equations are not required for the modify method, the nonlinear feedback control strategy with this modification is very effective and convenient to realize the chaos control for dynamical systems. Furthermore, modify method is used to overcome some problem in nonlinear feedback controller, which helps to achieve the convergence of dynamics system easily. This proposed method has certain significances for reducing the effort and complexity of controller implementation. Finally, numerical simulations are given to illustrate and verify the results.
  Keywords: Chaos control; nonlinear feedback control; nonlinear dynamical system; Lyapunov's second method.
  
  
• A new QPSO based hybrid algorithm for bound-constrained optimization problem and its application in engineering design problems  
  by NIRMAL KUMAR, AVIJIT DUARY, SANAT KUMAR MAHATO, ASOKE KUMAR BHUNIA 
  Abstract: The aim of this paper is to introduce a new hybrid algorithm for bound constrained optimization problem combining Quantum behaved Particle Swarm Optimization (QPSO) and binary tournamenting technique. Depending on the different options of binary tournamenting process six diverse forms of hybrid algorithm are introduced. Then the efficiency and performance of these hybrid algorithms are investigated through six well known benchmark bound constrained optimization problems. Computational results are compared graphically as well as numerically. Finally, this algorithm is utilized to solve the engineering design problem and results are compared with the recent algorithm available in the literature.
  Keywords: PSO; QPSO; Adaptive QPSO; Gaussian QPSO; Tournamenting; Hybrid algorithm; Engineering design problem.
  
  
• Linear Programming Method For Buckling Critical Load Of Foundation Piles In Nonlinear Foundation  
  by Weizhe Li, Ping Lou 
  Abstract: Linear programming method is presented for calculation of the Buckling critical load of the pile in nonlinear soils. MATLAB programming for buckling stability analysis of pile in nonlinear soils is self-provided. Cases analysis of buckling stability analysis for both test piles and model piles in nonlinear soils are done, and conclusions are derived as follows:1); the key parameters ? and V in the transient buckling stability equation has extremely remarkable linear relationship with each other; 2) the buckling critical load of the pile will decrease with the increase of horizontal load at the top of the pile; 3) critical buckling load value of the pile in nonlinear soils is much smaller than that of the pile by m-method, and it is advised that nonlinear effects of soils should be carefully considered in application for critical buckling analysis of pile in practice.
  Keywords: nonlinear soils; critical buckling analysis; transient buckling stability equation; fitting curve method ;critical buckling load; pile.
  
  
• Research On Robot Optimal Path Planning Method Based On Improved Ant Colony Algorithm  
  by Hui Tian 
  Abstract: In order to overcome the problem of poor convergence and obstacle avoidance when traditional methods are used to plan the optimal path of robot, a new optimal path planning method based on improved ant colony algorithm is proposed. Firstly, the odometer model, ultrasonic sensor model and robot motion model are constructed to obtain the environmental information and robot motion state information. Then, according to the adaptive transformation heuristic function of the target point and the principle of wolf swarm assignment, the pheromone is refreshed. On this basis, the core parameters of the improved ant colony algorithm are optimized by particle swarm algorithm, so as to complete the optimal path planning of the robot. The experimental results show that the overall mean value of collision avoidance of the proposed method is 0.97, and the planning performance is significantly better than that of similar planning methods, with considerable application value.
  Keywords: Improved ant colony algorithm; Robot; Optimal path; Planning; Particle swarm optimization.
  
  
• A covering method for continuous global optimization  
  by Ziadi Raouf 
  Abstract: In this paper we improve the reducing transformation method for solving a large class of global optimization problems. The reducing transformation method allows us to transform a multidimensional problem into a one-dimensional one of the same type, and then use the one-dimensional Evtushenko algorithm to obtain the global minimum. To accelerate the corresponding mixed algorithm (Reuducing transformation - Evtushenko), we have incorporated the Hook-Jeeves algorithm to explore promising regions. Our approach is suitable for solving a large class of global optimization problems on a rectangle of $mathbb{R}^n$ where the objective function is only continuous. This method converges in a finite number of iterations to the global minimum within a prescribed accuracy $delta>0$. Numerical experiments are achieved on some typical test problems and a comparison with well known methods is carried out to show the performance of our algorithm.
  Keywords: Global optimizationrn Reducing transformation methodrn Evtushenko's algorithmrn Hooke-Jeeves algorithm.
  
  
• Comprehensive Algebraic Proof that the Metric Time is Discrete: Strong Mathematical Challenge against Einsteins Continuum Spacetime Hypothesis of GR  
  by Yohannes Yebabe Tesfay 
  Abstract: In modern physics and cosmology, it is widely accepted that the general theory of relativity successfully predicts spacetime, gravity and the large scale structure of our universe. However, the core foundation and all the calculations of the general theory of relativity are based on the postulation of spacetime continuum. In this article, the author proposed to test that whether the metric time is continuous or discreet. Using the energy-time uncertainty relationship and the theory of abstract algebra, the author introduces theorem of quantum mechanical theory of time and gives a complete proof that the metric time is quantized and has the smallest value. The proof of the theorem is, therefore, a strong challenge the continuum spacetime hypothesis of the general theory of relativity and gives strong evidence to the scheme of quantum gravity.
  Keywords: abstract algebra; general relativity; metric time; quantization; and uncertainty principle.
  
  
• A fast ADI algorithm for nonlinear Poisson equation in heterogeneous dielectric media  
  by Wufeng Tian 
  Abstract: A nonlinear Poisson equation has been introduced to model nonlinear and nonlocal hyperpolarization effects in electrostatic solute-solvent interaction for biomolecular solvation analysis. Due to a strong nonlinearity associated with the heterogeneous dielectric media, this Poisson model is difficult to solve numerically, particularly for large protein systems. A new pseudo-transient continuation approach is proposed in this paper to efficiently and stably solve the nonlinear Poisson equation. A Douglas type alternating direction implicit (ADI) method is developed for solving the pseudo-time dependent Poisson equation. Different approximations to the dielectric profile in heterogeneous media are considered in the standard finite difference discretization. The proposed ADI scheme is validated by considering benchmark examples with exact solutions and by solvation analysis of real biomolecules with various sizes. Numerical results are in good agreement with the theoretical prediction, experimental measurements, and those obtained from the boundary value problem approach. Since the time stability of the proposed ADI scheme can be maintained even using very large time increments, it is efficient for electrostatic analysis involving hyperpolarization effects.
  Keywords: Nonlinear Poisson equation; Non-local dielectric media; Pseudo-transient continuation approach; Alternating direction implicit (ADI);Solvation free energy.
  
