International Journal of Computational Systems Engineering (54 papers in press)

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  Causal transformer and counterfactual reasoning: deconstruction analysis of the impact of teaching strategies on academic achievement ( Free Full-text Access )

  by Lipeng Zhang 
  Abstract: Modern education increasingly relies on data-driven decision-making, requiring causal inference methods to assess teaching strategies beyond correlations. Challenges such as time-varying confounders, unobserved counterfactuals, temporal dependencies of interventions, and heterogeneous responses limit strategy design and evaluation. Existing methods also struggle with temporal dynamics and complex causal structures. To address these issues, causal temporal contextual reasoning (CTCR) was developed, incorporating a dynamic disentanglement mechanism for time-varying confounders, a two-way causal representation module, and a counterfactual generation algorithm constrained by temporal logic. Experiments on higher education (Dataset-H), K12, and MOOCs datasets show CTCR's effectiveness. On Dataset-H, it achieves MSE-T 5.53 × 10^-2, PEHE 5.16 × 10^-1, and CP@K 0.89, outperforming comparative models. Performance volatility across K12 and MOOCs is ≤ 29.3%, and CTCR remains robust under 30% data sparsity and 5 dB noise, demonstrating strong generalization and reliability.
  Keywords: causal transformer; counterfactual reasoning; instructional strategy; CTCR; academic achievement; time-varying confounders.
  DOI: 10.1504/IJCSYSE.2025.10075320
   
  
• A Security protocol with trust model for WSN  
  by Ruizhi Chen  
  Abstract: The research introduces a novel secure routing protocol for wireless sensor networks by integrating a trust model. The protocol incorporates acceleration factors and penalty coefficients to enhance the trust mechanism, enabling faster identification of malicious nodes and swift reduction of their trust values. The protocol introduces the acceleration factor and penalty factor into the trust mechanism to increase its response speed and trust value drop rate. Performance tests reveal that the protocol exhibits over 60% faster trust value reduction speed than TLEACH and 37% faster than the TLES protocol under black hole attacks. Additionally, the protocol demonstrates a longer life cycle, with the first node expiring around round 422 and half of the nodes ceasing operation by round 537, surpassing the longevity of TLEACH and TLES. These findings highlight the protocol’s promising practical application potential.
  Keywords: wireless sensor network; WSN; security protocols; network structure; network security.
  DOI: 10.1504/IJCSYSE.2025.10059616
   
  
• Research on the Importance of Database System Security Performance Testing Technology to Computer Software Development  
  by Li Gao, Qiu Junlin, Huaqi Lu, Xiaolin Jiang, Shaohang Yi 
  Abstract: In order to make up for the lack of database security in traditional business systems and help managers realise real-time monitoring and auditing of database operations, the research is based on cloud-encrypted databases and combined with auditing technology to ensure database security and integrity during software development sex. In the study, two normal users and two abnormal users are used to operate the database to judge the effectiveness of the security audit scheme. The results show that the judgment value can distinguish normal users from abnormal users, and false detection rate can be kept below 0.03. As length of sliding window increases, detection rate of system shows an increasing trend, and the false detection rate shows a decreasing trend, but the change gradually slows down, and the impact on it decreases significantly after reaching 0.015. It should be noted that with the increase of the sliding window, the time complexity of the system is also increasing, which will have a certain impact on real-time performance of audit. The research results ensure that database managers can discover the existing problems in the first time, and formulate targeted solutions to improve the efficiency of software development.
  Keywords: big data; cloud computing; hidden Markov model; HMM; encrypted database; security audit.
  DOI: 10.1504/IJCSYSE.2025.10059710
   
  
• Two-way Sentiment Analysis Method of Multimedia Information Based on Deep Learning Algorithm  
  by Yingjie Liu, Baopeng Kan 
  Abstract: Emotional analysis can better understand the public's emotions and needs, and make better decisions based on the analysis results. However, there is still a lack of effective analysis methods in practical applications. Therefore, the study utilises a bidirectional emotion classification mechanism based on deep learning, and uses time series algorithms to predict the development trend of users' bidirectional emotions. A bidirectional emotion analysis method for multimedia information based on deep learning algorithms is proposed. The results show that the accuracy of emotional judgment in the analysis model is 82.5%, which is 11.1% higher than the machine learning model. At the same time, the prediction accuracy of the prediction model is around 84%, which is significantly better than the comparison method. This indicates that the bidirectional emotion model constructed through research can accurately analyse user emotions and provide reference for making development decisions in the multimedia field.
  Keywords: deep learning; two-way sentiment analysis; attention mechanism; time series; sentiment prediction.
  DOI: 10.1504/IJCSYSE.2025.10059764
   
  
• Feature Perception Based Graphic Advertising Image Generation Technology  
  by Huichao Zhang  
  Abstract: In order to meet the market demand for graphic advertising images, this article proposes a feature aware image generation technology for print advertising. This technology quantifies image features, uses simulated annealing algorithm to sample the quantised features, and then combines dictionary strategy to optimise probability models to predict feature distribution, ultimately generating the optimal graphic advertising image. The results show that in terms of iteration error rate, the simulated annealing algorithm tends to stabilise after 85 iterations, with an error rate of 0.015. In terms of colour feature extraction rates, the simulated annealing algorithm has extraction rates of 92%, 91.5%, and 89.1%, respectively. In expert evaluation, the expert evaluation scores all exceed 90 points. The above data indicates that the proposed method is feasible and can provide technical support for related advertising image generation.
  Keywords: graphic advertising; image generation; simulated annealing algorithm; lexicographic strategy; feature perception.
  DOI: 10.1504/IJCSYSE.2025.10060143
   
  
• Security Defence Technology for Webcast Integrating SSA and Reinforcement Learning  
  by Delu Wang  
  Abstract: This paper first introduces logistic chaotic mapping and random walk strategy to optimise traditional sparrow search algorithms, and combines them with support vector machines for intrusion detection. Subsequently, reinforcement learning and game model were integrated. The data prove that the loss function of the proposed detection method is the smallest and approaches to 10-6 infinitely when the iteration is 61 times. In the comparison of comprehensive F1 values for detection and defence, when the running time is 0.475 seconds, the F1 value of the proposed method is the highest, reaching 98.31%. In the analysis of defence success rates for different attack strategies, the proposed strategy can achieve a maximum of 99.78% against password intrusion in network live streaming, and can maintain 99.99% against security vulnerabilities in network live streaming security intrusion. This indicates that the proposed security defence technology has implemented various types of network live streaming security intrusion prevention.
  Keywords: sparrow search algorithm; SSA; reinforcement learning; online live streaming; defence; intrusion detection.
  DOI: 10.1504/IJCSYSE.2025.10060356
   
