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International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (36 papers in press) Regular Issues
Abstract: Personalised feedback in dance instruction is difficult to scale due to the reliance on expert supervision. General pose estimation models suffer significant accuracy degradation when directly applied to dance scenarios, owing to differences in movement styles, costume textures, and viewing angles. This paper proposes a multi-scale feature alignment and temporal domain adaptation network for dance-oriented pose estimation. The method captures human motion patterns at multiple granularities through hierarchical feature alignment and introduces a temporal domain adaptation mechanism to mitigate cross-domain distribution discrepancies, enabling effective knowledge transfer from general to dance-specific domains. Experiments demonstrate that the proposed method improves mean average precision by 12.7% over direct transfer baselines and achieves an area under the curve of 0.914 for action score consistency with expert ratings. This work provides a viable pathway toward intelligent, scalable feedback in digital dance education. Keywords: posture estimation; transfer learning; dance teaching; feature alignment. DOI: 10.1504/IJICT.2026.10079358
Abstract: Artificial intelligence is driving a paradigm shift toward active generation in English learning. However, generative English learning faces challenges such as high latency in hybrid data synchronisation and low accuracy in grammatical proofreading. To address this, this paper proposes a hybrid real-time synchronisation algorithm-driven framework for generative English learning. Adopting a hierarchical modular design, the framework integrates a log-stream real-time synchronisation engine with a dual-encoder grammatical proofreading engine. The synchronisation layer achieves millisecond-level data synchronisation through log encapsulation and final state extraction. The correction layer constructs a dual-encoder model that dynamically fuses cross-sentence contextual and intrasentential semantic features using a gated attention mechanism. Experimental results demonstrate that the proposed method achieves a minimum synchronisation delay of 0.91 ms and a syntax correction accuracy of 93.8%, significantly outperforming existing approaches. This research provides effective technical support for the intelligent advancement of generative English learning. Keywords: generative English learning; hybrid real-time synchronisation; log stream encapsulation; dual-encoder model; gated attention mechanism. DOI: 10.1504/IJICT.2026.10079359
Abstract: Colour matching is the core aspect of clothing design, directly influencing the visual appeal of the finished product. However, existing intelligent generation models often only focus on the realism of the images, but ignore the human visual systems preference for colour harmony. This leads to frequently occurring combinations of glaring or incongruous colour blocks in the generated results. To address this aesthetic deficiency, this paper proposes a generative adversarial network that integrates visual perception constraints. By introducing three computable constraints: colour harmony assessment, visual attention prediction, and texture continuity preservation, the model is guided to actively avoid visual conflicts during the generation process. Experiments show that this method improves the colour harmony score to 78.3, which is 12.4% higher than the current optimal model. The subjective score given by users reaches 4.62 (out of 5), significantly enhancing the visual comfort of the generated matching. Keywords: colour matching; visual perception; generative adversarial network; GAN; aesthetic computation. DOI: 10.1504/IJICT.2026.10079360
Abstract: With the advancement of educational informatisation, labour education effectiveness and student behaviour prediction have attracted increasing attention. Multi-modal deep learning provides new perspectives by integrating information from multiple data modalities. This study constructs an analysis and prediction model based on multi-modal deep learning and develops a cross-modal feature fusion mechanism optimised with an improved graph convolutional network (GCN). An attention-enhanced GCN is employed to explore dynamic relationships among student interactions and behaviours, enabling accurate analysis of labour education effectiveness and student behaviour prediction. Experimental results show that the proposed model achieves a behaviour recognition accuracy of 98.9%, labour education analysis accuracy of 95.2%, participation prediction error of 0.142, and risk warning accuracy of 0.923, while maintaining strong generalisation across diverse labour scenarios. Compared with traditional methods, the model demonstrates superior accuracy and stability, enriching the application of multi-modal deep learning and supporting labour education optimisation and personalised training. Keywords: multi-modal deep learning; labour education; student behaviour prediction; graph convolutional network; GCN; model optimisation. DOI: 10.1504/IJICT.2026.10079361
