Forthcoming and Online First Articles

International Journal of Computing Science and Mathematics

International Journal of Computing Science and Mathematics (IJCSM)

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International Journal of Computing Science and Mathematics (60 papers in press)

Regular Issues

  • Application of New Entropy Measure for Pythagorean Intuitionistic Fuzzy Sets   Order a copy of this article
    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.

  • Modified Exponential Family for Improved Searls Estimation of Finite Population Mean   Order a copy of this article
    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.

  • Two-Stages Explicit Schemes Based Numerical Approximations of Convection-Diffusion Equations   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • Analysis of Signed Petri Net   Order a copy of this article
    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.
    DOI: 10.1504/IJCSM.2020.10047015
  • COVID-19: Machine Learning Methods Applied for Twitter Sentiment Analysis of Indians Before, During and After Lockdown   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • Half-Sweep RSOR Iteration with Three-Point Linear Rational Finite Difference Scheme for Solving First-Order Fredholm Integro-Differential Equations   Order a copy of this article
    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.

  • Exact reliability formula for n-clients computer network with catastrophic failure and copula repair   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • Improved rough K-means clustering algorithm based on firefly algorithm   Order a copy of this article
    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.

  • Machine Learning Comparative Study for Human Posture Classification using Wearable Sensors   Order a copy of this article
    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.

  • A method of designing swinging-leg walking trajectory for biped robot on plat ground   Order a copy of this article
    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).

    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • Underwater Short Distance Magnetic Communication Based on Coupling Coils in Sealed Metal Bin   Order a copy of this article
    by Qi Chen, Linqi Xia, Wei Zhang 
    Abstract: With the more and more extensive demand of ocean exploration, the communication system of deep-water operation equipment in the ocean requires higher and higher performance. Due to the absorption of electromagnetic wave by seawater, underwater wireless communication is very difficult. In order to adapt to the complex working environment in the deep sea, it is necessary to adopt the communication system with the characteristics of non-contact, low power consumption, small size, two-way transmission and so on. In this paper, a set of underwater short distance magnetic coupling communication system equipment is developed. Combined with the parameter calculation formula of magnetic coupling coil, the short spiral coil is wound, and the experiment is carried out to simulate the seawater environment. The experimental results show that the system has the advantages of low power consumption, small volume, two-way transmission and good stability.
    Keywords: underwater; magnetic coupling; Non contact.

  • A Real-coded Chicken Swarm Optimization algorithm for solving Traveling Salesman Problem   Order a copy of this article
    by Min Lin, Yuhang Yang, Yiwen Zhong, Juan Lin 
    Abstract: Chicken swarm optimization (CSO) algorithm, which is inspired by the hierarchal structure and the behaviours of the chicken flock, was first presented for continuous optimization problems. The paper proposes a real-coded scheme of CSO algorithm (RCCSO) to solve Traveling Salesman Problem (TSP). In the RCCSO algorithm, each position vector represents a visiting sequence of cities. In a position vector, each dimension represents a city and is coded with a real number. The integer part of the real number represents the index number of the city, and the decimal part denotes the visiting order of the city. Using this coding scheme, the discrete neighbourhood of TSP is converted into a continuous neighbourhood. Two repair operators, relocation operator and replacement operator, are designed to guarantee that position vector is always a valid solution of TSP. Finally, the RCCSO algorithm is compared with many different types of intelligent optimization algo-rithms. Experimental results prove that the RCCSO algorithm can find the shortest path more quickly and effectively on most TSP data sets.
    Keywords: Chicken Swarm Optimization; Real-coded scheme; Traveling Salesman Problem; Swarm intelligence algorithm; Relocation operator; Replacement operator.

