International Journal of Computing Science and Mathematics (16 papers in press)

Regular Issues

• 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.
  
  
• Incorporation of Question Segregation Procedure in Visual Question Answering Models  
  by Souvik Chowdhury, Badal Soni, Doli Phukan 
  Abstract: There are various open issues in visual question answering (VQA). One of them is sometimes a model can predict Yes or No as an answer which is not relatable to the question and requires a descriptive answer and vice versa. To solve this issue in the VQA domain, in this paper, a question segregation (QS) technique is incorporated to classify the questions into three types (Yes/No, Other and Number). Then we successfully incorporated this technique with wo of the VQA models, stacked attention networks (SAN) and modular co-attention network (MCAN). We evaluate the performance of the QS and SAN models on two datasets VQA v.2 and CLEVR. We also studied and analysed the impact of Question Segregation on the performance of these two models on different datasets.
  Keywords: Visual Question Answering; Machine Learning; Deep Learning; CNN;LSTM.
  DOI: 10.1504/IJCSM.2023.10058564
   
  
• Data analysis using Maximum Probabilistic Rough Set in R Environment  
  by Kalyani Debnath 
  Abstract: To address data analysis problems in practice, numerous researchers have developed several models, yet it might be difficult to reduce data effectively without losing the original information. This paper proposes a new concept of non-parametric model which achieves the best attribute reduction without removing highly significant attributes. In this paper, the concept of maximum probabilistic rough set (MPRS) is introduced and its properties are discussed where it has been found that the positive region in MPRS is a superset of rough set (RS). Later, the implementation of MPRS is put forward to solve real life problems using R Language and it is compared with several existing methods to illustrate the advantages. Experimental results demonstrate that MPRS achieves better reduction without compromising consistency factor.
  Keywords: Rough set; Variable precision rough set; Attribute reduction; Maximum probabilistic based rough set; R language.
  DOI: 10.1504/IJCSM.2023.10059990
   
  
• A Spark-based Parallel Genetic Algorithm for Bayesian Network Structure Learning  
  by Naixin Wu 
  Abstract: The Bayesian network structure learning (BNSL) algorithm based on genetic algorithm (GA) has the problem of long search time and being prone to falling into local optima. When the sampling data is large, the single machine BNSL algorithm cannot obtain the BN structure within a limited time. To address this issue, this paper proposes a parallel BNSL algorithm based on the Spark framework with GA (PGA-BN). The three main stages of the proposed PGA-BN are population initialization, BIC score calculation, and evolution operators, which are all designed in parallel on each partition to accelerate based on Spark. The experiments are studied on two typical BN datasets with different sample sizes to evaluate the parallel performance of the PGA-BN algorithm. Experimental results showed that the PGA-BN is significantly faster than its single-machine version with the satisfied accuracy.
  Keywords: Bayesian networks; structure learning; genetic algorithm; parallel.
  DOI: 10.1504/IJCSM.2023.10061827
   
  
• Offshore Booster Station Site Selection based on Immune Optimization Algorithm  
  by Zhen Wang, Yukuan Wang, Fangyi Zong, Xiangdong Yan, Wende Ke, Lei Kou, Zhen Yu, Junhe Wan, Fangfang Zhang, Guohua Sun, Zhiqiang Hu 
  Abstract: In the development and construction of offshore wind power, the offshore booster station undertakes the important task of gathering the power and delivering it to the onshore grid. At the same time, it also serves as a spare parts warehouse providing broken parts for wind turbines. Its site selection aims to improve the maintenance efficiency and reduce the power loss caused by downtime. Considering factors such as the aging of wind turbines and the occurrence of random breakdowns, we study the site selection optimization problem of offshore booster stations. Firstly, the sum of the distances from the offshore booster station to all wind turbines is taken as the objective function. Then, a mathematical model of the offshore booster station site selection is constructed based on the objective function, and it is solved by immune optimization algorithm. Finally, the feasibility of the algorithm is verified by simulation experiments. The experimental results show that immune optimization algorithms have better performance compared to genetic algorithms.
  Keywords: offshore booster station: spare parts warehouse: site selection optimization: immune optimization algorithm.
  DOI: 10.1504/IJCSM.2024.10062148
   