Abstract: In response to the long-standing challenge of the mismatch between graduates skills and market demands, traditional static modelling methods struggle to capture dynamic correlations, resulting in limited matching accuracy. Based on this, this study has constructed a deep knowledge reasoning graph, integrating multi-source public data, and implementing dynamic inference and completion of skill relationships using graph neural networks. Experimental verification shows that compared to typical baseline models, this method has improved the area under the curve metric from 0.85 to 0.92, an increase of 0.07, and the accuracy from 81% to 89%, an improvement of 8%. At the same time, precision and recall have also improved by approximately 8% and 6% respectively, significantly enhancing the reliability and interpretability of skill demand prediction, providing an effective tool for the adjustment of higher education courses and personalised career recommendations. Keywords: knowledge graph; skill requirements; graph neural networks; data fusion. DOI: 10.1504/IJICT.2026.10079362
Abstract: Precisely measuring the influence of meteorological variables on electricity demand is crucial for promoting the sustainable development of the energy economy. To overcome existing models limitations in capturing the complex spatiotemporal dependencies between meteorological factors and electricity load, this study initially utilises maximum information coefficient analysis to investigate associations between meteorological variables and power demand. Weighting different meteorological factors based on maximum information coefficients enables a weighted summation to assess similarity between historical and forecast days. Building upon this foundation, we propose a transformer model with a deep decomposition architecture for electricity load forecasting. The model progressively extracts trend and periodic components from input meteorological sequences while refining intermediate variables. Leveraging self-attention mechanisms to highlight key features and perform aggregation, it ultimately achieves electricity load prediction. Experimental results demonstrate that the suggested model reduces the mean absolute error by at least 6.06%, making it well-suited for energy-economic electricity load forecasting. Keywords: weather forecasting model; electricity load forecasting; correlation analysis; transformer model; deep decomposition. DOI: 10.1504/IJICT.2026.10079363
Abstract: The accurate identification of weather precursors is essential for the stable functioning of power infrastructure. Addressing the issue of weather precursor signals being susceptible to noise in current research, this paper first analyses factors influencing extreme weather precursors and decomposes them using an improved empirical mode decomposition algorithm. By calculating correlation coefficients, the most significant influencing components are selected. An optimised threshold function is then introduced to optimise the deep residual contraction network, with multi-scale residual blocks used for feature extraction. A hybrid attention mechanism is designed to enhance key features. The maximum mean discrepancy loss is used to mitigate the distributional shift between the source and target domain features, enabling the detection of extreme weather conditions in power facilities under noise interference. Experimental outcome indicates that the average detection accuracy of the proposed model reaches 93.53%, outperforming the baseline model, thus demonstrating the effectiveness of the proposed model. Keywords: power facilities; weather precursor detection; empirical mode decomposition algorithm; deep residual shrinkage network; DRSN; attention mechanism. DOI: 10.1504/IJICT.2026.10079364
Abstract: The old routing methods that are mostly based on deterministic models cannot adjust to these dynamic conditions, resulting in poor network performance, increased outage likelihood, and greater delays. This paper will solve these problems by introducing an innovative solution that combines probabilistic modelling, reliability analysis, and the Griffon vulture optimisation (GVO) algorithm to communicate in an optimal way in smart grid optical networks. Software-defined networking (SDN) is used as a methodology to provide real-time monitoring and dynamic path discovery. This will reduce the latency, energy usage, and bit error rate (BER), giving it resilience against different situations. The results of the simulations depict that the end-to-end reliability increased by 15%, delay was reduced by 20%25%, and energy consumption decreased by 10%. The work presented in the proposed model can decrease the outage probability by 30%, which shows that it is an efficient way to optimise smart grid communication networks. Keywords: smart grid; optical communication; reliability optimisation; Griffon vulture optimisation; GVO; probabilistic modelling; software-defined networking; SDN. DOI: 10.1504/IJICT.2026.10079365
Abstract: Edge-IoT networks in smart cities, industrial automation, and environmental monitoring have problems because they are densely deployed, have limited resources, and have traffic that changes all the time. This causes interference, collisions, and energy waste. Traditional rule-based or fixed allocation approaches do not adjust to changing conditions in real time. This research presents a reinforcement learning-based spectrum and power coordination framework (RSPCF) that adaptively enhances device scheduling, transmission power, and channel selection in response to interference, traffic load, and remaining energy. The framework gets a packet delivery rate of 92%, which is better than current methods (74%85%), and it cuts latency down to 95 ms. It boosts throughput to 6.4 Mbps and makes energy use 85% more efficient. Also, packet collisions go down by 30%, and successful transmissions go up by 25%. This shows that dynamic Edge-IoT environments are more reliable and scalable. Keywords: edge-IoT; reinforcement learning; RL; spectrum management; power control; RSPCF; adaptive communication. DOI: 10.1504/IJICT.2026.10079384