  
• Mapped Gegenbauer rational collocation method for a class of Fredholm integral equations on the whole line  
  by Azedine Rahmoune, Ahmed Guechi 
  Abstract: Several real problems were modeled by integral equations defined on infinite intervals with underlying solutions decay to zero at infinity. The use of Hermite polynomials to approximate these functions are not suitable due to their wild behaviors at infinity. Therefore, it is useful to employ non-weighted orthogonal systems constructed from Jacobi polynomials. This paper aims at developing accurate collocation method to solve a class of linear Fredholm integral equations on the whole line using mapped Gegenbauer rational functions. The proposed approach uses a special accurate quadrature formula based on the mapped Gegenbauer-Gauss integration rule for approximating integrals appeared in the scheme. Moreover, the associated algorithm is easy to implement on any personal computer. Error analysis including convergence rates of the presented method are established. The accuracy and stability are also validated by two typical numerical examples.
  Keywords: Mapped Gegenbauer rational approximation; Fredholm integral equations; The whole line; Collocation method; Stability.
  
  
• Efficient graph-based algorithms for solving team formation problem  
  by Abdulla Qaddoumi, Youssef Harrath, Abdul Fattah Salman 
  Abstract: This research investigates the pair formation problem described as forming pairs of people to achieve certain objectives. This problem is a subcategory of the well-known grouping problem that is classified as NP-complete. The necessity of pairing people in teams is frequent in many real-life fields such as education, social life, and production environments. To solve the problem, a mathematical formulation and a weighted graph-based representation are proposed. Pairing people is usually affected by psychological and productivity factors such as expertise and managers' opinion. These factors are summarized to produce quantitative scores representing the fitness relationship of each person towards others. Four algorithms are proposed to maximize the total weight of the formed pairs. The proposed algorithms are implemented and benchmarked using data instances of various sizes. The performance of the algorithms is evaluated against two proposed upper bounds. The results showed that the edge-based first algorithm outperforms other algorithms.
  Keywords: Pair Formation; Graph Theory; Team Efficiency; Productivity; Manufacturing.
  
  
• Research On The Optimal Route Selection Method Based On Improved Ant Colony Algorithm  
  by Xiaoying Wei 
  Abstract: In order to overcome the problems of large pheromone gap and poor convergence in the existing tourism route selection methods, a new optimal tourism route selection method based on improved ant colony algorithm is proposed. This method analyzes the pheromone trajectory update process of ant colony algorithm. Based on the metropolis sampling criterion, it improves the probability of generating different solutions of generate function and reduces the probability of generating different solutions of generate function. Based on the volatile characteristics of pheromone, the shortest path for ant colony to return to nest is obtained, and the result of tourism route selection is optimized. The experimental results show that the proposed method can quickly converge to obtain the optimal solution with high user satisfaction, the highest satisfaction is 98.4%, which fully shows that the proposed method can complete the optimal tourism route planning.
  Keywords: Improved Ant Colony Algorithm; Pheromone; Generate Function; Metropolis Sampling Criteria; Best Route Selection.
  
  
• A gray wolf algorithm for feature and parameter selection of support vector classification  
  by Omar Qasim, Zakariya Algamal 
  Abstract: In classification problems, there are many data that contain a large number of features, some of which are irrelevant and cause confusion for the classifiers. The support vector classification (SVC) method is one of the most common methods used in classification. Feature selection, together with the parameters setting of SVC, such as the kernel parameter and the penalty parameter, significantly affects the classification performance of the SVC. In this study, the gray wolf optimization algorithm (GWO) is proposed to improve feature selection and determine the optimal parameter values of SVC simultaneously. Based on several benchmark datasets for diseases, the experimental results show that the proposed method, FOGWO-SVC, is capable in selecting the best features with best parameters determination. Further, the comparative results demonstrate that the FOGWO-SVC is better or comparable than other competitor algorithms in terms of classification accuracy and feature reduction.
  Keywords: Feature selection; gray wolf optimization; parameter determination; support vector classification; metaheuristic algorithms.
  
  
• Reformulation of Bilevel Linear Fractional/Linear Programming Problem into a Mixed Integer Programming Problem Via Complementarity Problem  
  by Anuradha Sharma 
  Abstract: The bilevel programming problem is a static version of the Stackelberg's leader follower game in which in which Stackelberg strategy is used by the higher level decision maker called the leader given the rational reaction of the lower level decision maker called the follower.The bilevel programming problem is a two level hierarachical optimization problem and is non-convex.This paper deals with finding links between the bilevel linear fractional/linear programming problem(BF/LP), the generalized linear fractinal complememtarity problem(GFCP) and mixed integer linear fractional programming problem (MIFP),The (BFLP) is reformulated as a(GFCP) which in turn is reformulated as an (MIFP),The method is supported with the help of a numerical example.
  Keywords: Bilevel Programming; Generalized complementarity problem; Mixed integer programming; Fractional programming.
  
  
• Short-Term Traffic Flow Prediction Model based on Deep Learning Regression algorithm  
  by Yang Zhang, Dong-rong Xin 
  Abstract: In view of the problem that the short-term traffic flow prediction under the condition of unsteady traffic flow, such as low precision and over-reliance on large sample historical data, proposing a novel short-term traffic-flow prediction method based on deep learning support vector regression (DL-SVR). A framework of the DL-SVR is built with a restricted Boltzmann machine (RBM) visible inputting layer, which is connected with several intermediate operating networks, and a radial SVR output layer. In addition, a T mutation particle swarm optimization algorithm is proposed to select the important parameter in DL-SVR. Experimental results show that the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the proposed short-term traffic-flow prediction method are better than other classic algorithms, and the real time also can meet the needs of practical use.
  Keywords: deep learning; support vector regression; short-term traffic flow; artificial neural network.
  