  
• Airborne network security protection technology based on hybrid K-means algorithm  
  by Yunna Shao, Bangmeng Xiang 
  Abstract: In order to reduce the security risks such as illegal acquisition of airborne network data and malicious attacks. Based on k-tree structure, weighted density method is used to accelerate K-means clustering. Weighted voting rules are proposed to enhance the training of labelled data sets. Finally, binary tree structure is used to design the classification model. The results showed that the detection rates of remote to local (R2L) and user to root (U2R) were increased by 7.98% and 7.64%, respectively. The research methods achieved 91.63%, 92.29%, 90.68% and 96.34% of the network information confidentiality, integrity and availability, and virus detection ability, respectively. The increases were 36.15%, 40.81%, 44.41% and 44.38%, respectively. The research model can detect airborne network attacks more comprehensively and accurately than the existing detection methods. It can be used to protect the personal information of network users, as well as the security of network communication processes.
  Keywords: K-means algorithm; airborne network security; semi supervised hierarchical classification; tri-means; Kd-tree; detection accuracy.
  DOI: 10.1504/IJCSYSE.2025.10060414
   
  
• Bank Marketing Model Based on Improved Neural Network Algorithm  
  by Tongdi Hou, Jie Chen 
  Abstract: In commercial banks, traditional marketing methods cannot directly and accurately predict customer needs and preferences, leading to a decline in bank competitiveness. With the progress of big data, deep learning has been applied in many fields. CNN has the characteristics of high-dimensional data and nonlinear data processing. Research using CNN to design marketing models, introducing gravity search algorithm to solve the problem of uncertain network structure selection and overfitting, and using bagging ensemble learning algorithm integration to improve generalisation ability. Due to the uncertainty of network structure in the simulated annealing algorithm, this algorithm was chosen to optimise CNN for comparison. The experiment showed that the CNN MSE optimised by the study was 0.0096, and compared with the comparative model MSE = 0.1021, the similarity between the predicted value and the actual value reached 87%. Therefore, the marketing model based on gravity search algorithm optimisation and bagging integration has good development potential.
  Keywords: Convolutional neural network; GSA; Simulated annealing algorithm; Bagging Integration; MSE evaluation.
  DOI: 10.1504/IJCSYSE.2025.10061137
   
  
• Intelligent Recognition English Translation Model Based on Speech Recognition  
  by Xiulian Han, Yawei Ran 
  Abstract: This article uses the physical model sampling survey method, mapping method and parameter analogy method to collect data, analyses the practicality of speech recognition from the four aspects of the model's translation speed, efficiency, language sense and connectivity, and creates a translation model suitable for intelligent recognition. The research results found that in terms of translation speed evaluation, there were 268 samples with the same evaluation by machines and humans, with a consistency rate of 96.58% and a correlation coefficient of 0.74; in terms of language perception evaluation, the consistency rate reached 99.87% and a correlation coefficient of 0.512; in terms of translation efficiency evaluation, the consistency rate was as high as 96.87%, and the correlation coefficient was 0.554; in terms of connectivity evaluation, the consistency rate was as high as 95.19%, and the correlation coefficient was 0.614.
  Keywords: speech recognition; speech signal; English translation model; translation speed.
  DOI: 10.1504/IJCSYSE.2026.10061819
   
  
• Impact of Computer Intelligent Healthcare Combined with Nursing Monitoring on the Efficacy and Medication Safety of Critically Ill Patients  
  by Guiqiang Ren, Yuan Zheng 
  Abstract: The article selected 120 severely ill patients admitted to a general hospital in a certain city from January 1, 2022 to December 31, 2022 as the research subjects, divided into an observation group (60 cases) and a control group (60 cases). The observation group used the system as auxiliary treatment, and the control group used traditional methods for treatment. In terms of changes in inflammatory indicators, the P values of reactive protein (CRP), neutrophil percentage (NEUT%), and lymphocyte percentage (LYM%) in the observation group upon admission and 14 days after treatment were 0.001, 0.033, and 0.026, respectively, showed statistically significant differences; the P values of the average changes in CRP, NEUT% and LYM% indicators of patients in the control group at admission and 14 days after treatment were 0.048, 0.206 and 0.118 respectively, and there was no significant difference in NEUT% and LYM%.
  Keywords: efficacy of critically ill patients; drug safety; computer intelligent healthcare; nursing monitoring; medical decision making.
  DOI: 10.1504/IJCSYSE.2026.10061820
   
  
• Abnormal behaviors identification method of college students by WOS-IForest under smart campus  
  by Ronghua Teng, Shuyu Teng, Junpeng Wang 
  Abstract: Based on isolated forests, a weighted optimum sub forest algorithm is therefore constructed and examined in response to the tiny fraction of aberrant data and large variations from normal data. Subsequently, a twin gated recurrent neural network model based on linear discriminant analysis loss function is examined and built using the features of data from college students. Ultimately, integrating the two results in a mechanism for recognising aberrant conduct in college students. The research results show that the algorithm proposed in the study has the shortest running time in different dimensional datasets, with an average running time of 124.5 ms and a maximum average accuracy of 98.76%. The average accuracy of the model designed for the study was 98.01%. Finally, the study employed the recognition approach of abnormal behaviour among college students to build a digital image of the students’ aberrant activity, with a pretty broad presentation impact.
  Keywords: smart campus; WOS-IForest; abnormal behaviour identification; twin network; digital portrait.
  DOI: 10.1504/IJCSYSE.2026.10061823
   
  
• Evaluation on Embedded Computer Information Network System Security Architecture  
  by Jing Liang  
  Abstract: With the continuous development of internet technology and computer technology, the security performance of information network system has begun to be widely concerned, and an enterprise's computer information network would cause serious consequences if it is hacked. For the security problem of the network system, this paper solved this problem by introducing the embedded system into the information network system. This method is based on the existing computer information system and utilises embedded technology to improve the security performance of the system and prevent advanced trojans from invading the information network. By embedding embedded systems into the existing information network systems of the enterprise, the average integrity increased from 92.8% to 97.3% in two months. For information network systems without embedded systems, the average integrity has dropped from 87.8% to 86.3%, which is already not high and has also decreased. The experimental results indicated that embedded systems are very effective in improving the security performance of computer information network systems.
  Keywords: system security; embedded system; computer information network; trusted platform module.
  DOI: 10.1504/IJCSYSE.2026.10062105
   
  
• Machine Learning Model of English Language Psychology Based on Data Mining Technology  
  by Hanhui Li, Zijiang Zhu, Chen Chen, Yi Hu 
  Abstract: The development of the times cannot be separated from language, which is the most basic tool for communication and the carrier for obtaining information. In recent years, data mining technology has achieved relatively good development, especially at the level of students English learning. Therefore, this paper built a machine learning model of English language psychology based on data mining technology. Compared with classroom teaching education, modern distance education was more conducive to the improvement of students' English performance, which could guide students to learn online and offline, and break the space of learning English. Therefore, it is meaningful to study the machine learning model of English language psychology based on data mining technology in this article.
  Keywords: machine learning model; English language; data mining technology; distance education; traditional learning model.
  DOI: 10.1504/IJCSYSE.2026.10062107
   