Abstract: In computational musicology, generating multi-part harmony for vocal melodies remains challenging because traditional rule-based and hidden Markov model methods struggle to capture long-range musical dependencies and cross-part harmonic constraints. A vocal melody multi-part harmony simulation framework based on encoder-decoder transformer is designed in this paper, motivated by solving the modelling failure of existing technologies when dealing with the hierarchical temporal structure of music and cross-channel harmonic constraints. Experimental results show that this method achieves 0.912 on normalised discounted cumulative gain at rank 5, a 6.5 percentage point improvement over standard transformer, and 0.468 on bilingual evaluation understudy score, a 12.2% increase over multi-generator music generation pre-training network. These data indicate that by introducing hierarchical temporal attention and differentiable harmonic constraint loss, the proposed framework can effectively simulate multi-part harmony arrangements that conform to musical norms without sacrificing melody integrity, providing an interpretable and controllable generation path for computer-assisted creation. Keywords: deep learning; vocal melody generation; polyphonic harmony; harmonic constraints. DOI: 10.1504/IJICT.2026.10079459
Abstract: Faced with the challenge of clustering complex graph structures in big data, traditional methods often separate graph preprocessing and deep representation learning, leading to suboptimal cognitive-resource allocation. Inspired by cognitive load theory (CLT), this paper proposes a co-clustering model that combines adaptive graph transformation and structural self-attention. The model actively reduces the intrinsic cognitive load of the input graph structure through a learnable graph transformer and efficiently allocates the associated cognitive load to construct clusters through a self-attention mechanism that incorporates structural priors. Experiments on real datasets, such as Amazon-Computers, show that the accuracy of the proposed model reaches 0.852, which is significantly improved by 4.0% compared with the optimal baseline (p < 0.001), and ablation experiments confirm the substantial contribution of each module. Our research results offer a new paradigm for combining cognitive theory with algorithm design in graph learning. Keywords: graph clustering; cognitive load theory; CLT; adaptive graph transformation; structural self-attention; big data. DOI: 10.1504/IJICT.2026.10079460
Abstract: Consumer reviews often contain the anchoring effect, a cognitive bias where users express emotions based on a reference point such as price or a comparison object. Traditional sentiment classification models struggle to distinguish between the anchor point and the true attitude, leading to misjudgements. To address this, we propose a fine-grained sentiment classification method that integrates anchoring psychological bias. By jointly extracting numerical and semantic anchors, we explicitly encode cognitive bias as learnable feature variables. A dual-channel interaction network dynamically fuses semantic and bias features. Experiments on Amazon and Yelp datasets show accuracies of 89.2% and 86.7%, improving by 3.5% and 2.9% over the baseline bidirectional encoder representations from transformers model, with area under the curve increases of 4.1% and 3.3%. These results verify that incorporating anchoring psychological features improves complex review sentiment classification, offering an interpretable cognitive perspective on consumers irrational expressions. Keywords: fine-grained sentiment classification; anchoring effect; cognitive bias; consumer reviews. DOI: 10.1504/IJICT.2026.10079461
Abstract: Online teaching platforms generate rich interaction traces, yet real-time evaluation still struggles to capture how learner understanding shifts during instruction. To address this gap, this study proposes a dual-stream framework for real-time cognitive transfer tracking in online teaching evaluation. First, behavioural evidence and semantic-cognitive evidence are encoded separately to preserve temporal rhythm and instructional meaning. Then, a cross-stream interaction mechanism aligns the two evidence sources and feeds a confidence-aware tracking module that stabilises state updates under noisy streaming inputs. Finally, the tracked states are mapped to process-sensitive evaluation outputs. Experimental results show that the proposed method achieved macro-F1 scores of 0.792 and 0.776 on two datasets, reduced temporal variation by 21.2 % compared with the strongest baseline, and maintained stable step-wise inference at 22.4 milliseconds. The framework demonstrates strong accuracy, robustness, and practical real-time usability. Keywords: online teaching evaluation; cognitive transfer tracking; dual-stream network; real-time learning analytics. DOI: 10.1504/IJICT.2026.10079462
Abstract: The fragmentation and heterogeneity of student behaviour data hinder traditional methods from constructing complete digital profiles for early academic risk perception. To address semantic alignment issues in multi-source data and capture temporal behaviour evolution, this study proposes a data fusion framework integrating attention mechanisms and temporal deep learning. It combines data from campus cards, learning management systems, and psychological assessments to build student profiles. Experiments show the model achieves an area under the curve of 0.937 in risk prediction, 4.1% higher than extreme gradient boosting. In early perception tasks 8 weeks in advance, the normalised discounted cumulative gain at 5 reaches 0.884, identifying 85.7% of potential at-risk students. The research demonstrates that multi-source heterogeneous fusion and temporal modelling synergistically enable early precise intervention. Keywords: multi-source heterogeneous data; student digital profile; academic risk warning; time-series deep learning. DOI: 10.1504/IJICT.2026.10079479