  
• Image Error Correction Of Hockey Players' Step-By-Step Pull Shooting Based On Bayesian Classification  
  by Hongping Li 
  Abstract: The target recognition accuracy of the traditional motion image error correction method is low, which leads to its poor application effect. In order to solve this problem, this paper proposes a new image error correction method based on Bayesian classification for hockey players' step-by-step pull shooting action.The SLIC method is used to distinguish the hockey players' step-by-step pull shooting action from the image background area, to obtain the proportion of the hockey players' step-by-step pull shooting action in the super pixel area. The hockey players' step-by-step pull shooting action with incomplete naive Bayesian classification model, and to correct the image error with interpolation method. The experimental results show that the accuracy of the method for the hockey players' step-by-step pull shooting action is higher than 98%, and the image quality is high after the error correction.
  Keywords: Bayes classification; Hockey players; Step-by-step pull shooting; Image error correction.
  
  
• Predicting the Amount of Files Required to Fix a Bug  
  by Ahmed Otoom, Maen Hammad, Sara Al-jdaeh, Sari Awwad, Sahar Idwan 
  Abstract: This paper proposes a classifier that can predict the amount of files required to fix a bug. A newly incoming bug can be classified into one of the three classes (categories): Small, Medium, or Large depending on the amount of files required to fix that bug. For this purpose, 5800 bug reports are studied from three open source projects. The projects are: AspectJ, Tomcat, and SWT. Then, feature sets are extracted for each project separately. The feature sets represent the occurrences of keywords in the summary and description parts of the bug reports. Due to the high dimensionality of the feature vectors, we propose to apply the well-known method, principle component analysis (PCA). The resulting feature vectors are then fed to a number of popular machine learning algorithms. For an enhanced performance, we experiment with multiclass support vector machine quadratic MSVM2. It provides improvements of classification accuracy ranging from 2.3%-22.3% compared to other classifiers.
  Keywords: software maintenanc; machine learning; bug reports; effort prediction; MSVM2.
  
  
• Classifying Defective Software Projects based on Machine Learning and Complexity Metrics  
  by Mustafa Hammad 
  Abstract: Software defects can lead to software failures or errors at any time. Therefore, software developers and engineers spend a lot of time and effort in order to find possible defects. This paper proposes an automatic approach to predict software defects based on machine learning algorithms. A set of complexity measures values are used to train the classifier. Three public datasets were used to evaluate the ability of mining complexity measures for different software projects to predict possible defects. Experimental results showed that it is possible to min software complexity to build a defect prediction model with a high accuracy rate.
  Keywords: software defects; defect prediction; software metrics; machine learning; complexity.
  
  
• A semi-analytical method for solving a class of non-homogeneous time-fractional partial differential equations  
  by Jianke Zhang, Luyang Yin 
  Abstract: In this paper, a new kind of hybrid method is established to get the analytical approximate solutions for a class of non-homogeneous time-fractional partial differential equations. This hybrid method is with respect to the fourier series and the Chelyshkov polynomials. Firstly, the Fourier series is presented to transform the time-fractional partial differential equations to the time-fractional ordinary differential equations. Secondly, the operational matrix based on Chelyshkov polynomials is proposed to attain the analytical approximate solutions with the polynomial least squares method. The fractional derivatives are in Caputo sense. Several numerical examples are given in this paper, whose results are shown in the form of graphs and data. The results point that this hybrid method is effective to solve this class of non-homogeneous time-fractional partial differential equations.
  Keywords: Fourier series; Chelyshkov polynomials; Caputo fractional derivative.
  
  
• Investigation on stroke deterministic parameters using Intuitionistic Fuzzy Set Environment  
  by Samriddhi Ghosh, Banhi Guha, Pulak Konar 
  Abstract: In this changing work-life environment, many kinds of diseases pop out. One of such life-threatening disease is stroke. Our research addresses how this disease can be recognized using one mathematical model using Intuitionistic fuzzy set with its different measures. The mathematical model produces comparisons between the several measures seen in different genders also. The comparisons have been drawn through a statistical tool.
  Keywords: Fuzzy sets; Intuitionistic fuzzy set (IFS); Stroke; Euclidean distance; Membership degree.
  
  
• Asymmetry approach to study for chemotherapy treatment and devices failure times data using modified Power function distribution with some modified estimators  
  by Azam Zaka, Ahmad Saeed Akhter, Riffat Jabeen 
  Abstract: In order to improve the already existing models that are used extensively in bio sciences and applied sciences research, a new class of Weighted Power function distribution (WPFD) has been proposed with its various properties and different modifications to be more applicable in real life. We have provided the mathematical derivations for the new distribution including moments, incomplete moments, conditional moments, inverse moments, mean residual function, vitality function, order statistics, mills ratio, information function, Shannon entropy, Bonferroni and Lorenz curves and quantile function. We have also characterized the WPFD, based on doubly truncated mean. The aim of the study is to increase the application of the Power function distribution. The main feature of the proposed distribution is that there is no induction of parameters as compare to the other generalization of the distributions, which are complexed having many parameters. We have used R programming to estimate the parameters of the new class of WPFD using Maximum Likelihood Method (MLM), Percentile Estimators (P.E) and their modified estimators. After analyzing the data, we conclude that the proposed model WPFD performs better in the data sets while compared to different competitor models.
  Keywords: Weighted distribution; Power function distribution; Characterization; Adequacy Model.
  
  
• Decision preference-based artificial bee colony algorithm for many-objective optimal allocation of water resources  
  by Wenjun Wang, Hui Wang, Changyan Li 
  Abstract: Freshwater becomes a scarce resource because of the rapid growth of population and serious water pollution. Optimal allocation of water resources is an effective method to tackle this issue. In general, water resources allocation is a complex optimization problem, which contains multiple optimization objectives and constraints. In this paper, we consider a many-objective water resources allocation problem with four objectives and several constraints. Unlike other many-objective optimization problems, water resources allocation prefers the current situation (economy, resident life, or ecological environment). A decision preference-based algorithm may be suitable for the problem. So, this paper proposes a decision preference-based ABC for many-objective optimal allocation of water resources. Firstly, each objective is assigned a weight factor based on decision preferences. By weighted sum of four objectives, the many-objective water resources allocation problem is converted into a single objective optimization problem. Then, an improved artificial bee colony algorithm is used to solve the problem. Finally, different decision preferences are tested. On the basis of different preferences, decision makers can obtain different allocation schemes. Simulation results show our approach can achieve promising performance.
  Keywords: water resources allocation; artificial bee colony; many-objective optimization; multi-objective optimization.
  
