International Journal of Computing Science and Mathematics (92 papers in press)

Regular Issues

• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• A Fusion Multi-Criteria Collaborative Filtering Algorithm for Hotel Recommendations  
  by Qusai Shambour, Mosleh Abualhaj, Qasem Kharma, Faris Taweel 
  Abstract: Recommender systems employ information filtering techniques to mitigate the problem of generating personalized recommendations in the digital world that is heavily overloaded with information. Recently, tourism industry becomes more and more popular and the number of online hotel booking sites with search engines has been increasingly growing. However, using such sites can be time consuming and burdensome for potential travellers. Accordingly, this paper proposes a Fusion Multi-Criteria User-Item Collaborative Filtering Recommendation algorithm that exploits the multi-criteria ratings of users and integrates MC user-based CF and MC item-based CF techniques to produce personalized hotel recommendations. Experimental results on two real-world multi-criteria datasets show the effectiveness of the proposed algorithm by outperforming other baseline single-criteria and multi-criteria CF recommendation approaches in terms of recommendation accuracy and coverage, in particular, when dealing with sparse datasets.
  Keywords: Multi-Criteria ratings; Collaborative filtering; Recommender systems; Hotel recommendations; Sparsity.
  
  
• Solving Capacitated Vehicle Routing Problem with Route Optimization based on Equilibrium optimizer Algorithm  
  by Ibrahim Fares, Aboul Ella Hassanien, Rizk M. Rizk-Allah, R. M. Farouk, Hassan M. Abo-donia 
  Abstract: This paper presents a new solving method for the Capacitated Vehicle Routing Problem (CVRP) based on new bio-inspired Equilibrium Optimizer (EO) algorithm. The CVRP considered as one of the NP-hard combinatorial optimization problems and most of the algorithms failed to reach optimality in these problems. The EO algorithm is a powerful technique in solving several combinatorial optimization problems. The performance of the EO algorithm in solving the CVRP compared with the artificial bee colony algorithm, the particle swarm optimization algorithm, and the whale optimization algorithm. The computational results obtained for the CVRP model illustrate the power of the EO algorithm over the competitor algorithms.
  Keywords: Metaheuristic; Combinatorial optimization; Computational complexity; Natured inspired algorithms; particle swarm optimization; Artificial bee colony.
  
  
• A Mathematical Model and Optimal Control for Listeriosis Disease from Ready-to-Eat Food Products  
  by Williams Chukwu, Farai Nyabadza, Joshua Asamoah 
  Abstract: Ready-to-eat food (RTE) are foods that are intended by the producers for direct human consumption without the need for further preparation. In the present study, a deterministic model of Listeriosis disease transmission dynamics with control measures is analyzed. Equilibrium points of the model in the absence of control measures were determined, and their local stabilities established. We formulate an optimal control problem and analytically give sufficient conditions for the optimality. The transversality conditions for the model with controls are also given. Numerical simulations of the optimal control strategies were performed to illustrate the results. The numerical findings suggest that the constant implementation of joint optimal control measures throughout the modelling time will be more efficacious in controlling or reducing the Listeriosis disease. The results of this study can be used as baseline measures in controlling Listeriosis from RTE food products.
  Keywords: Listeria; Contaminated Food Products; Food Contamination Threshold; Optimal Control Interventions; Numerical Simulations.
  
  
• Pricing American put options model with application to oil options  
  by Hajar NAFIA, Yamna ACHIK, Imane AGMOUR, Asmaa IDMBAREK, Naceur ACHTAICH, Youssef EL FOUTAYENI 
  Abstract: In this paper, we reformulate a problem of pricing American put options to linear complementarity problem. The space and the time are discretized with the finite difference method in the Crank-Nickolson approach, which leads to present the put option price as a solution of the linear complementarity problem. For solving this problem and evaluating the put options we use a fast algorithm. We apply our study for an example on oil options.
  Keywords: American option; European option; Linear complementarity problem; Black and Scholes model; Crank-Nickolson approach.
  
  
• Bi-level Optimization Model of Modular Product Family with Adaptability Consideration  
  by Xianfu Cheng, Minhua You, Xiaotian Ma 
  Abstract: Considering the adaptability of product platform in product family optimization, the product module can be divided into common module, adaptable module and customization module. Some of the design parameters in adaptable module have bi-level relationship with leader-follower characteristics. The objective function of the upper-level is to optimize the product family design, and that of the lower-level is to optimize the single product design. The bi-level optimization algorithm is designed, the genetic algorithm is designed to solve the upper-level model and the Fmincon function is designed to solve the lower-level model. For the overall optimization of modular product family with bi-level relationship, the upper-level module is to reflect the universality and adaptability of the product, and the lower-level module is more to meet the customization needs of customers. The upper-level and lower-level models can be solved by genetic algorithm. Finally, the feasibility of the method is verified by the example of the hoisting system.
  Keywords: product family; adaptable design; bi-level optimization; genetic algorithm.
  