  
• Application of LightGBM Algorithm in Risk Control of Investment Industry  
  by Zhao Guang 
  Abstract: Bond default risk has the potential to result in losses for investors, which might influence their choice of investments. The study uses the gradient boosting decision tree algorithm framework as its starting point, choosing the indicators from the four categories of macro factors, debt characteristics, financial factors, and non-financial factors. The study then further calculates the value of the information in order to screen out the final indicators, and constructs a bond default prediction model. The model is optimised by introducing genetic algorithm to get the final optimised bond default risk warning model. The results of experimental revealed that the model's accuracy was improved by 2.3% in comparison to using a single index factor, the corresponding AUC value after incorporating the study’s proposed index system into the model reached 0.9992, and the standard deviation of the model in the ten-fold cross-validation reached 0.0011. Results indicated that, when compared to the pre-improvement technique, the true rate of the study's improved model was 5.4% higher and the false-positive rate was 0.52% lower. It demonstrates that the model has higher predicted accuracy in addition to superior predictive stability, which can serve as a decision-basis for risk control in the investment industry.
  Keywords: LightGBM; indicator system; risk control; bond default; genetic algorithm.
  DOI: 10.1504/IJCSYSE.2026.10062133
   
  
• Hierarchical Planning and Design of Landscape Architecture Environment Based on VR Technology and Computer Vision Technology  
  by Siyi Wang, Ling Wei, Fang Wang, Rongyuan Xiong 
  Abstract: In the traditional environmental level planning and design of landscape architecture, the relevant personnel mostly rely on the analysis and modification of drawings to carry out the project construction and construction plan, and the design results are poor. In order to improve the intuitiveness and authenticity of designers in space vision, this paper analysed the use of virtual reality (VR) technology and computer vision (CV) technology to achieve the design of landscape architecture. This article analyses the terrain, topography, and hydrology in gardens using CV technology, providing a basis for virtual terrain design and hydrological modelling. The results showed that it can be seen that Scheme 2 has high intuitiveness and authenticity. With the continuous progress of science and technology, VR technology would play an increasingly important role in landscape architecture design.
  Keywords: virtual reality; VR; computer vision; CV; landscape architecture; levels of detail.
  DOI: 10.1504/IJCSYSE.2026.10062144
   
  
• Basketball Trajectory Tracking Based on Machine Vision Image  
  by Tao Liu, Qinhong He 
  Abstract: Based on the existing experience, this paper made a comparative study of the two existing basketball track tracking methods and technologies, which were background difference method and frame difference method. Two methods were used to compare the same group of video images. The corresponding data were obtained according to the calculation methods of the two methods, and the two groups of data were compared and analysed to show the advantages and disadvantages of the two methods. According to the data comparison of the above two methods, the background difference method had fast calculation speed and high real-time efficiency. The corresponding conclusion was drawn that when high-speed objects needed to be tracked in real-time, the background difference method could be used. When it was not necessary to track the moving track of the target in real-time, the frame difference method could be used to track the moving track.
  Keywords: basketball trajectory tracking; machine vision image; Kalman filtering; image recognition.
  DOI: 10.1504/IJCSYSE.2026.10062145
   
  
• Motion Video Evaluation and 3D Human Motion Simulation in Image Processing Oriented Sports Training  
  by Dandan Fan, Xiaodan Yang 
  Abstract: In this paper, image processing technology was used to analyse sports video in sports training, and 3D human motion simulation was carried out. In this paper, firstly, image processing of sports training video was carried out, including image transformation, greyscale transformation and image filtering. After that, the research of moving object detection was carried out, and the detection methods included inter frame difference method and background difference method. The common human models were introduced. Next, a 3D human motion model was constructed, and 3D human knowledge was recognised. In the experiment part, the video of running, walking, rope skipping and sit ups were analysed and simulated. It can help athletes better analyse the mistakes in the sports process, and improve the athletes' sports efficiency, so as to maximise the athletes’ sports ability.
  Keywords: 3D human motion simulation; motion video analysis; physical training; image processing; electronic imaging.
  
  
• Digital Network Communication Strategy of Brand Influence under the Background of Computer Multimedia Technology  
  by Xinyi Liu  
  Abstract: Internet has become an important channel for people to get information. Only by timely understanding of Internet trends can enterprises better communicate with consumers and effectively promote and shape their brands. This paper aims to explore the digital network communication strategy of brand influence under the background of computer multimedia technology. This paper analyses the difference between digital technology and traditional technology, probes into the influence and value of digital technology on brand communication, puts forward the image quality evaluation method, and conducts an experimental study on the digital network communication of brand influence. The experimental results show that under the background of computer multimedia technology, the brand awareness score is between 8.5 and 9.3 points, the brand interaction score is between 8.8 and 9.5 points, the brand satisfaction score is between 8.7 and 9.6 points, and the brand loyalty score is between 8.6 and 9.5 points.
  Keywords: brand influence; computer multimedia; digital network; communication strategy; image quality evaluation.
  DOI: 10.1504/IJCSYSE.2026.10062146
   
  
• Computer Vision-based Accurate Identification System for Damaged Parts of Athletes' High-strength Sports Injury Images  
  by Guoyang Huang  
  Abstract: With the continuous development of society, the application of computer vision (CV) is also increasing. CV is an important branch of AI. The problem it needs to solve is to understand the content in the image. Due to the fact that various parts of the body would be damaged to different degrees during the high-intensity exercise of sports athletes, the image recognition and analysis must be carried out during the treatment. The accuracy and efficiency of the existing relevant technologies to identify and process them are very low. To solve this problem, this paper proposed a high intensity motion damage image based on fish swarm algorithm, and applied it to gray scale conversion and damage recognition. By comparing particle swarm optimisation (PSO), genetic algorithm (GA) and the algorithm designed in this paper, the experiment in this paper was analysed from two aspects of recognition rate and time. According to the experimental data, when the number of recognised images was 50 and the number of experiments was 50, the recognition rates of PSO, GA and this algorithm were 64.33%, 66.86% and 94.57% respectively. When the number of recognised images was 35, the recognition time of PSO, GA and this algorithm was 0.768 s, 0.807 s and 0.532 s respectively. It was not difficult to see that the design method in this paper had excellent performance in recognition rate and recognition time. Therefore, the system designed in this paper was worthy of further promotion and application.
  Keywords: sports injury; computer vision; CV; sports athletes; accurate identification of image damaged parts; fish swarm algorithm; high-intensity sports.
  DOI: 10.1504/IJCSYSE.2026.10062164
   
  
• Virtual Simulation Technology of Embedded Systems in Multimedia Digital Signal Processing  
  by Shujuan Qu  
  Abstract: In order to improve the security of the embedded system, people have connected the embedded system with virtual simulation technology. In embedded systems, this article used virtual simulation technology to analyse embedded systems based on virtual simulation, and completed virtual simulation of reliability enhancement technology. By analysing the solutions of virtualisation technology in multimedia digital signal processing, a virtual simulation signal processing system was studied. Through experimental data, it has been proven that virtual simulation technology had better performance in signal frequency, transmission bandwidth, and signal denoising in embedded systems. The average value of the signal output cut-off frequency of Gaussian white noise was 3% higher than the signal transmission cut-off frequency.
  Keywords: embedded system; field programmable gate array; FPGA; digital signal processing; virtual simulation technology.
  DOI: 10.1504/IJCSYSE.2026.10062165
   
