Forthcoming and Online First Articles

International Journal of Business Intelligence and Data Mining

International Journal of Business Intelligence and Data Mining (IJBIDM)

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International Journal of Business Intelligence and Data Mining (22 papers in press)

Regular Issues

  • Enterprise financial risk early warning method based on PCA and SVM algorithms   Order a copy of this article
    by Yanyan Cao, Gechun Pei 
    Abstract: Aiming at the problems of low relevance and high false alarm rate of enterprise financial risk early warning, an enterprise financial risk early warning method based on PCA and SVM algorithm is proposed. Firstly, the sensitivity optimisation principal component analysis method is introduced, and the representative index is selected according to the threshold value to establish the index system. Then, support vector machine is introduced to store the data in the sample space, and the indicators are divided into positive and negative indicators. Finally, combined with FCM clustering algorithm, the early-warning decision function is constructed to realise the early-warning of enterprise financial risk. The experimental results show that the correlation of this method is higher than 0.915, the false alarm rate is lower than 2%, and the Matthews correlation coefficient is up to 1.00.
    Keywords: principal component analysis; PCA; support vector machine; SVM; corporate financial risks; risk warning; FCM clustering algorithm.

  • An influence-based k-nearest neighbour classifier for classification of data with different densities   Order a copy of this article
    by Hassan Motallebi, Amir-Hossein Fakhteh 
    Abstract: The k-nearest neighbour (KNN) is a simple and yet effective classification rule. To achieve robustness against outliers, several local mean-based extensions of the KNN classifier have been proposed which assign the query to the class with nearest local mean. However, using the conventional proximity measures causes poor performance in situations with multi-scale classes. Here, we propose a new local mean-based KNN classifier that uses a new modified distance measure which adjusts the proximity around each sample with respect to the situation and density. We scale the distance from the sample to each class with respect to the size of its influence set such that the sample seems closer to classes in which it has a higher number of reverse neighbours. We apply the proposed method on three local mean-based KNN classifiers. Our results show that the proposed method improves the performance of the local mean-based classifiers.
    Keywords: local mean-based KNN; modified distance measure; reverse KNN; gradient descent direction; different densities.
    DOI: 10.1504/IJBIDM.2024.10062901
     
  • Research on the correlation between corporate financial performance and strategic risk based on panel data model   Order a copy of this article
    by Su Guo, Junfu Cui, Taile Zhang 
    Abstract: In recent years, the trend of economic globalisation and multilateral trade has expanded rapidly, and the risk issues related to enterprises have received widespread attention. For this problem of the panel data model, the research uses the entropy theory of ordinal space to quantify the strategic risk of the enterprise, and constructs a panel-PLS estimation model to analyse the financial correlation of the enterprise. The results show that in most industries, the difference between the entropy value of rising and falling in the ranking of enterprises is 0.008, which is in a relatively stable state. Corporate strategic risk is negatively correlated with ROE and total asset turnover, and positively correlated with non-current asset turnover and dividend payout ratio. The above results can be used to predict the risk situation of the enterprise strategy and arrange targeted management decisions, which can promote the long-term development of the enterprise.
    Keywords: strategic risk; financial performance; ordinal space; panel data model; PLS.
    DOI: 10.1504/IJBIDM.2024.10062518
     
  • A three-way density peak clustering algorithm based on sinusoidal fuzzy entropy   Order a copy of this article
    by Yudong Meng, Xin Xu, Leyuan Yan, Yuan Cao 
    Abstract: The density peaks clustering (DPC) algorithm is an efficient and concise method for clustering that automatically detects density centres and noise points. However, it is vulnerable to inaccuracies in outlier identification due to the cutoff distance parameter. To address this drawback, this paper proposes a novel three-way density peak clustering algorithm called sinusoidal fuzzy entropy-based density peak clustering (SFE-DPC). An important aspect of SFE-DPC is determining the thresholds. To tackle this issue, a criterion using sinusoidal fuzzy entropy is developed, and genetic algorithms are utilised to search for the optimal thresholds. By employing the optimal thresholds determined through the proposed criterion, SFE-DPC divides each cluster into three sections, with points in the trivial region identified as outliers. To assess the performance of SFE-DPC, we evaluate it on UCI datasets that include outliers using three baseline measures (NMI, RI, and F1-score), and compare it to DPC, k-means, FCM, BIRCH, and SC methods. Experimental results confirm that SFE-DPC is more effective in outlier detection.
    Keywords: three-way clustering; 3WC; sinusoidal fuzzy entropy; SFE; outlier identification; genetic algorithm.
    DOI: 10.1504/IJBIDM.2024.10062646
     