  
• Research on prediction method on RUL of Motor of CNC machine based on Deep Learning  
  by Chuchu Rao, Renwang Li 
  Abstract: Abstract: To solve the problem of high fault frequency and sudden occurrence of the motor of CNC machine tool, the paper proposes a deep learning RUL(remaining useful life) prediction model based on DFS-LSTM. Through collecting the motor life cycle data by sensors, constructing the data set, then extracting the depth feature set from the original data by DFS?feature depth synthesis) , and the depth feature will be inputting into the LSTM(Long-short term memory) model for training, then the prediction model is obtained. In order to realize the function of predicting RUL, cut-off time function is designed in data processing, and residual life is calculated by data before cut-off time. The model is applied to the RUL prediction of the motor of CNC machine tool, and obtained a good prediction result.
  Keywords: motor;DFS;LSTM;Deadline time;RUL;CNC machine tool;predict;feature.
  
  
• Analysis of Signed Petri Net  
  by Payal , Sangita Kansal 
  Abstract: In this paper, the behavioral properties of Signed Petri net (SPN) are given along with the two techniques: Reachability Tree and Matrix equations to analyse the SPN. An actual case scenario of a restaurant model is given and analysed using the techniques mentioned in the paper. The benefits of using an SPN to model the restaurant system rather than using Petri net are also given.
  Keywords: Incidence matrix ; Petri net ; Signed Petri net.
  
  
• static gait planning method for quadruped robots on uneven terrain  
  by Wang Qixin, Wen Qi, Ke Wende, Li Huazhong, Yuan Quande 
  Abstract: In order to improve the stable walking ability of quadruped robot in uneven terrain, the discontinuous gait of quadruped robot is designed based on the stability margin calculated by the pressure center method.Gait planning includes body center of gravity trajectory planning and swing foot end trajectory planning. Sine acceleration curve is used to plan the body center of gravity trajectory to ensure the continuity of the whole movement process.Aiming at the robot with the ability of environment perception, a kind of rectangular swinging foot end trajectory is designed, which has both flexible adaptability and moving speed.The simulation results show that the quadruped robot can pass through the uneven terrain area in real time and stably using the proposed static gait planning method.
  Keywords: (quadruped robot; static gait planning; trunk track; foot end track).
  
  
• COVID-19: Machine Learning Methods Applied for Twitter Sentiment Analysis of Indians Before, During and After Lockdown  
  by H.S. Hota, Dinesh Kumar Sharma, Nilesh Verma 
  Abstract: This paper emphasizes the analyzing sentiment of Indian citizens based on Twitter data using Machine Learning (ML) based approaches. The sentiment of about 1,51,798 tweets extracted from Twitter social networking and analyzed based on tweets divided into six different segments, i.e., before lockdown, first lockdown, lockdown 2.0, lockdown 3.0, lockdown 4.0 and after lockdown (Unlock 1.0). Empirical results show that ML-based approach is efficient for Sentiment Analysis (SA) and producing better results, out of 10 ML-based models developed using N-Gram (N=1,2,3,1-2,1-3) features for SA, Linear Regression model with Tf-Idf (Term Frequency Inverse Term Frequency) and 1-3 Gram features is outperforming with 81.35% of accuracy. Comparative study of the sentiment of the above six periods indicates that negative sentiment of Indians due to COVID-19 is increasing (About 4%) during first lockdown by 4.0% and then decreasing during lockdown 2.0 (34.10%) and 3.0 (34.12%) by 2% and suddenly increased again by 4% (36%) during 4.0 and finally reached to its highest value of 38.57% during unlock 1.0.
  Keywords: Machine Learning (ML); Twitter; Sentiment Analysis (SA); Logistic Regression; COVID-19; Lockdown.
  
  
• Artificial bee colony algorithm with distance factor  
  by Min Zhou, Runxiu Wu, Hui Sun 
  Abstract: Aiming at the shortcomings of standard artificial bee colony (ABC) algorithms, such as weak local searching ability, poor diversity and easy to fall into local optimum, we propose the ABC algorithm with distance factor (DF_ABC). With the current optimal honey source as the reference, the new algorithm introduces the honey source distance reflecting the difference of the honey source location, and defines the distance factor controlling the searching direction of the algorithm through the honey source distance. The proposed searching strategy is capable of self-adaption. When the honey source distance is large, the particle can quickly jump to the peak (valley) where the global optimal point is located with the peak (valley) jumping ability; when the distance is small, the location information of the optimal honey source is used for local searching to speed up the algorithm convergence. The effectiveness of the proposed searching strategy is verified by results of the experiment on benchmark test functions. Compared with other improved algorithms, the proposed algorithm showcases the best comprehensive performance.
  Keywords: artificial bee colony (ABC) algorithm; distance factor; honey source distance; global detection; local development.
  
