International Journal of Business Intelligence and Data Mining (12 papers in press)

Special Issue on: OA Digitalisation, Information Systems and Artificial Intelligence in Business Processing

• Open Access

  Research on safety risk perception of electochemical energy storage power station under the background of environmental sustainable development ( Free Full-text Access )

  by DongLiang Deng, LiXin Yin 
  Abstract: The current risks faced by electrochemical energy storage power plants are diversified and complex, resulting in high false alarm rates, false negative rate, and long time consumption of traditional methods. Therefore, a safety risk perception method of electochemical energy storage power station under the background of environmental sustainable development has been proposed. Cluster analysis of multi-source risk indicator data using GMM to identify potential risk patterns; It calculates the weights of various indicators based on cloud models, screens key risk factors, and constructs a multi-level risk indicator system; by integrating real-time monitoring data with prior knowledge through Bayesian inference, dynamic risk probability updates and perception can be achieved. The experimental results show that the minimum false alarm rate of the proposed method is 2.86%, the minimum false negative rate is 2.78%, and the risk perception time varies between 0.3 s and 0.7 s, indicating high engineering application value.
  Keywords: background of environmental sustainable development; cloud model; electochemical energy storage power station; safety risk perception; Bayesian inference.
  DOI: 10.1504/IJBIDM.2025.10074955
   
  

• Open Access

  A comprehensive management method of audit databased on knowledge graph ( Free Full-text Access )

  by Xuena Lin 
  Abstract: A comprehensive management method of audit data based on knowledge graph is proposed to solve the problems of low F1 value, long data update delay time, and low data coverage in traditional methods. Firstly, using web crawling technology to automate the collection of audit data. Secondly, based on the preprocessed data, a BiLSTM CRF joint model is used to achieve audit entity recognition, and a graph convolutional network (GCN) is used to complete the relationship extraction task, thereby constructing an audit knowledge graph. Finally, an incremental learning mechanism is introduced to dynamically update the knowledge graph, and comprehensive management of audit data is achieved based on the updated knowledge graph. The experimental results show that the F1 value of the proposed method is between 0.81 and 0.89, the data update delay time is stable at 100-110ms, and the data coverage reaches over 90% after 10 iterations and remains stable
  Keywords: knowledge graph; KG; audit data; entity recognition; relationship extraction; dynamically update.
  DOI: 10.1504/IJBIDM.2025.10075009
   
  

Regular Issues

• Improving brain MRI segmentation of multiple sclerosis using an advanced CNN approach  
  by V. Biksham , Sampath Korra, B. Pradeep Kumar , Salar Mohammad 
  Abstract: Multiple sclerosis (MS) can be detected early by looking for lesions in brain magnetic resonance imaging (MRI). Recently, unsupervised anomaly detection algorithms based on autoencoders were presented for the automatic identification of MS lesions. However, because these autoencoder-based approaches were created exclusively for 2D MRI pictures (e.g., 2D cross-sectional slices), they do not take use of the complete 3D information of MRI. In this research work, a novel 3D autoencoder-based methodological solution for detecting MS lesion volume in MRI is offered. We begin by defining a 3D convolutional neural network (CNN) for complete MRI volumes and then construct each encoder and decoder layer of the 3D autoencoder using 3D CNN. For optimal data reconstruction, we additionally include a skip link between the encoder and decoder layers. In the experimental results, we compare the 3D autoencoder-based method to the 2D autoencoder models using training datasets from the Human Connectome Project (HCP) and testing datasets from the Longitudinal MS Lesion Segmentation Challenge, and show that the proposed method outperforms the 2D autoencoder models by up to 20% in MS lesion prediction.
  Keywords: multiple sclerosis; brain MRI; image segmentation; CNN; chronic disease; healthcare.
  DOI: 10.1504/IJBIDM.2025.10068668
   
  
• Analysing the effectiveness of financial news sentiments on stock price prediction of twelve Indian sectoral stock indices using a hybrid LSTM-GRU model  
  by Meera George, R. Murugesan 
  Abstract: Despite the growing interest in combining news sentiments with historical data to improve stock price prediction, a considerable gap exists in predicting the sectoral stock indices using this methodology. This study addresses this gap by predicting the closing price of 12 Indian sectoral stock indices through a hybrid deep-learning architecture. It employs a hybrid TFIDF-Doc2Vec feature extraction technique and an SVM classifier to extract the financial news sentiments. These sentiments are utilised to create a sentiment Index, combined with historical stock data to predict each sectoral stock index using a hybrid LSTM-GRU model. The study evaluates the effectiveness of financial news sentiments in sectoral stock prediction by comparing models with and without sentiments. Results demonstrate a notable influence of sentiments on the stock price prediction of ten sectoral stock indices with a pronounced impact on the NSEBANK index. This study offers valuable insights for investors in formulating sector-specific trading strategies. It also aids policymakers in market regulation and helps financial analysts improve forecasting models by incorporating financial news sentiments. Future research could explore the integration of multi-source investor sentiments with advanced deep-learning models for even more accurate stock price predictions across diverse sectors.
  Keywords: stock price prediction; sectoral stock indices; financial news sentiments; FNSs; hybrid TFIDF-Doc2Vec; hybrid LSTM-GRU.
  DOI: 10.1504/IJBIDM.2025.10070322
   
