International Journal of Business Intelligence and Data Mining (17 papers in press)

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

• Open Access

  DNN and BiGRU-based hierarchical attention network for intrusion detection ( Free Full-text Access )

  by Hui Yan, Hupeng Liu, Ping Yu, Xiaoqing Xu, Mingxin Li, Yunxin Long, Hanlin CHEN, Qi Wang, Duo Long 
  Abstract: To tackle the rising sophistication of cyberattacks and the critical need to enhance intrusion detection capabilities, this study presents a hybrid architecture combining a DNN with an attention mechanism and a BiGRU with an attention mechanism, BiGRU with attention, and an MLP classifier. The model integrates dual DNN-Attention and BiGRU-Attention submodules to capture high-dimensional static features and temporal dependencies, followed by feature fusion and classification via MLP. Evaluated on NSL-KDD and UNSW-NB15 datasets, the model achieves validation accuracies of 99.48% and 98.43%, respectively, with stable convergence and low loss. Comparative results demonstrate superior performance in accuracy, robustness, and generalization, confirming its effectiveness for practical cybersecurity applications.
  Keywords: intrusion detection; deep learning; bidirectional gated recurrent unit; attention mechanism; network security.
  DOI: 10.1504/IJBIDM.2026.10079023
   
  

• Open Access

  Rapid planning of multimodal transport path based on improved VSRB-RRT algorithm ( Free Full-text Access )

  by Fang Wang, Chunzheng Zhao, Xiaoya Huang, Yali Liang 
  Abstract: To address the problems of insufficient network coverage, low on-time delivery rate, and long planning scheme generation time in traditional methods, a rapid planning method of multimodal transport path based on improved VSRB-RRT algorithm is proposed. Multi modal transportation network topology model, and GA-PSO hybrid algorithm for hub location optimisation, to build an efficient network foundation for path search; Based on the results of hub site selection, an improved VSRB-RRT algorithm was designed and implemented. The algorithm improves exploration efficiency through variable sampling areas and bidirectional search mechanism, adapts to complex environments with dynamic step size adjustment, and optimises path quality through node pruning and B-spline curve smoothing, achieving fast and reliable multimodal transportation path generation. Experimental results show that the proposed method has a multimodal transportation network coverage rate of up to 97.12%, a peak on-time delivery rate of 98.67%, and a minimum scheme generation time of only 3.61s.
  Keywords: improved VSRB-RRT algorithm; multimodal transport path; rapid planning; network topology; GA-PSO hybrid algorithm.
  DOI: 10.1504/IJBIDM.2026.10079262
   
  

• Open Access

  A resource allocation method for digital online teaching platform based on classification mining ( Free Full-text Access )

  by Weiya Xu, Xi Lin 
  Abstract: In the process of deepening the evolution of digital education environment, online teaching platforms often suffer from problems such as the generalisation of heterogeneous resources, dynamic demand changes, and imbalanced system architecture. Therefore, a resource allocation method for digital online teaching platforms based on classification mining is proposed. Firstly, based on the improved particle swarm optimisation algorithm, the twin support vector machine is optimised for resource classification. Secondly, the matching degree is quantified by the load difference and imbalance degree of physical nodes, and the virtual machine resource demand is constrained within the physical machine capacity. Finally, by combining real-time resource consumption and node performance indicators, a configuration function and adaptation factor are constructed to achieve balanced and optimised allocation of teaching resources. The test results show that the method maintains a stable resource allocation balance of over 90%, and the resource allocation response time is always below 1.5 seconds.
  Keywords: classification mining; digitisation; online teaching platform; resource allocation; twin support vector machine; TWSVM; particle swarm optimisation; PSO.
  DOI: 10.1504/IJBIDM.2026.10079431
   
  

• Open Access

  A mathematical model of attribute-based encryption for mining clusters in big data ( Free Full-text Access )

  by Yumei Zhao, Huaxi Chen 
  Abstract: Traditional encryption methods are difficult to meet the fine-grained requirements of multi-user collaboration and dynamic access in big data scenarios, and there are still shortcomings in terms of recall rate, attribute hiding success rate, and encryption efficiency. Therefore, this article proposes a mathematical modeling method for encrypting big data attributes based on clustering mining. The structured dynamic fusion deep graph clustering framework enables effective big data cluster analysis with comprehensive result processing and completion. A bilinear group-based cryptographic model incorporating attribute policies is constructed, encompassing fundamental procedures including system setup, key derivation, index encryption, and trapdoor computation, thereby establishing mathematical representation for attribute-based big data encryption. Experimental evaluations demonstrate the approach achieves 97.85% average recall, 98.7% successful attribute concealment rate, with merely 75.44ms average encryption latency.
  Keywords: cluster mining; big data attribute encryption; mathematical modelling; deep map clustering mining network; bilinear group.
  DOI: 10.1504/IJBIDM.2026.10079433
   