  
• Using Artificial Intelligence to Construct a Character Expression and Action System for a 3D Human Model  
  by Bozuo Zhao, Danping Zhan, Canlin Zhang 
  Abstract: In recent years, with the continuous development of computer graphics technology and the wide application of artificial intelligence technology, three-dimensional human modelling technology based on artificial intelligence has gradually become a research hotspot. This article aims to use artificial intelligence to optimise the design of the system. The article introduces common 3D human modelling methods, and then optimises the 3D human reconstruction algorithm. Then, it elaborates on the process of generating complex virtual scenes and 3D facial modelling methods, and uses sequence images to achieve 3D human model reconstruction. Finally, a detailed analysis is conducted on the construction of a character expression action generation system. The experimental results show that the three-dimensional human body reconstruction algorithm designed in this paper reduces the time consumption by about 50% compared to traditional algorithms, and the error is reduced by about 30% compared to traditional algorithms.
  Keywords: virtual reality technology; 3D virtual human; model construction; character expression action system; artificial intelligence.
  DOI: 10.1504/IJCSYSE.2026.10062235
   
  
• Construction of a Network Platform for Student Behavioral Health Monitoring Based on Decision Support  
  by Keke Wang  
  Abstract: In the introduction, the significance of research on student behavioural health was introduced, and then academic research and analysis were conducted on the two key sentences of student behavioural health monitoring and decision support in building a monitoring network platform; an algorithm model was established, and decision support algorithm for student behavioural health assessment were proposed to provide theoretical basis for the construction of a network platform for student behavioural health monitoring based on decision support; at the end of the article, a comparative simulation experiment was conducted and the experiment was summarised and discussed; in the last experiment, based on the excellence evaluation criteria, it was calculated that the number of people who evaluated excellent before use was 11% of the total number, while the number of people who evaluated excellent after use was 33% of the total number.
  Keywords: health monitoring; online platform; decision support; student behaviour; health evaluation.
  DOI: 10.1504/IJCSYSE.2026.10062435
   
  
• Optimization Management Method of Enterprise Logistics Supply Chain Based on Artificial Intelligence(AI)  
  by Mo Kuang, Lili Weng, Da Kuang 
  Abstract: This article systematically analyses the specific current situation of the entire supply chain using value stream mapping (VSM) tools, and then optimises it from three aspects: real logistics, information flow, and time flow, in order to explore the management efficiency, supply chain costs, and supply chain risks of the logistics supply chain. In order to verify the effectiveness of AI in optimising enterprise logistics management methods, this paper selected the logistics SCM business segments of 12 listed enterprises as the experimental objects for comparison before and after, and evaluated the logistics SCM efficiency, cost management and supply chain risk respectively. The experimental results show that the optimisation management method of enterprise logistics supply chain based on AI had obvious effect on solving the problems existing in enterprise logistics supply chain, and the overall average improvement range was 23.38%.
  Keywords: logistics supply chain; supply chain management; physical distribution management; artificial intelligence; enterprise logistics.
  DOI: 10.1504/IJCSYSE.2026.10062508
   
  
• Sustainable Development of Green Finance in the Low Carbon Economy Era of the Internet of Things  
  by Chunshu Wang, Wei Bai, Li Zhao 
  Abstract: This paper used the method of combining theoretical analysis and empirical research, starting from the essence of green finance and low-carbon economy, to explain the necessity of financial institutions to carry out low-carbon finance. Based on this, a green financial development model based on LCE of the internet of things (IoT) was proposed to solve the problem of transformation and upgrading of financial institutions. This paper compared the traditional financial model with the green financial model under the low-carbon background. The results showed that the green financial development model has increased the market size of enterprises by about 4.62%, and the enterprise risk has been effectively controlled, reducing the enterprise operating costs. The vigorous development of green finance can further optimise the industrial structure and improve the allocation of resources, which is of great significance to promote the healthy and stable development of social economy.
  Keywords: green finance; low-carbon economy; LCE; internet of things; IoT; energy report; sustainable development.
  DOI: 10.1504/IJCSYSE.2026.10062591
   
  
• Research on the generation of correlation relations of electricity transmission based on improved Jaro-Winkler algorithm  
  by Xiangrui Zong, Bing Feng, Ning Liu, Yuefan Du, Jian Zheng, Bin Zhou 
  Abstract: At present, the data correlation query method in the field of electric power marketing has problems such as low efficiency and low accuracy. This paper improves the Jaro-Winkler character similarity algorithm by combining the editing distance algorithm to improve the matching rate of field names in the data table. Experimental results based on 2,356 data tables show that the improved algorithm is applied to the data table association relationship query, and its accuracy reaches 98%. Based on the improved Jaro-Winkler algorithm and Echarts framework, a visual display system of association relationship of power marketing data table is developed, which provides auxiliary support for business personnel to use data independently and efficiently.
  Keywords: Jaro-Winkler; string similarity; electricity marketing database; associative relationships.
  DOI: 10.1504/IJCSYSE.2026.10062594
   
  
• Optimisation Evaluation of Middle and Bottom Level Scheduling Algorithms Based on Embedded Wireless Communication and Big Data Query Processing Technology  
  by Haifeng Chen, Lili Ding 
  Abstract: In embedded wireless communication system and big data query processing technology, the quality of task scheduling algorithm largely determines the performance of the system, and how to optimise the real-time scheduling is a problem worth studying. In this paper, the scheduling algorithm analysis and big data query processing technology of embedded wireless communication system are optimised, the specific algorithm optimisation steps are given, and the system structure design diagram and operation process are analysed. This paper takes the classical elevator scheduling problem as the research object, studies the process correlation and response time of three scheduling algorithms, FCFS, RR and PSA, and uses the benefit function R in the optimisation content of the algorithm to analyse the values of the scheduling algorithm nos. 18 when R = 1 and R = 2. Research shows that most scheduling algorithms only consider queuing order and have randomness in the local distribution of data.
  Keywords: scheduling algorithm; wireless communication; embedded system; big data; query processing; elevator scheduling.
  DOI: 10.1504/IJCSYSE.2026.10062938
   
  
• E-commerce Customer Marketing Classification Technology Based on The Improved Ant Colony Clustering Algorithm  
  by Ming Zhong  
  Abstract: The vigorous development of the internet has driven the development of the e-commerce market economy. Facing a huge number of consumers, every e-commerce enterprise is facing the problem of customer classification. To address this issue, the collected customer characteristic data are processed by feature selection and principal component analysis for dimensionality reduction. According to the ant colony clustering model, a new two-dimensional data object load state matrix is introduced, and by improving the ant's. Observe the radius and introduce the Sigmoid function to improve the test accuracy. Test findings demonstrate that the F-measure value of the standard model is 0.846, and the F-measure value of the improved model is 0.934. The former has an error of 0.25 after 500 iterations, and the error of the later is 0.12 after 300 iterations. The average consumption time of the standard model test dataset is 51.64 s, and the average consumption time of the improved model is 28.12 s. The test's findings reveal that the improved method has smaller error value and shorter time consumption when dealing with discrete data, and its performance is better than the standard model, which can better classify customers. The growth of e-commerce has been greatly influenced by the research findings.
  Keywords: E-commerce; customer classification; marketing; data processing; ant colony clustering; ACC.
  DOI: 10.1504/IJCSYSE.2026.10062984
   
