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International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (26 papers in press) Regular Issues
Abstract: To address the limitations of traditional large language models in adapting to the intelligent construction of new power systems, as well as the insufficient recall of domain-specific knowledge, semantic associations, and topological relationships in conventional retrieval-augmented generation (RAG) architectures, this paper constructs a knowledge graph-enhanced RAG system for the power domain. It proposes optimisation methods including domain-adaptive semantic embedding, knowledge graph-enhanced multi-strategy retrieval, and dual-constrained compliance generation. Comparative experiments with traditional RAG models demonstrate that the proposed model outperforms conventional RAG in retrieval precision and hallucination suppression, effectively meeting the requirements of various core business scenarios in new power systems and demonstrating strong engineering value. Keywords: retrieval-augmented generation; RAG; power knowledge graph; large language model; LLM; power system intelligence. DOI: 10.1504/IJICT.2026.10080102
Abstract: In industry-education integration scenarios, existing knowledge tracking models are largely based on statistical correlations derived from historical interactions, making it difficult to distinguish between skill deficiencies and random errors; moreover, their black-box nature undermines the credibility of decision-making. This paper proposes a causality-enhanced attention mechanism that models causal pathways between skill mastery, exercise interactions, and job requirements using structural causal graphs. By introducing counterfactual reasoning to generate representations of knowledge states after interventions, the model focuses on actual changes in skills rather than superficial patterns in interaction sequences. Experiments on the ASSISTments and industry-education integration platform datasets demonstrate that the model achieves area under the curves of 0.834 and 0.871, respectively, representing improvements of 3.1% and 2.7% over the best baseline. normalised discounted cumulative gain @5 also significantly outperforms comparison methods, and the visualisation of causal attention weights reveals the models precise attribution of skill-related root causes. Keywords: knowledge tracking; causal inference; industry-academia integration; interpretability; attention mechanisms. DOI: 10.1504/IJICT.2026.10080103
Abstract: In order to solve the frequent loss of small symbols in complex music scores and the serious interference of five line staff backgrounds, a research proposes an end-to-end architecture that integrates music score recognition and style generation. The recognition stage adopts a multi-scale feature fusion convolutional recurrent neural network, combined with Haar wavelet transform to preserve edge details, and utilises note focusing module and quasi recurrent neural network for efficient transcription. The generation stage adopts a hierarchical cascade strategy, combining generative adversarial networks and Transformers to capture structural and spectral details, and then synthesising waveforms through WaveNet voice encoders. The results showed that the symbol accuracy on the DeepScores V2 dataset was 96.82%, and the symbol error rate under noise interference was only 5.15%. The research results provide effective technical support for the audio-visual conversion from visual music scores to auditory audio. Keywords: convolutional recurrent neural network; CRNN; music score recognition; generative adversarial network; GAN; transformer; music generation. DOI: 10.1504/IJICT.2026.10080147
Abstract: The digital trade flow patterns within manufacturing clusters are crucial for analysing supply chain resilience. However, existing graph neural network methods often treat enterprises within the cluster as homogeneous nodes, ignoring structural differences such as product specialisation levels and technical density, resulting in significant deviations in trade intensity predictions. This paper proposes the domain-aware attention graph network, which explicitly captures the heterogeneous trade relationships among enterprises by embedding learnable node domain feature modulation functions. Experiments show that domain-aware attention graph network achieves a determination coefficient of 0.842 in the trade intensity regression task, an improvement of approximately 0.049 compared to graph attention network; in the trade change direction classification task, its area under the curve reaches 0.891, an improvement of approximately 0.044 compared to graph attention network, indicating that the model can more accurately identify trade flow and intensity changes. Keywords: manufacturing cluster; digital trade flow; hierarchical attention mechanism; gravitational model. DOI: 10.1504/IJICT.2026.10080148