  
• SCENE TEXT DETECTION METHOD RESEARCH BASED ON MAXIMALY STABLE EXTREMAL REGIONS  
  by Lei Xu, Yi Liu, Lian Mou 
  Abstract: Text information is an important basis for people to understand the natural scene image. At first, an edge-enhanced MSER text detection method based on weighted guided filtering and Histograms of Oriented Gradients (HOG) features is proposed. Then, a two layers candidate text validation method from coarse to fine is proposed. In the first layer, a heuristic rule for validating candidate character regions is designed based on the shape features of text regions. In the second layer, the recognition of character regions is realized by using support vector machines (SVM) with 9-dimensional features such as Hu moment invariants and stroke width transformation. The proposed method is validated by the benchmark datasets ICDAR 2013. The experimental results show that the method is comparable with other most advanced methods.
  Keywords: Text detection; MSER; Edge enhancement; SVM.
  
  
• Succulent Link Selection Strategy for underwater sensor network  
  by Shahzad Ashraf 
  Abstract: In underwater environment, the sensor nodes are deployed for collecting information and sending back to the base station. Establishing astute communication link among these sensor nodes in a multi-link routing environment is a key challenge for all underwater routing protocols. A sagacious communication link can only guarantee the maximum data transfer rate. The link selection mechanism of three underwater routing protocol i.e, Energy-aware Opportunistic Routing (EnOR) protocol, Shrewd Underwater Routing Synergy using Porous Energy Shell (SURS-PES) and Underwater Shrewd Packet Flooding Mechanism (USPF) have been investigated. After analyzing performance results of these protocols interms of packet delivery ratio, end-to-end ?delay, network lifespan and energy consumption using NS2 with AquaSim 2.0 simulator. The protocol existing, with sagacious link selection mechanism in multi-link routing environment has been identified. The identification of this sagacious link selection mechanism is a novel approach which can give specific knowledge for targeted output without wasting resources for irrelevant objectives.
  Keywords: Flooding mechanism; underwater routing; link selection; sagacious link; sink node.
  
  
• An improved pseudospectral approximation of coupled nonlinear partial differential equations  
  by Avinash Mittal 
  Abstract: In this paper, we propose time-space Chebyshev pseudo-spectral method for the numerical solutions of coupled Burger\'s equation, Whitham-Broer Kaup shallow water model and coupled nonlinear reaction-diffusion equations. This technique is based on orthogonal Chebyshev polynomial function and discretizes using Chebyshev- Gauss- Lobbato(CGL) points. A mapping is used to transform the non-homogeneous initial-boundary value to homogeneous initial-boundary value. By applying the proposed method in both time and space, the problem is reduced into a system of a nonlinear coupled algebraic equation, which is solved using Newton-Raphson method. We estimate the errors in norms $ L_{2} $. The results obtained by the scheme are very accurate and effective. Presented numerical results confirm the spectral accuracy.
  Keywords: Coupled Burger\'s equation; Whitham-Broer Kaup shallow water model; Coupled nonlinear reaction diffusion equations; Pseudospectral method; Chebyshev-Gauss-Lobbato points.
  
  
• On a nonlinear integro-differential equation of Fredholm type  
  by Mohammed Charif Bounaya, Samir Lemita, Mourad Ghiat, Mohamed Zine Aissaoui 
  Abstract: This work concerns an analytical and numerical study of a nonlinear Fredholm integro-differential equation, in this respect, we treat the conditions ensuring the existence and uniqueness of the solution for a different varieties of kernels, thereafter, we construct a numerical approach based on the well known Nystr
  Keywords: Fredholm integro-differential equation; Fixed point; Nyström method; Successive approximations.
  
  
• Parameter Estimation for Mean-reversion Type Stochastic Differential Equations from Discrete Observations  
  by Chao Wei 
  Abstract: This paper is concerned with the parameter estimation problem for mean-reversion type stochastic differential equations from discrete observations. The Girsanov transformation is used to simplify the equation because of the expression of the drift coefficient. The approximate likelihood function is given, the consistency of the estimator and asymptotic normality of the error of estimation are proved. An example is provided to verify the results.
  Keywords: Parameter estimation; discrete observation; consistency; asymptotic normality.
  
  
• Research and Application of Lasso Regression Model based on prior coefficient framework  
  by Rongzhi Wu, Li He, Lei Peng, Zepeng Wang, Weigang Wang 
  Abstract: In recent years, the establishment of a suitable data model and data analysis with a few significant features have drawn many scientists' attention. The Lasso model can effectively process high dimensional data and keep the corresponding accuracy. Compared to the traditional regression, the Lasso regression model and its improved model can solve better variable selection. In this paper,a new Lasso improved method is proposed for the Lasso regression model. The prior information is incorporated into the model. This paper refers to the Lasso regression model based on the prior sparse framework and gives the corresponding algorithm. Additionally, it analyzes multiple sets of simulation and empirical data. The results show that the improved model has better performance than the traditional model with prior information.
  Keywords: Lasso model;prior information;Sparse framework.
  
  
• Deep Learning Based Tobacco Products Classification  
  by Murat Taskiran, Sibel Çimen Yetis 
  Abstract: Various images and videos are uploaded every day or even every second on Instagram. These publicly available images are easily accessible as a result of uncontrolled Internet use by young people and children. Shared images include tobacco products and can be encouraging for young people and children when they are accessible. In this study, it is aimed to classify tobacco and tobacco products with various Convolutional Neural Networks (CNNs) and to limit the access of young users to these classified tobacco products over the Internet. A total of 2008 public images were collected from Instagram, and feature vectors were extracted with various CNNs, which proved to be successful in the competitions and CNN was determined to be proper for classification tobacco products. The classification of 5 different the tobacco products classes was realized by using the networks and the classification performance rate was obtained as 99.50% for 402 test images via MobileNet, which gave the highest results 99.11% as average. In this way, the content that is encourage to use tobacco products, can be filtered with a high accuracy rate and a secure Internet environment can be provided for children.
  Keywords: tobacco products; convolutional neural network; classification; social media; health; instagram.
  