  
• A Survey of Blockchain : Concepts, Applications andChallenges  
  by Abhishek Taparia, Nizar Banu P K 
  Abstract: With the development of Bitcoin, organizations, be it businesses or institutions, are centring on leveraging Bitcoin's blockchain technology to non-monetary based applications to improve efficiency of the activities. Having various benefits like anonymity, decentralized, audibility etc. blockchain technology can be vastly implemented in various sectors other than financial too. This paper gives an overview the blockchain technology. It briefs about various technical concepts used in the blockchain, its types and where it can be used. It also discusses some proposed applications of the technology and tools or frameworks that can be used to develop such. It also presents the limitations of the technology.
  Keywords: Keywords: Blockchain; Hash Cryptography; Mining; Hyperledger; Proof-of-Work; Consensus Protocol; Smart Contracts.
  
  
• To solve multi-class pattern classification problems by grid neural network  
  by Ajendra Kumar, Preet Pal Singh, Dipa Sharma, Pawan Joshi 
  Abstract: Grid computing is employed to unravel massive computational problems by using large numbers of heterogeneous computers connected to the computing network. Job scheduling is an important part of the grid computing environment, which is employed to extend the throughput and reduce the turnaround and reaction time. This paper proposed a new scheduling algorithm called Feed forward neural network in the grid computing system (FFNNGC), which is used to solve some real-life problems related to the pattern classification. In the proposed method, we have used a feed-forward algorithm to find the output in the grid computing network, and the network training is done until the system converges to a minimum error solution. The pattern classification problem consists of 13 real-life, and artificial data set problems, including two class, multiclass and complex problems. Experiments were performed under these real-life problems, and the results indicated that the proposed method is helpful in such types of problems.
  Keywords: Grid Computing; pattern classification; Artificial Neural Network; Feed Forward algorithm.
  
  
• Half-Sweep RSOR Iteration with Three-Point Linear Rational Finite Difference Scheme for Solving First-Order Fredholm Integro-Differential Equations  
  by Ming-Ming Xu, Juamt Sulaiman, Labiyana Hanif Ali 
  Abstract: In this paper, we establish the three-point newly half-sweep linear rational finite difference-quadrature discretization scheme, which is the combination of the three-point half-sweep linear rational finite difference (3HSLRFD) scheme alone with the first-order quadrature scheme especially half-sweep composite-trapezoidal (HSCT) in discretizing the first-order linear Fredholm integro-differential equation (FIDE). Based on this established discretization scheme, the corresponding 3HSLRFD-HSCT approximation equation can be derived and then generate the large-scale and dense linear system. Furthermore, the numerical solution of the first-order linear FIDE can be obtained by implementing the Half-Sweep Refinement of Successive Over-Relaxation (HSRSOR) iterative method to solve the linear system. For the sake of comparison, the formulation of the full-sweep Gauss-Seidel (FSGS) and full-sweep Refinement of Successive Over-Relaxation (FSRSOR) methods are also presented as the control method. Finally, several numerical examples of the proposed problem are shown to demonstrate that the HSRSOR iterative approach gives the highest degree of supremacy in terms of number of iterations and execution time as compared to the other two existing methods.
  Keywords: First-order integro-differential equations; Half-sweep concept; RSOR iteration; Linear rational finite difference; Composite trapezoidal.
  
  
• Efficient projective algorithm for linear fractional programming problem based on a linear programming formulation  
  by Ahlem Bennani, Djamel Benterki 
  Abstract: In this paper, we are interested in solving a linear fractional programming problem that is converted into an equivalent linear program. The obtained problem is solved through an interior point method. In a first step, an adequate formulation of linear fractional programming problem into an equivalent linear program was proposed by Bennani et al., avoiding the increase of the dimension of the initial problem. Moreover, we successfully established a comparative numerical implementation of Ye-Lustig's algorithm to find the optimal solution. A comparative numerical study is carried out between this formulation and another classical one. The results obtained were very encouraging and showed clearly the impact of this formulation.
  Keywords: Linear fractional programming; Linear programming; Interior point method; Projective method.
  
  
• Exact reliability formula for n-clients computer network with catastrophic failure and copula repair  
  by Praveen Kumar Poonia 
  Abstract: In this paper, I have considered a general warm standby repairable k-out-of-n computer lab network with similar computers and all the computers are connected in parallel to a data server and a router. Failure rates of all n computers, data server and router are assumed to be constant and follow exponential distribution. Upon failure, every component moves into repair space and the repair supports general distribution and Goumbel-Hougard copula distribution. The objective of this paper is to evaluate the exact formulas for availability of the system, reliability of the system, mean time to failure and expected profit analysis in a way that numerical solutions can be obtained systematically in a reasonable computational time. This makes the computation uncomplicated and accurate. The problem is modelled as a finite series using supplementary variable technique, Laplace transform and copula repair. Lastly, the model is illustrated with graphs and an example for specific values of n and k.
  Keywords: k-out-of-n: G; computer network; availability; database server; catastrophic failure; Gumbel-Hougaard copula distribution.
  
