International Journal of Computing Science and Mathematics (47 papers in press)

Regular Issues

• 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.
  
  
• 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.
  DOI: 10.1504/IJCSM.2020.10047015
   
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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).
  
  
• 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.
  
  
• Underwater Short Distance Magnetic Communication Based on Coupling Coils in Sealed Metal Bin  
  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  
  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  
  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.
  
  
• Modelling and optimal control analysis of coffee berry disease with cost-effectiveness in the presence of temperature variability.  
  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.
  
  
• HYBRID MODEL FOR CLASSIFICATION OF DISEASE USING DATA MINING WITH PARTICLE SWARM OPTIMIZATION TECHNIQUE  
  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  
  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  
  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  
  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  
  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.
  
  
• Design Of Emergency Rescue Plans And Rescue Equipment For Confined Spaces  
  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  
  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  
  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
   
  
• An Integer Programming Model for Controlling Dengue Transmission  
  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  
  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  
  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  
  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.
  
  
• Stability and bifurcation study of interaction in the vermifiltration phase between predators and prey  
  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.  
  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  
  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  
  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  
  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  
  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  
  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.
  
  
• On the use of Container-based Virtualization for IoT Provisioning and Orchestration: A Survey  
  by Haleema Essa, Rawaa Qasha 
  Abstract: The Internet of Things has great potential to be adopted by applications covering several smart domains, as it consists of a set of physically linked objects that can be accessed through the Internet. Virtualization techniques play an important role in the field of IoT, especially for provisioning and orchestrating IoT applications to overcome the heterogeneity and diversity of the IoT components and environments that host the applications. Recently, container virtualization became the preferred technique for IoT applications due to providing execution isolation, portability, lightweight deployment, and reduced design time as compared with hypervisor-based virtualization. This survey presents a comprehensive study of provisioning and orchestrating the distributed IoT applications in different environments like Edge and Cloud Computing, and how containers can be used proficiently for provisioning and orchestrating IoT applications in these environments.
  Keywords: Internet of Things; IoT provisioning; IoT Orchestration; Container-basedrnvirtualization; Cloud Computing; Edge Computer.
  
  
• The time series modelling on exchange rate and inflation rate: finite normal mixture model  
  by Shi Ling Khek, Seuk Yen Phoong 
  Abstract: The economic growth of developing countries always a central issue concerned by all countries because any changes in global events might influence the performance of these countries. However, there are many debates on the relations between exchange rate and inflation rate in developing countries. In present study, finite mixture model is introduced to address the nexus between exchange rate and inflation rate among Malaysia, Thailand and the Philippines. With this, the monthly data with 182 observations are analysed using maximum likelihood estimation. The results reveal that negative relationship existed during growth situation while no interaction exhibited when a country is trapped into crisis period. Since this study presented the financial interaction among macroeconomic variables during different situations of country, hence, it is believed that statisticians and investors can get to know more details of these variables on these developing countries.
  Keywords: finite mixture model; maximum likelihood estimation; nonlinear time series analysis; Asian developing countries; exchange rate; inflation rate.
  
  
• Creep mechanical model of rock considering temperature effect  
  by Guilin Sun, Jiangchun Hu, Xiangyang Lu, Juncai Xu 
  Abstract: The temperature has an essential influence on the mechanical properties of rock. The present article proposes a novel creep model of rock, considering the temperature. The model explicated consists of three units: an improved Kelvin unit, an improved Maxwell unit, and an improved ideal viscoplastic unit. Based on the new model, the visco-elastic-plastic constitutive equation of rock with temperature effects is derived. When the temperature change is constant and the stress is stable, the creep equation of the system can be explicitly derived. By solving the equation with constant coefficients, the expression of rock strain is obtained. From the rock strain expression, it is demonstrated that the strain of rock is composed of a function of time and an exponential function..
  Keywords: rock; temperature; mechanical model; constitutive model.
  
