International Journal of Data Mining and Bioinformatics (7 papers in press)

Special Issue on: Big Data Industrial Application and Computing Innovation Part Three

• Open Access

  End-to-end learning algorithm for intelligent annotation and knowledge extraction of cross-modal scientific big data ( Free Full-text Access )

  by Zhi Shao, Shuqiang Liu 
  Abstract: This paper proposes an end-to-end learning algorithm for intelligent annotation and knowledge extraction of cross modal scientific research big data. Firstly, this paper takes scientific text, images, formulas, and tables as inputs, extracts heterogeneous features, and maps them to a shared semantic space using scientific bidirectional encoders from transformers, visual transformers, and tree transformers, respectively. Then, this paper weights and sums the annotation cross entropy loss, relationship extraction classification loss, and cross modal comparison alignment loss to form a joint loss function, which drives the collaborative optimisation of all module parameters in a single backpropagation. The results showed that on the benchmark dataset of scientific literature, the F1 score of cross modal named entity recognition reached 87.3%, the accuracy of relation triple extraction reached 83.6%, the accuracy of cross modal semantic alignment reached 85.1%, and the coverage rate.
  Keywords: cross-modal semantic alignment; end-to-end learning; intelligent data annotation; knowledge graph extraction; scientific big data.
  DOI: 10.1504/IJDMB.2026.10080104
   
  

Special Issue on: Hyperautomation and Big Data Methodologies Interactivity and Applications

• Open Access

  Museum social media content generation and personalised push algorithms based on social networking technology ( Free Full-text Access )

  by Shuo Wang, Jiahui Li, Yuxiang Wang, Mengyao Guo, Ze Gao, Kangcheng Deng 
  Abstract: The social media platforms used by museums face various challenges, such as inaccurate generation of cultural information, difficulty in accurately matching changes in user interests, and insufficient ability to adapt to immersive cross platform experiences. To address these issues, this study proposes an intelligent content generation and personalised push algorithm framework. This paper first develops an intelligent content generation model based on graph convolutional network (GCN) transformer, then proposes a dynamic interest model that includes context aware users, and finally proposes a push decision mechanism based on multi-objective particle swarm optimisation deep Q network (MOPSO-DQN). The experiment shows that the average historical matching accuracy of the GCN Transformer model is 92.27%, the cultural accuracy is 95.35%, the semantic consistency is 94.38%, 2 S. Wang et al. and the factual accuracy is 96.79%. The algorithm framework proposed in this study provides a reliable technical solution for the intelligent and digital distribution of museums.
  Keywords: social networking technology; museum social media; knowledge graph; KG; content generation; personalised recommendation; context-aware interest modelling.
  DOI: 10.1504/IJDMB.2026.10078865
   
  

Special Issue on: OA Artificial Intelligence for Biomedical, Health Service, and Public Health Knowledge Discovery

• Open Access

  Deep learning assisted generation of ethnic vocal music for health promotion: integrating LSTM, GAN, and emotional constraints for enhanced creativity and authenticity ( Free Full-text Access )

  by Shumin Sun 
  Abstract: Ethnic vocal music for health promotion carries rich cultural and historical significance, but its composition and preservation face challenges in the digital era. Deep learning offers new possibilities for automatic music generation; however, existing models often lack cultural adaptability and struggle to balance sound quality, style consistency, and emotional expression. This study proposes a hybrid deep learning model combining long short term memory networks and generative adversarial networks for ethnic vocal music for health promotion composition and sound quality optimisation. The LSTM module captures long range temporal dependencies and melodic structures, while the GAN module enhances audio fidelity through adversarial training. An emotion aware loss term and cultural style conditioning vectors are explicitly incorporated to improve emotional expression accuracy and stylistic authenticity. These findings demonstrate that integrating LSTM, GAN, and emotion constraints effectively enhances the quality, authenticity, and cultural relevance of AI generated ethnic vocal music for health promotion.
  Keywords: long short-term memory; LSTM; generative adversarial network; GAN; ethnic vocal music for health promotion; deep learning; music generation.
  DOI: 10.1504/IJDMB.2026.10080802
   
  

