Forthcoming and Online First Articles

International Journal of Data Mining and Bioinformatics

International Journal of Data Mining and Bioinformatics (IJDMB)

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International Journal of Data Mining and Bioinformatics (27 papers in press)

Regular Issues

  • Digital Architectural Decoration Design and Production Based on Computer Image   Order a copy of this article
    by Chan Zhou  
    Abstract: The application of computer image digitisation has realised the transformation of people’s production and lifestyle, and also promoted the development of the construction industry. This article aims to realise the research on architectural decoration design and production under computer network environment and promote the ecological development of indoor and outdoor design in the construction industry. This article proposes to use virtual reality technology in image digitisation to guide architectural decoration design research. In the comparative analysis of the weight of architectural decoration elements, among the calculated weights of secondary elements, the spatial function has the largest weight, which is 0.2155, and the landscape has the smallest weight, which is 0.0113. Among the three-level unit weights, the service area has the largest weight, which is 0.0976, and the fence frame has the smallest weight, which is 0.0119.
    Keywords: architectural decoration design; image digitisation; computer technology; virtual reality technology.
    DOI: 10.1504/IJDMB.2024.10060066
  • Urban Public Space Environment Design Based on Intelligent Algorithm and Fuzzy Control   Order a copy of this article
    by Ting Song, Yansong Li 
    Abstract: With the development of urban construction, its spatial evolution is also influenced by behavioural actors such as enterprises, residents, and environmental factors, leading to some decision-making behaviours that are not conducive to urban public space and environmental design. At the same time, some cities are vulnerable to various factors such as distance factors, transportation factors, and human psychological factors during the construction of public areas, resulting in a decline in the quality of urban human settlements. Urban public space is the guarantee of urban life. For this, in order to standardise urban public space and improve the quality of urban living environment, the standardisation of the environment of urban public space is required. The rapid development of intelligent algorithms and fuzzy control provides technical support for the environmental design of urban public spaces. Through the modelling of intelligent algorithms and the construction of fuzzy space, it can meet the diverse.
    Keywords: urban public space; environmental design; intelligent algorithm; fuzzy control.
    DOI: 10.1504/IJDMB.2024.10060067
  • Evaluation on Stock Market Forecasting Framework for AI and Embedded Real-time System   Order a copy of this article
    by Yu Lin  
    Abstract: Since its birth, the stock market has received widespread attention from many scholars and investors. However, there are many factors that affect stock prices, including the company’s own internal factors and the impact of external policies. The extent and manner of fundamental impacts also vary, making stock price predictions very difficult. Based on this, this article first introduces the research significance of the stock market prediction framework, and then conducts academic research and analysis on two key sentences of stock market prediction and artificial intelligence in stock market prediction. Then this article proposes a constructive algorithm theory, and finally conducts a simulation comparison experiment and summarises and discusses the experiment. Research results show that the neural network prediction method is more effective in stock market prediction; the minimum training rate is generally 0.9; the agency’s expected dilution rate and the published stock market dilution rate are both around 6%.
    Keywords: stock market forecast; embedded real-time system; artificial intelligence; back propagation neural network; dilution rate.
    DOI: 10.1504/IJDMB.2024.10060068
  • Design of Data Mining System for Sports Training Biochemical Indicators Based on Artificial Intelligence and Association Rules   Order a copy of this article
    by Dongbiao Liu 
    Abstract: Physiological indicators are an important basis for reflecting the physiological health status of the human body and play an important role in medical practice. Association rules have also been one of the important research hotspots in recent years. This study aims to create a data mining system of association rules and artificial intelligence in biochemical indicators of sports training. This article uses Markov logic for network creation and system training, and tests whether the Markov logic network can be associated with the training system. The results show that the accuracy and recall rate obtained are about 90%, which shows that it is feasible to establish biochemical indicators of sports training based on Markov logic network, and the system has universal, guiding and constructive significance, ensuring that the construction of training system indicators will not go in the wrong direction.
    Keywords: Artificial Intelligence; Association Rules; Data Mining; Biochemical Indicators.