  
• Research on the Application of Supply Chain Revenue Sharing Contract under Omni-channel Retail BOPS Mode  
  by Bingda Zhang, Zixia Chen, Zelin Chen, Yang Peng 
  Abstract: In the context of modern retail and mobile e-commerce, the Buy Online and Pick-up in Store (BOPS) model offers a promising opportunity to boost profits by seamlessly integrating online and in-store shopping resources in the omni-channel supply chain. This study employs the Stackelberg game model to optimize strategies and contracts for manufacturers and retailers in both online and traditional in-store shopping markets. This is the first innovative analysis of the impact of revenue-sharing contracts on the profit returns of different entities in the omni-channel supply chain. By incentivizing BOPS operations through revenue-sharing contracts, this approach protects traditional in-store shopping while capitalizing on the advantages of online and in-store integration to enhance profitability. The results of the system simulation and the numerical validation show that revenue-sharing contracts outperform traditional wholesale price contracts, achieving Pareto optimization and creating a win-win situation for all stakeholders who are involved in the omni-channel supply chain.
  Keywords: omni-channel retail; supply chain; BOPS; revenue sharing contract.
  DOI: 10.1504/IJCSM.2024.10062232
   
  
• An Online Paper Reviewer Recommendation Method based on the Combination of Authority and Activity  
  by Hua Zhao, Li Wang, QingTian Zeng, Wei Tao 
  Abstract: The existing paper reviewer recommendation methods pay more attention to research interests, ignoring the integration of reviewer's authority and activity. An online paper reviewer recommendation method combining authority and activity is proposed. Firstly, an expert citation network is established and PageRank algorithm is adopted to evaluate expert authority. Secondly, a method for predicting the reviewer's domain activity based on time cycle is proposed. This method constructs expert-keyword matrix with time cycle at first, and then the matrix is smoothed, optimally decomposed and normalized to get the prediction matrix to be used to predict reviewer's activity. Thirdly, a reviewer recommendation method which combines authority and activity is presented. Finally, experiments were carried out on the automatically collected data, and experimental results show that the proposed method is successful.
  Keywords: Paper Reviewer Recommendation; Authority; Activity.
  DOI: 10.1504/IJCSM.2024.10062233
   
  
• Research on Game Strategy between Firm and Streamer about Live-Streaming Marketing  
  by Xiaoping Chen, Zelin Chen, Zixia Chen 
  Abstract: The study examines the relationship between sales efforts during live-streams and pre-broadcast marketing initiatives, presenting a model that highlights a streamer's influence and product-matching reliability. Three supply chain scenarios are identified: Direct Selling (DC model); Live-stream Selling without prior strategy (NE model); and with a strategy (E model). On the basis of analyzing the preference orientation of streaming media influence on marketing strategies, this article uses a game-theoretic model citation and digital simulation to verify for the first time when firms should use live-stream models with pre marketing strategies. Results suggest that pre-broadcast strategies enhance streamer sales efforts. Streamers with high influence tend to avoid the E model; those with low influence prioritize pre-broadcast marketing. Those with moderate influence favor the E model as product-matching reliability increases. It's also noted that firms benefit from collaborating with influential streamers.
  Keywords: Live-stream marketing; Streamer attributes; Streamer influence; Marketing strategy; Game-theoretic approach.
  DOI: 10.1504/IJCSM.2024.10062273
   
  
• PKC-SPE: A Variant of McEliece Cryptosystem based on Systematic Polar Encoding  
  by Ritu Redhu, Ekta Narwal 
  Abstract: The public key cryptosystem, including RSA and elliptic curve cryptography, can be deciphered with the development of quantum computers. As a result, we must find a replacement for these algorithms, and Post Quantum Cryptography(PQC) is the best solution to this problem.This article examines PKC-SPE, the McEliece cryptosystem variant based on systematic polar encoding. A highly reliable and effective cryptosystem based on Systematic Polar Code (SPC) encoding is designed using the properties of polar codes. Here, we also examine the error performance, upper bound on error probability, and Bit Error Rate (BER) performance for a fixed code rate of 0.85 on various blocklengths with varying bit energy to noise power spectral density ratio (EbNo). As a result, this technique has better error performance and a higher encryption rate.
  Keywords: Public key cryptosystem; Polar codes; PQC; AWGN Channel; BER.
  DOI: 10.1504/IJCSM.2024.10062275
   