  • Learning-assisted intelligent risk assessment of highway project investment   Order a copy of this article
    by Hongwei Liu, Zihao Zhang 
    Abstract: Highway project has the characteristics of long construction period, large investment scale, high investment risk and high technical requirements. How to identify, classify and evaluate the project investment risk is one of the hot spots of domestic and foreign experts. Aiming at the problem of investment risk management, this paper takes 15 highway investment projects in the past decade as the research object. According to the problems found in the actual process of highway project development, the investment risk classification is realized, and the investment risk index system including 12 first-level indexes and 30 second-level indexes is established. The hierarchical weight model of highway engineering investment risk assessment is proposed. The intelligent evaluation of highway engineering investment risk by extreme learning machine and broad learning system algorithm is discussed. The comparative experimental results show that the improved intelligent evaluation model can evaluate and predict the investment risk of highway engineering projects more effectively. The R-square value of the improved intelligent evaluation model is increased by 0.35, and the accuracy is greatly improved. It can provide decision support for highway engineering project investment risk management.
    Keywords: risk assessment; highway; risk index system; extreme learning machine; broad learning system.

  • Texture feature extraction method of multi-focus image based on gradient fusion rule   Order a copy of this article
    by Dr. Ding, Guotao Zhao 
    Abstract: In order to improve the texture detection ability of multi-focus images, a texture feature extraction method based on gradient fusion rule is proposed. The texture information detection and imaging model of multi-focus image is constructed. The texture block feature matching model of multi-focus image is constructed by using the method of region information block detection. Combined with gradient fusion rule model, the analytic rule function of texture information gradient fusion of multi-focus images is constructed. Through gradient fusion rule distribution and texture information feature decomposition results of images, multi-order moment feature decomposition is adopted to extract texture features of multi-focus images. The simulation results show that the texture feature extraction of multi-focus images by this method has higher output recognition and better texture distribution fusion performance, which improves the imaging quality and texture information enhancement effect of multi-focus images.
    Keywords: gradient fusion rules; Multi-focus image; Texture; feature extraction.

  • Modelling and optimal control analysis of coffee berry disease with cost-effectiveness in the presence of temperature variability.   Order a copy of this article
    by Abdisa Melese, Oluwole Makinde, Legesse Obsu 
    Abstract: In this paper, we propose and analyze a nonlinear deterministic mathematical model for the impact of temperature variability on coffee berry disease in coffee plants. In the analysis of the model, we derived the basic reproduction numbers at minimum temperature T0 and maximum temperature Tm which help us in establishing the local and global stability of disease-free and endemic equilibrium points. The global stability of endemic equilibrium is determined by using a Lyapunov function. We proved that the model exhibits forward and backward bifurcation by the concept of center manifold theory. Sensitivity indices are also discussed. We extend the proposed model into the optimal control problem by incorporating three controls. We also analyze the necessary conditions for the optimal control of the disease by applying Pontryagin minimum principle. In addition, we investigate the cost-effective analysis to determine the most effective strategy with minimum costs. Finally, we present the numerical simulations.
    Keywords: mathematical model; coffee berry; optimal control; cost-effectiveness; temperature variability.

    by Akhilesh Shrivas, Rashmi Gupta, Ragni Shukla 
    Abstract: This paper presents the hybrid model based on Particle Swarm Optimization (PSO), K-means, and Self Organizing Map (SOM), combined with different data mining-based classifiers for identification and classification of various health-related diseases. The main contribution of this research work is to develop a robust and computationally efficient predictive hybrid model using K-means and SOM unsupervised clustering techniques to facilitate the classification of data. The clustering algorithms help to reduce the unclustered instances or wrongly instances from the database, while the PSO is used to optimize the features of datasets. Both of these methods help to improve classification accuracy and reduce uncertainty. The proposed hybrid-based model diagnosed different diseases, namely Chronic Kidney Disease (CKD), Breast cancer, and Hepatitis disease, is one of the best results compared with other results reported after comparison in this literature. The results confirmed that the proposed hybrid system achieved better performance in measures of accuracy, sensitivity, and specificity.
    Keywords: hybrid model; classification; data mining; self-organizing map; SOM; particle swarm optimization; PSO; decision tree; DT.