• Open Access

  An auxiliary assessment tool for mental health in preschool children within art therapy scenarios under IAPSO and CBAN model ( Free Full-text Access )

  by Dan Su, Jinghua Shan, Qing Wang, Hongmei Liu, Jian Jia 
  Abstract: Traditional psychological assessment of children’s drawings in preschool children relies heavily on manual feature extraction, resulting in strong subjectivity and inconsistent evaluation standards. Existing deep learning models also encounter difficulty in capturing hidden psychological correlation features embedded in drawings. In addition, conventional optimisation algorithms are prone to falling into local optima during hyperparameter optimisation, which limits assessment accuracy. To address these limitations, an intelligent assessment model integrating improved adaptive particle swarm optimisation (IAPSO) and a convolutional bidirectional attention network (CBAN) is constructed. The proposed tool does not replace the professional judgement of therapists or directly evaluate the effectiveness of therapeutic interventions. Instead, it analyses the features of children’s drawing images and outputs standardised psychological state classification references, thereby providing quantitative support for state monitoring during the art therapy process.
  Keywords: psychological assessment of children’s drawings; art therapy; deep learning; preschool children’s mental health.
  DOI: 10.1504/IJDMB.2026.10080803
   
  

Special Issue on: OA Big Data Industrial Application and Computing Innovation - Part 3

• Open Access

  Standardised storage model for exercise physiological data from wearable devices and mobile terminals ( Free Full-text Access )

  by Jun Xie, Wenbin Chen 
  Abstract: The existing system has low efficiency in data fusion and unified storage, making it difficult to support cross device data integration and longterm management. Therefore, this paper constructs a standardised storage model for multi terminal sports physiology data. This paper first designs a unified data description structure to standardise core fields such as user ID, device ID, timestamp, and data type, and then constructs a hierarchical storage architecture for sports physiology time series data. Finally, by combining batch writing and data compression mechanisms, this paper designs a unified data encoding rule and time index structure to improve data storage efficiency and query performance. The experiment shows that the standardised storage model proposed in this paper has significant advantages in write speed, query speed, compression, and scalability. The average accuracy of data fusion reaches 99.4%; query latency less than 100 ms.
  Keywords: wearable device data integration; mobile terminal health monitoring; standardised data storage; multi-source physiological data; time-series data management.
  DOI: 10.1504/IJDMB.2026.10080250
   
  

Special Issue on: OA Hyperautomation and Big Data Methodologies Interactivity and Applications

• Open Access

  Sports rehabilitation training methods based on deep reinforcement learning in brain-computer interface ( Free Full-text Access )

  by Jianhua Hao 
  Abstract: The existing traditional rehabilitation training methods have complex signal processing, low decoding accuracy, and low training efficiency. Therefore, this paper uses deep reinforcement learning (DRL) algorithm to optimise the brain computer interface (BCI) system and optimise and improve the sports rehabilitation training method. Firstly, this paper introduces the current application status of brain computer interface technology in sports rehabilitation and points out the limitations of traditional methods. Then, this paper explains the BCI system implemented using deep reinforcement learning and highlights the benefits of using deep neural networks and proximal strategy optimisation techniques to collect and process patient electroencephalogram (EEG) data. According to the experimental results, compared with traditional methods, the BCI system based on deep reinforcement learning significantly improves training effectiveness and patient engagement, and shortens rehabilitation and recovery time by 4-5 days.
  Keywords: brain computer interface; BCI; deep reinforcement learning; DRL; sports rehabilitation training; signal processing; personalised rehabilitation; training effect.
  DOI: 10.1504/IJDMB.2026.10080328
   
  

• Open Access

  Research on the application of deep learning in automatic colouring and composition optimisation of animation scenes ( Free Full-text Access )

  by Fang Liu 
  Abstract: This paper proposes an integrated framework for reference-guided animation video colourisation, combining a stylised multimodal transformer (SMT) with style-aware contextual learning (SACL). By jointly modelling reference appearance, target structure, region-level correspondence, and temporal dependencies, the framework improves colour transfer accuracy, perceptual fidelity, and temporal consistency. Experiments on AnimeRun and ATD-12K demonstrate superior performance over existing methods in PSNR, SSIM, LPIPS, and temporal warping error, highlighting its robustness and practical potential.
  Keywords: deep learning; multimodal transformer; style-aware learning; visual content generation; computational creativity.
  DOI: 10.1504/IJDMB.2026.10080808