    DOI: 10.1504/IJDMB.2024.10060195
  • Application of AI Intelligent Technology in Natural Resource Planning and Management   Order a copy of this article
    by Hui Cheng  
    Abstract: This article studies the application of artificial intelligence technology in natural resource planning and management. This article first introduces the background of NR and AI intelligent technology, then conducts academic research and summary on NR planning management and AI intelligent technology; then establishes an algorithm model based on multi-objective intelligent planning algorithm; finally, conducts simulation experiments and conducts experiments summary and discussion. The experimental results show that the average efficiency value of the four stages of NR planning and management before use is 5.25, and the average efficiency value of the four stages of NR planning and management after use is 7. The difference in the average efficiency value before and after use is 1.75. It can be seen that the use of AI intelligent technology can effectively improve the efficiency of natural resource planning and management.
    Keywords: natural resources; planning management; AI intelligence technology; resource management; multiple target.
    DOI: 10.1504/IJDMB.2024.10060785
  • Research on low voltage current transformer power measurement technology in the context of cloud computing   Order a copy of this article
    by Chao Yan, Peng Tao, Hongxi Wang, Chunrui Li, Yushuai Zhang 
    Abstract: As IOT develops drastically these years, the application of cloud computing in many fields has become possible. In this paper, we take low-voltage current transformers in power systems as the research object and propose a TCN-BI-GRU power measurement method that incorporates the signal characteristics based on the transformer input and output. Firstly, the basic signal enhancement extraction of input and output is completed by using EMD and correlation coefficients; secondly, multi-dimensional feature extraction is completed to improve the data performance according to the established TCN network; finally, the power prediction is completed by using BI-GRU, and the results show that the RMSE of this framework is 5.69 significantly lower than other methods. In the laboratory test, the device after being subjected to strong disturbance, its correlation coefficient feature has a large impact, leading to a large deviation in the prediction, which provides a new idea for future intelligent prediction.
    Keywords: cloud computing; low voltage current transformer; power prediction; empirical mode decomposition; EMD; gated recurrent unit; GRU.
    DOI: 10.1504/IJDMB.2024.10061059
  • Dual network control system for bottom hole throttling pressure control based on RBF with big data computing   Order a copy of this article
    by Yanghou Chen 
    Abstract: In the context of smart city development, the managed pressure drilling (MPD) drilling process faces many uncertainties, but the characteristics of the process are complex and require accurate wellbore pressure control. However, this process runs the risk of introducing un-modelled dynamics into the system. To this problem, this paper employs neural network control techniques to construct a dual-network system for throttle pressure control, the design encompasses both the controller and identifier components. The radial basis function (RBF) network and proportional features are connected in parallel in the controller structure, and the RBF network learning algorithm is used to train the identifier structure. The simulation results show that the actual wellbore pressure can quickly track the reference pressure value when the pressure setpoint changes. In addition, the controller based on neural network realises effective control, which enables the system to track the input target quickly and achieve stable convergence.
    Keywords: controller; identifier; MDP; neural network; radial basis function; RBF.
    DOI: 10.1504/IJDMB.2024.10061267
  • Design of intelligent financial sharing platform driven by consensus mechanism under mobile edge computing and accounting transformation   Order a copy of this article
    by Qiang Li 
    Abstract: The intelligent financial sharing platform in the online realm is capable of collecting, storing, processing, analysing and sharing financial data through the integration of AI and big data processing technologies. However, as data volume grows exponentially, the cost of financial data storage and processing increases, and the asset accounting and financial profit data sharing analysis structure in financial sharing platforms is inadequate. To address the issue of data security sharing in the intelligent financial digital sharing platform, this paper proposes a data-sharing framework based on blockchain and edge computing. Building upon this framework, a non-separable task distribution algorithm based on data sharing is developed, which employs multiple nodes for cooperative data storage, reducing the pressure on the central server for data storage and solving the problem of non-separable task distribution. Multiple sets of comparative experiments confirm the proposed scheme has good feasibility in improving algorithm performance and reducing energy consumption and latency.
    Keywords: mobile edge computing; intelligent finance; data sharing; blockchain; non-separable task.