  • A personalized travel route recommendation method based on improved greedy algorithm   Order a copy of this article
    by Qun Shang 
    Abstract: In order to improve the low accuracy, recall rate and popularity of the traditional method, this paper proposes a personalized tourism route recommendation method based on the improved greedy algorithm. Firstly, the attractiveness rating index of tourist attractions is established, the weight of the index is determined by the normalization method, and the attractiveness rating is obtained by combining the condition evaluation matrix and potential evaluation matrix. Then, the greedy algorithm is improved by decomsolving the maximal link subgraph to find the biggest node of influence, and the individual contribution value of tourism route resource knowledge is calculated by using the improved greedy algorithm. Finally, personalized tourism route resources are recommended according to different individual contribution values. The results show that the maximum recommendation accuracy of this method can reach 96%, the maximum recall rate can reach 80%, and the maximum is close to 4.8.
    Keywords: Maximal unicom subgraph; Perceptual sorting; Greedy decision-making range; Attractiveness rating; Improved greedy algorithm; Personalized recommendation.

  • Improved Handwritten Digit Recognition Using Artificial Neural Networks   Order a copy of this article
    by Debabrata Swain, Badal Parmar, Hansal Shah, Aditya Gandhi 
    Abstract: Handwritten digit recognition is one of the major challenging problems concerning real-time applications. It has found utilization in various fields like postal mail arranging and healthcare. There is a necessity for a framework that can apprehend the penmanship of all age groups with precision. In our proposed system, an acute number recognition system is implemented by neural networks. Different types of optimizers are employed to enhance the performance of the system. An optimizer is an essential part that aids with tracking down the ideal arrangement of weights and their values for improving accuracy. The Modified National Institute of Standards and Technology (MNIST) digit dataset has been utilized for experimentation. This work addresses how the general accuracy of a neural network can be improved by utilizing more befitting optimizers. Extensive experimentation was performed, and the model achieved recognition accuracy of 99.87% with an RMSProp optimizer.
    Keywords: handwritten digit recognition artificial neural networks; optimizers; MNIST dataset; hyper-parameters; deep learning; Adagrad; Adam; RMSprop; Gradient Descent; Stochastic gradient descent; Mini-Batch gradient descent.

  • Solving First-Order Fuzzy Initial Value Problems Using One-Step Scheme with Second and Third Fuzzy Derivatives   Order a copy of this article
    by Kashif Hussain, Oluwaseun Adeyeye, Nazihah Ahmad 
    Abstract: Fuzzy differential equations model the uncertain behaviour of the dynamic model. In cases where the exact solution does not exist, numerical methods are adopted to obtain an approximate solution of these equations. Although there are several numerical approaches in the literature, the accuracy of the existing numerical methods for solving linear and non-linear first-order fuzzy initial value problems (FIVPs) in terms of absolute error could be improved. For this reason, this article develops a one-step implicit method with the presence of higher fuzzy derivatives to obtain the numerical solution of both linear and non-linear first-order FIVPs. First, the convergence properties of the one-step method are described in detail using the definition of zero-stability and consistency for linear multistep methods. Then the one-step scheme with second and third fuzzy derivatives is adopted to solve some first-order FIVPs. The results indicate that the proposed method efficiently solves fuzzy initial value problems.
    Keywords: Fuzzy Set Theory; Fuzzy Derivatives; Fuzzy Initial Value Problems; First-Order; One-step; Implicit Method.

  • Comprehensive assessment and prediction of urban resilience: A case study of China   Order a copy of this article
    by Yao Wang, Zhe Liu 
    Abstract: Urban resilience is widely used to describe the capability of cities to fend off internal and external risks, reduce losses and recover quickly. The assessment and prediction of urban resilience can help cities to develop strategies and plans to deal with unknown disaster risks. This paper employs the method of combining The Entropy Weights Method and Technology for Order Preference by Similarity to an Ideal Solution to establish the urban resilience comprehensive evaluation model, and uses the grey prediction model and Back propagation neural network to predict the urban disaster resilience value. The results have suggested that the resilience of cities in different dimensions is generally high in eastern China, while the urban resilience in Northeast China is not higher than the average, and the regions with an average level of resilience are concentrated in Central China.
    Keywords: Urban Resilience; EWM; TOPSIS; BP neural network; Grey model.

