International Journal of Business Intelligence and Data Mining (14 papers in press)

Regular Issues

• Open Access

  Lightweight CNN-transformer hybrid network for English speech recognition ( Free Full-text Access )

  by Yan Li, Weiguo Huang, Cui Gu 
  Abstract: Speech recognition is the core technology for achieving human-computer interaction, among which English speech recognition has extremely high practical value in global communication scenarios. Although CNN-based speech recognition models are good at extracting local features, they cannot effectively capture global semantics. In contrast, transformer-based models outperform CNN in extracting global semantics, but their model parameters and computational complexity are high, making it difficult to deploy and run on resource constrained devices. Inspired by this, we propose a lightweight CNN-transformer hybrid network (LwCTHNet) for English speech recognition. LwCTHNet effectively integrates local feature extraction, frequency domain detail supplementation, and global semantic capture capabilities by alternately stacking 3 x 3 convolution layers, wavelet enhanced convolution modules, and lightweight transformer modules. In addition, it also achieves multi-scale feature learning through skip connections and enhances feature discriminability by using a mixed loss function that combines cross entropy loss and contrastive loss. The experimental results on three English speech recognition datasets show that the proposed method not only has the minimum parameter size, but also achieves an approximately optimal word error rate. This indicates that the proposed LwCTHNet method has achieved a good balance in recognition performance, computational complexity, and parameter size.
  Keywords: lightweight model; English speech recognition; transformer; multi-scale feature learning.
  DOI: 10.1504/IJBIDM.2026.10077674
   
  
• Research on the impact of digital marketing campaign strategies on consumer buying intention  
  by Koteswararao Dondapati, Naga Sushma Allur, Durga Praveen Deevi, Himabindu Chetlapalli, Sharadha Kodadi, Thinagaran Perumal 
  Abstract: Consumer psychology and shopping motivation also stay abreast of changes in technology. To address an immense number of audiences and to understand the purchaser’s behaviour, campaigns for digital marketing are pretty crucial for an organisation. However, the purchasing propensity cannot be precisely measured and portrayed by traditional tools. With this limitation, the study managed to deliver an ML-based consumer buying intention analysis method, based on analysis from consumer data through online advertisements using machine learning algorithms. Technique for Order of Preference by Similarity to Ideal Solution applied will allow ML-CBIAM to have precise all-inclusive understanding of customer habit and preference. Simulated results indicate that ML-CBIAM is superior to the state-of-the-art methods in terms of accuracy and coverage in predicting purchase intent through different campaign techniques. This approach helps firms optimise marketing strategies, increase profits, and strengthen customer relationships.
  Keywords: digital marketing campaign; consumer buying intention; machine learning; consumer analysis.
  DOI: 10.1504/IJBIDM.2026.10077344
   
  
• Cloud-based business intelligence in an E-commerce environment for small and medium-sized enterprises  
  by Akhil Raj Gaius Yallamelli, Vijaykumar Mamidala, Mohanarangan Veerappermal Devarajan, Rama Krishna Mani Kanta Yalla, Thirusubramanian Ganesan, Aceng Sambs 
  Abstract: Business intelligence (BI) addresses comprehensive quality management approaches. The interconnected facilities applicable to BI in many instances became more difficult, expensive, and rigid. Therefore, this paper suggests cloud-based business intelligence in the e-commerce environment (CB-BIE) method is used to find solutions for small and medium-sized enterprises. In the CB-BIE method, five customers are interviewed, and a questionnaire session is conducted for 10 small and medium-sized enterprises (SMEs). Each of these aspects covers a particular field that clients pay great attention to when introducing a cloud BI solution. The results indicate that some of the most significant performance measures are software, seamless network services, sensitive responses to requests for customer service, managing vast volumes of data, and implementing costs. CB-BIE method noticed that industry-specific applications for monthly and quarterly charges and emails or phones to access the company are desired.
  Keywords: business intelligence; BI; cloud computing; E-commerce.
  DOI: 10.1504/IJBIDM.2026.10077472
   
  

Special Issue on: Exploring AI Methods and Applications for Data Mining

• Open Access

  Study on fault diagnosis and recovery for digital distribution networks: ITOT fusion ( Free Full-text Access )

  by Chunmei Zhang, Xingque Xu, Yongjian Li, Silin Liu, Jianyi Li 
  Abstract: This study proposed a fault diagnosis and recovery method of digital distribution network based on ITOT fusion. Build a digital distribution network operation data acquisition architecture using ITOT fusion technology, and perform PCA dimensionality reduction on the collected data. Input the data into a genetic algorithm optimised wavelet neural network to obtain fault diagnosis results. Build a digital distribution network fault recovery model based on the fault diagnosis results, and solve the fault recovery model using the BPSOGWO algorithm to obtain the optimal fault recovery strategy. In diagnosing faults, the proposed method attains a remarkable average accuracy of up to 97.55%, the average fault recovery rate is 96.88%, and the fault recovery time varies between 0.8 s and 1.9 s.
  Keywords: ITOT fusion; digital distribution network; fault diagnosis; fault recovery; optimised wavelet neural network; BPSOGWO algorithm.
  DOI: 10.1504/IJBIDM.2026.10077297
   