Abstract: In order to meet the demand for the digital modelling and intelligent analysis of students learning behaviour, a predictive evaluation framework is proposed, which combines feature engineering with LightGBM. Through the construction of tags and the extraction of training samples from college English course behaviour log, the construction strategy and interpretable output mechanism of the model are discussed. The results show that the proposed model achieves an F1-score of 0.889 on the test set, with an average response latency of less than 230 ms, and a performance fluctuation of less than 1.6% in 5 times cross validation, demonstrating robust deployment stability and generalisation. Keywords: learning behaviour prediction; LightGBM algorithm; educational data mining; Intelligent teaching analytics; learning behaviour modelling. DOI: 10.1504/IJICT.2027.10080149
Abstract: Accurate prediction of copper ore mineralisation potential remains a challenging task in mineral resource exploration. To address the limitations of existing methods in characterising complex mineralisation processes and their unsatisfactory predictive performance, this paper proposes a novel deep learning framework with three key innovations. First, multi-source geological data are pre-processed using an entropy-weighted principal component analysis strategy to remove redundant features. Second, a meanshift-improved synthetic minority over-sampling technique is applied to oversample the training set, effectively balancing positive and negative sample ratios. Third, asymmetric convolution modules and a multi-spectral channel attention module are integrated into the ResNeXt50 network, significantly enhancing the model's ability to extract features from small-sized, multi-channel mineralisation data. Experimental results demonstrate that the proposed method achieves a prediction accuracy of 95.14%, outperforming the best benchmark method by at least 3.03%, and enables efficient simulation of copper ore mineralisation. Keywords: multi-source geological data; deep learning; synthetic minority over-sampling technique. DOI: 10.1504/IJICT.2026.10080150
Abstract: Personalised English vocabulary recommendation remains difficult because learner behaviour changes quickly while lexical relations are highly structured. To address this issue, this study proposes an attention-enhanced knowledge graph recommendation model for vocabulary learning. First, learner interaction sequences are encoded to capture recent review rhythm, repeated errors, and short-term preference shifts. Then, lexical relations are organised through a vocabulary knowledge graph to preserve semantic, derivational, and pedagogical connections among words. Finally, both sides are fused in a personalised scoring stage to generate the next-word recommendation list. Experimental results show that the proposed method achieved 0.742 Precision, 0.694 Recall, 0.781 NDCG, and 0.726 MRR on Dataset A, and remained consistently superior on Dataset B. The model shows strong ranking accuracy, stable recommendation behaviour, and good educational suitability. Keywords: personalised vocabulary recommendation; knowledge graph; attention mechanism; English learning. DOI: 10.1504/IJICT.2026.10080151
Abstract: Traditional studies on bel canto resonance rely mainly on subjective auditory evaluation, lacking standardised quantitative methods. It is difficult to accurately capture the internal correlations and variation patterns of multi-scale resonance characteristics, it also makes the optimisation of bel canto teaching and singing skills lack of scientific quantitative basis. To address this issue, this paper proposes a dedicated multi-scale feature extraction algorithm and an attention-based feature fusion algorithm. Combined with acoustic experiments on soprano singing signals, we conduct a systematic quantitative study. Results show that this method can effectively extract multi-scale features of bel canto resonance. The recognition rate of the fused features for bel canto vocal types is significantly higher than that of the single feature, and the average recognition rate of all vowels is more than 90%, which can accurately reveal the characteristic distribution of bel canto resonance. Keywords: bel canto; resonance characteristics; multiscale feature; acoustic quantitative analysis. DOI: 10.1504/IJICT.2026.10080152
Abstract: As engineering ethics teaching faces the contradiction between overload of case resources and increasing demand for individualisation, this study aims to develop a system that can provide accurate and explainable recommendations. By constructing the KGDP-RM model, which integrates knowledge graphs with differential privacy protection, we employ graph neural networks to learn case semantic association, and attention mechanism and Bayesian network are introduced to realise dynamic preference capture and causal explanation. The accuracy rate of the model on the EdNet dataset reaches 0.821, the F1-value is 0.813, representing an improvement of 0.038 over the best-performing baseline. Its performance degrades by only 0.024 in a 10% noise environment. The user relevance and satisfaction scores reach 4.3 and 4.1 points respectively. In addition, the ablation test confirms that the knowledge graph module contributes the most. Overall, the multi-technology integration framework proposed in this study effectively solves the problems of sparsity, privacy security and interpretability in teaching resource recommendation. Keywords: engineering ethics case teaching; personalised recommendations; knowledge graph; differential privacy; interpretability. DOI: 10.1504/IJICT.2026.10080173