    DOI: 10.1504/IJDMB.2024.10061501
  • Educational Countermeasures of different learners in virtual learning Community based on artificial Intelligence   Order a copy of this article
    by Xiangning Deng 
    Abstract: In order to reduce the challenges encountered by learners and educators in engaging in educational activities, this paper classifies learners’ roles in virtual learning communities, and explores the role of behaviour characteristics and their positions in collaborative knowledge construction networks in promoting the process of knowledge construction. This study begins with an analysis of the relationship structure among learners in the virtual learning community and then applies the FCM algorithm to arrange learners into various dimensional combinations and create distinct learning communities. The test results demonstrate that the FCM method performs consistently during the clustering process, with less performance oscillations, and good node aggregation, the ARI value of the model is up to 0.90. It is found that they play an important role in the social interaction of learners’ virtual learning community, which plays a certain role in promoting the development of artificial intelligence.
    Keywords: big data; FCM algorithm; social relations; virtual learning community; VLC.
    DOI: 10.1504/IJDMB.2024.10061560
  • Design of an Intelligent Financial Sharing Platform Driven by Digital Economy and Its Role in Optimizing Accounting Transformation Production   Order a copy of this article
    by Yun   Ye 
    Abstract: With the expansion of business scope, the environment faced by enterprises has also changed, and competition is becoming increasingly fierce. Traditional financial systems are increasingly difficult to handle complex tasks and predict potential financial risks. In the context of the digital economy era, the booming financial sharing services have reduced labour costs and improved operational efficiency. This paper designs and implements an intelligent financial sharing platform, establishes a fund payment risk early warning model based on an improved support vector machine algorithm, and tests it on the Financial Distress Prediction dataset. The experimental results show that the effectiveness of using F2 score and AUC evaluation methods can reach 0.9484 and 0.9023, respectively. After using this system, the average financial processing time per order decreases by 43%, and the overall financial processing time decreases by 27%. Finally, this paper discusses the role of intelligent financial sharing platform in accounting transformation and optimisation of production.
    Keywords: digital economy; financial sharing; accounting transformation; production optimisation; SMOTE-SVM.
    DOI: 10.1504/IJDMB.2024.10061580
  • Spearman dependence function-based goodness-of-fit test for the gene's relation   Order a copy of this article
    by Selim Orhun Susam, Burcu Hudaverdi 
    Abstract: A gene network represents the relationship between different groups of genes with various functions, aiming to depict how genes collaborate and influence each other’s activities within a biological system. This relationship can be effectively explained using copulas. Therefore, it is crucial to determine which copula best fits the gene data and provides the most accurate explanation of the relationships between gene groups. In this study, our objective is to introduce a Spearman dependence function-based goodness-of-fit test using Bernstein polynomial approximation. We apply this test to identify a copula model that can effectively explain the relationships between gene groups. A Monte Carlo simulation study is conducted to assess the performance of the proposed test. Next, we analyze histone gene groups using data from yeast cell regulation, as provided by Eisen et al.(1998). Specifically, we investigate the dependence model structures of gene interactions for eight histone genes.
    Keywords: Spearman dependence; copula goodness-of-fit test; Bernstein copula; histone genes.
    DOI: 10.1504/IJDMB.2025.10061726
  • An empirical study on construction emergency disaster management and risk assessment in shield tunnel construction project with big data analysis   Order a copy of this article
    by Liyu Lu, Meiling Ji, Xi Wen, Yong Xiang 
    Abstract: Emergency disaster management presents substantial risks and obstacles to shield tunnel building projects, particularly in the event of water leakage accidents. Contemporary water leak detection is critical for guaranteeing safety by reducing the likelihood of disasters and the severity of any resulting damages. However, it can be difficult. Deep learning models can analyse images taken inside the tunnel to look for signs of water damage. This study introduces a unique strategy that employs deep learning techniques, generative adversarial networks (GAN) with long short-term memory (LSTM) for water leakage detection i shield tunnel construction (WLD-STC) to conduct classification and prediction tasks on the massive image dataset. The results demonstrate that for identifying and analysing water leakage episodes during shield tunnel construction, the WLD-STC strategy using LSTM-based GAN networks outperformed other methods, particularly on huge data.
    Keywords: disaster management; shield tunnel construction; STC; water leakage detection; big data; deep learning; generative adversarial networks; GAN; long short-term memory; LSTM.