  • Bagging Method-Based Classifier Chains for Multi-label Classification   Order a copy of this article
    by Jiaman Ding, Haotian Tan, Runxin Li, Yuanyuan Wang, Lianyin Jia 
    Abstract: Classifier chains is one of the main methods to deal with multi-label classification. For classifier chains, it is important to order labels according to the correlations among labels and to find the corresponding feature subsets. Therefore, we propose a bagging method-based classifier chains for multi-label (BMCC4ML in short). Firstly, unique feature subset for each label is selected by measuring feature importance of labels based on ReliefF. Next, considering the positive and negative correlations from a global perspective, all the labels are in order by calculating the correlations among labels. After that, inspired by ensemble learning methods, the ensemble classifier chains were constructed according to the strategy that the number of chains and the repeat rate of label order are higher than given thresholds to improve the prediction accuracy. Experiments on publicly accessible multi-label datasets demonstrate that BMCC4ML achieves more prominent results than other related approaches across various evaluation metrics.
    Keywords: Multi-label classification; Ensemble chains; ReliefF; Feature selection; Bagging Method.

  • Design Of Emergency Rescue Plans And Rescue Equipment For Confined Spaces   Order a copy of this article
    by JianYe Cui, Youchun Li, Haiping Li, Shan Gao, Yue Zhou 
    Abstract: AbstractThe emergency rescue work in a confined space is not only difficult, but also has a high risk of danger. According to the pothole and small space operating environment, a pothole and small space emergency rescue program and design of the mechanical structure of the relevant equipment, the equipment through the lifting way into the pothole or small space, and approach the unconscious trapped persons, telescopic carriage to hold up part of the human body, and then use the arm tied to the wrist or ankle, after the walking mechanism will rescue objects transported to the hole, relying on lifting equipment to equipment Hang out with the rescue object.
    Keywords: Keywords: confined space; emergency rescue; rescue equipment; structural design.

  • An option pricing model with adaptive interval-valued fuzzy numbers   Order a copy of this article
    by Qiansheng Zhang, Jingfa Liu, Haixiang Yao 
    Abstract: This paper proposes an option pricing model with interval-valued fuzzy interest rate, volatility and stock price. The interval-valued fuzzy pattern of Black-Scholes option formula is also investigated. With the presented option pricing model the European option price can be evaluated by adaptive interval-valued fuzzy number. Utilizing the proposed interval-valued fuzzy option valuation formula, the option investor can pick European option price with an acceptable interval belief degree for the later transaction.
    Keywords: Interval-valued fuzzy number; Option pricing; Possibilistic mean; Black-Scholes formula.

  • Artificial bee colony with multiple search strategies and a new updating mechanism   Order a copy of this article
    by Xin Li, Kai Li, Tao Zeng, Tingyu Ye, Luqi Zhang, Hui Wang 
    Abstract: The imbalance of exploration and exploitation is a weakness in artificial bee colony (ABC) algorithm. To overcome this deficiency, this paper presents an improved ABC (namely IABC) by employing multiple search strategies and a novel updating method. Firstly, a concept of marginal group is introduced to construct an exploration search strategy. Then, an exploitation search strategy is designed utilizing some excellent solutions. Thirdly, the probabilistic selection strategy is modified on this basis of some elite solutions. In the experiments, 22 benchmark problems were utilized to prove the effectiveness of IABC. Test results indicate that IABC achieves stronger optimisation capabilities than the other five ABCs.
    Keywords: Artificial bee colony; marginal group; multiple search strategies; selection mechanism.
    DOI: 10.1504/IJCSM.2022.10048859
  • Research on intelligent path planning technology of logistics robot based on giraph architecture   Order a copy of this article
    by Xin Xiong, Runan Huang, Cuixian He 
    Abstract: In the development of modern e-commerce, logistics robot is an important part of intelligent warehouse. Especially in large-scale warehouses, the operation efficiency of logistics robots largely depends on the processing ability of patrol efficiency. In order to solve the problems of difficult path planning and complex operation in the logistics robot operating system, based on the analysis of the traditional path algorithm, an improved logistics robot path planning algorithm based on graph architecture, CAA*path planning algorithm, is proposed and used to solve the conflict free path planning problem of logistics robot in intelligent logistics warehouse. The improved CAA*path planning algorithm is compared with the traditional a*algorithm, ant colony algorithm, simulated annealing algorithm, ant colony optimization algorithm and genetic algorithm. The simulation analysis shows that the improved CAA*path planning algorithm is more efficient in time operation. Under the same number of tasks and the same map specification, the improved CAA*path planning algorithm takes less time and runs more efficiently.
    Keywords: Logistics robot; Giraph architecture; Route planning; Cloud computing; intelligence; Warehouse.

