International Journal of Data Mining and Bioinformatics (16 papers in press)

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
   
  

• Open Access

  Enhancing enterprise decision-making via deep learning-based graph integration ( Free Full-text Access )

  by Hengxi Guo 
  Abstract: Enterprise decision-making increasingly depends on data that are high-dimensional, time-varying, and strongly connected across departments, agents, and operational processes. Conventional analytics models often treat these data as static or independent records, which limits their ability to capture changing relationships and support timely decisions. To address this limitation, we propose a deep learning-based graph framework for enterprise decision support. The proposed dynamic enterprise synthesis graph (DESG) represents enterprise agents, resources, and operational states as nodes and links in a dynamic graph, allowing the model to learn both temporal changes and relational dependencies. On top of this graph representation, we develop an adaptive multi-agent intent orchestration (AMAIO) module that adjusts agent-level decisions according to local states, learned interaction weights, and global enterprise objectives. In this way, the framework connects local operational behaviour with overall decision goals while remaining adaptable to partially observable and resource-constrained environments. Experiments on multiple benchmark datasets show that the proposed method achieves better accuracy, recall, F1-score, and AUC than representative recurrent, convolutional, and transformer-based baselines. The results indicate that combining dynamic graph modelling with adaptive multi-agent coordination can improve the robustness, coherence, and efficiency of enterprise-scale decision-making systems.
  Keywords: multi-agent systems; temporal graph models; multi-agent decision coordination; computational enterprise analytics; intelligent decision support.
  DOI: 10.1504/IJDMB.2026.10081098
   
  

• Open Access

  M3DecideNet: multi-modal attention-driven fusion for enterprise management decision support ( Free Full-text Access )

  by Wenfang Luo, Hongyu Zhang 
  Abstract: Enterprise management decision making requires the integration of diverse data modalities, including financial metrics, operational statistics, and external market indicators. The heterogeneity and dynamic characteristics of these modalities create substantial challenges for effective decision support. Traditional approaches often fail to capture complex interactions among multiple modalities, resulting in limited predictive accuracy and insufficient interpretability. To address these limitations, this study introduces M3DecideNet, a multi modal attention driven fusion framework for enterprise management decision support. M3DecideNet serves as the overall decision support framework and is organised through three hierarchical components. A formalised problem definition establishes the mathematical foundation for multi modal decision modelling. M3FusionNet functions as the core modelling component within M3DecideNet, extracting modality specific features and integrating them through attention-based weighting to generate a unified representation. The adaptive decision fusion strategy, namely ADFS, operates as the decision refinement layer by introducing context aware modulation and regularisation to improve robustness, adaptability, and interpretability. Empirical evaluations show that M3DecideNet achieves superior predictive performance compared with existing methods while providing scalable and actionable insights across diverse enterprise scenarios. The proposed framework offers a structured, robust, and interpretable solution for modern enterprise management decision making.
  Keywords: enterprise management decision; multi-modal data fusion; attention mechanisms framework; adaptive decision strategy; predictive performance evaluation; real-time business applications.
  DOI: 10.1504/IJDMB.2026.10081099
   
  

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
   
  

• Open Access

  AI technology and real-time interaction for music therapy system under edge computing ( Free Full-text Access )

  by Binjie Liu 
  Abstract: In the existing systems based on cloud computing and virtual reality (VR), playing data may require multiple interactions with remote cloud servers, thus increasing communication delay. Edge computing (EC) migrates some computing and storage tasks from centralised cloud to nodes closer to users. This study focuses on the design of a real-time interactive music therapy assistant system integrating edge computing and deep learning (DL), and takes the improved YOLOv3 model as the edge recognition component. The system collects users' performance data through sensors, and performs local preprocessing and model reasoning at edge nodes. The results show that compared with the contrast system based on DBN, the proposed system can evaluate performance information with higher recognition accuracy and provide feedback with lower delay and resource occupation. These results support the technical feasibility and interactive usability of the system.
  Keywords: edge computing; EC; music therapy system; real-time interaction; intelligent assistance; artificial intelligence.
  DOI: 10.1504/IJDMB.2026.10081277
   
  