  
• An Enhanced Harmony Search Integrated with Adaptive Mutation Strategy  
  by Ying Deng, Yiwen Zhong, Lijin Wang 
  Abstract: Aiming at making improvements on solutions to function optimization problems, an enhanced harmony search, called EHS, is proposed by hybridizing differential mutation strategies. EHS employs the differential mutation strategies after a solution generated by harmony search, then the solution is integrated into the differential mutation strategies as a target or current vector. Moreover, four differential mutation operators, including target-to-rand/1, target-to-rand/2,rntarget-to-best/1, and target-to-best/2, are invoked adaptively in a random way.rnExtensive experiments on CEC2014 benchmark functions demonstrate EHS is effective and efficient with the combination of harmony search and the differential mutation strategies.
  Keywords: harmony search; differential mutation; adaptive strategy; current vector; constructive algorithm.
  
  
• A ranking paired based artificial bee colony algorithm for data clustering  
  by Haiping Xu, Zhengshan Dong, Meiqin Xu, Geng Lin 
  Abstract: Data clustering aims to partition a dataset into $k$ subsets according to a prespecified similarity measure. It is NP-hard, and has lots of real applications. This paper presents a ranking paired based artificial bee colony algorithm (RPABC) to solve data clustering. First, a chaotic map is employed to generate initial food sources. Second, in order to speed up the search, RPABC uses a ranking paired learning strategy to produce new positions. Finally, the best food source is utilized to guide the search in the onlooker bees\' phase. Several datasets from the literature are used to test the RPABC. The computational results show that the proposed method is able to provide high quality clusters, and is more stable than the compared algorithms.
  Keywords: data clustering;artificial bee colony;ranking.
  
  
• Sinc Collocation Method: Solution of a Class of Strongly Nonlinear Two-Point Boundary Value Problems  
  by Mohammad Nabati, Ali Barati, Mehdi Jalalvand, Jalil Rashidinia 
  Abstract: In this study, Sinc-collocation methods based on single and double exponential transformations for finding solution of a class of nonlinear second order two-point boundary value problems were developed, and their properties were enumerated. The presented methods are shown to reduce the solution of nonlinear two-point boundary value problems to the system of nonlinear algebraic equations. The convergence and error analysis of the method has been investigated, also the upper bound of the error has been calculated as exponential form. To show the efficiency, ability and high accuracy of the method, several examples have been considered. The obtained results of Sinc methods based on single and double exponential transformations were compared with each other, and also with those of the existing numerical results of methods reported in the literature. The numerical results confirm that these methods rapidly converge and have a considerably efficient and accurate nature.
  Keywords: Sinc function; collocation method; nonlinear problems of BVPs; single and double exponential transformations.
  
  
• Method of characteristic points for composite Rydberg interatomic potential  
  by Takalani Malange, Samuel Surulere, Michael Shatalov, Andrew Mkolesia 
  Abstract: The Interpolation function in Mathematicatextsuperscript{textregistered} was used to identify the experimental data sets of copper atom as a potential energy curve. The characteristic points of the resulting energy curve were considered in three domains, each having five, four and two constraints respectively. The analytic forms of the extended-Rydberg potential (cubic, quartic and quadratic) were used for the curve fitting of the estimated parameters for the potential energy curve. The unknown parameters of each respective analytic form of the extended-Rydberg potentials were estimated using the minimization of the formulated goal function. This was done by an effective one-dimensional search for the (alpha_i)-parameter ((i=1,ldots, 3)). The results of the estimated values of the potential energy curve using the experimental data values indicate that the method of characteristic points gave modestly good estimates for the characteristic points of the composite Rydberg interatomic potential.
  Keywords: extended-Rydberg potential; composite potential; characteristic points; energy potentials; minimization.
  
  
• Source Selection and Transfer Defect Learning based Cross-Project Defect Prediction  
  by Wanzhi Wen, Ningbo Zhu, Bingqing Ye, Xikai Li, Chuyue Wang, Jiawei Chu, Yuehua Li 
  Abstract: Software defect is an important metrics to evaluate software quality. Too many defects will make the software unavailable and cause economic losses. The aim of SDP (Software Defect Prediction) is to find defects as early as possible. Based on this, source project selection and transfer defect learning based cross-project defect prediction STCPDP is proposed. This method firstly sets the threshold of the metrics to predicting the defect more effectively, secondly computes the similarity between different project versions to find the appropriate train sets, and finally combines the popular transfer defect learning method TCA+ to predict software defects based on the logistic linear regression model. Experimental results show that when the defect probability threshold is about 0.4, STCPDP has better performance based on F-measure metric, and STCPDP can effectively improve the popular CPDP models.
  Keywords: cross-project defect prediction; feature selection; logistic regression; source project selection; transfer defect learning.
  