  • An Integer Programming Model for Controlling Dengue Transmission   Order a copy of this article
    by Anuradha Mahasinghe, KKWH Erandi, Sanjeewa Perera 
    Abstract: Prevailing denguecontrol strategies in many developing countries yield only limited benefits due to nonoptimality of those strategies. In this paper we demonstrate how the same strategies could be altered using the same amount of resources in order to yield more fruitful results. Accordingly, we develop a binary integer programming model, aimed at minimizing the total number of susceptible individuals with highrisk of being infected with dengue, by identifying the most influential dengueinfected individuals who could undergo an epidemiological isolation, subject to the conditions imposed by the topological properties of the epidemiological network and budgetary constraints. Further, we analyze the proposed epidemiological isolation to examine its adequacy in a realworld implementation.
    Keywords: dengue control; integer programming; binary optimisation; epidemiological isolation.

  • A Granger Causality Analysis between Stock Prices and Exchange Rates: Evidence on Four Countries   Order a copy of this article
    by Seuk Yen Phoong, Cheng Zhi Lim, Seuk Wai Phoong 
    Abstract: This study investigates predictive causality between stock prices and exchange rates over the period of January 2005 to May 2020 in Malaysia, Singapore, China and the United States of America. The methodology applied in this study allows for structural changes during the global financial crisis as past studies have shown that tests can be biased towards non-rejection if structural breaks are not accounted for. Thence, Gregory and Hansen cointegration and Granger causality tests are implemented in this study. Results revealed that unidirectional Granger causality relations are established in Malaysia, the United States and Singapore. However, signs for causal effects differ even though two markets have the same unidirectional Granger causality relation. Additionally, stock prices and exchange rates are found to be cointegrated in United States and Singapore. Meanwhile, there is no causal effect between stock prices and exchange rate in China, for both the short run and long run period.
    Keywords: structural breaks; Granger causality; stock prices; exchange rates; cointegration.

  • Applicability of Double Power Law model for the statistical analysis of meteorological wind velocity time series data   Order a copy of this article
    by Shashank Bhardwaj, Sammedkumar Patil, Vijesh Joshi 
    Abstract: This paper is devoted to the statistical analysis of wind velocity variance time-series data obtained over the Bay of Bengal to investigate the turbulent energy cascade. The spectral distribution of wind velocity variance followed two different power laws. It was proposed that these power laws express the inertial subrange and the dissipation range. So, to cover these spectral ranges of frequencies, a double power-law (DPL) model was employed. The statistical analysis conducted on the power spectrum provided equations of both the power laws with high confidence levels. The Heaviside functions helped to formulate a single equation describing the entire spectral density. It was observed that the double power-law (DPL) model was very compliant with the -5/3 power law, and the statistical tests concluded that this model was a better fit with a close to 96% R^2 score when compared to a 90% R^2 score of -5/3 power law.
    Keywords: double power law; -5/3 power law; turbulence; inertial subrange; time series analysis.

  • A supervised learning algorithm based on spike train inner products for recurrent spiking neural networks   Order a copy of this article
    by Xianghong Lin, Xiaomei Pi, Xiangwen Wang 
    Abstract: For the recurrent spiking neural networks (RSNNs), constructing an efficient supervised learning algorithms is difficult because of their complex recurrent structure and implicit nonlinear spike firing mechanism. This paper presents a supervised learning algorithm based on spike train inner products in RSNNs. The proposed algorithm transforms the discrete spike train into a continuous function using a special kernel function, and we design the corresponding error function for the backpropagation process. The proposed algorithm is successful applied to spike train learning and pattern classification problems. The experimental results show that our algorithm has higher accuracy than the algorithm for feedback-based online local learning of weights (FOLLOW). Therefore, it is an effective method to solve the spatio-temporal pattern learning problems.
    Keywords: Supervised learning; recurrent spiking neural networks; spike train inner product; kernel function.