  

Regular Issues

• 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: 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
   
  
• Privacy-preserving legal credit risk assessment framework driven by generative AI with interpretability analysis  
  by Di Teng 
  Abstract: The increasing reliance on credit risk assessment models within legal frameworks necessitates a balance between data utility and privacy preservation. This paper introduces a privacy-preserving legal credit risk assessment framework driven by generative AI, specifically leveraging a privacy-preserving generative adversarial network (ppGAN). The proposed framework aims to generate synthetic financial and legal data that preserves privacy while maintaining high utility for downstream legal credit risk models. The core idea of our approach is to leverage generative adversarial networks (GANs) to synthesise data that mimics real-world patterns without compromising sensitive information. Furthermore, we analyse the interpretability of these models, enabling stakeholders to understand the decision-making process behind risk assessments. The ppGAN architecture is evaluated against several baseline methods, including tGAN, MedGAN, HealthGAN, and DP-GAN, in terms of privacy protection, utility (classification accuracy, F1-score), and interpretability. Experimental results demonstrate that ppGAN achieves superior privacy protection and delivers high-performance credit risk predictions.
  Keywords: privacy-preserving; generative AI; legal credit risk assessment; generative adversarial networks; GAN; interpretability analysis.
  DOI: 10.1504/IJBIDM.2026.10078551
   
  
• AI-driven multi-modal legal dispute question and answer system: legal reasoning and evaluation  
  by Lin Lin 
  Abstract: With the growing integration of artificial intelligence (AI) into legal services, AI-driven legal question answering (QA) systems have garnered significant attention. However, most existing systems focus on legal knowledge retrieval and text generation, often failing to effectively address complex legal disputes that require nuanced reasoning based on core legal terms. To address this gap, we propose a novel AI-driven core keyword legal dispute question answering system (CKLQ), which combines core keyword extraction, legal dispute categorisation, and legal knowledge retrieval for handling a variety of legal disputes. It uses a multi-modal reasoning framework to enhance performance in categorising legal disputes and accurately extracting core keywords from complex legal texts. We evaluate CKLQ using accuracy, F1-score, answer accuracy, and explanation accuracy across three comprehensive legal datasets: LawBench, Legal Case Corpus, and Contract Disputes. Performance is validated against five state-of-the-art baseline models including LegalBERT, LexGLUE, LawGPT, LawBench Evaluation (original), and LegalBERT+RAG.
  Keywords: core keyword extractionl; legal dispute classification; legal reasoning; multi-modal reasoning; legal question answering system.
  DOI: 10.1504/IJBIDM.2026.10078552
   
  
• Real-time cross-lingual speech translation for English teaching via federated transfer learning and multi-modal knowledge distillation  
  by Baoying Sun, Yingwei Liu, Ying Huang 
  Abstract: This paper presents a novel approach for Real-Time Cross Lingual Speech Translation using Federated Transfer Learning and Multi-Modal Knowledge Distillation. Real-time translation between languages is critical for many applications in today’s globalised world, but existing solutions often face significant challenges such as high latency, computational overhead, and data privacy concerns. In this work, we address these challenges by introducing a decentralised learning framework using Federated Transfer Learning to enable privacy-preserving model training across edge devices, and multi-modal knowledge distillation to transfer knowledge from a large, accurate teacher model to smaller, more efficient student models. Our approach not only reduces the latency and computational cost of speech translation systems but also ensures high translation quality. Experimental results demonstrate that our model outperforms traditional methods in terms of translation accuracy, latency, and model size, making it well-suited for deployment on edge devices with limited resources.
  Keywords: real-time cross-lingual speech translation; federated transfer learning; FTL; multi-modal knowledge distillation; edge computing; privacy-preserving machine learning.
  DOI: 10.1504/IJBIDM.2026.10078553
   
  
• Athlete fatigue recognition and performance analysis via multimodal deep learning with cross-modal attention mechanisms  
  by Xue Han, Fujiang Cui, Feng Wang, Wei Wang, Haibo Wang 
  Abstract: In modern sport science, timely detection of athlete fatigue and accurate assessment of performance degradation are critical for enhancing training efficiency, reducing injury risk, and optimising competitive outcomes. This study proposes a multimodal deep learning framework with attention mechanisms that integrates physiological (e.g., ECG/EMG), biomechanical (e.g., IMU motion data) and visual (e.g., body pose or facial cues) modalities for simultaneous fatigue-state classification and continuous performance regression. The designed architecture employs dedicated encoders per modality, followed by a crossmodal attention fusion module and dualtask heads for classification and regression. Experiments conducted on publicly available datasets demonstrate that the proposed method outperforms eight baseline algorithms, achieving 95.76% accuracy in fatigue recognition and 0.108 MAE in performance prediction. Ablation studies confirm that each modality and the attention fusion contribute significantly to overall performance.
  Keywords: athlete fatigue recognition; multimodal deep learning; attention mechanism; performance regression; cross-modal fusion.
  DOI: 10.1504/IJBIDM.2026.10078554
   