    DOI: 10.1504/IJDMB.2024.10061756
  • Natural language processing based machine learning psychological emotion analysis method   Order a copy of this article
    by Yang Zhao  
    Abstract: To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary’s accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An F-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.
    Keywords: emotion dictionary; psychological emotion analysis; conditional random field.
    DOI: 10.1504/IJDMB.2024.10061757
  • Research on facial dataset cleaning in mixed scenes based on spatiotemporal correlation   Order a copy of this article
    by Siguang Dai 
    Abstract: Researching methods for cleaning mixed scene facial datasets can improve the performance and reliability of mixed scene facial recognition algorithms. Therefore, the paper proposes a facial dataset cleaning method in mixed scenes based on spatiotemporal correlation. The 2DPCA algorithm is used to reduce the dimensionality of the data set, and the composite multi-scale entropy is used to decompose, reconstruct and arrange the image sequence after the dimensionality reduction. The autocorrelation coefficient and the number of interrelation between image sequences were determined, and the anomaly detection of data set was realised by combining spatio-temporal correlation. Sparse representation was used to repair the abnormal images, and the images with high similarity were deleted to clean the mixed scene face data set. The experimental results show that the minimum anomaly rate of our method is 0.5%, the success rate is between 94% and 96%, and the minimum time cost is 0.2 s.
    Keywords: spatiotemporal correlation; mixed scenes; facial dataset; dataset cleaning; 2DPCA algorithm; composite multi-scale entropy; sparse representation.
    DOI: 10.1504/IJDMB.2025.10061768
  • Identification of Potential Biomarkers of Esophageal Squamous Cell Carcinoma using Community Detection Algorithms   Order a copy of this article
    by Bikash Baruah, Domum Karlo, Manash Pratim Dutta, Subhasish Banerjee, Dhruba K. Bhattacharyya 
    Abstract: Potential biomarker genes are uncovered in this research by developing a unique methodology through the employment of six eminent community detection algorithms (CDAs) on four RNAseq esophageal squamous cell carcinoma (ESCC) datasets. RNAseq datasets are preprocessed using galaxy server followed by the identification of a subset of differentially expressed genes (DEGs). CDAs are applied separately on control and disease samples of DEGs to extract the hidden communities of the datasets. To identify the significant communities, ESCC elite genes are extracted from Genecards for subsequent downstream analysis towards the identification of potential biomarkers. Topological analysis is performed to support critical gene identification based on elite genes followed by a biological investigation. For biological investigation, gene enrichment and pathway analysis are implemented. Finally, a group of genes EPHB2, ABLIM3, ACER1, ABCD4, ARF6, ADRA1D, ATP6V1D, CLTB, ATP6V0A4, and AP1M1 are identified as ESCC possible biomarkers that carry both topological and biological significance.
    Keywords: community detection algorithm; CDA; potential biomarker; esophageal squamous cell carcinoma; ESCC; Elite gene; topological analysis; biological significance.
    DOI: 10.1504/IJDMB.2025.10061876
  • Research on bioinformatics data classification method based on support vector machine   Order a copy of this article
    by Hui Yan, Yunxin Long, Chao Lv, Ping Yu, Duo Long 
    Abstract: Due to the problems of low classification accuracy and long classification time in traditional biological information data classification methods, a biological information data classification method based on support vector machine is proposed. Acquire bio-information data through gene expression and analyse its characteristics. According to the data analysis results, carry out outlier detection and data scaling for the acquired bio-information data. Based on the processing results, use mutual information to measure the correlation and redundancy, select the bio-information data features through the feature selection algorithm of minimum redundancy and maximum correlation, and take the selected bio-information data features as data samples. Through support vector machine, the classification decision function is established under the conditions of linear and non-separable data samples to obtain the classification results of biological information data. The experimental results show that the proposed method has higher classification accuracy and shorter classification time.
    Keywords: support vector machine; bioinformation; data classification; minimum redundancy and maximum correlation; feature selection.