  • A condensed hybrid feature selector for enhancing classifier performance using TOPSIS and improved Rao optimisation   Order a copy of this article
    by A.S. Karthik Kannan, S. Appavu alias Balamurugan, Millie Pant 
    Abstract: A wide range of fields are increasingly utilising high-dimensional data, such as text mining, bioinformatics, image processing, and pattern recognition. A classification system is less efficient as a result of the curse of dimensionality problem, which incurs high computational costs. With its efficacy to identify the optimal features from the feature space, an approach to selecting the more informative features based on MCDM and improved Rao optimisation methods is proposed. As a measure of the candidate solution's fitness, the proposed work uses the classifier error rate and feature selection ratio. A comparison of the proposed method with state-of-the-art methods is conducted using 12 popular benchmark datasets. A hybrid approach outperforms the standard strategy in terms of selected features and classification accuracy, according to the results of the experiment.
    Keywords: hybrid feature selector; classification; multi-criteria decision making; MCDM; TOPSIS; improved Rao optimisation.
    DOI: 10.1504/IJBIDM.2024.10062647
     
  • A method for evaluating the quality of teaching reform based on fuzzy comprehensive evaluation   Order a copy of this article
    by Weimiao Cui, Chen Zhang, Xiaoqi Zhang 
    Abstract: In order to improve the comprehensiveness of evaluation results and reduce errors, a teaching reform quality evaluation method based on fuzzy comprehensive evaluation is proposed. Firstly, on the premise of meeting the principles of indicator selection, factor analysis is used to construct an evaluation indicator system. Then, calculate the weights of various evaluation indicators through fuzzy entropy, establish a fuzzy evaluation matrix, and calculate the weight vector of evaluation indicators. Finally, the fuzzy cognitive mapping method is introduced to improve the fuzzy comprehensive evaluation method, obtaining the final weight of the evaluation indicators. The weight is multiplied by the fuzzy evaluation matrix to obtain the comprehensive evaluation result. The experimental results show that the maximum relative error of the proposed method's evaluation results is about 2.0, the average comprehensive evaluation result is 92.3, and the determination coefficient is closer to 1, verifying the application effect of this method.
    Keywords: fuzzy comprehensive evaluation; teaching reform; quality assessment; factor analysis; fuzzy entropy; fuzzy evaluation matrix.
    DOI: 10.1504/IJBIDM.2024.10062027
     
  • Prediction method of college students' achievements based on learning behaviour data mining   Order a copy of this article
    by Haiqing Zhang, Chao Zhai 
    Abstract: This paper proposes a method for predicting college students' performance based on learning behaviour data mining. The method addresses the issue of limited sample size affecting prediction accuracy. It utilises the K-means clustering algorithm to mine learning behaviour data and employs a density-based approach to determine optimal clustering centres, which are then output as the results of the clustering process. These clustering results are used as input for an attention encoder-decoder model to extract features from the learning behaviour sequence, incorporating an attention mechanism, sequence feature generator, and decoder. The characteristics derived from the learning behaviour sequence are then used to establish a prediction model for college students' performance, employing support vector regression. Experimental results demonstrate that this method accurately predicts students' performance with a relative error of less than 4% by leveraging the results obtained from learning behaviour data mining.
    Keywords: learning behaviour; data mining; college students; achievement prediction method; K-means; support vector regression.
    DOI: 10.1504/IJBIDM.2024.10062026
     