  
• Intelligent precision advertising via deep learning and multi-source data fusion  
  by Lijing Xu 
  Abstract: With the rapid growth of digital marketing, intelligent advertising recommendation systems play a crucial role in improving delivery efficiency and user experience. Yet, conventional methods often suffer from limited multi-source data fusion, weak interpretability, and poor adaptability across domains. To address these challenges, we propose an intelligent precision advertising framework that integrates heterogeneous data sources using graph neural networks, employs a hybrid deep learning architecture with reinforcement learning for accurate user-ad matching and dynamic optimisation, and incorporates probabilistic modelling to enhance targeting accuracy and cost efficiency. Experiments on real-world datasets show that our approach improves click-through rate by 32.7%, conversion rate by 28.4%, and reduces cost per conversion by 21.5%, while maintaining strong adaptability in dynamic scenarios. These results demonstrate the frameworks potential to shift advertising recommendation from static, rule-based delivery to a personalised, interpretable, and real-time paradigm suitable for next-generation marketing systems.
  Keywords: precise advertising recommendation; artificial intelligence; multi-source data fusion; dynamic optimisation; deep learning.
  DOI: 10.1504/IJBIDM.2026.10078555
   
  
• Real-time lightweight pose estimation for sports motion analysis on mobile and edge platforms  
  by Haibo Wang, Ningning Li, Chao Liu, Bin Wu 
  Abstract: This paper proposes a lightweight motion posture recognition method tailored for real-time sports motion analysis on mobile and edge platforms, which adopts MobileNetV2 as the backbone, augmented with depth-wise separable convolutions and depth-wise transpose convolutions to ensure high-resolution feature up-sampling. A skip concatenation mechanism is introduced to preserve fine-grained spatial information, thereby improving the accuracy of athlete keypoint localisation. To enable real-time feedback in sports scenarios, a full-dataflow pipelining strategy is designed to optimise concurrent operations across feature extraction, pose estimation, and post-processing. Loop unrolling and double buffering are applied to maximise parallelism and minimise memory latency. The proposed approach outperforms state-of-the-art lightweight models on the COCO Keypoints and MPII Human Pose benchmarks. Additionally, it delivers real-time processing speeds of 68 FPS on mobile devices and 180 FPS on edge accelerators. With a compact model size of only 5.2 MB, it ensures efficient deployment in resource-constrained environments.
  Keywords: lightweight pose estimation; mobile motion recognition; real-time processing; depth-wise separable convolutions; DwTConv.
  DOI: 10.1504/IJBIDM.2026.10078556
   
  
• Efficient lightweight AI-driven animation generation for IoT systems via dynamic encoding and contrastive learning  
  by Peng Guo 
  Abstract: We introduce efficient lightweight AI-driven animation generation, named ELA-AG, a novel framework tailored for IoT systems demanding real-time performance under stringent resource constraints. ELA-AG innovates by combining three core components: 1) dynamic encoding to compress temporal-spatial redundancies across adjacent frames; 2) contrastive animation learning to enforce latent alignment across time and eliminate flicker; 3) a resource-aware optimisation objective that integrates model efficiency metrics (FLOPs, memory footprint, energy, latency) directly into training. We evaluate ELA-AG across three datasets — AnimeRun, CartoonMotion-50K and our IoT-AnimeCam — with comparisons against CartoonGAN, AnimeGAN2, DualAST and other baselines. Quantitative results demonstrate that ELA-AG achieves superior perceptual quality (PSNR, SSIM, FID) and temporal coherence (LPIPS, TWE), while achieving notable improvements in efficiency — higher FPS, halved energy consumption, and reduced model size. Comprehensive ablations confirm that ELA-AG achieves a 47% reduction in energy consumption, 1.5 x higher FPS, and a 19.2% lower FID compared with DualAST, while preserving superior temporal coherence. These results set a new benchmark for Pareto-efficient animation synthesis on resource-constrained IoT platforms, ensuring high-quality, temporally consistent animations without sacrificing speed or energy.
  Keywords: efficient animation generation; lightweight AI; dynamic encoding; contrastive animation learning; resource-aware optimisation; temporal coherence; energy-aware AI.
  DOI: 10.1504/IJBIDM.2026.10078607