Abstract: This paper proposes a blind source separation scheme based on an improved sine cosine algorithm (SCA) to address the challenge of separating multiple sound sources in music signals. By constructing a linear instantaneous hybrid model, introducing adaptive control parameters and local disturbance mechanisms to optimise the standard SCA, and constructing the objective function with reconstruction error and independence constraints, the optimal separation matrix is searched for. Experiments have shown that the improved SCA achieves an average SDR of 11.5 dB in two source scenarios, which is 1.55 dB higher than the standard SCA. In three source scenarios, the SDR is 10.2 dB, which outperforms traditional algorithms such as ICA and NMF. It has higher accuracy, stability, and noise resistance, providing an effective technical solution for blind source separation of music signals. Keywords: improved SCA algorithm; music signal; blind source separation; intelligent optimisation; signal processing. DOI: 10.1504/IJICT.2026.10079480
Abstract: To address the challenges of high noise and low latency in real-time data processing for wide-area digital metering devices in new energy grid connection environments, this paper proposes a lightweight spatio-temporal fusion network. This method synergistically integrates multi-head attention mechanisms with causal convolutions. A dual-channel noise filtering module is innovatively designed to dynamically suppress composite noise in complex electromagnetic environments, while an online incremental learning framework is incorporated to mitigate data distribution drift caused by fluctuating renewable generation. Validation tests on the Institute of Electrical and Electronics Engineers 37-node test system demonstrate that our method achieves a voltage/current measurement accuracy of 98.2% with an end-to-end latency below 15 ms. These results not only meet the stringent real-time monitoring requirements of smart grids but also provide a highly robust and adaptable solution for future wide-area power metering systems. Keywords: deep learning algorithms; wide area power metering; real-time data processing; online learning; noise suppression. DOI: 10.1504/IJICT.2026.10079481
Abstract: Relatively important nodes have become a key issue in the field of complex network research, and along with the advent of the era of big data, more and more novel techniques are involved in the research of complex networks. Different from the traditional method of analysing node importance based on network structure, this paper combines computer science methods with complex network significant node mining, and proposes a method of mining relatively important nodes based on label propagation SIGELAP (Significant node Identification algorithm based on Graph Embedding and LAbel Propagation algorithm). The method first maps the network into a vector representation using the network representation learning method. Then, the vector representation of the network is fed to the machine learning algorithm as input, and then label propagation algorithm is used to classify the nodes and to mine the relative nodes. Through experiments on real networks such as the 911 telephone network and the SARS virus propagation network, the SIGELAP algorithm proves to be accurate and applicable in mining relatively important nodes. Keywords: complex network; important nodes; label propagation; graph embedding. DOI: 10.1504/IJICT.2026.10079482
Abstract: With the rapid growth of fashion e-commerce and rising demand for personalised dressing, clothing compatibility calculation has become essential for intelligent fashion recommendations. To address the limitations of existing methods in capturing fine-grained visual features and modelling complex item-to-item matching relationships, this study proposes an end-to-end compatibility model combining transformer and graph convolutional networks (GCN). The model leverages transformer to extract fine-grained visual semantic features of clothing items, adopts GCN to model deep matching relationships between items, and outputs the final compatibility score through a learnable multi-branch weighted fusion module. Experimental results on the Polyvore and FashionVC datasets show classification accuracies of 92.35% and 91.78%, with a highest AUC of 95.12%, significantly outperforming five mainstream and two state-of-the-art (SOTA) models. In complex scenarios involving six items and cross-category combinations, accuracy remains above 89%, with over 93% consistency with human labelling. This robust intelligent matching solution can be widely applied in online retail, virtual dressing, and personalised fashion content production. Keywords: clothing matching compatibility; graph convolutional network; GCN; transformer; feature extraction. DOI: 10.1504/IJICT.2026.10079483