    DOI: 10.1504/IJDMB.2025.10061944
  • Log anomaly detection and diagnosis method based on deep learning   Order a copy of this article
    by Zhiwei Liu, Xiaoyu Li, Dejun Mu 
    Abstract: In order to improve the accuracy of log anomaly detection and diagnostic effectiveness, this paper proposes a deep learning based log anomaly detection and diagnosis method. Firstly, analyse the log data and obtain the corresponding relationship between the log keys and log parameters. Secondly, using deep learning to capture association features, a convolutional neural network bidirectional long short-term memory (CNN-BiLSTM) deep learning model is constructed. Finally, learning context sequence feature information from both positive and negative directions through bidirectional input, and implementing log anomaly detection and diagnosis based on the results of context sequence feature information. The experimental results show that the accuracy of log anomaly detection in this method can reach 98.6%, the time required for log anomaly detection can reach 1.1 s, and the recall rate for log anomaly detection is 96.8%. The log anomaly detection effect is good.
    Keywords: deep learning; one hot encoding; context sequence features; log exception.
    DOI: 10.1504/IJDMB.2025.10062017
  • Classification and retrieval method of personal health data based on differential privacy   Order a copy of this article
    by Guanpeng Xu, Liang Zhao 
    Abstract: Research on personal health data classification and retrieval methods can improve the accuracy and efficiency of medical decision-making, promoting the development of personalised medicine. To overcome the issues of low accuracy, long retrieval time, and low satisfaction in traditional methods, a classification and retrieval method of personal health data based on differential privacy is proposed. The method involves encrypting personal health data using linear regression model and differential privacy, constructing a classification objective function through integrated manifold learning to classify the encrypted results of personal health data. Binary hash codes are used to retrieve the classification results, and the decrypted retrieval results are provided to users for personal health data classification and retrieval. The experimental results demonstrate that this method achieves a maximum accuracy of 96.8% in personal health data classification and retrieval, with a minimum retrieval time of 20 ms and an average satisfaction of 97.1% for the retrieval results.
    Keywords: differential privacy; personal health data; classification and retrieval; linear regression model; encrypted results; binary hash code.
    DOI: 10.1504/IJDMB.2025.10062018
  • A Novel Intelligent-based Intrusion Detection and Prevention System in the Cloud Using Deep Learning with Meta-Heuristic Strategy   Order a copy of this article
    by Srilatha Doddi, Thillaiarasu N 
    Abstract: Cloud computing serves diverse options for end-users to minimise costs, and services are easily accessible through online platforms. While the users access the services remotely, the attackers launch cyber-attacks to disrupt the services. Cloud security analysts treat the security of the cloud as a potential area of research to minimise the impacts of abnormal behaviour. One of the potential solutions to detect attacks is the development of the next-generation intrusion detection and prevention system (IDPS). Hence, this paper proposes an efficient IDPS using a hybridised model known as hybrid firebug-squirrel swarm algorithm-based ensemble classifiers (HF-SSA-EC). Initially, the NSL-KDD cup 1999 dataset is considered for experimental analysis. The efficient features are extracted via restricted Boltzmann machines (RBM) layers of the deep belief network (DBN) model. The extracted features are submitted to the ensemble classifiers (ECs), which use naive Bayes (NB), support vector machines (SVM), deep neural networks (DNN), and recurrent neural networks (RNN) for identifying the intrusions. EC parameter optimisation using a hybridised HF-SSA meta-heuristic improves performance. Finally, the prevention model eliminates malicious nodes from detected intrusions. Meta-heuristic clustering is used in the preventative model. The experimental results reveal that the recommended IDPS outperforms existing models.
    Keywords: intrusion detection and prevention system; IDPS; cloud computing; restricted Boltzmann machines; RBM; deep feature extraction; firebug swarm optimisation; FSO; squirrel search algorithm.
    DOI: 10.1504/IJDMB.2025.10062482
  • Prediction Method of Commercial Customers' Mental Health Based on Data Mining   Order a copy of this article
    by Yanhua Shen, Bing Gao 
    Abstract: For commercial customer management, mental health prediction is crucial, therefore, a data mining based method for predicting the mental health of commercial customers is proposed. Firstly, the K-means algorithm is used to mine and process the psychological health test data of commercial customers. Secondly, develop a program for evaluating the psychological health of commercial customers, construct a judgment matrix, and calculate weight coefficients to obtain the evaluation results of the psychological health level of commercial customers. Finally, based on the evaluation results of mental health level as input and the predicted results of mental health, a BP neural network is used to build a commercial customer mental health prediction model. The experimental data shows that after the proposed method is applied, the mining results of commercial customers’ mental Health data are consistent with the actual results, and the minimum error of commercial customers’ mental health prediction is 0.4%.