  
• Emotion feature optimization based on PCA-GRA analysis  
  by Guohua Hu, Guoyan Meng, Qingshan Zhao, Xiaoxia Zheng 
  Abstract: The interference and redundancy of speech emotional features will directly affect the recognition performance of emotional features. In order to enhance the ability of emotional features to recognize speech emotion, dimension reduction method was used to optimize emotional features. Four emotion (sad, angry, happy and neutral) voices were selected from Berlin speech database and CASIA corpus, and traditional emotion features (prosodic features, formant features and MFCC features) were extracted. In order to reduce the mutual interference between features, PCA was used to extract the principal components of features to get independent features. Meanwhile, in order to get the emotional features that are highly related to the emotional type, GRA was used to select the main features from the principal components and design experiments for comparison. The experimental results show that PCA-GRA dimensionality reduction method can reduce the correlation of features and get the feature set with small redundancy, so as to improve the recognition effect of speech emotion.
  Keywords: emotion recognition; principal component analysis (PCA); grey relational analysis (GRA).
  
  
• Insight into 2-step continuous block method for solving mixture model and SIR model  
  by M.K. Duromola, A.L. Momoh, M.A. Rufai, Isaac L. Animasaun 
  Abstract: Understanding of the solutions of first-order ordinary differential equations, mixture model and SIR model in order to develop deep insight and exploration are major problems before the experts, biologists, scientists, and mathematicians. In all these problems, the governing equations are either single first-order or coupled ordinary differential equations kind of initial value problem. In this paper, a polynomial function $q(x)$ that passes through the points $(x_n, y_n)$, $(x_{n+1}, y_{n+1})$, . . . , $(x_{n+2}, y_{n+2})$ was adopted as the basis function that leads to third derivatives continuous 2-step block method suitable to solve first order initial value problems of ordinary differential equations (ODEs). Upon using the newly proposed scheme to solve linear ordinary differential equations (i.e. mixture theory) and nonlinear ordinary differential equation (i.e. SIR model), it is worth concluding that the algorithm is not only efficient but minimizes error.
  Keywords: 2-step method; First order differential equations; Continuous schemes; Multi-step collocation; Third derivative formula.
  
  
• A mollified approach to reconstruct an unknown boundary condition for the heat conduction equation of fractional order  
  by Afshin Babaei, Seddigheh BAnihashemi 
  Abstract: We consider an inverse problem of time fractional heat conduction problem. It is shown that the problem is ill-posed. A method is investigated based on the finite differences to find heat distribution and boundary values. The discrete mollification regularization is applied to obtain a stable numerical solution. Finally, some test problems are investigated to show the ability of the proposed scheme.
  Keywords: Heat conduction equation; Caputo’s fractional derivative; Ill-posed problem; Mollification; Finite difference method.
  
  
• Self-adaptive Network Structure Tuning Method Based on NSGA-III  
  by Lei Du, Zhihua Cui 
  Abstract: Neural networks are arising a wave in the various areas of artificial intelligence and they have adopted in daily life successfully. The structure tuning of neural networks is crucial when building the relative models. The structure of neural networks is usually designed and tuned with experience and plenty of attempts. To reduce the difficulty and cost of structure tuning meanwhile improving its rationality, we propose a new method to tune the structure of neural networks adaptively. In this method, the related structure parameters are optimized. A many-objective algorithm is employed as the optimized tool to get a better structure. We design the experiments combining Convolutional Neural Network (CNN) with Non-dominated Sorting Genetic Algorithm III (NSGA-III). The related experiments are conducted on the MNIST and Malware image datasets. Results show that the method has promising performance on neural networks tuning and can improve the robustness.
  Keywords: Neural network structure tuning; CNN; Many-objective algorithm; NSGA-III.
  
  
• A closed-form general solution for the distance of point-to-parabola in two dimensions  
  by Chang-Chien Chou 
  Abstract: This paper resolves a closed-form general solution for the distance from a point to a parabola in a two dimensional plane. This is the quickest way to answer the question with exact value. Although the method for resolving the general solution is somewhat fundamental, so far there is no efficient way provided in the literature than we do in this work. The closed-form general solution takes O(1) instant time answering the question and thus should be archived. Versatile applications need to compute the distance among parabolas in 2D. Practitioners in all fields may access the source code in the appendix for rapid conducting to their applications.
  Keywords: Shortest Path; Shortest Path of Parabola; Computer-Aided Design and Manufacturing; Computer Graphics.
  
  
• Firefly Algorithm Based on Intelligent Single Particle Learning  
  by Wenping Chen, Jun Ye, Runxiu Wu, Guangming Liu, Ping Kang 
  Abstract: The particles in the population of the firefly algorithm learn from each other using an all-attractive model, and the algorithm has a strong ability of social learning and global detec-tion. However, the influence of each particle in the algorithm is the same, which cannot reflect the role of dominant particles. For example, the algorithm ignores the role of the global optimal particle, resulting in weak self-learning and local development ability of the algorithm. To solve this problem, this paper proposes an intelligent single-particle learning firefly algorithm. The algorithm divides the iterative process into two stages, the first stage adopts the standard firefly algorithm to evolve, after all particle iterations are completed, the global optimal particles at the current time are found; in the second stage, the intelligent single particle optimization is used According to the greedy strategy, the global optimal particle carries on a certain number of iter-ations according to the greedy strategy. After the learning of all dimensions is finished, the above two stages are repeated until the termination condition of the algorithm is reached, and the optimization of the algorithm is completed. The iterative process in the first stage ensures the sociality and global detection ability of the particle, and the second stage enhances the abil-ity of self-learning and local development of the algorithm and alternates the two stages to en-sure the balance of the detection and development ability of the algorithm. In the experimental part, the effects of algorithm parameters and data dimensions on algorithm performance are discussed in detail, and the algorithm in this paper is compared with the standard firefly algo-rithm and the newly proposed improved firefly algorithm on 12 benchmark functions. The ex-perimental results show that the algorithm in this paper has better performance.
  Keywords: firefly algorithm; intelligent single particle optimization; all-attractive model; detection and development.
  
  
• Revised version of exponentially fitted pseudo-Runge-Kutta Method  
  by Shruti Tiwari, Ram Pandey 
  Abstract: In this paper, we have proposed the revised version of exponentially fitted (ef) pseudo-Runge-Kutta method (ef-PRKM). The motivation behind the revision is to fill the leakage of error in the internal stages during the fitting process. Generally, the internal stage operator, in an ef-PRKM or ef-Runge-Kutta method (ef-RKM) integrates two exponential functions exp(
  Keywords: Pseudo-Runge-Kutta method; Exponential fitting; Local truncation error; Initial value problem; Oscillatory solution.
  