  • Integrating MOOC online and offline English teaching resources based on convolutional neural network   Order a copy of this article
    by Kelu Wang, Dexu Bi 
    Abstract: In order to shorten the integration and sharing time of English teaching resources, a MOOC English online and offline mixed teaching resource integration model based on convolutional neural networks is proposed. The intelligent integration model of MOOC English online and offline hybrid teaching resources based on convolutional neural network is constructed. The intelligent integration unit of teaching resources uses the Arduino device recognition program based on convolutional neural network to complete the classification of hybrid teaching resources. Based on the classification results, an English online and offline mixed teaching resource library for Arduino device MOOC is constructed, to achieve intelligent integration of teaching resources. The experimental results show that when the regularisation coefficient is 0.00002, the convolutional neural network model has the best training effect and the fastest convergence speed. And the resource integration time of the method in this article should not exceed 2 s at most.
    Keywords: MOOC English; online and offline; mixed teaching resources; intelligent integration method; convolutional neural network.
    DOI: 10.1504/IJBIDM.2024.10062025
     
  • A personalised recommendation method for English teaching resources on MOOC platform based on data mining   Order a copy of this article
    by Yalei Yan, Liya Chen, Wenjing Wang 
    Abstract: In order to enhance the accuracy of teaching resource recommendation results and optimise user experience, a personalised recommendation method for English teaching resources on the MOOC platform based on data mining is proposed. First, the learner's evaluation of resources and resource attributes are abstracted into the same space, and resource tags are established using the Knowledge graph. Then, interest preference constraints are introduced to mine sequential patterns of user historical learning behaviour in the MOOC platform. Finally, a graph neural network is used to construct a recommendation model, which adjusts users' short-term and short-term interest parameters to achieve dynamic personalised teaching recommendation resources. The experimental results show that the accuracy and recall of the resource recommendation results of the research method are always higher than 0.9, the normalised sorting gain is always higher than 0.5.
    Keywords: data mining; MOOC platform; English teaching resources; personalised recommendation; knowledge graph; neural network.
    DOI: 10.1504/IJBIDM.2024.10062024
     
  • Evaluation method of teaching reform quality in colleges and universities based on big data analysis   Order a copy of this article
    by Xiumin Wang 
    Abstract: Research on the quality evaluation of teaching reforms plays an important role in promoting improvements in teaching quality. Therefore, an evaluation method of teaching reform quality in colleges and universities based on big data analysis is proposed. A multivariate logistic model is used to select the evaluation indicators for the quality evaluation of teaching reforms in universities. And clustering and cleaning of the evaluation indicator data are performed through big data analysis. The evaluation indicator data is used as input vectors, and the results of the teaching reform quality evaluation are used as output vectors. A support vector machine model based on the whale algorithm is built to obtain the relevant evaluation results. Experimental results show that the proposed method achieves a minimum recall rate of 98.7% for evaluation indicator data, the minimum data processing time of 96.3 ms, the accuracy rate consistently above 97.1%.
    Keywords: big data analysis; colleges and universities; reform quality evaluation; multivariate logistic model; whale algorithm; support vector machine model.
    DOI: 10.1504/IJBIDM.2024.10062022
     
  • A method for evaluating the quality of college curriculum teaching reform based on data mining   Order a copy of this article
    by Xuepeng Huang 
    Abstract: In order to improve the evaluation effect of current university teaching reform, a new method for evaluating the quality of university course teaching reform is proposed based on data mining algorithms. Firstly, the optimal data clustering criterion was used to select evaluation indicators and a quality evaluation system for university curriculum teaching reform was established. Next, a reform quality evaluation model is constructed using BP neural network, and the training process is improved through genetic algorithm to obtain the model weight and threshold of the optimal solution. Finally, the calculated parameters are substituted into the model to achieve accurate evaluation of the quality of university curriculum teaching reform. Selecting evaluation accuracy and evaluation efficiency as evaluation indicators, the practicality of the proposed method was verified through experiments. The experimental results showed that the proposed method can mine teaching reform data and evaluate the quality of teaching reform. Its evaluation accuracy is higher than 96.3%, and the evaluation time is less than 10ms, which is much better than the comparison method, fully demonstrating the practicality of the method.
    Keywords: data mining; university courses; teaching reform; quality evaluation.
    DOI: 10.1504/IJBIDM.2024.10062023
     