Abstract: With the vigorous development of the global experience economy, the problem of overloading tourist traffic in popular scenic areas has become increasingly prominent, posing a huge threat to public safety. The traditional centralised cloud computing monitoring architecture, when dealing with massive sensing data, has bottlenecks such as communication congestion, high end-to-end latency, and high energy consumption at the edge nodes. This study proposes a communication-efficient distributed sensor network framework (CEDF), adopting a three-layer collaborative architecture of edge-fog-cloud, and downloading the feature extraction task to the edge nodes, while introducing an event-triggered communication protocol based on adaptive dynamic thresholds and a distributed state consensus algorithm. In a real scenic area with an area of approximately 25 square kilometres, 500 heterogeneous sensor nodes were deployed for on-site verification. The results showed that compared with the traditional continuous periodic transmission (CPT) and standard edge clustering (SEC) architectures, CEDF reduced the total network data communication overhead by over 90%, compressed the end-to-end closed-loop control delay to within 30 milliseconds, and increased the average tourist circulation efficiency by 34%. This research provides an engineering paradigm for the large-scale deployment of smart tourism systems. Keywords: distributed sensor network; event-triggered communication; real-time tourist flow monitoring; edge computing; smart tourism; congestion control; standard edge clustering; SEC; continuous periodic transmission; CPT. DOI: 10.1504/IJICT.2026.10080257
Abstract: Cross-modal hashing facilitates efficient retrieval across diverse modalities by mapping shared semantics into a compact binary space. Despite significant progress, conventional approaches frequently encounter two bottlenecks: 1) a preoccupation with Euclidean geometry that overlooks intrinsic graph structures, making them susceptible to data noise/outliers; 2) a reliance on a single latent subspace which fails to capture the complexity of multi-modal distributions. To circumvent these limitations, we introduce refining crossing modal retrieval via two stage graph hashing (RCRT). In this initial phase, collective matrix factorisation is integrated with a customised graph convolutional network (GCN) to extract modality-specific latent representations. By merging Euclidean features with non-Euclidean structural priors, this stage robustly mitigates the impact of anomalous data points. Subsequently, a global approximation strategy aligns these heterogeneous subspaces to distil high-level common semantics, followed by the derivation of discrete hash codes that preserve semantic consistency. In the second phase, a linear hash function is optimised via a local similarity preservation mechanism, ensuring the fine-grained topology is maintained within the Hamming space. Across multiple benchmark datasets, RCRT demonstrates superior performance over all current SOTA methods. Keywords: discrete optimisation; crossing modal retrieval hashing; graph convolutional. DOI: 10.1504/IJICT.2026.10080279
Abstract: To improve the accuracy of key indicator evaluation in power engineering, this study proposes a comprehensive evaluation method based on K-nearest neighbour (KNN) algorithm. Aiming at the limitations of traditional methods in processing nonlinear and high-dimensional data, an evaluation system including ten key indicators such as engineering cost, construction period, and equipment performance was constructed to conduct experiments on 50 power engineering project data in a certain region. The results show that compared with traditional methods, the KNN model has a 15% improvement in evaluation accuracy, a more reasonable allocation of indicator weights, and can effectively identify key factors affecting engineering quality and efficiency. It also exhibits strong robustness in dealing with nonlinear problems and is suitable for power engineering of different scales and types. This method provides a scientific basis for decision-making in power engineering and has promotional value. Keywords: KNN algorithm; power engineering; index analysis; comprehensive evaluation. DOI: 10.1504/IJICT.2026.10080314
Abstract: Recent advances in artificial intelligence (AI) have enabled automated support for academic writing; however, many platforms remain tool-centric, providing isolated corrections rather than process-aware instructional support. This paper proposes a novel AI-enhanced academic writing instruction platform designed from an ICT perspective, focusing on the specific rhetorical needs of Sino-French and Sino-African cross-cultural academic communication. The framework integrates a three-layer architecture: a multi-level linguistic feature extraction layer using fine-tuned transformer models, a learner modelling layer driven by longitudinal data analytics, and an adaptive feedback engine. To validate the system, a 16-week empirical study was conducted with 342 undergraduate students. Analysis of a corpus comprising 1,250 academic essays reveals that the platform significantly enhances discourse coherence (18.4% improvement) and lexical complexity (12.6% increase) compared to traditional automated writing evaluation tools. Statistical significance was confirmed via a paired-sample t-test (p < 0.01), and effect size calculations (Cohens d = 0.76) suggest a robust impact on student writing quality. The results demonstrate that embedding evaluation directly into the system operation enables a continuous feedback loop that fosters long-term linguistic development in specialised academic contexts. Keywords: artificial intelligence; academic writing instruction; learning analytics; intelligent educational systems; information and communication technologies; ICTs. DOI: 10.1504/IJICT.2026.10080315