Abstract: Semantic communication overcomes traditional capacity limits by extracting the meaning of information; however, existing separative coding methods overlook differences in the semantic importance of images, resulting in critical semantic information being easily lost when channel conditions deteriorate. This paper proposes a joint source-channel coding scheme based on dual attention: spatial and channel attention are embedded at the encoding stage to dynamically focus on semantically critical regions, and the transmission dimension is adaptively adjusted according to the signal-to-noise ratio to achieve joint optimisation of semantics and the channel. Experiments on the CIFAR-10 and Kodak24 datasets demonstrate that at a signal-to-noise ratio of 10 decibel, the peak signal-to-noise ratio reaches 33.68 decibel, representing a 1.23 decibel improvement over the state-of-the-art method; under 0 decibel adverse conditions, it maintains 26.4 decibel, a 4.3 decibel improvement over traditional separative coding. This method effectively enhances both image transmission robustness and reconstruction quality. Keywords: semantic communication; attention mechanism; joint source-channel coding. DOI: 10.1504/IJICT.2026.10079484
Abstract: Performing arts instruction has long relied on subjective experience, making it difficult to achieve precise and personalised learning feedback. While traditional artificial intelligence methods can process quantitative data, they struggle to capture ambiguous concepts such as expressive movement and emotional delivery, posing a core challenge for assessment. To address this, this study innovatively integrates fuzzy logic with deep learning to construct an intelligent evaluation framework capable of understanding the grey areas of artistic expression. Experiments conducted on public dance datasets demonstrate that compared to traditional precise algorithms, this approach improves overall accuracy from 0.82 to 0.91 and significantly enhances the normalised discounted cumulative gain metric for ranking quality. This research validates the effectiveness of integrating fuzzy logic in reconstructing artistic teaching methods, offering a new pathway toward more human-centred and explainable intelligent artistic guidance. Keywords: fuzzy logic; performing arts education; personalised assessment; artificial intelligence; AI; multimodal. DOI: 10.1504/IJICT.2026.10079485
Abstract: The scale of resources in university libraries has grown exponentially. Traditional recommendation methods struggle to simultaneously model heterogeneous user-resource relationships and deep semantic features, leading to significant performance drops in cold-start scenarios. To address this, we propose dual-channel graph neural network with attention fusion, a dual-channel graph neural network with semantic fusion. It captures high-order topological structure via relation-aware graph convolution and extracts semantic representations from metadata using attention mechanisms. An adaptive gating mechanism dynamically fuses both features. On a dataset with 156,942 resources and 1.8 million borrowing records, the method achieves an area under the curve of 0.937 and NDCG@10 of 0.581, outperforming light graph convolution network by 3.2% and 4.5%. Generalisation is verified on the Amazon Books dataset. The dual-channel architecture effectively addresses data sparsity in academic resource recommendation, supporting smart library services. Keywords: resource recommendation; semantic fusion; adaptive gating; university library. DOI: 10.1504/IJICT.2026.10079486
Abstract: The development of the internet has driven the growth of e-commerce for agricultural products and has also advanced logistics. However, as market demand increases and consumer expectations for delivery rise, the issue of insufficient delivery capacity in e-commerce has become more prominent. This study constructed a delivery path optimisation model that integrates fuzzy time windows. Initially, density-based spatial clustering of applications with noise (DBSCAN) and particle swarm optimisation-based K-means clustering are employed to group customer points by density and identify their centroids, and an improved ant colony optimisation (ACO) algorithm was used to dynamically solve the vehicle routing problem with time window constraints. Experimental results demonstrate that the mean square error of the proposed hybrid clustering algorithm converges to 0.0426 within 139 s, balancing analysis efficiency and clustering accuracy. This model effectively balances computational overhead and optimisation quality, while reducing merchant operating costs and improving customer satisfaction. Keywords: clustering analysis; DBSCAN-IPSO-K-means; ant colony optimisation; ACO; time windows; rolling time domain; delivery route. DOI: 10.1504/IJICT.2026.10079513
Abstract: With the digital transformation of smart education, objective and efficient classroom teaching quality evaluation has become a critical challenge for optimising teaching effectiveness. Traditional manual assessment methods suffer from strong subjectivity, low efficiency, and delayed feedback, while existing AI-driven approaches face bottlenecks of single-modal limitation, insufficient multimodal semantic fusion, and poor scene generalisation. To address these issues, this paper proposes a multimodal transformer for teaching evaluation framework, which deeply integrates speech, text, and behavioural modalities to realise interpretable teaching quality assessment. This framework designs a tri-modal feature extraction module, a cross-modal transformer fusion encoder with adaptive modal weighting. Experimental results show that the model achieves a macro-average F1 score of 0.88 across three core evaluation dimensions, significantly outperforming mainstream baselines. This work provides a robust technical solution for intelligent teaching evaluation and a benchmark for future research. Keywords: teaching quality evaluation; multimodal transformer; cross-modal fusion; speech-text-behaviour. DOI: 10.1504/IJICT.2026.10079514