  
• Analyze the brain imagine patterns under different functional electrical stimulation on the upper limb movement BCI system  
  by Tang Rongnian, Xiao Songyuan, Xie Xiaofeng, Hou Yao, Xie Hongnan, Zhao Xiaokang, Gaodi Xu 
  Abstract: The brain computer interface (BCI) systems based on motor imagery was widely used for upper limb rehabilitation of stroke patients. Functional electrical stimulation (FES) can generate artificial muscle contraction by sending electrical pulses to limbs to achieve the rehabilitation. Previous work mainly focused on the qualitative analysis of feedback, rarely on the quantitative analysis of feedback with FES. In this paper, we quantitatively analyzed the effect of FES on motor imagery. By analyzing the classification performance under different FES values from the in-house experimental dataset, we can infer that the greater the intensity of FES, the greater the enhancement of the classification effect of motor imagery task.
  Keywords: Brain computer interface; Motor imagery; EEG signal; Functional electrical stimulation; Rehabilitation.
  DOI: 10.1504/IJCSM.2024.10062346
   
  
• Efficient Line-Search Modified Bat Algorithm for Solving Large-Scale Global Optimisation Problem  
  by Enas Suhail, Ahmed Zekri, Mahmoud El-Alem 
  Abstract: An efficient line-search modified bat algorithm (EMBA) is proposed to solve large-scale global optimisation problems. A balance between exploration and exploitation abilities is achieved. Firstly, a line search to an accurate step size of a particle towards the global optimum is presented. The generated step size depends on the proximity of the particle to the global optimum and it is directly proportional to the dimension of a problem. This proportion makes EMBA capable to handle the high probability of an explosion in the initial values of objective functions in large-scale optimisation problems. Secondly, the velocity of a particle is clamped within pre-defined boundaries and penalised, if necessary, to ensure that both velocity and position of a particle are within their boundaries. These modifications combined make EMBA able to converge to the global optimum in few iterations. The numerical results show the efficiency of EMBA when comparing with other well-established algorithms.
  Keywords: Optimisation problems; global optimisation; large-scale problems; bat algorithm; meta-heuristics; nature-inspired minimizer; numerical comparison.
  DOI: 10.1504/IJCSM.2024.10062352
   
  
• Implementation of Oppositional Slime Mould Algorithm in Power Dispatch Problem  
  by Kanchan Pawani, Manmohan Singh 
  Abstract: Economic load dispatch (ELD) is crucial for power system operation, balancing electricity supply and demand while minimising costs. The goal of ELD is to minimise the overall cost of generating electricity and meets operational constraints. These constraints make the system complex. The complexity increases with the dimensionality of the problem. To tackle the complexities of the problem, a hybrid optimisation technique is implemented. This paper introduces an optimised opposition slime mould algorithm to solve this problem. The proposed algorithm uses slime reproductive behaviour and an opposition learning strategy to avoid exploitation and balance exploration. The accessibility of the proposed algorithm is calculated by the fuel cost and the compact solution. The performance and effectiveness are verified through benchmark functions, the CEC14functions and real-world load dispatch problems. The applied algorithm has yielded better results on ELD problems, benchmark functions and CEC14 functions than popular algorithms.
  Keywords: Economic load dispatch; Heuristic algorithms; Slime mould algorithm; Opposition-learning; Constraints.
  DOI: 10.1504/IJCSM.2024.10062408
   