    Keywords: commercial customers; mental health; enterprise development; data mining technology; prediction model construction.
    DOI: 10.1504/IJDMB.2025.10062484
  • Consumer purchasing intention and marketing data mining model in the digital economy   Order a copy of this article
    by Yang Zhao 
    Abstract: In recent years, there has been a rapid development of science and technology in China. This article uses a marketing data mining model to conduct research on consumers' purchasing intention. The research is based on traditional questionnaire surveys, further Bayesian inference and shopping basket analysis, to analyse how to enhance buyers' desire in what environment they would enhance their purchase desire. Through shopping basket analysis and Bayesian inference, this article found that the purchase information, purchase demand, and purchase behaviour of the buyer could obtain the purchasing intention of the buyer, which can also formulate reasonable marketing strategies for businesses. In the product analysis conducted in this article, it can be found that the highest number of purchases of vegetables and fruits was 14,192. Based on similar analysis, the correlation degree between vegetables and fruits in small categories was 0.800. The article made a reasonable analysis of the two and concluded that putting vegetables and fruits together for sale was the best choice. This article also made a correlation analysis for other products that were analysed as having a high number of purchases. It was believed that the higher the correlation between the two products, the higher the sales volume would be.
    Keywords: consumer purchasing intention; marketing mining model; Bayesian inference; shopping basket analysis.
    DOI: 10.1504/IJDMB.2024.10061658
  • Cross-border e-commerce logistics distribution optimisation based on IoT artificial intelligence algorithm   Order a copy of this article
    by Haofeng Huang 
    Abstract: In recent years, cross-border e-commerce has developed rapidly, but logistics and distribution still face major obstacles. The emergence of the internet of things (IoT) provides opportunities for it. This paper aims to study the utilisation of IoT in enhancing cross-border e-commerce logistics distribution. This paper determines the ideal logistics distribution path based on the intelligent ant colony algorithm, artificial fish swarm method and a hybrid algorithm combining the two. The experimental results show that the development trend of Taobao's online shopping user scale has increased from 37.65% in 2015 to 59.59% in 2020; the JD online shopping user scale has increased from 30.74% in 2015 to 42.27% in 2020; and Tmall online shopping user scale has increased from 28.53% in 2015 to 34.98% in 2020. The research results show that the optimal path must be selected in logistics distribution to meet the requirements of low cost and high efficiency.
    Keywords: logistics distribution optimisation; cross-border e-commerce; artificial intelligence algorithm; internet of things; IoT.
    DOI: 10.1504/IJDMB.2024.10059957
  • An enterprise operation management method based on mobile edge computing and data mining   Order a copy of this article
    by Mingzhao Liu, Lei Wei 
    Abstract: E-commerce data, as an important part of enterprise online marketing data, can help enterprises understand customer needs and improve sales efficiency. Aiming at the problems of low mining efficiency, slow periodic convergence and high redundancy of traditional data mining technology, this paper constructs an e-commerce data mining method oriented to edge computing. Firstly, a fuzzy clustering algorithm is applied to e-commerce data mining, where the fuzzy partition matrix and clustering centre are obtained by optimising the objective function, and the membership function and clustering centre are repeatedly updated to a fixed range, to obtain different types of e-commerce data mining results. Secondly, Rpack edge computing deployment algorithm is adopted to build a more efficient network architecture for e-commerce data mining. Finally, the experimental results show that the average mining accuracy is higher than 99.5% and the average running time is less than 60 ms when the algorithm is applied to e-commerce data mining, which can provide decision-making basis for e-commerce enterprises. In addition, the deployment of edge computing can provide a good method for enterprise operation management.
    Keywords: network marketing; edge computing; e-commerce data; data mining; enterprise operation management.