  

• Open Access

  Improved multi-objective quantum genetic algorithm for low-carbon economic dispatch in power grids ( Free Full-text Access )

  by Jinhui Ma, Xincun Shen, Li Zhang, Lu Chen 
  Abstract: To improve the low-carbon economic regulation effect of the power grid, reduce carbon emissions and operating costs, a low-carbon economic regulation method for the power grid based on improved multi-objective quantum genetic algorithm is proposed. Firstly, integrate photovoltaic, wind power, energy storage, and electricity market data, use local outlier factor algorithm to clean the data and fill in missing values. Secondly, establish a multi-objective optimization model that includes power generation costs, carbon emissions, and demand response costs. Finally, an improved quantum genetic algorithm is proposed to enhance solution efficiency through quantum gate updates and intelligent population management, achieving low-carbon economic dispatch of the power grid. The results showed that under the control of the proposed method, the highest average carbon emissions were 0.39 tons of CO?/MWh, the highest cost was only 135000 yuan/MWh, and the average new energy consumption rate reached 91.87%.
  Keywords: low carbon economic regulation; multi-objective quantum genetic algorithm; power generation cost; clean data.
  DOI: 10.1504/IJBIDM.2026.10077375
   
  

• Open Access

  Automatic correction of subjective sentence similarity questions based on text image recognition ( Free Full-text Access )

  by Kaichen Tang, Xuyang Tu 
  Abstract: Subjective questions play a vital role in educational and vocational assessments, yet manual grading presents challenges to both efficiency and fairness. To address these challenges in sentence similarity tasks, this study proposed an automated correction method by leveraging text-image recognition. A hardware module for data acquisition via image capture was employed, and a VGGNet-based model was used for highly accurate text recognition. Building on the recognised text, a novel automatic grading approach was introduced that integrated a T5 pre-trained model with a pointer network within a ‘pre-training + fine-tuning’ paradigm. Experimental results demonstrated the effectiveness of the method, achieving a text recognition accuracy of 98.61%, with a low error rate of 2.87% and a processing time of 1.21 seconds. These findings highlight the potential of the system for reliable and efficient automated assessment.
  Keywords: text image recognition; sentence similarity; subjective questions; automatic correction; VGGNet; T5 pre training model; pointer network.
  DOI: 10.1504/IJBIDM.2026.10077486
   
  

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

• Open Access

  Study on multimodal ideological and political teaching material push on MOOC online learning platform ( Free Full-text Access )

  by Yiming Qu, Yanhua Wang 
  Abstract: The expected goal is to address the issues of low coverage, high latency, and high average absolute error in traditional push methods for ideological and political teaching resources. This study focuses on the multimodal ideological and political teaching material push method on MOOC online learning platform. Firstly, the k-means clustering method is used to label the learner data and construct platform user profiles; Secondly, by combining significant data block detection methods, significant learning features are extracted. Using polynomial naive Bayes model, classify according to the modality of ideological and political teaching resources; Finally, based on collaborative filtering algorithms, different modalities of ideological and political teaching resources are pushed to learners with different learning needs. Through experiments, it has been proven that the coverage rate of our method can reach over 90%, with a push delay of only 105ms and an average absolute error of only 0.13.
  Keywords: MOOC online learning platform; multimodal; ideological and political teaching resources; user profiles; significant data block detection.
  DOI: 10.1504/IJBIDM.2025.10075381
   
  

• Open Access

  Prediction of carbon emissions throughout the lifecycle of zero carbon substations based on Lasso-GRNN neural network model ( Free Full-text Access )

  by Ting Zeng, Yueqing Chen, Liuhuo Wang, Mingpeng Yuan, Zhangqi Lv, Dianbin Wang 
  Abstract: In order to solve the problems of low recall rate, low prediction accuracy, and long prediction completion time of carbon emission prediction factors in traditional methods, a prediction method of carbon emissions throughout the lifecycle of zero carbon substations based on Lasso-GRNN neural network model is proposed. Lasso-GRNN neural network model is constructed by using the key indicators for predicting the carbon emissions of a zero carbon substation throughout its entire lifecycle as input variables and the carbon emission values as output variables. The model uses Lasso to screen key indicators and inputs them into the GRNN neural network to obtain accurate prediction results. Experimental results show that the proposed method has a maximum recall rate of 98.12% for the influencing factors of carbon emissions throughout the entire life cycle of zero carbon substations, a maximum prediction accuracy of 98.51%, and a minimum prediction completion time of 0.68s.
  Keywords: Lasso-GRNN neural network model; zero carbon substation; full lifecycle; carbon emissions forecast; indicators.
  DOI: 10.1504/IJBIDM.2026.10076223
   
  

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
   
  

Special Issue on: Knowledge Discovery from Big Data to Spur Social Development Part 2

