Forthcoming Articles
International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (21 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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. |
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