  
• Data mining of abnormal behaviour in English learning based on dynamic grid generation algorithm  
  by Le Yang  
  Abstract: To address the limitations of traditional data mining methods for detecting abnormal behaviour in English learning - such as low accuracy, low recall rates, and long processing times - this paper proposes a joint data mining method based on a dynamic grid generation algorithm. The proposed approach begins by collecting instances of abnormal behaviour in English learning to construct a behavioural dataset. Next, the RANSAC algorithm eliminates erroneous feature matching points, while the dynamic grid generation algorithm performs data feature matching. Finally, an abnormal behaviour data mining function is constructed to identify anomalies in English learning behaviours. Experimental results demonstrate the methods effectiveness, achieving a recall rate of 96.5%, an accuracy of 99.15%, and a processing time of just six seconds, confirming its efficiency in mining abnormal behaviour data in English learning.
  Keywords: feature matching; dynamic grid generation algorithm; data mining; Ransac algorithm.
  DOI: 10.1504/IJBIDM.2025.10072627
   
  
• A collective intelligence based project team formation using bio-inspired techniques  
  by Ankita Verma, Vaishnavi Agarwal, Shubhangi, Sakeena Rizvi, Savita Singh 
  Abstract: The outcome of a project is greatly influenced by technical variables along with human and social elements of the development process. The knowledge possessed by team members and their ability to collaborate effectively are essential in managing these complexities. Team formation involves assembling specialists with diverse skills to perform a task efficiently. This paper addresses team formation for generalised tasks by formulating a set of experts from an expertise network who can collaborate effectively. It utilises collective intelligence to build teams, maximising potential based on individual skill levels. The proposed model captures team dynamics by considering trust, which is vital for effective interactions. Team formation is formulated as an optimisation problem with two objectives: 1) maximising knowledge; 2) maximising collaboration. To achieve this, nature-inspired approaches such as NSGA-II and ant colony optimisation (ACO) are employed. Results demonstrate the methods effectiveness from both computational and pedagogical perspectives.
  Keywords: team formation; collective intelligence; bio-inspired computing; genetic algorithm; project development.
  DOI: 10.1504/IJBIDM.2026.10074392
   
  
• Fusion technique for handling imbalanced and overlapped dataset to improve the performance of existing classifiers  
  by Sunil Kumar, S.K. Singh, Vishal Nagar 
  Abstract: The effectiveness of the classifier in efficiently classifying the data is diminished by imbalanced class data. Employing a supervised method to address imbalanced data may result in the exclusion of majority classes. The overlap of class instances can complicate the learning process of the classifier. In this paper, three different algorithms were proposed to handle the imbalance problem. The proposed algorithms MCROR, RbImbD and Fused-MCRb with different working mechanisms were applied to balance the majority and the minority classes present in the given dataset. 23 publicly available dataset and 12 synthesised dataset were used to evaluate the performance of the proposed algorithms. The results were calculated on different performance metrices: precision, sensitivity, F1 score and geometric mean. The values obtained for different metrices shows that the proposed algorithms significantly improved the performance of the classifier and handle the imbalance classes in the given dataset.
  Keywords: class imbalance; instances; random forest; classifier; machine learning.
  DOI: 10.1504/IJBIDM.2026.10074673
   
  

Special Issue on: Innovative AI driven 3D Modelling for Business Intelligence Tools

• Leveraging traditional business culture for business intelligence: a scalable parameter server architecture with distributed machine learning  
  by Chengcai Xing 
  Abstract: Yanan, a significant historical and cultural hub in China, is being revitalised and utilised to drive development in various spheres. The citys traditional commercial and cultural resources are being harnessed to contribute to its political, cultural, educational, and economic growth. Yanan models other historically significant regions, demonstrating how heritage can be leveraged for contemporary development. Advanced machine learning approaches are used to overcome scalability and robustness issues in large-scale data-driven systems. The parameter server architecture decentralises the training process of machine learning models, enabling efficient handling of vast datasets and high computational demands. This design enhances fault tolerance and ensures seamless operation under challenging conditions. Intelligent simulations and tests validate the efficacy of these machine-learning approaches in modelling the evolution and application of traditional commercial culture. These simulations provide a dynamic and accurate representation of how cultural and business practices can adapt and thrive in modern contexts. The reliability and precision of machine learning models in capturing complex patterns and trends inherent in cultural and economic transitions are underscored through rigorous testing. This exploration highlights the innovative intersection of technology and tradition, showcasing how machine learning can play a transformative role in preserving and advancing historical and cultural assets.
  Keywords: traditional commercial and cultural resources; Yan’an historical value; distributed machine learning; parametric server architecture.
  DOI: 10.1504/IJBIDM.2025.10069536
   