  
• Research on Localization Algorithm of Large Irregular Workpiece for Industrial Robot  
  by Zhen Zheng, Shilin Wu, Qinxia Huang, Jun Yang 
  Abstract: Aiming at the problem of large irregular workpiece positioning difficulty and large error, a new workpiece positioning method is presented. The research object of this paper is a large porous irregular workpiece with holes as the processing location of the workpiece. For this porous workpiece, a special calibration tool is designed and an appropriate calibration algorithm is selected to complete the calibration of the tool. And a relative calibration method is designed. This method uses a small number of holes on the workpiece as the calibration point, and combines the data of the 3D model of the workpiece to calculate the poses of all the holes of the workpiece. Using the robot teaching method, take the pose data of part of the hole on the workpiece, and calculate the calibration error by combining the hole pose obtained above. The experimental results show that the deviation meets the calibration requirements.
  Keywords: Industrial robots; Large irregular workpieces; Calibration of tools; Workpiece positioning; Calibration algorithm.
  
  
• Improved rough K-means clustering algorithm based on firefly algorithm  
  by Ye Ting Yu, Jun Ye, Lei Wang 
  Abstract: The rough K-means clustering algorithm has a strong ability to deal with data with uncertain boundaries. However, this algorithm also has limitations such as sensitivity to initial data selection, as well as it use of fixed weights and thresholds, which results in unstable clustering results and decreased accuracy. In response to this problem, combined with the firefly algorithm, the original algorithm has been improved from three aspects. Firstly, based on the ratio of the number of objects in the dataset to the product of the difference of the objects in the dataset, a more reasonable method of dynamically adjusting the weights of approximation and boundary set is designed. Secondly, a method of adaptively realizing the threshold ? associated with the number of iterations is given. Then, by constructing a new objective function, and take the objective function value as the firefly brightness intensity to perform the search and update iteration of the initial cluster center point, the optimal solution obtained by each iteration of firefly is taken as the initial center position of the algorithm. Experiment result shows that the new algorithm has improved the clustering effect.
  Keywords: Rough K-means algorithm; firefly algorithm; Cluster center; Lower approximation and boundary set; Objective function.
  
  
• Deep denoiser prior and smoothed projection landweber inspired block-wise compressed sensing  
  by Chunmei Zong 
  Abstract: How to use effective image prior to reconstruct high-quality images is a key problem in compressed sensing reconstruction. By introducing instantiation priors, traditional optimization model-based compressed sensing reconstruction methods enjoy good structural analysis ability. To further improve the reconstruction quality, the optimization model-based method is combined with deep learning to introduce a deep denoiser prior into BCS-SPL algorithm via a plug and play technique. Notably?the denoising operator is obtained by training a multi-scale residual network with data-driven discriminant learning method. Multi-scale network can extract different scale feature information about the image, and the introduced deep prior is beneficial for reconstructing high-quality images. Experimental results exhibit that the proposed method can effectively improve the image reconstruction quality without the expense of too much computational complexity.
  Keywords: compressed sensing; deep learning; plug and play; deep denoiser prior.
  
  
• Machine Learning Comparative Study for Human Posture Classification using Wearable Sensors  
  by Aaron Rababaah 
  Abstract: Human posture classification plays important role in number of applications including elderly monitoring, workplace ergonomics, sleeping patterns studies, sports, fall detection, etc. Despite of the fact that the topic is well-studied in the literature, many studies utilize one to few models to investigate the classification reliability of different postures. In this paper we present a rich study of the problem with six primary machine learning algorithms and an overall of nine different models considered in training and testing the real world collected data of human subjects. In this study, six different postures are addressed namely: sleeping, sitting, standing, running, forward bending and backward bending. Two accelerometers were attached to the chest and thigh areas of human subjects where each sensor produced three different readings for x, y, and z axes. A total of six signal readings were collected per each posture which made-up the feature vector. Close to 45000 samples were recoded for all postures to be used for training and testing different machine learning algorithms. The study considered two categories of models, supervised and unsupervised learning algorithms namely: Neural Network - Multi-layer perceptron, Nearest neighbor classification, Discriminant analysis, Self-organizing maps, K-Means and Gaussian mixture model. After intensive training and testing of all algorithms, Multi-layer perceptron and K-Means outperformed other algorithms with an impressive classification accuracy of 99.88% and the lowest performing algorithm at 73.95% was the Gaussian mixture model as data may not follow Gaussian probability distribution.
  Keywords: Human posture; Wearable sensors; Machine learning; Neural Network; Multi-layer perceptron; Nearest neighbor classification; Discriminant analysis; Self-organizing maps; K-Means; Gaussian mixture model; clustering; classification; signal processing.
  