Abstract: Ethnic dance movement recognition is critical for cultural heritage preservation and intelligent dance teaching, yet traditional methods struggle to capture spatial correlations of human joints and dynamic motion features of dance movements. This paper proposes a recognition method fusing graph convolutional network and motion attention mechanism to address these problems. The graph convolutional network models human skeletal joints as graph structures to extract spatial topological features, while the motion attention mechanism adaptively weights dynamic motion information of different joint sequences to enhance key movement feature representation. Evaluations on public and self-built ethnic dance datasets show the method outperforms traditional convolutional and single graph network models by 8.2% and 5.7% in recognition accuracy, respectively. It effectively captures the unique spatial and dynamic characteristics of ethnic dance movements, providing a reliable technical solution for ethnic dance digitalisation and intelligent analysis. Keywords: ethnic dance movement recognition; graph convolutional network; GCN; motion attention mechanism. DOI: 10.1504/IJICT.2026.10079515
Abstract: This paper addresses the challenge of difficult early warning of muscle injuries during exercise training, proposing a new method that integrates the time-frequency characteristics of surface electromyography signals with biomechanical simulation analysis. Traditional methods cannot comprehensively reflect the functional state of muscles during dynamic movements. This study uses wavelet transformation to deeply explore the latent fatigue and injury precursors in the signals, and simultaneously simulates the mechanical responses of the musculoskeletal system, thereby achieving real-time quantitative assessment of injury risk. Experimental results on public datasets show that compared with traditional time-domain analysis methods, this method increases the accuracy of injury identification from 82.4% to 91.7%, and the area under the curve for risk prediction also improves from 0.86 to 0.93. This method provides a more accurate and reliable decision support tool for monitoring training loads and preventing injuries in athletes. Keywords: surface electromyographic signal; time-frequency analysis; injury risk assessment; biomechanical simulation. DOI: 10.1504/IJICT.2026.10079516
Abstract: As fund management in colleges and universities faces the challenges of low efficiency and intensified operational risks, this study aims to build a new intelligent decision-making model to realise process automation and decision optimisation by integrating robotic process automation (RPA) and artificial intelligence technology. This paper adopts a hierarchical architecture to integrate data layer, process layer, decision layer and application layer, and uses OCR, LSTM and random forest technologies to automatically process invoice identification, fund forecast and risk monitoring. Although the model has achieved remarkable results in improving the efficiency of capital use and reducing risks, it still has limitations such as strong data dependence, and it is necessary to optimise the lightweight and cross-scenario adaptability of the algorithm in the future. Generally speaking, this study provides empirical support for financial intelligence in colleges and universities, and promotes the innovative practice of integrating automation and decision-making. Keywords: colleges and universities; money management; RPA robots; process optimisation; intelligent decision-making. DOI: 10.1504/IJICT.2026.10079517
Abstract: The lack of personalised adaptation in higher vocational curriculum systems is increasingly prominent. To address this, we propose a knowledge graph-based curriculum system construction and path optimisation model (KG-VCSPOM). The model integrates two modules: a knowledge graph construction module using BERT and GGAT to extract course entities and relationships, and a path optimisation module combining GCN embedding and reinforcement learning to compute optimal learning sequences. Tests show the model achieves an F1-score of 84.6% (baseline CF: 71.2%), a noise robustness attenuation rate of 6.2%, and a user satisfaction score of 4.5. Performance remains stable as data scales to 100,000 entries. These results confirm the model's advantages in improving recommendation accuracy, adaptability, and practicality, providing an innovative solution for intelligent higher vocational education. Keywords: knowledge graph; higher vocational education; curriculum system; path optimisation. DOI: 10.1504/IJICT.2026.10079518
Abstract: To reduce word error rate of speech recognition in English interactive system, a key technology of speech recognition based on improved BiLSTM-HMM model is proposed. Firstly, BiLSTM-HMM speech recognition model is constructed by combining BiLSTM network and HMM model. Then, on the basis of BiLSTM-HMM model, attention mechanism is introduced, and model structure and data alignment are improved. Meanwhile, improved BiLSTM-HMM model is proposed, and it is applied to the English interactive system built on voice communication technology. Finally, verification is carried out on LibriSpeech dataset. The results shows that recognition time of improved BiLSTM-HMM model on LibriSpeech dataset is 3,541 s, and its error rate is 0.35; in English interactive system, error rate of English speech recognition for users is 0.34. Therefore, the key technology of speech recognition based on improved BiLSTM-HMM model can be used for speech recognition in English interactive system, and can effectively reduce the error rate of speech recognition in English interactive system, which has certain practical application value. Keywords: English interactive system; speech recognition; BiLSTM network; HMM model; speech communication technology. DOI: 10.1504/IJICT.2026.10079519