  

Special Issue on: Knowledge Discovery from Big Data to Spur Social Development

• Deep learning based DAVS-UNet for medical image segmentation  
  by Zihui Zhu 
  Abstract: Deep learning based Convolutional Neural Networks (CNNs) and transformers are widely used in medical image processing tasks, while the State Space Sequence Model (SMM) architecture is proposed to address its limitations in improving the scaling efficiency and solving the transformed quadratic scale problem. Inspired by the Mamba architecture, this paper proposes Dual-Attention Vision Scaled-UNet (DAVS-UNet) for medical image segmentation, in which Adaptive Multi-scale Selection (AMS) is applied to the input image for better capturing details at different scales and extracting input features. Furthermore, Atrous Space Pyramid Pooling (ASPP) is introduced to expand the sensory field by collecting global contextual information after the final encoder. The experiments on a large number of publicly available datasets illustrate that DAVs-UNet shows excellent performance on the ISIC2017, ISIC2018, Synapse datasets, and outperforms existing SSM-architecture networks employed in medical image segmentation tasks. The code is available at https://github.com/zhzhuac/DAVS.
  Keywords: attention mechanism; multi-scale information; state space models; convolutional neural networks; CNNs; adaptive multi-scale selection; AMS; atrous space pyramid pooling; ASPP.
  DOI: 10.1504/IJBIDM.2026.10070330
   
  
• GA-TabNet: a novel approach for early dropout prediction in MOOCs based on genetic algorithms and TabNet  
  by Houssam Eddine Aouarib, Fatima Zohra Laallam, Salah Eddine Henouda, Mohamed Fouzi Djouhri 
  Abstract: Massive open online courses (MOOCs) represent one of the most effective educational methodologies due to their cost-effectiveness, flexibility, ubiquity, and their role in facilitating and improving education. MOOCs possess the capacity to revolutionise global education; nevertheless, the high dropout rates often undermine their effectiveness. The emergence of machine learning, deep learning techniques, and educational big data enables academics to address the student dropout problem through big data analytics. This study addresses the critical challenges of student dropout prediction by proposing GA-TabNet, an innovative model that combines a Genetic Algorithm with TabNet for early dropout prediction. The results of this study were validated using the Open University Learning Analytics Dataset. The proposed model attained an average accuracy exceeding 92%. Furthermore, it outperformed traditional predictive models, including Support Vector Machine, Long Short-Term Memory, Logistic Regression, MultiLayer Perceptron, Decision Trees, and Random Forest models, by margins ranging from 0.79% to 4.79%.
  Keywords: student dropout prediction; massive open online course; MOOC; TabNet; genetic algorithm; big data; machine learning; ML; deep learning; DL.
  DOI: 10.1504/IJBIDM.2026.10073518
   
  
• Enhancing physical education with kinect motion tracking and context personalisation  
  by Yuqiu Zhang 
  Abstract: This study proposes a novel approach for physical education (PE) that integrates kinect motion tracking, deep learning, and context personalisation. The system combines real-time feedback and adaptive learning paths to optimise student participation, motivation, and physical skill development. An ablation study was conducted to compare the effectiveness of the full system with three other configurations: kinect-only motion tracking, kinect with context personalisation, and kinect with deep learning. The experimental results indicate that the full system, which combines all three components, significantly outperforms the other configurations in terms of motivation, physical performance improvement, and engagement. Specifically, the full system achieved the highest improvement in skill development (90%), engagement (98%), and motivation, suggesting that the combination of kinect motion tracking, context personalisation, and deep learning is most effective for enhancing PE outcomes. This research contributes to the digital transformation of physical education. It provides a new pathway to leverage technology for improving both student motivation and performance.
  Keywords: physical education; kinect; deep learning; context personalisation; teaching methods.
  DOI: 10.1504/IJBIDM.2026.10074262
   
  
• Integrating data mining and transformer models for socially beneficial bicycle scheduling in urban bicycle-sharing  
  by Wenxin Ma, Jiabin Liang, Dazhou Li 
  Abstract: The rapid growth of bicycle-sharing systems in urban settings necessitates the implementation of effective scheduling strategies to optimise resource allocation and tackle challenges such as uneven distribution of bicycles and operational inefficiencies. This study introduces an innovative integration of data mining techniques and transformer models aimed at enhancing bicycle scheduling. By leveraging the MARO resource scheduling platform, we simulate bicycle mobility and scrutinise demand patterns through electronic fence clustering. We evaluate two distinct scheduling strategies: a dynamic programming-based approach and a transformer-based methodology. Experimental findings reveal that the proposed transformer model markedly decreases average time overhead and path distances in comparison to conventional methods, thereby fostering more efficient and socially advantageous bicycle-sharing systems. This research significantly contributes to the optimisation of urban transportation and the advancement of sustainable mobility solutions.
  Keywords: bicycle scheduling; data mining; transformer model; urban bicycle-sharing; resource optimisation.
  DOI: 10.1504/IJBIDM.2026.10074963