  
• Improved Total Difference Method (ITDM): A New Approach to Solving Transportation Problem Based on Modifications of Total Difference Method 1 and Integration of Total Ratio Cost Matrix  
  by Muhammad Sam'an, Yosza Dasril, Nazarudin Bin Bujang, Farikhin Farikhin 
  Abstract: In this paper, Initial Basic Feasible Solution is referred to as Initial Feasible Solution (IFS). There are two phases in solving transportation problem (TP). An IFS is determined in the first phase by using the least distribution cost, followed by calculation of the optimal solution through the modification of total difference method (TDM 1), integrated with total ratio cost matrix (TRCM) in the second phase. In some cases, it has been found that TP has equal values of the distribution least costs so that the existing methods generate two or more IFS values. The newly developed algorithm obtain the optimal solution of TP. A total of 26 numerical examples were selected from reputed journals to evaluate the performance of the newly developed algorithm. The computational performances were compared to the existing methods in the literature and the results showed that this algorithm not only solve TP with similar values optimal solution but also produce better minimal solutions than existing methods.
  Keywords: transportation problem; initial feasible solution; optimal solution;rntotal ratio cost matrix.
  
  
• Deep Learning of Human Posture Image Classification using Convolutional Neural Networks  
  by Aaron Rababaah 
  Abstract: Human posture classification plays important role in number of applications including elderly monitoring, workplace ergonomics, sleeping patterns studies, sports, fall detection, etc. In this paper a study of deep learning applied to human posture image classification using convolutional neural networks (CNNs) is presented. Typical computer vision workflow includes in the early stages: data conditioning, feature extraction, dimensionality reduction/feature selection whereas, in CNNs, these stages are not required which provides a big advantage of automatic feature extraction. In this work, CNNs are applied to human posture classification. The input data is collected using an RGB digital camera within an indoor environment targeting 10 different postures from 4 different human subjects including standing with 5 different variations, sitting with 2 different variations, bending and sleeping with two different variations. More than 6000 samples were collected for training and validation. Since number of features is among the most important parameters of CNN models, 7 independent experiments were conducted each of which has a different number of filters/kernels ranging within [1, 32]. The results of the experimental work showed that number of features influenced the classification accuracy significantly as the lowest CNN model produced 91.76% and the highest model produced 98.57% classification accuracy.
  Keywords: deep learning; convolutional neural networks; human posture classification; image processing; machine vision.
  
  
• Modeling and Empirical Analysis of the VMI-3PL System of Cloud Service Platform in Industry Supply Chain  
  by Zixia Chen, Zelin Chen 
  Abstract: The cloud service platform of industrial supply chain gathers chain-enterprises such as manufacturers, retailers, logistics service providers and customers, among which VMI warehousing hosting is a core function of the service platform. Based on the comprehensive analysis of the manufacturer's optimal production strategy and inventory strategy of raw materials and products, the paper constructed the VMI-3PL supply chain system model that is composed of "single manufacturer, single logistics service provider and multiple retailers" under specific constraints for the first time. Furthermore, the paper emphasizes the influence on the cost of industrial supply chain of several parameter such as the production demand ratio of the manufacturer, the inventory holding cost and order fixed cost of the third-party logistics service provider. Empirical analysis shows that VMI-3PL supply chain system can avoid high inventory cost of manufacturers, thereby effectively reducing the average ordering cost of retailers and the average total cost of supply chain.
  Keywords: industrial supply chain; cloud service platform; VMI-3PL; system model; empirical analysis.
  
  
• A new artificial bee colony algorithm based on modified search strategy  
  by Kai Li, Minyang Xu, Tao Zeng, Tingyu Ye, Luqi Zhang, Wenjun Wang, Hui Wang 
  Abstract: Artificial bee colony (ABC) is an efficient global optimization algorithm. It has attracted the attention of many researchers because of its simple concept and strong exploration. However, it exhibits weak exploitation capability. To improve this case, a novel ABC with modified search strategy (namely MSABC) is proposed in this work. In MSABC, some modified elite solutions are preserved and used to guide the search. In addition, MSABC uses the modified elite solutions to generate offspring to replace the probability selection in the onlooker bee phase. To evaluate the capability of MSABC, 22 classical problems are tested. Results demonstrate MSABC achieves superior performance than five other ABC variants.
  Keywords: Artificial bee colony (ABC); Elite solution; Search strategy; Probability selection.
  
  
• A method of designing swinging-leg walking trajectory for biped robot on plat ground  
  by Yingli Shu, Quande Yuan, Jian Zhang, Huazhong Li, Yuzhen Pi, Wende Ke 
  Abstract: The periodic walking of biped robot involves the alternate movement of supporting leg and swinging leg. In order to quickly plan the gait, it is necessary to select the key posture of biped walking on the premise of maintaining the stability of the robot. Based on the known information, the spline curve is designed and solved to construct the ankle trajectory of the swinging leg of the robot. Simulation results showed the feasibility of the method.
  Keywords: biped robot; trajectory; walking; zero moment point (ZMP).
  