Abstract: Effective carbon management requires accurate prediction of energy consumption patterns, yet conventional models struggle with complex temporal dependencies. This study proposes a novel integrated framework combining transformer-based generative adversarial networks with Bayesian optimisation (BO-TransGAN) for energy forecasting and carbon optimisation. Using 2,000 hourly records of energy use and emissions, data were pre-processed via imputation, outlier removal, and normalisation. The TransGAN captures nonlinear temporal dependencies through adversarial learning, while Bobcat optimisation tunes hyperparameters for enhanced convergence and stability. BO-TransGAN achieves 0.987 accuracy, 0.995 R2, minimal losses, and low training (2.12s) and run times (7.35s). It generates realistic synthetic sequences and provides actionable insights for reducing carbon emissions, offering a scalable tool for sustainable energy planning and real-time carbon management. Keywords: carbon management; generative adversarial networks; GANs; energy consumption prediction; sustainable energy planning; energy-carbon pattern modelling. DOI: 10.1504/IJICT.2026.10080316
Abstract: Traditional business English emails generated by artificial intelligence are often rigid and lack flexibility, making it difficult to meet complex and variable communication needs. To address this issue, this paper explores the application potential of generative adversarial networks (GANs) in this field. It designs a new conditional generative adversarial network architecture, enabling the model to learn and imitate the appropriate style and accurate content of professional business letters. Experiments on an open business email dataset show that, compared with mainstream generation models, the method proposed in this paper improves the practicality of generated emails by 15.7% in human evaluation and increases the accuracy of key terms by 8.2%. This demonstrates that generative adversarial network technology can effectively reconstruct the quality and adaptability of automatically generated business emails, providing a new path for enhancing the professionalism of intelligent communication tools. Keywords: generative adversarial network; GAN; business English email; natural language generation; style control. DOI: 10.1504/IJICT.2026.10080359
Abstract: This paper proposes a student information management platform that integrates support vector machine (SVM) modelling with data mining to improve academic early warning and personalised guidance. The platform adopts a four-layer architecture covering data, processing, application, and presentation functions. Multi-source student data, including academic records, behavioural information, and consumption data, are cleaned through missing value imputation, outlier correction, and feature selection to form model-ready datasets. An SVM model with an RBF kernel is developed for predicting course-failure risk and performance fluctuations, with parameters optimised by grid search (C = 10, = 0.1). Using 5,000 undergraduate records, the model is compared with decision tree, KNN, and logistic regression methods. Results show that the SVM model achieves 89.6% accuracy, outperforming decision tree and KNN models. The platform also maintains acceptable response and prediction times under 500 concurrent users. Keywords: intelligent management of student information; support vector machine; SVM; educational data mining; EDM; academic risk early warning; platform design. DOI: 10.1504/IJICT.2026.10080360
Abstract: Traditional manual English writing scoring is inefficient and subjective, while existing automated systems lack semantic understanding and instructional interpretability. This study proposes an intelligent human computer collaborative scoring system integrating large-scale model representation learning with knowledge reasoning. The system combines representation, reasoning, and collaboration through a fine-grained feature learning algorithm and a domain knowledge map, enabling multidimensional evaluation and traceable scoring decisions. Experimental results on 8,800 English compositions demonstrate that the proposed system achieves 91.7% scoring accuracy (36.8% higher than traditional models), 72% lexical error detection coverage with a 5.2% omission rate, and a human-computer semantic consistency coefficient (SCC) of 43% in evaluating high-level dimensions like semantic coherence and argumentation logic. Keywords: large model representation learning; knowledge reasoning; English writing scoring; human-computer collaborative; explainable artificial intelligence. DOI: 10.1504/IJICT.2026.10080361