  
• Scale Parameter Recognition Of Blurred Moving Image Based On Edge Combination Algorithm  
  by Dongbo Lv 
  Abstract: In order to overcome the problems of the traditional fuzzy motion image scale parameter recognition method with high recognition error and poor denoising effect, the paper proposes a new fuzzy motion image scale parameter recognition method based on edge combination algorithm. A fuzzy moving image denoising method based on fuzzy wavelet threshold denoising is used to remove noise in the blurred moving image. The scale parameter of the blurred moving image after denoising is extracted through the blurred moving image degradation model. According to the scale parameters of the blurred moving image, the edge combination algorithm is used to realize the recognition of the scale parameters of the blurred moving image. Experimental results show that the proposed method has the best quality of the blurred motion image after denoising, the lowest misrecognition rate of the scale parameter of the blurred motion image, and the application performance is remarkable.
  Keywords: Edge combination algorithm; Blurred; Moving; Image; Scale; Parameter recognition.
  
  
• Combination of a 2D-RCA model and ANNs for texture image segmentation  
  by Assia Ayache, Soumia Kharfouchi, Fouad Rahmani 
  Abstract: Image segmentation is defined as the partition of an array of measurements taken on an image on the basis of homogeneity. In this paper, a region growing technique is used to achieve image segmentation by merging some starting points or internal small areas if they are homogeneous according to a measurement of a local region property. A 2D random coefficients autoregressive model (2D RCA) is fitted in order to identify the different textures present in the image. First, an estimation procedure using a generalized method of moments (GMM) technique is proposed to extract some local region properties. For this, a gradient-based neural network (GNN) is used to estimate the 2D RCA model parameters from a given texture. The cost function of the proposed (GNN) is based on a strong matching of the statistical moments of the corresponding 2D-RCA model and the sample moments of population image data. Experimental results demonstrate the effectiveness and the relevance of the proposed method.
  Keywords: Image segmentation; 2D RCA models; ANNs; GMM.
  
  
• A Decision Model to Improve the Performance of Inventory Management for Deteriorating Items Considering Temperature  
  by Shilpy Tayal, Neeraj Dhiman, S.R. Singh 
  Abstract: Here an inventory system for time and temperature dependent deterioration with constant demand rate has been developed. If we discuss about a cold storage warehouse then the items stored maintain its quality in a particular range of temperature. A temperature value higher or lower than this range results in an increased rate of deterioration. Considering all the possible cases of temperature the optimal value of unit time profit have been calculated at different points in the assumed range. With the help of numerical example it is concluded that unit time profit is maximum at that temperature value which is feasible for that particular item. With numerical analysis the optimality of the system in all the three cases has also shown graphically. Further to verify the system stability sensitivity analysis has been performed and the system is found to be quite stable.
  Keywords: Inventory; Temperature and Time Dependent Deterioration; Demand; Shortages; Partial Backlogging.
  
  
• A hybrid grid-based many-objective optimization algorithm for software defect prediction  
  by Junyan Wang 
  Abstract: How to apply limited test resources to detect error module is one of the challenges of software defect prediction problem. To solve the problem, a many-objective software defect prediction model is proposed by considering the probability of detection and false alarm rate, the Balance value and F-measure as defect prediction objectives. At the same time, a hybrid grid-based many-objective optimization algorithm is designed to solve the model. In the designed algorithm, the adaptive dominant region operator is introduced into the grid-based many-objective optimization algorithm to improve the performance of algorithm in balancing dynamically the convergence and diversity of population. The simulation results show that the proposed algorithm has better performance in solving many-objective the software defect prediction problem.
  Keywords: Software defect prediction problem; the probability of detection; false alarm rate; many-objective optimization.
  
  
• Diabetic Retinopathy using Image Processing and Deep Learning  
  by Debabrata Swain, Sanket Bijawe, Prasanna Akolkar, Aditya Shinde, Mihir Mahajani 
  Abstract: Diabetic Retinopathy is one of the most non-uniform and confront regions to diagnose as it is exceptionally perplexing. In the circle of retinopathy, the number of times intensive assessments are required to be done to determine upon the diabetes mellitus or blindness that patient might be facing. Various professionals may take different amount of time to recognize diabetic retinopathy. So, a framework is required that can effectively and precisely analyze the retinal conditions with no of such limitations. This paper presents a two- stage method to effectively predict the level grading of diabetic retinopathy. The first stage involves preprocessing the retinal image and reducing the noise from an image. The second stage involves building a convolutional neural network architecture for predicting diabetic retinopathy level. It is a hurdle of diabetes that can affect the retinal nervous and lead to total or partial loss of vision.
  Keywords: Diagnostic;Diabetic Retinopathy;machine intervention;image preocessing; artificial neural networks.
  
  
• Application of New Entropy Measure for Pythagorean Intuitionistic Fuzzy Sets  
  by Taruna , H.D. Arora, Pratiksha Tiwari 
  Abstract: Pythagorean fuzzy set (PFS) theory has been commonly used to tackle vague information and imprecise expressions in real-world decision-making contexts. In this paper, we have explained the concept of the probability of the characteristics of a fuzzy set in the Pythagorean as an extension of the fuzzy set of intuitions. In contrast to past measures, the new proposed measures are simpler, closer to the real meaning, and better reflective properties. New generalized parametric measures of exponential entropy on intuitionistic fuzzy sets are characterized. Further, fundamental properties are demonstrated. Through dissecting the consequences of the model, it has been demonstrated that this technique is progressively dependable and increasingly e?ective in handy applications for introducing the level of fuzziness of IFS. Numerical illustration is revealed to validate the methods and compare their effectiveness with existing IFS measures.
  Keywords: fuzzy entropy; intuitionistic fuzzy sets; Pythagorean fuzzy sets; linguistic variables; multi-criteria decision making.
  