  • Learning behaviour recognition method of English online course based on multimodal data fusion   Order a copy of this article
    by Liangjie Li 
    Abstract: The conventional methods for identifying English online course learning behaviours have the problems of low recognition accuracy and high time cost. Therefore, a multimodal data fusion-based method for identifying English online course learning behaviours is proposed. Firstly, the analytic hierarchy process is used for decision fusion of multimodal data of learning behaviour. Secondly, based on the fusion results of multimodal data, weight coefficients are set to minimise losses and extract learning behaviour features. Finally, based on the extracted learning behaviour characteristics, the optimal classification function is constructed to classify the learning behaviour of English online courses. Based on the transfer information of learning behaviour status, the identification of online course learning behaviour is completed. The experimental results show that the recognition accuracy of the proposed method is above 90%, and its recognition accuracy is and can shorten the recognition time of learning behaviour, with high practical application reliability.
    Keywords: multimodal data fusion; English online course; learning behaviour; behaviour recognition.
    DOI: 10.1504/IJBIDM.2024.10062021
     
  • An English MOOC similar resource clustering method based on grey correlation   Order a copy of this article
    by Xianzhuang Mao 
    Abstract: Due to the problems of low clustering accuracy and efficiency in traditional similar resource clustering methods, this paper studies an English MOOC similar resource clustering method based on grey correlation. Principal component analysis was used to extract similar resource features of English MOOC, and feature selection methods was used to pre-process similar resource features of English MOOC. On this basis, based on the grey correlation method, the pre-processed English MOOC similar resource features are standardised, and the correlation degree between different English MOOC similar resource features is calculated. The English MOOC similar resource correlation matrix is constructed to achieve English MOOC similar resource clustering. The experimental results show that the contour coefficient of the proposed method is closer to one, and the clustering accuracy of similar resources in English MOOC is as high as 94.2%, with a clustering time of only 22.3 ms.
    Keywords: grey correlation; principal component analysis method; English MOOC; feature selection; clustering of similar resources.
    DOI: 10.1504/IJBIDM.2024.10062019
     
  • Study on personalised recommendation method of English online learning resources based on improved collaborative filtering algorithm   Order a copy of this article
    by Xiuqin Zhang, Jigang Xie 
    Abstract: In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.
    Keywords: improve collaborative filtering; English learning resources; personalised recommendation; matrix decomposition; K-means clustering; heuristic clustering.
    DOI: 10.1504/IJBIDM.2024.10062020
     
  • A data classification method for innovation and entrepreneurship in applied universities based on nearest neighbour criterion   Order a copy of this article
    by Xiuhong Qin, Na Li 
    Abstract: Aiming to improve the accuracy, recall, and F1 value of data classification, this paper proposes an applied university innovation and entrepreneurship data classification method based on the nearest neighbour criterion. Firstly, the decision tree algorithm is used to mine innovation and entrepreneurship data from applied universities. Then, dynamic weight is introduced to improve the similarity calculation method based on edit distance, and the improved method is used to realise data de-duplication to avoid data over fitting. Finally, the nearest neighbour criterion method is used to classify applied university innovation and entrepreneurship data, and cosine similarity is used to calculate the similarity between the samples to be classified and each sample in the training data, achieving data classification. The experimental results demonstrate that the proposed method achieves a maximum accuracy of 96.5% and an average F1 score of 0.91. These findings indicate a high level of accuracy, recall, and F1 value for data classification using the proposed method.
    Keywords: nearest neighbour criterion; innovation and entrepreneurship; data classification; decision tree algorithm; dynamic weight; cosine similarity.
    DOI: 10.1504/IJBIDM.2024.10063366
     