Abstract: Existing gymnastics motion capture and simulation methods struggle with high-dynamic movements, rapid joint changes, and unnatural simulations. To address these pain points, we propose an end-to-end intelligent gymnastics movement analysis framework integrating deep learning spatiotemporal representation with 3D deformable transformers. We introduce a 3D deformable attention mechanism for limb tracking and a modal feature fusion strategy combining RGB and bone pose data. Additionally, a multi-objective optimisation framework with heuristic rules and parallel computing improves simulation authenticity and efficiency. Experimental results demonstrate high accuracy: the average 3D coordinate error for difficult churning is 35.6 mm, key pose recognition is 78%, and swivel tracking accuracy reaches 91.2%. The real-time tracking delay is as low as 12.3 ms, significantly outperforming traditional methods. Keywords: 3D vision transformer; multi-objective optimisation; gymnastics motion capture; action simulation; real-time performance. DOI: 10.1504/IJICT.2026.10080363
Abstract: With the wide application of multimedia technology in English teaching, how to effectively correlate the semantic information of cross-media data (such as text, audio and images) has become a key challenge. This paper aims to construct a cross-media semantic association model for college English teaching (ODBA-CMSA) based on output-driven hypothesis and bidirectional attention feature learning to improve the accuracy and interpretability of grading. The model optimises semantic representation through multi-modal embedding, bi-directional attention mechanism, and output-driven loss. The test results show that the model is significantly better than the baseline in accuracy rate (89.5%) and F1-score (89.0%), and its robustness (accuracy rate only decreases by 5.1% under noise) and practicability (user satisfaction 4.4 points) are outstanding, which verifies its effectiveness in complex teaching environment. This study not only provides an efficient semantic association solution, but also enhances the transparency of the model through interpretability design, which provides a new idea for the development of intelligent teaching system. Keywords: output drive; two-way attention; cross-media; college English; semantic association. DOI: 10.1504/IJICT.2026.10080438
Abstract: The precise analysis of singing techniques is of vital importance for intelligent vocal education. The existing methods process singing by segmentation, which destroys the characteristics of continuous changes of techniques, resulting in poor recognition of dynamic techniques such as vibrato. To address this issue, this paper proposes a neural ordinary differential equations waveform modelling network. This framework treats singing as a continuous process and simulates the physical laws of vocal cord vibration, enabling the model to more naturally capture the subtle changes in techniques. Experiments show that the comprehensive recognition accuracy of this method reaches 0.874, and the comprehensive evaluation index reaches 0.932. Compared with the current optimal method, it has improved by 4.2% and 3.1% respectively. The recognition of trills has also improved by 12.3%. This research provides high-precision and interpretable technical support for intelligent vocal education. Keywords: neural ordinary differential equations; singing technique; physics-informed machine learning; continuous-time dynamic modelling. DOI: 10.1504/IJICT.2026.10080439
Abstract: This paper presents an information and communication technology (ICT) enabled adaptive PID power regulation framework for cooling tower pumps. A cyber-physical system (CPS) architecture integrates real-time sensor data through industrial IoT communication links, enabling bidirectional data exchange between field devices and a remote adaptive tuner. The tuner dynamically updates PID gains using streaming operational data under practical network constraints, including time-varying delay and packet loss. Unlike conventional methods, the proposed framework embeds physical knowledge into networked control loops, ensuring reliable performance in non-ideal communication environments. Experimental results demonstrate that compared to state-of-the-art benchmarks, the ICT-based control reduces system response time by 18.5% under variable cooling loads and lowers total pump power consumption by 12.3%. Furthermore, statistical significance analysis confirms the robustness of the proposed approach against both extreme environmental disturbances and adverse network conditions. Keywords: cyber-physical system; CPS; adaptive PID; cooling tower pump; industrial IoT. DOI: 10.1504/IJICT.2026.10080440
Abstract: During the building operation and maintenance stage, the factors influencing carbon emissions are complex and highly coupled. The existing diagnostic methods based on correlation analysis or black-box models are unable to reveal the underlying causal mechanisms. This paper integrates the static topology and equipment attributes in the building information model as prior knowledge into a constrained causal discovery framework, learns causal structures from the dynamic data of the internet of things, and realises the diagnosis from correlation descriptions to causal tracing. Experiments on public datasets show that the accuracy rate, recall rate, and area under the curve value of this method in identifying key influencing factors are 0.92, 0.88, and 0.95 respectively, which are 14%, 11%, and 7% higher than the standard Peter-Clark algorithm, and can track key causal chains such as 'outside temperature, air conditioning set temperature, carbon emissions'. Keywords: building information model; causal discovery; carbon emission; traceability diagnosis. DOI: 10.1504/IJICT.2026.10080097
