This study presents a method for constructing digital art knowledge graphs based on deep recurrent neural network (DRNN).A digital art knowledge graph is initially constructed by extracting visual features with ResNet50 and identifying textual entities via a CNN-BiLSTM-CRF model.Then, a DRDA model with bidirectional gated recurrent unit (GRU) and neighbour-aware attention is proposed for graph completion.Experiments on DBPedia50k and DBPedia500k show DRDA's superiority over three baselines.On DBPedia50k, DRDA improves head prediction MRR by up to 55% and achieves the lowest MR in tail prediction, though trailing slightly in Hits@10.On DBPedia500k, DRDA consistently outperforms baselines with MR reductions of 59-406 and MRR gains of 2%-19%.Further analysis identifies optimal depth and neighbour parameters, validating the model's scalability and its effectiveness in capturing complex semantic dependencies in large-scale multimodal art data.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

The rapid development of intelligent communication technologies has brought innovative opportunities to the field of music education, as traditional models are no longer sufficient to meet students' learning needs.This study employs a convolutional neural network (CNN) as the core algorithm to process audio and dance data, extracting high-level features such as rhythm and body movements through its multi-layered hierarchical structure.A support vector machine (SVM) algorithm is used to assess student abilities, while the convolutional neural network processes data to extract features, and intelligent sensor technology is integrated to build a teaching platform.The study found that the support vector machine achieved an accuracy of 93.7% in music feature classification, while the convolutional neural network improved the accuracy of dance movement classification to 96.3%.This model significantly improves the accuracy of teaching assessment, providing an intelligent solution for music and dance education and promoting humancomputer interactive teaching.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

This study presents a WebGPU-based visualisation framework aimed at enhancing the processing, rendering, and interactivity of large-scale, multidimensional datasets in educational contexts.By integrating artificial intelligence (AI) tools with advanced visualisation techniques, the framework enables efficient data preprocessing, interpolation, and volume texture generation for seamless web-based visualisation.Utilising datasets from oceanographic simulations and educational performance metrics, the system demonstrates versatility across domains.Comparative experiments show that the WebGPU-based solution significantly outperforms previous WebGL-based implementations, reducing rendering time and increasing frame rates.User surveys report high satisfaction in functionality, personalisation, usability, and compatibility.These findings highlight the potential of AI-driven educational data analytics and visualisation tools to support decision-making, enhance user engagement, and promote data literacy in academic and professional training environments.

Music preferences serve as crucial behavioural clues for decoding mental health states.Music provides a continuous and emotionally rich behavioural signal that is less influenced by social desirability biases compared to self-reported data, making it a robust indicator for mental health assessment.However, traditional analysis methods struggle to simultaneously account for the temporal dynamics of music listening and its rich semantic information, resulting in limited decoding efficacy.Previous studies attempted hybrid models but often faced overfitting or computational inefficiency, which motivated our design of a more integrated framework.To address this, we propose an innovative framework that integrates temporal convolutional networks with pre-trained language models to capture both the sequential patterns of music consumption and the emotional semantics of lyrics content.Our validation on a public dataset containing over 100,000 records demonstrates that this model achieves approximately 8.5% higher accuracy than single-modal benchmark methods in mental health state assessment tasks.It also effectively identifies specific musical features associated with depressive and anxious tendencies.This work provides a novel technical pathway for achieving non-invasive, dynamic mental health screening.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.