  • Research on multi-objective network optimization of power material distribution based on improved ? constraint method   Order a copy of this article
    by Guanghui Wang, Yumin Li 
    Abstract: Aiming at the multi-objective distribution path optimization of power materials, a multi-objective distribution planning system based on improved ? constraint algorithm is designed. Then a local power material transportation example in China is selected to simulate and test the system. The results show that the optimal solution routes obtained by the system and the planning system based on the naive ? constraint algorithm are completely consistent except for the material demand node j5?j7?j8. At the same time, the average total transportation time and average total transportation cost of the system within ?3 calculation accuracy are 386.55 min and 283.06 k$ respectively, which are 4.62%, 9.41%, 6.33% and 1.56%, 6.98%, 3.45% less than the corresponding data of the optimal solution of the system based on the naive ? constraint algorithm, particle swarm optimization algorithm and fast RCNN algorithm, respectively. The data show that the algorithm can shorten the time and cost of transportation tasks, and the planning effect is better.
    Keywords: ? Constraint method; Electric power materials; Multi objective planning; Transportation route; Cost control.

  • Stability and bifurcation study of interaction in the vermi filtration phase between predators and prey   Order a copy of this article
    by Madhan Kumar, Mullai Murugappan 
    Abstract: The issue of waste water disposal that poses a major challenge especially in the industrial sector is discussed in this article. Vermifiltration is used to transform toxic waste to non-hazardous waste. Our focus is on the survival of living organisms which is involved in the process of vermifiltration. We formulate and build a prey predator model with stage structure for the predator population. Model equilibria are observed and studied. The proposed model is expanded by incorporating time delays in the model. The global stability (with and without delay) of the model is discussed in detail. Our findings show that the increase in the density mortality rate of the predator maintains the equilibrium to a certain degree and Hopf bifurcation occurs in the model beyond this. The system exhibits oscillatory behavior when the gestation time delay reaches the threshold level. Further, computational simulations are demonstrated and biological explanations are provided.
    Keywords: vermifiltration; prey-predator; time delay; equilibria; stability; Hopf bifurcation.

  • Modeling and Analyzing the Daily Temperature of Several Cities using Mixture Gaussian Distributions.   Order a copy of this article
    by Zuhair Al-Hemyari, Jamal N. Al Abbasi 
    Abstract: The rise in the surface temperature of the Earth since the nineteenth century is a clear indication of the negative and significant impact humans have had upon it. One of the results of this is many researchers have studied the problems of meteorological problems. Some of the important applications for modeling the daily maximum and minimum temperatures, reveal the importance of modeling the data of these phenomena using mixed distributions. In this paper, heavy statistical computations are carried out. Several single and mixture Gaussian distributions have developed the modelling of data of three cities over 40 years of daily maximum/ minimum temperatures. In addition, the computations of Expectation Maximization Algorithm Estimators, the tests of goodness, and the frequency distributions are developed in order to carry accurate analyses. Additional indicators, and the conclusions, revealed that the three cities are to be affected from the Global Warming in the near future.
    Keywords: Gaussian Distribution; Mixture Distributions; Goodness of Fit; Daily Temperatures.

  • An improved pigeon-inspired optimization for continuous function optimization problems   Order a copy of this article
    by Ding Guo-shen, Dong Fengzhong 
    Abstract: Pigeon-inspired Optimization (PIO) is a new heuristic searching algorithm with a simple structure that requires only simple parameters. However, analogous to other intelligent algorithms, the limited optimization method and the swarm diversity eroded its global search ability. To resolve this issue, this paper presents an improved pigeon-inspired optimization (IPIO). First, we analyze the shortcomings of PIO systematically from its construction and use the Markov chain to expound quantitatively its convergence, proving that the algorithm can converge to the global optimum with probability one under suitable condition. Second, a new solution generating method is introduced that tackles the limitation of local optimum. Finally, 29 benchmark functions are used to test the performance of IPSO. The computational results show that the presented IPSO is superior to other improved versions of PIO proposed in recent literature, including MPIO, CMPIO, and HCLPIO, on most test functions.
    Keywords: Pigeon-inspired Optimization; Evolutionary algorithms; Global optimization; Convergence.