Abstract: In the emotional understanding of children's stage narratives, children's emotional expressions are often embedded within the narrative flow of the performance. However, existing methods focus on isolated facial expressions or speech features, ignoring the dynamic evolution of emotions over time and their significant migration within the narrative context. This paper proposes the adaptive attention flow network, which introduces an attention flow field based on conservative flux constraints to dynamically track key emotional nodes across time, space, and narrative. The attention weights are regarded as information flows that can be transferred across frames, ensuring smooth evolution and lossless transmission of emotional representations along the narrative timeline. Experiments show that the classification accuracy of the adaptive attention flow network reaches 84.6% with an area under the curve of 0.91, outperforming the timesformer baseline (74.2% accuracy and 0.83 area under the curve) by 10.4 percentage points and 0.08, respectively. Keywords: emotional computing; children's stage narrative; attention flow; multimodal fusion. DOI: 10.1504/IJICT.2026.10080101
Abstract: In competitive sports, sources of stress for athletes - such as critical moments in competition, audience distractions, and coaches' expectations - vary greatly from person to person. Traditional questionnaire-based methods are subjective and lag behind events, making it difficult to support real-time interventions. Single recommendation algorithms, lacking prior knowledge of stressor types, tend to produce 'one-size-fits-all' strategies, which can exacerbate mental fatigue. To address this challenge, this paper proposes an identification framework that integrates hierarchical clustering with hybrid collaborative filtering: first, typical stressor patterns are extracted through clustering, and then personalised stress relief strategies are generated based on these patterns. Validation using publicly available physiological stress datasets shows that, compared to traditional collaborative filtering, our method improves accuracy from 78.3% to 86.7%, increases the area under the curve from 0.83 to 0.89, and raises the normalised discounted cumulative gain @10 from 0.72 to 0.81. Keywords: identification of competitive stressors; cluster analysis; collaborative filtering; personalised recommendations. DOI: 10.1504/IJICT.2026.10080100
Abstract: In online learning scenarios, the complex dependencies between knowledge concepts are often simplified, and the dynamic changes in learners' cognitive states are difficult to capture. As a result, personalised learning path recommendations generally lack the capacity to achieve global optimisation. To address this, the paper proposes a knowledge graph-enhanced reinforcement learning approach for personalised learning path optimisation. This method constructs a heterogeneous knowledge graph and introduces a graph attention network (GAT) to enhance concept representation, then employs an actor-critic agent to learn optimal decision strategies under composite reward guidance. On the MOOCCubeX dataset, the method improves Precision@5 from 0.504 to 0.547 and normalised discounted cumulative gain@5 from 0.489 to 0.535; on the MOOPer dataset, Precision@5 improves from 0.462 to 0.508, and normalised discounted cumulative gain@5 improves from 0.441 to 0.488. Keywords: knowledge graph; reinforcement learning; personalised learning paths; agents. DOI: 10.1504/IJICT.2026.10080099
Abstract: The speed at which digital technology advances and the need for low-carbon development make coordinated policies all the more relevant for sustainable development. This paper introduces a comprehensive analytical framework that merges text analysis with time series modelling to methodically examine the relationship between digital and environmental policies. By converting policy documents into quantitative indicators, a more accurate and dynamic evaluation of their efficacy becomes possible, surpassing shortcomings of conventional qualitative approaches. The research uses provincial data to analyse the long-term impacts of synchronised policy implementation on economic growth, ecological preservation, and technological progress. Better coordination leads to greater productivity, less pollution, and faster innovation, though these effects differ greatly across regions. The key transmission channels are industrial upgrading and more efficient resource use. The research offers a practical and clear method to comprehend policy interactions and improve policy design. Keywords: digital development; policy coordination; text analysis; time series methods; data-based research. DOI: 10.1504/IJICT.2026.10080098 |
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