  
• Research on user behavior detection algorithm of e-commerce platform based on machine learning  
  by Yuanyuan Tang  
  Abstract: This paper first introduced ML, including C4.5 algorithm and support vector machine algorithm in decision tree algorithm, and introduced random forest algorithm based on ML. Then, the user behaviour of EC platform was analysed and detected. First, the problems to be solved in the EC platform behaviour analysis are determined. Then, the data was collected, and then the collected data is characterised and analysed. The extracted data was divided into training set and test set, and the algorithm model was used to analyse the data. In the experiment part, three ML algorithms, C4.5 algorithm, support vector machine algorithm and random forest algorithm, were used for data analysis. The performance of user data analysis of the three algorithms was analysed by training set and ten fold cross validation. The relative error of model classification was the lowest, which showed that ML algorithm has good data analysis ability and good application effect in the field of EC platform user behaviour detection.
  Keywords: user behaviour detection; e-commerce platforms; machine learning; artificial intelligence.
  DOI: 10.1504/IJCSYSE.2026.10062985
   
  
• Financial Risk Monitoring and Prevention of Exhibition Enterprises Based on Security Cloud and Edge Computing Framework  
  by Jiang Wang  
  Abstract: Therefore, this paper has analysed the financial risk characteristics and causes of exhibition enterprises, and then used security cloud and edge computing to build financial risk monitoring and prevention measures, so as to improve the quality of financial risk management of exhibition enterprises. The financial risk monitoring and prevention effect of exhibition enterprises under the security cloud was higher than that of the original financial risk monitoring and prevention. Among them, the financial risk monitoring effect was 9.4% higher than the original one, and the financial risk prevention effect was 9.7% higher than the original one. In short, both AI and edge computing can improve the financial risk bearing capacity of enterprises.
  Keywords: financial risk monitoring; security element and edge computing; artificial intelligence; AI; network security.
  DOI: 10.1504/IJCSYSE.2026.10063124
   
  
• Design of athlete physical training system based on a smart wearable device  
  by Shikai Cai  
  Abstract: Smart wearables are any object that incorporates electronic technology or a gadget worn close to the body. Information may be tracked in real-time with the help of these athletes' progress; coaches no longer need to depend just on timings and splits on precise measurements of position, distance, velocity, and acceleration. The challenging characteristic of such physical training is the athlete's irregular optimality, scalability and generalisability. The gathering and quality of data is a significant obstacle to sports analytics. Even though there is a mountain of data, gathering and cleansing it is not always easy. Data quality is another potential issue; incomplete or erroneous data is a real possibility. Hence, in this research, smart sensors enabled intelligent physical monitoring systems on IoT platform (SS-IoT) technologies, which have been improved for sports monitoring systems with the athlete's physical training. The BP neural network establishes the athletes' physical training for data processing and monitoring in that physical control mechanism. Accurately predicting an athlete's physical state via simulation is a cutting-edge scientific method for increasing the efficiency of physical training. The experimental results show the SS-IoT achieves an accuracy ratio of 90%, efficiency ratio of 90.6%, prediction ratio of 91%, performance ratio of 95%, and error rate of 8.56% compared to other methods.
  Keywords: physical training; athlete; smart sensor; internet of things; BP neural network; data processing.
  DOI: 10.1504/IJCSYSE.2026.10063125
   
  
• Application of Matrix-based Genetic Algorithm in Foreign Trade CRM  
  by Yu Tang, You Zhou 
  Abstract: Genetic algorithm is a bionic optimisation algorithm in a macroscopic sense, which can optimise the combination of numerical values. On this basis, this paper proposes a matrix genetic algorithm combined with matrix algorithm, and applies the algorithm to foreign trade CRM, aiming to improve the efficiency of customer relationship management (CRM). This article first introduces genetic algorithm, then analyses the operating mechanism and overall architecture of the foreign trade customer management system. Finally, a mathematical model is established using matrix genetic algorithm, and dynamic management of foreign trade customer relationships is achieved through fuzzy management matrix. Research shows that the algorithm can be used to search for customer information of foreign trade enterprises, extract useful information for decision-making, and the information extraction efficiency is 5.8% higher, so that foreign trade enterprises are in a favourable position in the fierce market competition. It ultimately gets the highest profit.
  Keywords: matrix algorithm; genetic algorithm; foreign trade CRM; matrix genetic algorithm.
  DOI: 10.1504/IJCSYSE.2026.10063347
   
  
• Improve Text Classification Accuracy by Using Fuzzy-Convolutional Neural Network Model  
  by Xuan Wang, Jing Su 
  Abstract: Most of the text data we can see in daily life is fuzzy, which fuzzy information will increase the noise and reduce the classification accuracy. In order to solve this situation, this paper proposes a network model that fuses fuzzy neural network (FNN) and convolutional neural network in text classification (TextCNN), namely text fuzzy-convolutional neural network (TextFCNN). Firstly, the model uses FNN and TextCNN to obtain two sets of classification results; secondly, the fuzzy inference system is combined to further eliminate the fuzzy characteristics and achieve more correct classification outcomes. In the movie review (MR) dataset, the model was improved by 1.41% and 6.38% in accuracy compared to the single neural network FNN and TextCNN, respectively. Compared with other text classification methods, the accuracy of TextFCNN is improved by 0.33% 3.19%. Experimental results show that the network model TextFCNN can indeed improve the effect of the classifier.
  Keywords: text classification; fuzzy neural network; FNN; convolutional neural network; fuzzy theory; natural language processing; NLP.
  DOI: 10.1504/IJCSYSE.2027.10063460
   
  
• Application of Multimedia Interaction in Museum Display Space Design  
  by Bin Wang 
  Abstract: The visual based gesture recognition algorithm was used to optimize the museum interactive project. Through the scientific control, the visitors' scores before and after the project optimisation were investigated and counted. The score range was 0-100. The higher the score, the better the experience. The statistical results showed that before using the algorithm to optimize museum interactive projects, the average scores of visitors on virtual books and interactive projection were 85.6 and 89.03, respectively. After optimisation, the average scores for these two projects were 93.51 and 95.42, respectively. Based on this, it could be concluded that the optimised interaction method had higher attractiveness and fun and could better attract the attention of visitors and enable them to have a deeper understanding of museum exhibits and knowledge. This further proved the effectiveness and importance of vision based gesture recognition algorithm in museum interactive projects.
  Keywords: Museum Display Space; Interactive Mode; Gesture Recognition Algorithm; Virtual Book.
  DOI: 10.1504/IJCSYSE.2026.10063886
   
  
• Data Collection and Protection of Personnel Evaluation under Differential Privacy  
  by Yue Wu, Yaping Pan, Gang Wang, Shenghong Wang, Zhenfen Zhang 
  Abstract: This article utilized DP (differential privacy) technology to ensure that sensitive information of individuals was effectively protected during data collection and processing. Firstly, it used the LN (Laplace Noise) distribution to perform DP protection on the raw data, and compared it with exponential noise and Gaussian noise. Secondly, it divided the data into unrelated groups, added noise to each group, and adjusted DP parameters to balance data protection and data availability. Then, this article utilized the GD (Gradient Descent) algorithm to optimize parameters to maximise data availability and allocate privacy budgets to different data processing operations. Finally, during the data collection process, this article randomly selected samples and introduced random trap data points to reduce the risk of individual identification. It used metrics such as information loss and KL (Kullback Leibler) divergence to evaluate the degree of privacy leakage in DP protection.
  Keywords: Data Privacy Protection; Differential Privacy Techniques; Information Noise Addition; Privacy Measurement Metrics; Individual Identification Risk.
  DOI: 10.1504/IJCSYSE.2027.10064042
   