  
• Research on Internal Risk Control System of P2P Network Lending  
  by Bo PENG 
  Abstract: With the advent of new regulatory models, various types of P2P problem platforms have emerged endlessly, and bankruptcies have continued to erupt, causing investors to suffer great losses and also affecting the sound development of the P2P network lending industry. The platform system is taken as an example to analyze the internal control problems existing in the system itself, and based on this, it analyzes the risks existing in the P2P industry. It is intended to start with the top-level design of risk management control, combining third-party ratings, guarantee institutions and fund custody, other factors to optimize the internal control risk of the online lending platform, so as to strengthen the platform\'s own information intermediary function, improve the information disclosure mechanism, and resist the risk defense capability, and look forward to the good development of this emerging industry of Internet finance.
  Keywords: P2P; network lending; internal control; risk prevention.
  
  
• Modelling the impact of lock-downs in top-4 COVID-19 spreading states of India  
  by Adarsh Anand, Mohini Agarwal, Niyati Aggrawal, Navneet Bhatt 
  Abstract: Pandemics like COVID-19 being a highly infectious disease has severely affected the mankind and business activities. Seeing the critical situation, the honourable Prime Minister of India has called the lockdown in the entire country in order to supress the spread of this pandemic. While there are many debates about the spread of disease and lockdown in the entire country. We wish to mathematically understand the diffusion of this pandemic in context of four highly infected states of India. Moreover, through this article we wish to examine the impact of these lockdown period for understanding the spread-ness of COVID-19.
  Keywords: Bass Model; Coronavirus; Diffusion; Lockdown; Pandemic.
  
  
• Modified Exponential Family for Improved Searls Estimation of Finite Population Mean  
  by S.K. Yadav, Dinesh Sharma, Madhulika Dube 
  Abstract: This paper proposes a modified exponential ratio type Searls (1964) class of estimators for the estimation of population mean under simple random sampling scheme. The suggested estimator utilizes the known information on highly correlated auxiliary attribute. The theoretical derivations for the bias and mean squared error for the proposed are retained up to the approximation of order one and the performance properties of the estimator are compared with the well-established ratio, product, modified ratio and modified product estimators of the mean of the population for the characteristic under study. The efficiency conditions of the suggested estimator over these estimators are also obtained and the theoretical findings are verified using the empirical data sets. The efficiencies of the estimators are judged on the basis of mean squared errors of the sampling distributions around the true population mean of the study variable.
  Keywords: Study variable; Auxiliary Attribute; Exponential ratio estimator; Bias; MSE.
  
  
• Impacts of wind and anti-predator behaviour on predator-prey dynamics: A modelling study  
  by Prabir Panja 
  Abstract: In this paper, a predator-prey interaction model of anti-predator behaviour and wind effects has been developed. It is assumed that in the absence of predator prey growlogistically. It is also assumed that the prey shows anti-predator behaviour (group defense) against their predator. To analyze our proposed model, the impacts of wind direction have been incorporated. It is considered that due to wind effect the density of predator decrease.Next,wehave studied boundedness of all solution of the system and then all possible equilibrium points are determined. The local stability of our proposed system has been analyzed around these equilibrium points. Hopf bifurcation of our proposed system has been investigated with respect to the anti-predator behaviour (eta), wind effects (alpha) and inverse inhibitory effect of predator (b1). It is found that the effects of wind direction and anti-predator behaviour of prey can be stabilized our proposed system. Finally, some numerical simulation results have been presented to verify our theoretical findings.
  Keywords: Prey; Predator; Wind; Anti-predator behaviour; Hopf bifurcation.
  
  
• Two-Stages Explicit Schemes Based Numerical Approximations of Convection-Diffusion Equations  
  by Mohammad Izadi 
  Abstract: A wide family of finite difference methods for the convection-diffusion problems based on an explicit two-stages scheme and a seemingly implicit method are presented. In this paper, to have a greater stability region while keeping the second-order accuracy, a family of methods which combines the MacCormack and Saul'vey schemes is proposed. The stability analysis of the combined methods is investigated using the von Neumann approach. In each case, it is found that it is the convection term that limits the stability of the scheme. Based on the von Neumann analysis, valuable stability limits in terms of mesh parameters for maintaining accurate results are determined in the analytic manner and demonstrated through computer simulations. Two model problems consist of linear advection-diffusion and nonlinear viscous Burgers equation are given to illustrate some properties of the present technique such as stability and ability to propagate discontinuities.
  Keywords: Convection-diffusion equations; Finite difference approximation; MacCormack scheme; Saul'yev scheme; Stability analysis.
  
  
• Network Optimization Solutions of University Computer Laboratory Based on Virtualization Technology  
  by Wei Liu, Wei Zhao 
  Abstract: Information technology is widely used in our work and life. Colleges and Universities have set up a large number of information related courses to meet the needs of information training, and built a large number of computer laboratories to meet the teaching needs of these courses. In the process of laboratory construction and management, there are various problems, such as high failure rate, low utilization and complex management requirements. This paper carries out a detailed analysis of these problems, in order to solve these problems, we provide some suggestions for the construction of computer laboratory based on the virtualization technology, so as to effectively improve the utilization rate of the laboratory equipment and reduce the construction cost and management difficulty. Considering the difficulties encountered in the construction of laboratory virtualization, this paper analyses various factors that will affect the network performance, and puts forward the network optimization solution of university computer laboratory based on virtualization.
  Keywords: computer laboratory; virtualization; network optimization.
  
  
• Spherical based routing protocols for three-dimensional MANET  
  by Alaa Eddien Abdallah, Ebaa Fayyoumi, Emad Eddien Abdallah, Ahmad Qawasmeh, Islam Almalkawi 
  Abstract: Position-based routing protocols usually assume that mobile nodes are distributed in $2D$ space. Thus many of the previously proposed routing algorithms do not support many practical scenarios if MANET nodes are dispersed in three-dimensional environments. In this article, a couple of geographical-based routing protocols were proposed for 3D MANET, so-called, Spherical routing and Greedy-Spherical routing. In the Spherical algorithm, two dimensional Face routing is used on the internal surface of a predefined sphere after projecting each mobile node on that surface. Greedy-Spherical starts with the Greedy routing algorithm as long as there is progress toward the location of the target node. If the next node is not closer to the destination than the current node, Greedy-Spherical shifts to Spherical routing. We evaluate the newly proposed algorithms by simulation, which shows a considerable improvement in packet delivery compared with traditional known algorithms.
  Keywords: Ad hoc network; Localized routing; Delivery rate; Position based routing.
  