  • Intelligence assistant using deep learning: use case in crop disease prediction   Order a copy of this article
    by U. Dinesh Kumar, Manaranjan Pradhan, Shailaja Grover, Naveen Kumar Bhansali 
    Abstract: In India, 70% of the Indian population is dependent on agriculture, yet agriculture generates only 13% of the country's gross domestic product. Several factors contribute to high levels of stress among farmers in India, such as increased input costs, draughts, and reduced revenues. The problem lies in the absence of an integrated farm advisory system. A farmer needs help to bridge this information gap, and they need it early in the crop's lifecycle to prevent it from being destroyed by pests or diseases. This research involves developing deep learning algorithms such as ResNet18 and DenseNet121 to help farmers diagnose crop diseases earlier and take corrective actions. By using deep learning techniques to detect these crop diseases with images farmers can scan or click with their smartphones, we can fill in the knowledge gap. To facilitate the use of the models by farmers, they are deployed in Android-based smartphones.
    Keywords: agro-analytics; convolutional neural networks; CNNs; crop disease detection; data augmentation; intelligent assistant.
    DOI: 10.1504/IJBIDM.2024.10063463
     

Special Issue on: Artificial Intelligence in Online Higher Educational Data Mining

  • Reflections on strategies for psychological health education for college students based on data mining   Order a copy of this article
    by Die Meng, Beibei Ma, Shiying Li 
    Abstract: In order to improve the mental health level of college students, a data mining based mental health education strategy for college students is proposed. Firstly, analyse the characteristics of data mining and its potential value in mental health education. Secondly, after denoising the mental health data of college students using wavelet transform, data mining methods are used to identify the psychological crisis status of college students. Finally, based on the psychological crisis status of college students, measures for mental health education are proposed from the following aspects: building a psychological counselling platform, launching psychological health promotion activities, establishing a psychological support network, strengthening academic guidance and stress management. The example analysis results show that after the application of the strategy in this article, the psychological health scores of college students have been effectively improved, with an average score of 93.5 points.
    Keywords: data mining; college students; psychological health; educational strategies; stress management.
    DOI: 10.1504/IJBIDM.2024.10063976
     
  • Evaluation method for the effectiveness of online course teaching reform in universities based on improved decision tree   Order a copy of this article
    by Lin Yang, Haiqing Zhang 
    Abstract: Aiming at the problems of long evaluation time and poor evaluation accuracy of existing evaluation methods, an improved decision tree-based evaluation method for the effectiveness of college online course teaching reform is proposed. Firstly, the teaching mode of college online course is analysed, and an evaluation system is constructed to ensure the applicability of the evaluation method. Secondly, AHP entropy weight method is used to calculate the weights of evaluation indicators to ensure the accuracy and authority of evaluation results. Finally, the evaluation model based on decision tree algorithm is constructed and improved by fuzzy neural network to further optimise the evaluation results. The parameters of fuzzy neural network are adjusted and gradient descent method is used to optimise the evaluation results, so as to effectively evaluate the effect of college online course teaching reform. Through experiments, the evaluation time of the method is less than 5 ms, and the evaluation accuracy is more than 92.5%, which shows that the method is efficient and accurate, and provides an effective evaluation means for the teaching reform of online courses in colleges and universities.
    Keywords: improving decision trees; online courses; teaching reform; impact assessment.
    DOI: 10.1504/IJBIDM.2024.10063977
     