  
• Personnel Evaluation of Data Encryption Transmission and Storage Technology for Cloud Computing Environment  
  by Xu Zhang, Peidong Du, Qingzhao Hu, Zuohu Chen, Miao Wang, Long Wang 
  Abstract: In response to the current evaluation by personnel that data encryption transmission and storage technology has problems such as limited storage space resources, slow encryption speed, and low encryption accuracy. This article studied the existing problems in the cloud computing environment. In the cloud computing environment, stream cipher encryption methods were utilised to encrypt movie evaluation data, and combined with chaotic sequence systems, the encryption and decryption process of the data was completed. Then, transport layer security protocols and hash functions were utilised to verify user data and ensure data integrity. At the same time, data storage technology in cloud computing systems was studied, and unstructured storage technology was utilised to store evaluation data, effectively improving the speed of data storage. The encryption accuracy of data transmission obtained by the stream cipher encryption method was above 96.85%, and the average encryption accuracy of 50 experiments was 97.95%, which was 9.01% higher than the average encryption accuracy of the digital signature algorithm method. In response to the problem of limited storage space resources in cloud computing environments, this article utilised stream cryptography and unstructured storage technology to effectively ensure the security of data transmission and improve the space and speed of data storage.
  Keywords: unstructured storage technologies; USTs; evaluation of data; encrypted transmission; cloud computing environments; stream cipher; homomorphic encryption.
  DOI: 10.1504/IJCSYSE.2027.10064044
   
  
• RBF Neural Network Model Construction for Enterprise Financial Big Data Analysis  
  by Na Feng 
  Abstract: The study builds a system of financial indicators first, and then uses the fast density peak clustering (FDPC) algorithm and the Adam algorithm to optimise the radial basis function (RBF) network to create a model for predicting financial risk. The results reveal that the initial accuracy of the FDPC Adam RBF model is higher than 60%, and it tends to converge at four iterations, resulting in an accuracy of 95.6%. The FDPC Adam RBF model achieved a minimum value of 0.183 in mean square error (MSE). In summary, it can be seen that the RBF neural network model for enterprise financial big data analysis is significantly better than other common neural network models in terms of computational efficiency and prediction accuracy, making it more suitable for deep analysis of financial data and risk warning. This conclusion provides strong support for the application of advanced artificial intelligence technology in the financial field.
  Keywords: financial crisis; financial indicators; radial basis function; RBF; fast density peak clustering; FDPC; Adam.
  DOI: 10.1504/IJCSYSE.2027.10064058
   
  
• The Regulation Method of Agricultural Internet of Things Services Based on Dynamic multi-objective Optimization  
  by Chaoqun Huang, Qianlan Liu, Wenbin Qian 
  Abstract: In response to the complex and ever-changing environmental impacts faced in the current construction of agricultural internet of things technology. A supervision method for agricultural internet of things services based on dynamic multi-objective optimisation is proposed. The poor dynamic capabilities in the intelligent agricultural internet of things can be solved by constructing a decomposed algorithm. According to the findings, it performed well in convergence, hypervolume value and extreme point accuracy. This algorithm could propose the optimal service matching scheme based on a single-target service strategy, with good diversity. In addition, the calculation time of this algorithm was relatively short. Compared with the other two comparison methods, it led by 3.59s and 8.39s, respectively. Meanwhile, the average service cost of this algorithm was relatively low. It reduced the average service cost by 16.39% and 25.00%, respectively. Overall, the dynamic multi-objective optimisation agricultural internet of things regulation method has performed well in practical application, significantly improving accuracy. It can provide the highest quality service at the lowest cost within the shortest service time. In summary, this research provides an effective solution for the regulation of internet of things services in the intelligent agriculture.
  Keywords: internet of things; IoT; MOO algorithm; dynamic multi-objective optimisation algorithm; agriculture.
  DOI: 10.1504/IJCSYSE.2027.10064212
   
  
• Investigation and Implementation of Enterprise Strategic Management Evaluation Algorithm Based on IoT Big Data  
  by Chen Chen, Jia Hou 
  Abstract: With the rapid development of modern economy, many enterprises begin to introduce modern management mode. However, decision makers often cannot work out the best strategic management plan due to the limitation of their own information access, which makes the benefits of enterprises fall short of expectations or cause the waste of enterprise resources. The Internet of Things integrates all aspects of information related to people’s products, quietly changing people's lifestyles, and is undoubtedly the trend of future development. This paper studies the evaluation algorithm of enterprise strategy management method based on IoT big data, and focuses on the intelligent calculation of enterprise IoT big data. The experimental data show that the accuracy rate of strategic management evaluation is 98.23%. In the survey sample, the fastest survey time of the algorithm is 8 minutes, and the user satisfaction is as high as 90%.
  Keywords: evaluation algorithms; IoT big data; strategic enterprise management; intelligent computing.
  DOI: 10.1504/IJCSYSE.2026.10065687
   
  
• Design and Management of Enterprise E-commerce Financial System Based on Machine Learning  
  by Li Fu, Yi Yao 
  Abstract: The rapid development of network technology makes it closely related to peoples daily life, from the increasing number of users to the rapid development of express delivery industry, it shows the convenience brought by the network to human beings. In todays world where almost everyone is shopping online, this is both an opportunity and a huge challenge. In the fierce competition in the e-commerce market, how to retain users and attract more new users has become the key to its development. Today, with the rapid development of the internet and artificial intelligence, the competition of e-commerce is ultimately a contest of technology. Whoever can make their products more intelligent, more convenient and more accurate can occupy a place in this industry. This paper mainly studies the problems existing in the e-commerce management system, and proposes an artificial intelligence-based machine learning method to establish a users behaviour model and predict it. On this basis, it is combined with the traditional ant algorithm, and the learning method in the model framework is compared with XGBoost algorithm. The results showed that the optimised XGBoost algorithm had a good application prospect, and its prediction accuracy exceeded 85%.
  Keywords: Enterprise E-commerce System; Machine Learning; Order Management; B/S Mode; Support vector machine.
  DOI: 10.1504/IJCSYSE.2027.10066185
   
  
• Wireless Network Intrusion Detection using Feature Sampling and Selection Approach in an IoT Environment  
  by Zhujia K.  
  Abstract: It will be necessary to offload a large amount of computing tasks onto edge servers in order to keep up with the growing demand for IoT applications that utilise edge computing. In turn, this will help companies meet the surging demand for these apps. As soon as data transfer begins, this safeguard must kick in. The researchers in this study built a multi-attack intrusion detection system (IDS) for edge-assisted internet of things (IoT) using a BP neural network and an RBF neural network. To identify the anomaly and prioritise the attributes for each attack, the BP neural network's capabilities are utilised to their fullest potential. The goal of detecting infiltration by several attacks required the construction of a neural network based on the radial basis function (RBF). In the proposed multi-attack scenario, the results likewise show a high level of accuracy.
  Keywords: internet of things; IoT; neural network; multi-classification; intrusion detection system; IDS; network attacks; false positive rate; undetected rate.
  DOI: 10.1504/IJCSYSE.2027.10066357
   