  
• Safety and Energy-saving Driving Behavior Evaluation with Driving Feature Constraint TOPSIS Method  
  by Xinlei Wei, Yingji Liu, Wei Zhou, Haiying Xia, Xuan Dong 
  Abstract: With the development of road traffic, transportation enterprises pay more attention to safety and environmental protection, and the safety and energy-saving evaluations are of great significance in the management of vehicles. In view of the different characteristics of the evaluation system in the TOPSIS evaluation method, the objectivity and accuracy of the evaluation are reduced. This method takes into account the characteristics of the bad driving behavior index system based on the GPS data and improves the TOPSIS evaluation method by using index extremum constraint on the evaluation process. The experimental results show that the proposed method improves the objectivity and accuracy of the evaluation, which is always the same as the actual situation.
  Keywords: Safety and energy-saving evaluation; TOPSIS; Bad driving behavior.
  
  
• KERNEL BASED APPROXIMATION OF VARIABLE-ORDER DIFFUSION MODELS  
  by Marjan Uddin, Muhammad Awais 
  Abstract: In this paper, a numerical scheme is constructed which is based on radial basis functions (RBF) and Coimbra variable time fractional derivative of order 0 < A(t,x) < 1. The derivative due to Coimbra can efficiently modelrna dynamical system whose fractional order behavior varies with time as wellrnas space. The stability, convergence of the RBF based numerical scheme isrndiscussed and the developed numerical scheme is validated for various 1D andrn2D anomalous diffusion models with different fractional variable order eitherrna function of t or x . The accuracy and efficiency of the numerical scheme isrnachieved by comparing the results for available results in the literature.
  Keywords: RBF; Variable order; Fractional order; Anomalous Diffusion; Numerical approximation; Coimbra Derivative.
  
  
• IRPSM-Net:Information Retention Pyramid Stereo Matching Network  
  by Yun Zhao, Jiahui Tang, Xing Xu, Xiang Zhou 
  Abstract: In order to prevent the lack of information in the stereo matching process and improve the disparity map accuracy. The information retention pyramid stereo matching network (IRPSM-Net) was proposed a novel architecture that can relieve the limitation of accuracy and retention the original information of the image. The proposed network consisted an information retention pyramid module (IRPM) without batch normalization to retain the image information. And the training process was optimized by group normalization, which further improves the effect of stereo matching. The ablation experiments show that our method can effectively improve the accuracy of 0.17% in the threshold 3 pixels of KITTI2012 stereo dataset and 0.09% in the whole region of KITTI2015 stereo dataset. It showed that the improvement of IRPSM-Net can effectively improve the quality of the generated disparity map.
  Keywords: Stereo matching;Multi-scale;Information retention pyramid;Group normalization.
  
  
• An enhanced multi-objective particle swarm optimization with levy flight  
  by Hai-ying Lan, Gang Xu, Yu-qun Yang 
  Abstract: In the scope of multi-objective particle swarm optimization (MOPSO) research, avoiding premature convergence remains a challenge. To address this issue, the article develops an enhanced multi-objective particle swarm optimization with Levy flight (LF-MOPSO). In LF-MOPSO, swarm is made to evolve based on the original MOPSO to accelerate convergence. Then, Levy flight is adaptively activated to maintain diversity, so as to deal with the premature convergence when Pareto frontier is stagnant. It realizes the transformation between shrinkage and divergence of population diversity by self-adaptive conversion mechanism, which further improves the search ability of MOPSO. LF-MOPSO has been contrasted with some recently improved MOPSOs, the experimental outcomes indicate that LF-MOPSO ensures the better approximation to the Pareto optimal frontier, and gains the non-dominated solutions with good diversity and distribution.
  Keywords: Multi-objective optimization; Particle swarm optimization; Levy flight; Non-dominated solution.
  
  
• VGBNet: A Disease Diagnosis Model Based on Local and Global Information Fusion  
  by Yong LI, Xinyu ZHAO, Manfu MA, Qiang ZHANG, Hai JIA, Xia Wang 
  Abstract: There are significant differences in the data volume of different types of diseases in the electronic medical record data. Moreover, mainstream auxiliary diagnosis and prediction models either ignore local information or ignore global information. In response to these problems, this paper use a method of fusion random resampling to balance the data set, Using graph convolutional neural network to extract global features, combined with a bidirectional self-attention network, a VGBNet model is used to link local and global features to achieve diagnosis and prediction of diseases. This model can not only deal with unbalanced data but also combine global and local features to improve the accuracy of disease-assisted diagnosis and prediction. A large number of experiments show that the performance of this model has improved compared with BERT and GCN. This is of great significance to the precise auxiliary diagnosis of diseases.
  Keywords: Unbalanced Data Set; Disease Prediction; Graph Convolutional Neural Network; Attention Mechanism.
  