  • High quality management of higher education based on data mining   Order a copy of this article
    by Lihui Yang, Xiuhong Qin, Wenhong Liu 
    Abstract: In order to improve the quality of higher education, student satisfaction, and employment rate, a data mining based high-quality management method for higher education is proposed. Firstly, construct a high-quality evaluation system for higher education based on the principles of education quality evaluation. Secondly, the association rule mining method is used to construct a university education quality management model and determine the weight of the impact indicators for high-quality management of university education. Finally, the fuzzy evaluation method is used to determine the high-quality evaluation function of higher education, and the results of high-quality evaluation of higher education are obtained. High-quality management strategies are developed based on the evaluation results to improve the quality of education. The experimental results show that the student satisfaction rate of this method can reach 99.3%, and the student employment rate can reach 99.9%.
    Keywords: association rule mining; indicator weight; fuzzy evaluation; data mining.
    DOI: 10.1504/IJBIDM.2024.10063978
     

Special Issue on: Methods and Applications of Data Mining in Business Domains II

  • Online teaching data distribution method based on learning behaviour big data mining   Order a copy of this article
    by Jing Chang 
    Abstract: To overcome the problems of low accuracy and recall of traditional online teaching data distribution methods, this paper proposes an online teaching data distribution method based on learning behaviour big data mining. Firstly, collect online teaching data and pre-process the distribution data; then, generate online teaching data distribution rules through triangular fuzzy clustering; finally, based on the learning behaviour big data mining method, the data is divided into fuzzy metrics, membership functions are established to update distribution rules, and big data mining is used to design data distribution schemes. The experimental results show that the distribution accuracy of our method can reach 99.89%, and the parameter recall rate can reach 97.89%. The actual results are in line with the expected results and have a good distribution effect.
    Keywords: learning behaviour; big data mining; online teaching; data distribution.
    DOI: 10.1504/IJBIDM.2025.10065173
     
  • An English learning behaviour data mining based on improved ensemble learning algorithm   Order a copy of this article
    by Lin Fan, Pengqi Cao, Yunxia Du 
    Abstract: In order to enhance the learning effectiveness of English learners, this paper proposes an English learning behaviour data mining method based on improved ensemble learning algorithm. A web crawler is used to collect behavioural information of learners during the process of learning English, and learner profiles are constructed. The data is pre-processed, and collaborative filtering algorithms are employed to extract features of English learning behaviours. By treating English learning behaviour features as input vectors and data mining results as output vectors, an improved stacking ensemble learning model based on chain rules is constructed. This model is utilised to obtain data mining results for English learning behaviour. The experimental results show that the normalised difference accuracy of the proposed method is always above 90%, and the mAP value is always above 93%, indicating that the proposed method has high accuracy and good mining effect in English learning behaviour data mining.
    Keywords: ensemble learning; English learning; learning behaviour; data mining; chain rules; stacking ensemble learning model.
    DOI: 10.1504/IJBIDM.2025.10065187
     
  • Web server log data pre-processing for mining zakat user profile using association rules   Order a copy of this article
    by Mohamad Farhan Mohamad Moshin, Wan Hussain Wan Ishak, Yuhanis Yusof, Jastini Mohd Jamil, Alwi Ahmad 
    Abstract: The internet’s transformative impact on businesses and marketing strategies underscores the pivotal role of websites in establishing credibility and disseminating information to customers. To measure website effectiveness, tracking visitor behaviour is essential. This study focuses on web log data from Lembaga Zakat Negeri Kedah (LZNK), a Malaysian government institution managing zakat which utilises web analytics and mining to gain insights into website usage. The objectives of this paper are two-fold: firstly, to detail the pre-processing of weblog data to ensure reliability for data mining. Secondly is to employ association rule mining to extract user patterns from pre-processed weblog data. To achieve this, the web logs were obtained from the LZNK’s website spanning from 2016 to November 2020 with a focus on user access in 2020. The findings reveal critical aspects of user behaviour including the most visited pages, popular page combinations, user interests, relationships between pages, and the impact of the entry page. Implementing these insights can enhance the LZNK website’s usability, user satisfaction, and highlighting the importance of adapting to evolving user preferences and technological advancements.
    Keywords: association rule; data pre-processing; user profile; web log; web mining.
    DOI: 10.1504/IJBIDM.2025.10065199