  
• Fault Transient Waveform Recognition in Actual Transmission Lines Using Multi-Scale Convolution and Lightweight Channel Attention DenseNet  
  by Nailong Zhang, Jie Chen, Chao Gao, Xiao Tan 
  Abstract: Ensuring the proper functioning of power transmission lines is crucial for society and everyday life Accurate fault identification methods are essential for line maintenance and inspection Existing methods based on simulated data overlook the complexity of real-world fault data, which limits their reliability This study presents a method for identifying the data types of real transmission line fault transient waveforms using a multi-scale convolutional channel attention DenseNet The method consists of two main components: data preprocessing and fault recognition The imbalance of fault samples is addressed using the Synthetic Minority Over-sampling Technique (SMOTE), and the Gramian Angular Field method converts complex time series data into image data Multi-scale convolution further extracts fault feature information, while a lightweight channel attention block enhances discriminative capacity Experimental results demonstrate that the method effectively addresses sample imbalance issues and performs well on small datasets It accurately identifies fault types and exhibits excellent generalization performance.
  Keywords: transmission lines; fault identification; deep learning; DenseNet; multi scale; channel attention.
  DOI: 10.1504/IJCSM.2024.10062814
   
  
• Zipper Quintic Fractal Interpolation Function for Curve Fitting  
  by Sneha ., Kuldip Katiyar 
  Abstract: In this paper, we introduce a class of novel C^2-zipper rational quintic fractal interpolation functions (Zipper-RQFIF) with variable scalings, in the form of rational type which has a quintic polynomial in the numerator and a quadratic polynomial in the denominator with three shape control parameters. We restrict the scaling functions and shape control parameters so that the proposed Zipper-RQFIF is positive, when the given data set is positive. Using this sufficient condition, some numerical examples of positive Zipper-RQFIF are presented to support our theory. This paper approaches the zipper rational quintic fractal interpolation problem as a generalisation of both quintic fractal and affine zipper fractal interpolants which shows more versatility and flexibility than classical and fractal interpolation functions (FIFs).
  Keywords: Zipper; Zipper Fractal Interpolation Function (ZFIF); Positivity; Rational Quintic Fractal Interpolation Function (RQFIF); Iterated function system (IFS).
  DOI: 10.1504/IJCSM.2024.10062995
   
  
• VGG16 and Bi-LSTM fused with an Attention Mechanism for Human Action Recognition in Infrared Images  
  by Cheng Gao, Chao Tang, Anyang Tong, Wenjian Wang 
  Abstract: Action recognition has long been a popular subject of research in computer vision because of its wide prospects for application. Infrared videos are suitable for monitoring in any kind of weather, and can ensure the privacy of the data. We propose a method of human action recognition in infrared videos by fusing the visual geometry group 16 (VGG16) and bi-directional long short-term memory (Bi-LSTM) with an attention mechanism. First, we extract infrared images from an infrared video and pre-process them. Second, we use the VGG16 model to extract the spatial features of the images through convolution and pooling, and apply the Bi-LSTM fused with the attention mechanism to extract their temporal features. Finally, the two networks obtain the results of classification through the score fusion strategy at the decision level. The method is tested on various infrared data sets and the results show that it is effective.
  Keywords: human action recognition; deep learning; fusion model; infrared video; attention mechanism.
  DOI: 10.1504/IJCSM.2024.10063126
   
  
• New Numerical Model for the Direct Solution of Higher Order Ordinary Differential Equations  
  by Olusola E. Abolarin, Bamikole Gbenga Ogunware 
  Abstract: A unique continuous three-step hybrid block method for the solution of second, third, and fourth order initial value problems with constant step size was proposed in this research. A linear multi-step collocation approach was applied in the derivation of the new method with the use of power series approximate solution as an interpolation polynomial. The fourth derivative of the power series was collocated at the entire grid and off-grid points, while the fifth and sixth derivatives of the polynomial were collocated at the endpoint only. The numerical integrators that formed the block were also derived by evaluating the continuous scheme along with its derivatives at the non-interpolating points within the selected interval of the integration. The basic properties of the developed method were properly investigated. The comparison of the results with the existing methods showed that the new block method was better in accuracy than the existing methods.
  Keywords: Hybrid Block Method; Collocation; Higher Order Ordinary Differential Equations; Interpolation; stiff ODEs; Linear ODEs and Power series.
  DOI: 10.1504/IJCSM.2024.10063415