• Open Access

  Data mining and mediating factor identification of ideological leadership: machine learning modelling of college students’ mental health and educational big data ( Free Full-text Access )

  by Yang'an Chen, Qiongfang Feng 
  Abstract: To address the deficiencies in existing research, including the absence of an artificial intelligence (AI)-adapted evaluation system for college students’ ideological leadership and weak empirical support, this study adopts a mixed-methods approach. It integrates structural equation modelling (SEM), partial least squares structural equation modelling (PLS-SEM), deep convolutional neural network (DCNN), random forest (RF), support vector machine (SVM), and grounded theory, conducting analysis based on homologous de-sensitised multi-source student data from the authors’ institution. The dataset is stratified and subjected to comprehensive statistical testing. This study clearly defines core concepts, conducts ablation experiments to validate the contributions of individual modules, refines ethical protection protocols for sensitive data, constructs a ‘technology-interaction-value internalisation’ mechanism, and proposes a collaborative optimisation pathway. The research sample is subject to geographical limitations, which warrant expansion and refinement in subsequent studies.
  Keywords: artificial intelligence; deep learning; ideological leadership; college students; random forest; structural equation modelling; SEM.
  DOI: 10.1504/IJDMB.2026.10081278
   
  

• Open Access

  Prediction of players’ in-game purchase behaviour based on high-dimensional data and evolving graph convolution ( Free Full-text Access )

  by Ziyu Yun 
  Abstract: With the swift development of mobile games and online multiplayer games, the in-game purchase behaviour of players has become a core component of game revenue. Accurate prediction of players’ purchase behaviour can help operators optimise marketing strategies; it also improves user experience and player retention. To this end, the study proposes a prediction model of in-game purchase behaviour based on the evolving graph convolutional network (EGCN). The model first fuses the category features, temporal behaviours, and continuous features of players; it also extracts key behaviour patterns through an embedding layer and one-dimensional convolution. Then, EGCN is constructed to capture the dynamic changes in players’ social relations over time; the multi-classification prediction of future in-game purchase behaviour is realised by using a gating mechanism and spatial-temporal attention to adaptively focus on key nodes and time points.
  Keywords: player behaviour prediction; in-game purchase; evolving graph convolution; high-dimensional features; top-K hit rate.
  DOI: 10.1504/IJDMB.2026.10081279
   
  

• Open Access

  Task-driven human-AI collaboration framework for generative AI-assisted visual communication design ( Free Full-text Access )

  by Yufeng Zhang 
  Abstract: Generative artificial intelligence (AI) offers new possibilities for visual communication design in new media environments, but its application often suffers from uncontrolled generation processes and unclear human-AI role boundaries. This study proposes a structured framework for generative AI-assisted visual communication design specifically for new media tasks. The framework organises the design process into requirement analysis, prompt engineering, multi-round generation, human screening and integration, and publication feedback. A task decomposition mechanism clarifies the functional roles of generative AI and human designers across different design stages, aiming to balance generation efficiency with design controllability. Ablation experiments confirm the contribution of each framework component. This framework provides an operational methodology for human-AI collaborative visual design in new media contexts.
  Keywords: generative artificial intelligence; visual communication design; new media; human-AI collaboration; prompt engineering.
  DOI: 10.1504/IJDMB.2026.10081341
   
  

• Open Access

  Relational GCN-based multi-objective optimisation for elderly-friendly community design ( Free Full-text Access )

  by Yan Zeng, Jiayu Wen 
  Abstract: Aging community renewal requires multi-objective spatial optimisation balancing accessibility, safety, sociability, and comfort. Existing methods rely on static empirical rules and struggle with complex spatial relationships. This study proposes a hybrid graph convolutional network (GCN) framework integrating relational GCN, graph attention, and gated recurrent units. A heterogeneous community graph is constructed to encode physical, social, and functional interactions. An adaptive multi-objective loss dynamically balances conflicting goals. Evaluated on two real communities, the model improves social space utilisation by 61.4% in old neighbourhoods and achieves 92.7 accessibility in new communities. It outperforms baseline GCNs and traditional planning in comprehensive scores. The proposed method provides a data-driven tool for intelligent aging community renewal.
  Keywords: aging community renewal; spatial layout optimisation; graph convolutional network; multi-objective optimisation; elderly-friendly design; graph attention network.
  DOI: 10.1504/IJDMB.2026.10081342
   
  