Abstract: High-dynamic power optical networks are characterised by frequent topology changes and resource constraints, which challenge traditional routing mechanisms in meeting quality-of-service requirements. This paper proposes a multi-path deep Q-network (DQN) routing mechanism tailored for such environments. By integrating multi-path transmission with deep reinforcement learning, the mechanism optimises delay, packet loss rate, and load balancing through a multi-objective reward function. Built upon a layered graph model of the optical network, the approach incorporates a multi-path selection strategy and employs DQN for intelligent routing decisions under dynamic conditions. Simulation results demonstrate that the proposed mechanism effectively reduces end-to-end delay and packet loss under high traffic loads, while improving resource utilisation and network robustness, making it suitable for efficient data transmission in high-dynamic power optical networks. Keywords: high-dynamic optical networks; multi-path routing; deep Q-network; DQN; deep reinforcement learning; load balancing. DOI: 10.1504/IJICT.2026.10079520
Abstract: Aiming at the problems of inconsistent terminology, strong label coupling, insufficient generalisation, and difficult real-time deployment in power communication customer service texts, this paper proposes a multi-label classification method based on binary relevance (BR) and gradient boosting decision tree (GBDT) with adaptive label correlation weighting. We design an automated training set construction scheme using data cleaning, Jieba segmentation, TF-IDF, and Word2vec to build a standardised label system. The BR-GBDT model decomposes multi-label tasks into binary classification tasks and uses mutual information-based label correlation weights to overcome the label independence defect of traditional BR. Experiments on 12,800 real work orders show that the method achieves Hamming Loss 0.072, Subset Accuracy 82.3%, Macro-F1 89.5%, and Micro-F1 92.1%. The average single-sample prediction time is 18.3 ms under real system load, meeting the 30 ms real-time requirement. The model exhibits strong cross-domain adaptability, noise robustness, and low hardware dependence, and is interpretable via feature importance and per-label performance analysis. Comparisons with CNN-LSTM, BERT, and classic multi-label methods verify its superiority. This method supports efficient, real-time, and deployable multi-label classification for power communication customer service systems. Keywords: power communication; multi-label text classification; binary relevance; BR; gradient boosting decision tree; GBDT; label correlation; model interpretability. DOI: 10.1504/IJICT.2026.10079521
Abstract: A new media public opinion classification model is proposed to address limitations of traditional methods, including high-dimensional sparseness, poor semantic understanding, and category imbalance. The model integrates a three-branch decision feature selection algorithm (combining mutual information and improved term-category information) with a multi-channel random undersampling mechanism. A hybrid architecture combining Transformer, LSTM, and CNN captures global context, sequence dependence, and local features. Experiments on Weibo and Douyin datasets achieve accuracies of 89.47% and 87.93%, with macro F1 values of 83.26% and 81.17%, respectively. The model improves negative opinion recognition F1 by 6.97% over a pre-trained language model, with inference time of 28.5ms (120 samples) and stable 15.3% CPU usage. This work provides an accurate, efficient, and robust engineering solution for new media public opinion monitoring. Keywords: new media public opinion classification; three-way decision feature selection; multi-channel random undersampling; imbalanced data classification. DOI: 10.1504/IJICT.2026.10079522
Abstract: In power communication scenarios, incremental pre-training of large models faces challenges such as limited bandwidth and limited edge device resources , and existing gradient compression methods are difficult to adapt to dynamic network environments. To this end, this paper proposes a power-aware adaptive gradient sparsity compression (PAGSC) algorithm, which achieves efficient training by fusing dynamic sparsity, hierarchical quantisation and channel adaptive scheduling mechanisms. The test results show that on data sets such as CIFAR-100 and UCI power, PAGSC reduces the communication volume to 12% of the baseline, with only 2% accuracy loss, reduces training time by 19%, increases bandwidth utilisation to 85.6%, and significantly optimises resource use. To sum up, the algorithm effectively balances accuracy and efficiency. However, further research on hyperparameter adaptive optimisation and noisy channel robustness is needed. The innovation of this paper lies in providing a deployable lightweight training solution for the power internet of things. Keywords: power communication; large model incremental pre-training; gradient sparse compression. DOI: 10.1504/IJICT.2026.10079523