• Deep learning-based football training movement analysis and penalty feedback research  
  by Fubin Dai, Shuai Li, Zhigang Li 
  Abstract: Recent advances in video understanding have enabled referee assistance in football, but reliance on single views or costly VAR systems limits their use in training and lower-tier contexts. In this work, we first propose a multi-view deep learning system for analysing football training movements and generating automated penalty feedback. The system processes four-view video sequences through video encoders, followed by a novel cross-view attention fusion module (CAFM) that adaptively integrates features from different viewpoints. Finally, to address view inconsistency and class imbalance in real-world data, we introduce a view completion strategy with augmentation and apply a class-balanced loss for classification. Experiments conducted on the SoccerNet-MVFoul dataset demonstrate that our method achieves 60.67% accuracy and 46.30% balanced accuracy in foul action classification. For foul severity classification, our approach reaches 53.63% accuracy and 54.14% balanced accuracy. Visualisation of attention weights confirms that the model successfully identifies the most informative viewpoints for each foul instance. These results show that the proposed system is effective and interpretable, offering a promising direction for referee assistance in non-professional football scenarios such as youth academies, amateur clubs, and grassroots training sessions.
  Keywords: computer vision; football; video assistant referee; VAR; video classification.
  DOI: 10.1504/IJBIDM.2026.10077556
   
  
• Sports training fatigue recognition using surface electromyography signals on wearable devices  
  by Jing Li, Wenwen Pan 
  Abstract: How to timely assess the fatigue level of athletes to avoid muscle injury is critical for sports daily training. However, it is impossible to use huge device to monitor muscle fatigue level of athletes during training. This paper designs a lightweight muscle fatigue estimation system using surface electromyography (sEMG) signals to tackle these issues. First, the sEMG signals are collected using wireless sEMG sensors worn by athletes. Then, the collected sEMG signals are transmitted to edge device which integrates real-time sEMG signal processing and the lightweight artificial intelligence model deployment. The former one extracts time domain features, frequency domain features and time-frequency features of sEMG signals. The later one adopts relative margin support vector ordinal regression which is sparse to reflect the ordinal relationship between different fatigue levels. The experimental results show the proposed scheme can reach least mean absolute error and satisfy the computing resource limits of edge nodes.
  Keywords: surface electromyography; sEMG; edge computing; fatigue recognition; ordinal regression; wearable devices.
  DOI: 10.1504/IJBIDM.2026.10077587
   
  
• SportVAE: athletic heart rate anomaly detection via wearable sensors using enhanced variational autoencoders  
  by Peng Liu, Yang Yu 
  Abstract: Traditional HRV analysis struggles to distinguish exercise-induced variations from genuine cardiac irregularities, especially in high-intensity activities. To address these issues, this study proposes SportVAE, an enhanced Variational Autoencoder (VAE) for detecting heart rate anomalies of athletes during physical activities via wearable sensors. The proposed model incorporates temporal attention, a bidirectional LSTM encoder for capturing heart rate dynamics, an adaptive weighting mechanism to balance reconstruction error and KL divergence based on intensity, and domain adaptation layers for generalization across sports. Tested on 10,000+ hours of data from 2,000 athletes, it achieved 93.2% accuracy, 91.5% recall, and a 92.3% F1-score, outperforming existing methods. The model adapts across sports, handles varying intensities, and is efficient enough for wearable devices, contributing to both theory and practice in athletic health monitoring.
  Keywords: variational autoencoder; VAE; athletic heart rate monitoring; anomaly detection; deep learning; temporal attention mechanism; wearable technology.
  DOI: 10.1504/IJBIDM.2026.10077588
   
  
• A stroke lesion segmentation method based on volume-balanced data partitioning and dual-branch ensemble network  
  by Siyu Zhao, Baoqiang Li 
  Abstract: Accurate segmentation of ischemic stroke lesions from MRI is crucial for clinical decision-making, including subtype classification and prognosis assessment. However, the heterogeneous size and appearance of lesions in T1-weighted MRI, along with class imbalance, pose significant challenges. In this study, we propose a dual-branch ensemble framework integrating nnU-Net and nnResU-Net to leverage their complementary strengths in global representation and local detail preservation. Furthermore, we introduce a volume-balanced cross-validation strategy to ensure consistent distribution of lesion sizes across training folds, addressing the imbalance problem at the data level. Experiments on the publicly available ATLAS R2.0 dataset demonstrate the superiority of our method. Our ensemble approach achieves a Dice score of 0.6601, volume difference (VD) of 9188 mm³, lesion-wise F1-score (L-F1) of 0.5349, and a simple lesion count (SLC) error of 4.6735 across five-fold cross-validation. These results outperform state-of-the-art baselines, including U-Net, TransUNet, and SwinUNETR. Qualitative visualisation further confirms that our model produces lesion segmentation results most closely aligned with expert annotations. To further enhance clinical applicability, the framework can be deployed in edge-computing environments, enabling low-latency and resource-efficient lesion segmentation close to the point of care.
  Keywords: ischemic stroke; magnetic resonance imaging; deep learning; medical image analysis; segmentation.
  DOI: 10.1504/IJBIDM.2026.10077882