    DOI: 10.1504/IJDMB.2024.10060916
  • Development strategy of online English teaching based on attention mechanism and recurrent neural network recommendation method   Order a copy of this article
    by Linli Du, Yan Xu 
    Abstract: In the era of artificial intelligence, a profound examination of the significance and purpose of contemporary English education in tertiary institutions assumes paramount importance. This paper endeavours to explore sustainable development strategies for English education. Firstly, a recurrent neural network model is employed to meticulously analyse the learning characteristics of teachers and students engaged in English studies. These characteristics are predominantly extracted from library search engines, encompassing articles, journals, works, and keywords. Secondly, the attention mechanism is skilfully integrated to capture users' focus on information. Thirdly, the gated recurrent unit is utilised to acquire session information and provide users with pertinent content recommendations, thereby enhancing the recommendation's capacity for generalisation. The experimental results demonstrate that the proposed model attains the highest mean average precision when compared with traditional personalised search methods. Additionally, the effectiveness of the attention mechanism and the click feature within this model is also corroborated. On one hand, this model inspires college students to take the initiative in their learning and empowers them to independently assimilate the valuable knowledge resources of online English teaching. On the other hand, this model facilitates teachers in cultivating a more innovative generation of students within the realm of artificial intelligence.
    Keywords: recurrent neural network; recommendation system; online English teaching development strategy.
    DOI: 10.1504/IJDMB.2024.10061450
  • Design of a financial reporting management generation system based on Bi-LSTM model and MultiWord-Embedding method   Order a copy of this article
    by Yu Wang 
    Abstract: A text classification-based financial report management generation system is proposed to address the issues of slow data collection and low data integration efficiency in existing financial report management generation systems. The overall system focuses on financial data collection and classification, with a processor, human-machine interaction module, financial data collection and classification module, financial data storage module, financial data backup module, and financial statement generation module. In the financial data collection and classification module, an improved text classification method of Bi-LSTM+MultiWord-Embedding with lexical attention mechanism is proposed to address the problem of insufficient feature extraction ability of financial text data in traditional text classification methods by combining the features of a financial text. The experimental results show that the accuracy and recall of the system in this paper reach 0.93 and 0.91 respectively, which can achieve accurate and stable financial text data classification.
    Keywords: deep learning; text classification; neural networks; financial report management generation system.
    DOI: 10.1504/IJDMB.2024.10061302
  • Rural e-commerce data analysis based on data mining and its enlightenment to rural digital economy management   Order a copy of this article
    by Jingyang Tang 
    Abstract: In the context of the era of big data, the comprehensive enhancement and managerial refinement of rural e-commerce can be achieved through the profound and real-time scrutiny of rural e-commerce data. However, only relying on a single fusion algorithm is not enough to meet the needs of data monitoring system. This research manuscript concentrates on the realms of multi-source data acquisition, transmission, display, and fusion algorithms, resulting in a fully automated rural e-commerce data monitoring system crafted through the skilful use of data mining and various other computational operations. In addition, this paper selects six keywords that reflect consumers' attention appropriately, and uses dimensionality reduction techniques on historical transaction data. Finally, simulation experiments show that the combined model has higher prediction accuracy than the single prediction model, and the average prediction accuracy of rural e-commerce data in the next seven days reaches about 97%.
    Keywords: multi-source data; rural e-commerce; MDS dimension reduction model; long short-term memory; LSTM prediction model; digital transformation; data mining.
    DOI: 10.1504/IJDMB.2024.10061878
  • Design of performance assessment and management model for regional technological innovation under the background of machine learning   Order a copy of this article
    by Wenjing Zhang, Hanyuan Zhang, Kang Tian, Huaping Zhang 
    Abstract: This study addresses the limitations and primitiveness of performance evaluation management in regional technological innovation enterprises. We propose a methodology based on the random forest algorithm to overcome these issues. The methodology involves preprocessing raw data from a technology company's project management system through data cleaning, feature selection, and feature transformation. Using the ID3 algorithm, we construct an index weight evaluation model by recursively creating a decision tree and selecting features based on information gain criteria. The refined model generates a performance evaluation total score. Experimental results demonstrate that the random forest algorithm achieves a satisfactory assessment of regional technological innovation performance, with a testing accuracy of 94.20%. These findings establish a scientific foundation for performance evaluation management, enabling enterprises to enhance accuracy and efficiency.
    Keywords: performance evaluation management; management mode; random forest; decision tree; machine learning.
    DOI: 10.1504/IJDMB.2024.10061775