  
• An Approach to Security Evaluate Crucial Data Stored in a Cloud Platform Using BCSS Technique  
  by Rajkumar Veeran, Priyadharshini G 
  Abstract: The rise of malicious attacks like ransomware has made data security and privacy a critical challenge in the digital world. In 2020, the European Medicines Agencys COVID-19 vaccine data breach highlighted this issue. To address these challenges, we propose a blockchain cloud storage system (BCSS) that integrates blockchain technology with cloud storage for enhanced security. BCSS provides tamper-proof, timestamped records without third-party interference, offering superior security compared to traditional storage methods. Our system also ensures high storage flexibility and reduced implementation costs. Experimental results show BCSS reduces computational costs by 35.21%, increases remote access flexibility by 94.33%, and achieves a 97.43% success rate in security and maintenance, meeting the research objectives.
  Keywords: cloud storage; Blockchain; Consensus Mechanism; Security and Remote Access; Blockchain Cloud Storage System (BCSS).
  DOI: 10.1504/IJCSYSE.2027.10066360
   
  
• Exploring Aesthetic Dimensions in AI-Generated Music Compositions  
  by Huafang Liu, Zhangwei Wang 
  Abstract: The newest models of how music makes us feel (AEM) have only been around for a few years. They are based on studies from psychology and neuroscience. The main things that these models show are the thinking and processing that happens in the brain. Some of the real-world study that these models are based on, on the other hand, is related to Western tonal music. It is common for CCM to be out of tune and not organised in a way that makes sense. Through a comparison to classic-romantic music (CM), this study looked deeply into the visual parts of listening to contemporary classical music (CCM). The text was subject to a qualitative content analysis, and the groups' created main and secondary issues were tested against each other. We found big differences between CM and CCM in the areas of expectations, bodily and emotional responses and fun features.
  Keywords: AI-generated music; contemporary classical music; data augmentation data wrangling.
  DOI: 10.1504/IJCSYSE.2027.10066575
   
  
• A System of Systems ARIMAX Model of Coronavirus Propagation Dynamics on the United States East Coast  
  by Teddy Cotter 
  Abstract: During the SARS-CoV-2 coronavirus pandemic there has been a focus on forecasting the spread of COVID-19 cases. Multiple SIR model variants and time series models have been published worldwide with a goal of precisely predicting the near-term number of cases in a particular region. A major focus was on forecasting hospital bed capacity. Conversely, the dynamics of and factors contributing to or inhibiting the propagation of coronavirus have been discussed informally or assumed known with little research into their systemic effects. This research seeks to contribute to knowledge of the systemic dynamics and factors that contribute to or inhibit the propagation of coronavirus during the period March 2020 to December 2022. ARIMAX models were developed within a system of systems theoretical framework to identify the dynamics of and factors promoting or inhibiting the propagation of coronavirus on the East Coast of the United States as the system of systems of interest. Model results demonstrate that propagation dynamics vary by state within systemic states' niches in the East Coast system of systems. This system of systems time series modelling approach may be extended to modelling the propagation dynamics of future pandemic diseases.
  Keywords: coronavirus; ARIMAX; system of systems; SoS.
  DOI: 10.1504/IJCSYSE.2027.10066579
   
  
• Spatial Coupling Vibration Calculation Method of Wind-Vehicle-Bridge System based on Finite Element Model  
  by Miaoxi Yang  
  Abstract: This study suggests the Finite Element Model-based Spatial Coupling Vibration Calculation Method (FEM-SCVCM) for a convenient and comprehensive analysis of the WVB system. A spatial coupling vibration model is recommended to simulate the WVB association to resolve the vehicles' reactions efficiently and accurately. A 3D FEM considers the interaction of wind, bridges and vehicles. Furthermore, the FEM has been employed to calculate the bridge's vibration response to a variety of excitations, such as vehicle loads and wind loads. Other influence aspects like wind force, tire model, and road unevenness are considered. The results suggest that the bridge deck is the primary location of the severe vibrations. Additional bridge stresses and strains have been obtained, showing that the highest stresses occurred around the load application points, and the maximum strains occurred in the bridge's centre.
  Keywords: Finite Element Model; Spatial coupling vibration calculation method; Wind-vehicle-bridge system; Backpropagation Neural Network.
  DOI: 10.1504/IJCSYSE.2027.10066888
   
  
• Design and Implementation of Computer Adaptive Test System Based on Big Data  
  by Cundong Tang, Li Chen, Zhiping Wang, Yi Wang, Wusi Yang 
  Abstract: This paper first reviewed the analysis of elements of computer adaptive testing, and then analysed the operation of computer adaptive testing system, system functional structure, database design and functional module design. Then the application of big data in the design and implementation of computer adaptive test system was proposed, and the maximum information method was analysed and integrated to improve the planning and implementation of computer adaptive test, so as to verify the hidden links and values behind data and data tables. According to experiments and surveys, the big data and maximum information method were applied to the construction of computer adaptive test system, and a new type of computer adaptive test system was built. Compared with the traditional test system, this new type of test system had 33% higher satisfaction.
  Keywords: computer adaptive test system; big data; open computer science; maximum information method.
  DOI: 10.1504/IJCSYSE.2026.10067925
   
  
• A Finite State Machine Model for Mitigating Mobile Money Social Engineering Attacks  
  by Selorm Kofi Tagbo, Felix Adebayo Adekoya, Patrick Kwabena Mensah 
  Abstract: This article aims to design and propose the fundamental principles of finite state machines to build a novel mobile money fraud prevention model. The study employs a set of guidelines to construct an abstract model encompassing mapped real-life social engineering attack scenarios to suggest ways by which they can be prevented. The results obtained from the model implementation indicate that finite-state machines help in reducing social engineering attacks to a large extent. Regarding originality, while other traditional models depend on predefined systemic rules, the finite state machine model proposes an adaptable and dynamic framework, which boosts the entire fraud mitigation process. Practically, the enhanced capability of the proposed model would greatly help mobile money service providers reduce losses significantly from these attacks.
  Keywords: social engineering attack; phishing; finite state machine; FSM; mobile money fraud; financial fraud prevention; ontology.
  DOI: 10.1504/IJCSYSE.2027.10068479
   
  
• Mathematical Model Construction for Interactive Distance Learning of English at the University Level  
  by Yahui Shi  
  Abstract: To promote remote interactive learning of college English, this study focuses on English reading and constructs a corpus containing four emotions: surprise and sadness. Meanwhile, the principal component analysis method is used to optimise the emotional features of speech, and the first 19 principal components are extracted to calculate the linear contribution value of the original features. Then, the iterative binary algorithm 3 was used to synthesise evaluation indicators such as emotion and intonation, and a comprehensive evaluation model was constructed to further construct the corresponding oral learning system. The results indicate that the comprehensive evaluation models performance improved with the inclusion of emotion indicators. The accuracy increased by 12.10%, the adjacent agreement rate was 93.80%, and the Pearson correlation coefficient was 0.81. The research findings provide a methodological reference for interactive distance learning of college English.
  Keywords: University English; Distance learning; SVM algorithm; ID3 algorithm; Speech emotion; Spoken language.
  DOI: 10.1504/IJCSYSE.2027.10069441
   