Abstract: Corporate loan risk prediction is challenged by high-dimensional features, nonlinear relationships and severe class imbalance. To improve prediction accuracy and robustness, an improved LightGBM-based corporate loan risk prediction model is proposed. The framework integrates D-B-SMOTE for minority sample balancing, a feature importance fusion selection mechanism for adaptive feature screening, and a hybrid loss function combining focal loss and Huber loss to enhance sensitivity to default samples and reduce noise interference. In addition, an SSA-GWO hybrid optimisation strategy is introduced for hyperparameter tuning, and a distributed parallel training architecture is constructed to improve scalability. Experimental results show that the proposed model achieves an accuracy of 0.893, recall of 0.822, F1-score of 0.837 and AUC of 0.886, demonstrating strong robustness and generalisation ability in enterprise loan risk prediction. Keywords: corporate loans; feature fusion screening; LightGBM; risk prediction; sparrow search algorithm; SSA. DOI: 10.1504/IJICT.2026.10079524
Abstract: To improve the stability of distributed collaborative media data transmission, this paper proposes a two-stage distributed collaborative media transmission optimisation method based on improved K-means algorithm and improved bee evolutionary genetic algorithm (BEGA). By dividing distributed collaborative media transmission optimisation into clustering and routing stages, and respectively using K-means algorithm improved by introducing a differential evolution algorithm, as well as BEGA algorithm improved in parameters, crossover method, and mutation method for clustering and optimisation, the optimisation of distributed collaborative media transmission is achieved. The simulation results show that when number of deployment node is 100, 300, and 500, number of surviving nodes drops to zero approximately after 700 rounds, the packet transmission rate is about 70% after 1,000 rounds, the network remaining energy drops to zero approximately after 1,000 rounds, and network energy consumption balance remains at a high value before 800 rounds. Compared with the contrast method, it has more efficient and stable data transmission performance. From this, it can be concluded that the proposed method can improve the stability of distributed collaborative media data transmission, providing a reference for achieving more efficient and stable data transmission in distributed collaborative media. Keywords: distributed collaborative media; transmission optimisation; K-means algorithm; BEGA algorithm. DOI: 10.1504/IJICT.2026.10079525
Abstract: Teaching Chinese tea culture to international students remains difficult in a conventional classroom because key elements of tea practice, including utensil handling, brewing sequence, and the cultural atmosphere of the tea room, are hard to reproduce authentically. This study presents the design and classroom implementation of an immersive virtual simulation learning system based on a browser/server architecture to address this gap. The system integrates three learning scenes, namely the tea culture corridor, tea garden roaming and tea room experience, and organises procedural tasks through a finite-state-machine interaction model. Multimodal guidance, including voice explanation, subtitles, icons, and real-time prompts, is provided to support learners from diverse linguistic and cultural backgrounds. The system was applied in three rounds of teaching involving 300 international students. Classroom records showed stable completion of tea-making tasks, effective retention of core cultural knowledge, and comprehensive learning logs for instructional review. Compared with the baseline classroom condition, the virtual simulation classes demonstrated higher engagement, task completion, and satisfaction at the descriptive level. These findings suggest that browser-based virtual simulation can serve as a practical instructional approach for cross-cultural tea culture education in universities. Keywords: virtual simulation; cross-cultural education; learning system; human-computer interaction; learning behaviour analysis. DOI: 10.1504/IJICT.2026.10079531
Abstract: To address the problems of low prediction accuracy and poor real-time performance in traditional cable tunnel construction behaviour prediction methods, a prediction method based on Qwen3-VL temporal enhancement is proposed. First, multi-source data related to cable tunnel construction (including construction equipment operation parameters, environmental monitoring data and process execution records) are collected and preprocessed to eliminate noise and data redundancy. Then, the temporal enhancement module of Qwen3-VL is optimised to enhance the models ability to capture time-series features of construction behaviour, effectively mining the temporal correlation and evolution rules among multi-dimensional construction data. Finally, the enhanced feature set is input into the prediction network to realise accurate prediction of key construction behaviours such as non-standard operation, process deviation and equipment failure risk. Experimental results show that compared with traditional machine learning methods and general deep learning models, the proposed method improves the prediction accuracy by 8.3%-12.7% and shortens the prediction response time by 15%-22%, which can provide reliable technical support for safe and efficient construction of cable tunnels. Keywords: cable tunnel; construction behaviour; prediction optimisation; Qwen3-VL; temporal enhancement. |
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