  • A particle swarm optimization based recurrent neural network method for predicting the trend of Chinese engineering education reform   Order a copy of this article
    by Xianjie Peng, Xiaonan Sun, Jiangtao Xu 
    Abstract: The existing studies focusing on investigating the trends of education reform mainly adopt simple clustering methods to find the relationships between the current reform directions. To solve this problem, this paper first proposes an improved particle swarm optimization algorithm; second, a particle swarm optimization based recurrent neural network method is proposed to predict the trend of the engineering education reform in China. In the proposed algorithm, the length of the recurrent neural network and the weights of the network are evolved by the particle swarm optimization algorithm. In the experiment, a dataset of the effort made in Chinese engineering education reform is built and adopted to train the proposed method, the testing results show that the proposed method outperforms the basic recurrent neural network in prediction. Furthermore, the prediction and discussion of the future trend of Chinese engineering education reform are presented.
    Keywords: Education reform; time series; recurrent neural networks; swarm intelligence.
    DOI: 10.1504/IJCSM.2022.10051042
  • A method for an approximate numerical solution of two point boundary value problems: nonstandard finite difference method on semi open interval   Order a copy of this article
    by Pramod Pandey 
    Abstract: In this article, a nonstandard finite difference technique considered with semi uniform nodes for the numerical solution of two point boundary value problem subject to Dirichlet boundary conditions in the semi open domain. To overcome the discretization issue at right open end of the domain, i.e. at infinity semi uniform nodes were used. The boundary condition at infinity is taken into account precisely by the so generated semi uniform nodes. The numerical experiment with a proposed method carried out by considering model problems including singular problem. The method proposed in the article produces a decent approximate solution of the considered problems and totally conquers the issue of boundary condition at infinity and singularity.
    Keywords: Boundary Condition at Infinity; Nonstandard Finite Difference; Quasi Uniform Step Length; Semi Open Interval; Singular Problem.

  • Numerical Approximation of Population Growth Autonomous System through Fourth Stage Geometric Mean based Explicit Runge-Kutta Method   Order a copy of this article
    by Vijeyata Chauhan, Pankaj Kumar Srivastava 
    Abstract: The future prediction of population numbers is an important factor in effectively managing the population models. A rise of pandemics like Covid-19 and its mutated versions have enhanced the need of standard numerical prediction method to deal with such problems. Keeping this in mind, the proposed study introduces a tetra geometric mean based explicit fourth order Runge-Kutta method to deal with population growth autonomous system. These population growth systems arise in population dynamics or economic industries or in chemical reactions or in biological growth models etc. Mathematically all such problems can be modeled as autonomous differential equations. In present study, two such numerical problems based on transmission of infectious disease have been solved. A one step explicit Runge-Kutta based numerical technique has been developed to solve such differential equation models. The convergence establishment of the developed method boosted the effectiveness of the proposed scheme. The proposed method is reviewed and compared with other competing methods through numerical problems based on pandemics. The results obtained are compared and the proposed method is found more companionable as compared to other fourth order methods.
    Keywords: Verhulst Equation; Logistic model of population Explicit Runge-Kutta method; Geometric mean; Autonomous initial value problem.

  • Global Optimization Procedures For Solving Systems Of H   Order a copy of this article
    by Djaouida Guettal, Mohamed Rahal 
    Abstract: In this paper, the Shubert-Piyavskii's algorithm (SPA) proposed for solving problems of global optimization with Lipschitz data, is extended to the class of H
    Keywords: Global optimization; Systems of equations-inequations; Hölder functions; Shubert-Piyavskii's method.