  
• Optimal Searls Estimation of Population Variance under Systematic Sampling Scheme: A Simulation Study  
  by S.K. Yadav, Dinesh Sharma, Abhishek Yadav, Surendra Kumar 
  Abstract: This paper proposes an improved estimation of population variance, utilizing known auxiliary information in a systematic sampling scheme. To enhance population variance estimation, we suggest a Searls (1964) type estimator utilizing known auxiliary parameters. The bias and Mean Square Error (MSE) are derived up to an approximation of first degree. The optimal values of the Searls characterizing constants are obtained, and the corresponding least mean squared errors are also obtained. The suggested estimators are theoretically compared with the competing estimators. The efficiency conditions of the suggested estimators over competing estimators are obtained. The theoretical efficiencies are verified using a real primary data set collected from a block of Barabanki District of Uttar Pradesh State in India. The estimator with lesser MSE or higher Percentage Relative Efficiency (PRE) is preferred for elevated population variance estimation in a systematic random sampling scheme.
  Keywords: Study Variable; Auxiliary Variable; Systematic Sampling; Bias; MSE; PRE.
  
  
• Solving multi-objective bi-matrix games with intuitionistic fuzzy goals through aspiration level approach  
  by Zhoushun Zheng, Mohamed Brikaa 
  Abstract: The main aim of this paper is to develop an approach to solve multi-objective bi-matrix game with intuitionistic fuzzy (IF) goals, which are called IF multi-objective bi-matrix games for short. In this article, the solution approach for such game is presented by introducing aspiration level approach, and IF non-linear programming problem is constructed to find the optimal solution for such type of multi-objective bi-matrix games. Furthermore, it is shown that this multi-objective bi-matrix game with IF goals is an extension of the multi-objective bi-matrix game with fuzzy goals. Finally, a numerical example is incorporated to demonstrate the implementation and applicability process of the proposed approach.
  Keywords: non-linear programming; intuitionistic fuzzy set; multi-objective bi-matrix games; game theory; intuitionistic fuzzy goals; aspiration level approach.
  
  
• A Support System for Coronary Artery Disease Detection using Deep Dense Neural Network  
  by Debabrata Swain 
  Abstract: Due to the development of modern gadgets and equipment, human life became very much luxurious. Hence for performing any work physical efforts are reducing day by day. This leads a person more prone to coronary artery disease which is a variety of cardiac syndrome. It has become one of the principal causes of mortality in the whole world. For the better and accurate identification of the disease, different researchers have explored many intelligent prediction systems. In this paper, an effective coronary artery disease prediction system is proposed using a deep dense neural network. The proposed model is an adaptive version of dense neural network with the addition of deep hidden layers structure and dropout. Here the data is collected from heart disease data sets present in the UCI repository. The repository consists of data taken from various geographical locations like Longbeach, Cleveland, Hungary, and Switzerland. The classifier has shown a classification accuracy of 95.32%.
  Keywords: Support Vector Machine; Random Forest; Decision Tree; Coronary Artery Disease(CAD); Deep Dense Neural Network.
  
  
• Establishment of traders optimal pricing Strategy for fresh product and used product with stock and trade cost associated demand  
  by R.P. Tripathi 
  Abstract: The recovery of used commodities is a major problem for inventory managers due to limited resources available in the universe. In this paper it is considered that a retailer sells the new commodity to purchaser and receives money, Buyer uses the product and again sells the used items. As like mango is used by living things such as men, birds, animals etc. After using it the rest part will again used for oil, medicine, fertilizers, seeds and others. Demand is assumed to be stock- sensitive for new products and price linked for used items. We formulate mathematical method for finding total yearly profit. Optimal solution is obtained by differential calculus. Numerical example and sensitivity study is providing to authenticate the authenticity of planned model. Executive phenomenon is also discussed.
  Keywords: Inventory; variable demand; recent and worn item; revenue.
  
  
• Nonparametric approximation of the characteristics of the D/G/1 queue with finite capacity.  
  by Faïrouz Afroun, Djamil Aïssani, Djamel Hamadouche 
  Abstract: In this work, we consider the finite capacity D / G /1 queue. First, the modeling of the system in question by an embedded Discrete-Time Markov chain is considered. Secondly, the aim is to illustrate the use of the discrete kernel method for the estimation of the stationary characteristics of this chain, when the general distribution that governs it is an unknown function. To support and illustrate our proposals, two extensive simulations studies are carried out.
  Keywords: Deterministic queues; Markov chains; smoothing parameter; discrete kernels; errors; simulation.
  
  
• Applications of Neutrosophic Set Theory in an Industry for Distribution of Projects and Its Maple Implementation  
  by Bizuwork Derebew, Shanmugasundaram P, Srinivasarao Thota 
  Abstract: In this paper, we proposed Average Composition Relation Method (ACRM) using the notion of neutrosophic set (NFS) operations and composition relations to identify the suitable contractors for allotment of projects. Identifying suitable contractors in any industry is not only based on bid price. Before allowing bid price, we want to ensure the prequalification criteria which affect non-price related factors like design, quality, time management, experience, financial problems. We cannot measure these factors in classical mathematics qualitatively because it is imprecise, vague and uncertain in nature. The expectations of the selection of contractors and the project criteria are recorded via Neutrosophic set and the projects allotment is effectively done in two stages. The proposed algorithm is validated through a case study. The implementation of the proposed algorithm in Maple is also discussed and sample computations are presented.
  Keywords: Neutrosophic Sets; Neutrosophic relations; Composition relations; Maple Programming.
  
  
• Forecasting foreign tourist arrivals in India using a single time series approach based on rough set theory  
  by Kriti Kumari, Haresh Sharma, Shalini Chandra, Samarjit Kar 
  Abstract: In this study, a hybrid approach based on single forecasts and rough set theory (RST) is proposed for forecasting of foreign tourist arrivals (FTAs) to India. In the formulation of the proposed hybrid method, the FTAs time series data is first forecasted using four time series models: Naive I, Naive II, Grey, and vector error correction (VEC) models. Then the RST is applied to generate an appropriate weight coefficient and the single forecasting results are combined via weight coefficient. The study also compares the forecasting results of the hybrid method with single forecasts and other combination methods such as simple average (SA) and the inverse of the mean absolute percentage error (IMAPE). Empirical results show that the proposed hybrid approach performs better than the other single forecasting modelsrn
  Keywords: Hybrid approach; Time series forecasting; tourist arrivals; single forecasts; rough Set.rn.