  
• Research on Intelligent Clothing Design Integrating Visual Communication and Embedded System - Taking the Blind Safety Clothing as an Example  
  by Zhenzhen Sun  
  Abstract: This study aims to design an intelligent clothing that integrates visual communication. The clothing has an embedded system of alarm and safety detection, which can protect the travel safety of the blind. The result shows that the clothing has good warning function, and it still has warning effect at 30 metres without natural light. The fall detection and protection function has a separate detection module for judgment, and the judgment time is within 0.5 seconds. However, there are still shortcomings in the research and design of clothing. Due to the existence of embedded devices, intelligent clothing is less comfortable than ordinary clothing. The hardware quality of different embedded devices cannot be fully guaranteed during mass production, and some products have not been tested for their service life. In the future, user group management and services can be carried out through the cloud platform.
  Keywords: Visual communication; Smart clothing; Embedded equipment; Clothing for the blind.
  DOI: 10.1504/IJCSYSE.2027.10070035
   
  
• Learning Platform Integrating Computer Technology and RecBC Recommendation Model in the Context of Education Informatization  
  by Yanqi Ruan 
  Abstract: In order to help students get suitable courses for themselves in a huge number of online courses, the research firstly constructs a course recommendation model based on the contribution of short-term preference reconstruction behaviour, based on which computer technologies such as bidirectional long and short-term memory networks are introduced to get a recommendation model that integrates the enhancement of learning behaviours. The research results show that the sparsity of the MOOC dataset is 99.43%, which is high sparsity. And in the comparison experiments with mainstream algorithms, the recommendation model incorporating learning behaviour enhancement is more than 5% higher than other algorithms in the two evaluation indexes of hit rate and normalised discount cumulative gain. In summary, the proposed model and learning platform have good robustness and generalisation ability, and can be applied in many fields.
  Keywords: Information technology in education; Computer technology; Recommendation model; Learning platform; Short-term preference.
  DOI: 10.1504/IJCSYSE.2027.10070400
   
  
• Methods to Enhance Student Classroom Participation through Big Data and Deep Learning  
  by Mingfang Zhou, Guofang Li, Yameng Bai 
  Abstract: This study investigates the efficiency and applications of big data and deep learning in enhancing classroom engagement. Traditional teaching methods often lack personalisation, leading to passive participation. Leveraging data analytics and AI, this research identifies key engagement drivers, enabling tailored interventions for improved student involvement. A mixed method approach was adopted, combining a systematic literature review, theoretical analysis, and first-hand surveys from teachers and students. Predictive models were developed, demonstrating superior accuracy over conventional methods in assessing engagement factors. The findings provide actionable, data-driven strategies for educators to boost participation. By integrating advanced technologies, instructors can make informed decisions, fostering better learning outcomes. This study bridges theory and practice, offering a foundation for future research on AI-enhanced education.
  Keywords: Big data; Deep learning; Student participation in class; Educational technology; Data-driven instruction.
  DOI: 10.1504/IJCSYSE.2027.10072910
   
  
• A Practical Study of a Gamified Motivational Curriculum for Physical Education based on Optimal Interactive Artificial Intelligence  
  by Yan Li, Lei Yang, Yue Yin, Jingyan Sun, Jialing Yang 
  Abstract: With the rise of nationwide sports, my countrys online sports education industry has developed rapidly. However, the overabundance of courses can easily lead to user fatigue. To provide personalised, precise course services and enhance application practicality, this study combines user body and movement information with personality dynamics (PD) theory to construct an improved user model. Furthermore, an intelligent partner matching system based on convolutional neural networks was designed, and a fuzzy hierarchical evaluation index system was established. The experimental results showed that the matching accuracy, recall rate, and F1 of the intelligent schoolmate matching model IUM-SR designed in this study were 95.31%, 95.24%, and 94.17%, significantly higher than the comparison matching algorithms. Experiments verified the effectiveness of this system and index system using backend data from a mobile sports education application.
  Keywords: Convolutional neural network; Physical education; Gamification; PD; User model; Learning partner matching.
  DOI: 10.1504/IJCSYSE.2027.10073462
   
  
• Personalised Teaching Mode of Music Education based on AI  
  by Yucong Song 
  Abstract: This paper presents the development and implementation of an AI-driven personalised teaching model for music education. The aim is to address the "one-size-fits-all" challenge and meet diverse student needs in music literacy, skill mastery, and creativity. We review the evolution of AI-based personalised teaching in music, examine key technologies, and analyse practical challenges. The personalised teaching model includes tailored curriculum content, flexible teaching methods, customised resources, real-time feedback mechanisms, and a diversified evaluation system. An innovative intelligent music teaching system, leveraging pre-trained language models, automates the acquisition and expression of music knowledge, providing personalised learning experiences. The results highlight the system's effectiveness in promoting technological innovation, modernising music education, enhancing student engagement, and fostering equitable, high-quality music education.
  Keywords: Artificial intelligence; Big models; Personalization; Music teaching; Teaching models.
  DOI: 10.1504/IJCSYSE.2027.10073835
   
  
• Using Big Data Analysis to Optimise English-Chinese Bilingual Classroom Teaching design  
  by Fenxiang Zhang 
  Abstract: With the rapid development of big data technology and its wide application in various fields, this study is devoted to exploring the role of big data in English-Chinese bilingual classroom teaching design. Through the comprehensive use of questionnaire survey and data analysis, the study reveals the importance of optimising teaching methods and improving student participation in improving learning results. This study found that interactive teaching methods and high student engagement are positively correlated with learning outcomes. In addition, big data analysis shows great potential in revealing educational trends and optimising teaching strategies. Based on these findings, this study suggests that educators adopt more interactive teaching methods in bilingual teaching and use big data tools to analyse student learning behaviours to improve teaching results. Future research could further explore the application of big data technologies in different educational Settings and the role of these technologies in educational innovation.
  Keywords: Big data; English-Chinese bilingual teaching; Teaching methods; Student engagement; Educational technology application.
  DOI: 10.1504/IJCSYSE.2028.10074052
   
  
• Enhancing User Experience with Prompt Recommendation Engine  
  by Aparna Chitta, Ajay Kumar Akasapu, H. Patra 
  Abstract: Prompt recommendation engines represent a novel approach to enhancing user experience by leveraging advanced language understanding to deliver personalized and relevant recommendations. These engines greatly boost engagement and satisfaction by creating concise prompts that address user interests and goals in domains such as e-commerce, search, and content discovery. This project uses prompt engineering and recommendation systems, thereby simplifying activities and improving interactions. Using Decision Tree Classifiers, Logistic Regression, and Support Vector Machines (SVMs), the system delivers personalized suggestions, with the SVM model having the highest accuracy and precision on a simulated dataset of text prompts across seven categories. These engines provide contextual cues in cases where users find roadblocks while looking for new information, easing the process. This strategy promises to transform user interactions with digital platforms by providing a personalized, seamless, and intuitive experience that boosts happiness and loyalty in a variety of disciplines.
  Keywords: Prompt Engineering; Recommendation Systems; Machine learning; Decision tree; Logistic Regression; Support Vector Machine.
  DOI: 10.1504/IJCSYSE.2028.10074714