  
• Numerical simulation and convergence analysis for Riemann-Liouville fractional initial value problem involving weak singularity  
  by Sudarshan Santra, Jugal Mohapatra 
  Abstract: The present work considers a Riemann-Liouville fractional initial value problem associated with homogeneous initial condition involving a weak singularity near the origin. Due to presence of initial singularity, an initial layer occurs at $t=0$. The L1 scheme is introduced on a uniform mesh to approximate the solution. The convergence analysis shows that the present method is more accurate and produces less error compared to some existing methods on any subdomain away from the origin while, the proposed method is comparable over the entire region. Numerical examples and comparison results are provided in order to show the effectiveness of the proposed method.
  Keywords: Riemann-Liouville fractional IVP; Caputo fractional derivative; L1 scheme; Error analysis.
  
  
• Performance Analysis of Internet Router Using Markovian Quasi Birth and Death Process  
  by Abhilash Vollala, Malla Reddy Perati 
  Abstract: In networking, network nodes play a crucial role, and their performance analysis has a greater significance for providing high quality service. Here, asynchronous network node with self-similar input traffic is modelled into single server queuing system with a finite buffer, where input process is Markov Modulated Poisson Process (MMPP), and service follows exponential distribution. It is intended to study the behaviour at arbitrary times, which carried out by using continuous time Markov chain and a finite quasi birth and death process (QBD). Transient state probability vector of transition rate matrix is obtained, which, in turn, gives performance metrics. Queuing behaviour is studied through performance metrics, namely blocking probability and mean waiting time (MWT) at arbitrary times, and comparison is made between steady and transient cases.
  Keywords: network node; queuing system; continuous time Markov chain, MMPP; self-similar traffic; exponential distribution; state probability vector, transition rate matrix.
  
  
• Computational advantage in evaluating oscillatory integral using quadratic spline  
  by Ramachandran Mankali 
  Abstract: Quadratic spline is used in the Levins method in a conventional manner when evaluating the oscillatory integral. Generally, Levin method involves solving a linear system of equations and this requires O(n3) computations. The quadratic spline interpolation approach however has a computational advantage and needs solving recurrence relations that involves only O(n2) computations, where (n) is the number of selected nodes. The stability of these recurrence relations is analysed, and it is shown that the proposed method for large (n), is not ill-conditioned and solution is trustable. It is also shown that linear piecewise and cubic interpolation do not offer such advantages. The paper outlines how this reduction in computation can be extended to two-dimensional oscillatory integrals. A bound on the numerical solution is obtained in terms of the frequency. We consider numerical examples, including an application from scattering problem, to assess the performance of the proposed Levin method.
  Keywords: Oscillatory integral ; Levin method ; Quadratic Spline.
  
  
• Numerical solution of partial differential equations based on new radial basis function  
  by Allal Ali, Lakehali Belkacem 
  Abstract: This paper proposes a numerical approach for solving partial differential equations (PDEs) using a new radial basis function (RBF) and collocation method. The new RBF is a coupled of the infinitely smooth RBFs and the shift of thin plate spline of order three, named the coupled shifted radial basis function (CSRBF). The CSRBF collocation method is both accurate and stable for the solution of PDEs, almost independent of the shape parameter included in this method. In addition, the CSRBF method overcomes the problem of searching for the optimal shape parameter in global RBF methods and produces a relatively well-conditioned system. The numerical examples given verify the performance of the CSRBF method.
  Keywords: Radial basis function; Coupled shifted of radial basis function; Collocation method; Shape parameter; Partial differential equations.
  
  
• Traffic Accident Prediction Based on Artificial Bee Colony Algorithm and Self-adaptive Fuzzy Wavelet Neural Network  
  by Zhicheng Li 
  Abstract: This study combines an artificial bee colony algorithm with a fuzzy wavelet neural network to establish a new traffic accident prediction model. This study improves the traditional artificial bee colony algorithm and introduces the self-adaptive mutation operation of differential evolution algorithms and the selection operator, crossover operator, and mutation operator of a genetic algorithm into the traditional artificial bee colony algorithm. This study also uses the improved artificial bee colony algorithm to optimize the weights and thresholds of the fuzzy wavelet neural network and to make up for the randomness of the weights and threshold selection of the wavelet neural network. This study combines the good nonlinear fitting ability of the self-adaptive fuzzy wavelet neural network with the strong robustness of the artificial bee colony algorithm to build a model for predicting traffic death tolls.
  Keywords: traffic accidents; prediction; improved artificial bee colony algorithm; fuzzy wavelet neural network; differential evolution algorithm.
  DOI: 10.1504/IJCSM.2022.10054977