  
• Dense Deep Stochastic Configuration Network with Hybrid Training Mechanism  
  by Weidong Zou, Yuanqing Xia, Weipeng Cao 
  Abstract: Thanks to the supervised parameter generation strategy and non-iterative training mechanism, Deep Stochastic Configuration Network (DSCN) has achieved very efficient modeling efficiency in scenarios with relatively small problem complexity. However, the increasing number of hidden layers and the amount of training data have issued a challenge to the implementation of DSCN. To solve this problem, we propose a Dense DSCN with a Hybrid Training mechanism (HT-DDSCN), which extends the network structure of the DSCN to a dense connection type and combines three typical optimization techniques and one universal control strategy to optimize the calculation process of the output weights. Extensive experiments on four benchmark regression problems show that HT-DDSCN can significantly improve the generalization ability and the stability of DSCN.
  Keywords: Deep Stochastic Configuration Network; Randomized Neural Networks; Generalization Ability.
  
  
• Time series granulation-based multivariate modeling and prediction  
  by Mengjun Wan, Hongyue Guo, Lidong Wang 
  Abstract: The typical characteristics of time series data exhibit a large data size, high dimensionality, and high correlation. To better extract high-level representative information for time series, this study proposes a novel granular vector autoregressive (GVAR) model, which incorporates granular computing with VAR models to predict the main varying ranges of the multivariate time series. The proposed model first utilizes the principle of justifiable granularity to construct information granules, which capture the cardinal information hidden in the time series. Then, the granular VAR model is built based on the upper and lower bounds of the constructed information granules simultaneously. Here, the interval least squares (ILS) algorithm is employed to estimate the models coefficients, and the regressive order is determined by the Bayesian information criterion (BIC). Finally, experimental studies are conducted to illustrate the effectiveness and practicality of the proposed prediction model.
  Keywords: Information Granule; Multivariate Time Series; Granular Prediction.
  
  
• Theoretical Model Analysis and Case Simulation of Spherical Involute Surface  
  by Zixia Chen, Yan Zhao, Jingyu Liu, Zelin Chen 
  Abstract: The theoretical model of spherical involute surface is the premise and foundation of 3D modelling design technology of spiral bevel gear. The forming process of the surface is also the forming process of its mathematical model. Based on the generating principle of spherical involute on cone and surface analysis, the system model is gradually divided into blocks from curve to surface, and the surface of bevel gear is obtained by parameter coupling. The modelling process of curve, surface or initial spiral can be independently referenced. It can realize the precise modelling of complex spiral profile surfaces such as variable spiral angles. In this paper, the mathematical model of spiral bevel gear was simulated and analyzed on the MATLAB2019b platform. The simulation results had verified the correctness of the algorithm of the spiral bevel gear model and the practicability of design optimization.
  Keywords: spiral bevel gear; spherical involute surface; mathematical modelling; MATLAB simulation.
  
  
• Multi-objective Cellular Memetic Algorithm  
  by Xianghong Lin, Tingyu Ren, Jie Yang, Xiangwen Wang 
  Abstract: This paper presents a multi-objective cellular memetic algorithm (denoted by MOCMA) based on k-means clustering, which integrates the clustering-based local search method into multi-objective cellular genetic algorithm. Specifically, according to the objective function values of individuals in each generation, the k-means clustering is used to control the similar individuals gathered in a cluster. Meanwhile, to explore the search space efficiently and get the Pareto optimal solutions in objective space, one individual is selected randomly to undergo local search from each cluster and it will be improved than before. The MOCMA is applied to constrained and unconstrained problems. We analyze the influence of cluster number on the performance of the algorithm, and compare the MOCMA with other evolutionary multi-objective optimizers. It indicates that the proposed MOCMA is efficient for solving the multi-objective optimization problems.
  Keywords: cellular genetic algorithm; multi-objective optimization; k-means clustering; pareto optimal set; local search.
  
  
• Research on x-vector speaker recognition algorithm based on Kaldi
  by hong zhao, lupeng yue, weijie wang, xiangyan zeng 
  Abstract: This paper presents a convolutional neural network with an attention mechanism for analyzing the spectrogram in an x-vector based speaker recognition system. First, the convolutional neural network (CNN) is used to extract the features of the spectrogram. Then, an attention mechanism is designed to calculate the frame weight in the statistical pooling layer. Finally, probability linear discriminant analysis (PLDA) is used as a back end classifier. The system is implemented using Kaldi speech recognition tools and tests on the Voxceleb1 database. The experimental results show that the combination of spectrogram and CNN gains a relative improvement of 6.7% in equal error rate (EER) compared with the x-vector baseline system. The attention mechanism for the statistical layer further leads to a relative improvement of 26.1%. Overall the proposed method outperforms state-of-the-art methods on the Voxceleb1 database.
  Keywords: spectrogram, attention mechanism, x-vector, speaker recognition, Kaldi.