• Open Access

  Data mining identification of AI-enabled leadership psychological factors affecting entrepreneurial perception of music undergraduates ( Free Full-text Access )

  by Honghao Yu 
  Abstract: Music students increasingly face a labour market that expects them to become potential music entrepreneurs. However, existing entrepreneurship and AI-facilitated leadership education often fails to connect their core artistic strengths with entrepreneurial mindsets. This study explored the relationship between three AI-facilitated leadership-related psychological dimensions risk-taking, innovativeness, and proactivity and Chinese music students perceptions of entrepreneurship education. Using a cross-sectional survey design, 196 undergraduate music students from four universities, who had completed a 14-day entrepreneurship education program, were surveyed. Exploratory factor analysis supported a four-factor structure (risk-taking, entrepreneurship education, innovativeness, and proactivity), with factor loadings ranging from approximately 0.52 to 0.83. All subscales demonstrated acceptable internal consistency. The results highlight the need to design AI-facilitated leadership development curricula within music entrepreneurship education, intentionally fostering students risk-taking, proactivity, and innovativeness while explicitly linking these capabilities to their artistic strengths and future career trajectories.
  Keywords: AI-facilitated leadership; entrepreneurship education; risk-taking; proactivity; music majors.
  DOI: 10.1504/IJDMB.2026.10081343
   
  

Special Issue on: OA Computational Intelligence for Healthcare Innovations in Data-Driven Diagnosis, Treatment, and Personalised Medicine

• Open Access

  Dynamic analysis of health physical fitness based on multi-sensor fusion ( Free Full-text Access )

  by Peng Bi, Nan Zhang 
  Abstract: Traditional health physical fitness assessment is static and relies on single indicators. To enable real-time dynamic analysis, we propose a multi sensor fusion method that integrates physiological and fitness test data. After collection, we apply SVM (optimised by chaotic firefly algorithm) and D-S evidence theory for decision level fusion, and construct a dynamic state space model to track fitness evolution. Experiments on freshmen to seniors at East China Normal University and Shanghai Jiao Tong University School of Medicine, using national physical health standards, measure BMI, cardiopulmonary endurance, lower limb strength, flexibility, and strength. Results show that normal university students’ fitness and cardiopulmonary function decline progressively with grade (male BMI +6.5, female +1.7), while medical university students remain stable due to better diet and exercise habits. The proposed system enables intuitive, real-time fitness analysis, helping schools identify weaknesses and design targeted interventions.
  Keywords: health physical fitness; physical fitness dynamics; multi sensor fusion; health testing; mathematical technology.
  DOI: 10.1504/IJDMB.2026.10081346
   
  

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
   
  

• Open Access

  Computer graphics and image processing and applications based on advanced algorithms ( Free Full-text Access )

  by Jingde Niu 
  Abstract: Existing image restoration methods rely on a single model, which is difficult to process. Therefore, this study proposes a fused multi-level hybrid restoration framework. Firstly, this paper designs a multi-stage transformer using the Restormer framework to handle compression loss and colour distortion in stages, in order to avoid error accumulation. Then, U-Net semantic segmentation and partial convolution are used to connect high-precision features through skip connections, accurately locate defect areas, and dynamically update mask weights. Finally, this paper uses a diffusion model (denoising diffusion probability model) to gradually generate high fidelity details while preserving the animation art style. The experimental results show that a peak signal-to-noise ratio (PSNR) of 29.45 dB and a structural similarity index of 0.85 were achieved on the ATD-12K dataset. In the case of low compression quality (Q = 10), the PSNR value remained at 21.45 dB.
  Keywords: advanced algorithm; graphics and image processing; image restoration; compression artefact; restormer framework.
  DOI: 10.1504/IJDMB.2026.10081344
   
  

• Open Access

  Applying generative adversarial networks to implement intelligent film restoration and colour enhancement algorithms ( Free Full-text Access )

  by Boxu Li, Jialin Gang 
  Abstract: The existing movie restoration methods based on conditional generative adversarial networks (cGAN) lack collaborative modelling mechanisms for structural constraints and colour generation, resulting in structural distortion and colour drift in the restoration results. This study introduces Wasserstein distance into the cGAN framework and constructs a conditional Wasserstein generative adversarial network. Firstly, the degraded film frames are converted into lab space, and the L channel is used as the conditional input for the U-Net encoder. Secondly, the discriminator uses a patch structure combined with Wasserstein distance to constrain the authenticity of the chromaticity distribution. Finally, during the training process, L1 reconstruction, VGG-16 perception, and gradient consistency loss are jointly optimised. The experimental results show that the peak signal-to-noise ratio of this method reaches 26.45 and 25.78 dB on the MPI Sintel and DAVIS test sets, respectively.
  Keywords: generative adversarial network method; intelligent film restoration; colour enhancement method; structural consistency modelling; multiobjective joint optimisation.
  DOI: 10.1504/IJDMB.2026.10081345