International Journal of Information and Communication Technology (14 papers in press)

Regular Issues

• Open Access

  Design of transfer learning pose detection algorithms for dance instruction ( Free Full-text Access )

  by Lei Chen 
  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
   
  

• Open Access

  Hybrid real-time synchronisation algorithm for generative English learning ( Free Full-text Access )

  by Ping Lu, Fangfang Xu 
  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
   
  

• Open Access

  Generative adversarial networks for colour pairing in fashion design based on visual perception constraints ( Free Full-text Access )

  by Hongjuan Niu 
  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
   
  

• Open Access

  Application of multi-modal deep learning in labour education effectiveness analysis and student behaviour prediction in colleges and universities ( Free Full-text Access )

  by Feng Qin 
  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
   
  

• Open Access

  A deep knowledge reasoning graph for graduate skill requirements ( Free Full-text Access )

  by Shiyu He, Guoping Dan 
  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
   
  

• Open Access

  Quantification of the impact of meteorological large models based on transformer on electricity load ( Free Full-text Access )

  by Sheng Chen, Lei Zhang, Hewen Bai 
  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
   
  

• Open Access

  Precursor detection for extreme weather in power facilities using deep residual shrinkage networks ( Free Full-text Access )

  by Jie Zhang, Yuhui Peng, Chengjun Ren 
  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
   
  

• Open Access

  Probabilistic modelling and reliability analysis of smart grid optical communication networks ( Free Full-text Access )

  by Jiao Li, Jie Deng, Song Cheng, Yuhang Pang, Xuan Wang, Yang Liu 
  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
   
  

• Open Access

  Reinforcement learning-based AI framework for interference control in edge-IoT networks with limited resources ( Free Full-text Access )

  by Xuefen Jin, Yongju Li, Jie Yao 
  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
   
  

• Open Access

  3D visual generation system based on the fusion of multi-view geometry reconstruction and deep learning ( Free Full-text Access )

  by Guofeng Hu, Yan Lin 
  Abstract: A 3D vision generation system combining multi-view geometric reconstruction and deep learning is proposed to improve robustness, reconstruction completeness, and rendering realism in complex scenes with weak texture, occlusion, and lighting variation. A cross-attention neural matcher enhances feature matching, a geometry-constrained cost volume network improves dense reconstruction, and a geometry-guided NeRF optimisation increases rendering realism. Geometry-aware attention reduces occlusion and repetitive texture interference, while sparse geometric priors improve weak-texture regions and unify geometric consistency with visual realism. Experiments show 91.7% matching accuracy and 84.2% recall, with pose errors of 0.19° and 1.14 cm. Dense reconstruction achieves 0.278 mm accuracy and 0.362 mm completeness on DTU, and 79.88 F-score on tanks and temples. Rendering reaches PSNR 34.72, SSIM 0.971, and LPIPS 0.039. The system enables robust, realistic end-to-end 3D generation from mobile image sequences for autonomous driving, industrial modelling, and digital twins.
  Keywords: feature matching; dense reconstruction; geometric reconstruction; multi-view; deep learning; DL.
  DOI: 10.1504/IJICT.2026.10079250
   
  

• Open Access

  A trustworthy traceability and permission management system for academic publishing processes based on blockchain and AI ( Free Full-text Access )

  by Yu Su, Yu Zhang, Haozhe Dai, Zizhen Di 
  Abstract: Academic publishing is crucial for knowledge dissemination and upholding scientific integrity, but existing mechanisms suffer from significant drawbacks such as lack of transparency, disputes regarding authorship, biased peer reviews, and unilateral commercial publisher control. Such challenges diminish confidence and hinder the process of research dissemination. To overcome them, the present study suggests a secure and traceable publishing model that combines Blockchain and artificial intelligence (AI). The system utilises a permissioned Blockchain (Hyperledger Fabric) with self-sovereign identity (SSI) to ensure tamper-proof logging, privacy-preserving authorship authentication, and role-based access control. Meanwhile, transformer-based AI models like DistilBART for summarisation, RoBERTa for sentiment analysis and BART-MNLI for intent classification are utilised to process peer reviews in real time, providing editorial decision support and increased efficiency. The architecture provides end-to-end traceability of review incidents, protects against immoral authorship behaviours, and locks away AI-derived insights immutably with human input. Built as a modular and micro-service-compatible architecture, it facilitates scalability, auditing, and transparency throughout the publishing process. This body of work illustrates how Blockchain and AI may be harnessed to develop a reliable, automated, and fair scholarly communication system.
  Keywords: blockchain; academic publishing; self-sovereign identity; SSI; peer review automation; role-based access control; RBAC; artificial intelligence; trustworthy systems; scholarly communication; review summarisation; sentiment analysis; intent classification.
  DOI: 10.1504/IJICT.2026.10079251
   
  

• Open Access

  Interactive teaching system based on an improved collaborative filtering algorithm and matrix decomposition ( Free Full-text Access )

  by Hui Zhang, Min Lu 
  Abstract: Providing accurate personalised resource recommendations for learners has become a key issue in improving teaching effectiveness. However, existing methods still face problems such as heavy computational burden, sparse data, and cold start when dealing with large-scale dynamic learning scenarios. Therefore, this study proposes a hybrid model integrating improved collaborative filtering, enhanced matrix decomposition, and knowledge mapping. The experimental results showed that the proposed model achieved a precision of 0.732, recall of 0.705, F1-score of 0.718, and RMSE of 0.721. For cold-start users, highly relevant recommendations increased from 28% to 64.6%. In practical teaching applications, knowledge mastery improved by 21.5%, and quiz scores increased by 12.7%. The proposed hybrid model exhibits strong robustness. It also exhibits teaching adaptability and practical value. The model improves recommendation accuracy and learning effectiveness. It provides an efficient and feasible technical solution for personalised resource recommendation in intelligent teaching systems.
  Keywords: collaborative filtering; CF; matrix decomposition; knowledge graph; KG; interactive teaching system; personalised recommendation.
  DOI: 10.1504/IJICT.2026.10079270
   
  

• Open Access

  Dynamic diagnosis of students state in English classes based on feature decoupling and improved active learning algorithm ( Free Full-text Access )

  by Dongfeng Liu, Xiaolong Ren 
  Abstract: Traditional subjective observation for diagnosing students' state in English classes suffers from bias, low efficiency, and inability to capture multi-dimensional information. This study builds a dynamic diagnosis system integrating text, audio, and video data. It employs an improved active learning algorithm with diversity constraints to optimise annotation, a semantic-state decoupling framework using triplet loss to reduce interference, feature alignment and cross-modal attention fusion to improve feature quality, and parallel deployment to accelerate response. Experimental results show multi-modal fusion achieves 90.5% accuracy, 8% higher than the best single modality. The active learning strategy yields 83.1% sample utilisation and 91.9% model accuracy within 180 minutes. The decoupling mechanism lowers diagnostic error to 9.0% in high-semantic complexity scenarios, and parallel deployment cuts response delay to 80ms. The system significantly enhances diagnostic accuracy and efficiency, providing reliable technical support for personalised and real-time teaching intervention in English classes.
  Keywords: students' status in English class; dynamic diagnosis; multi-modal data fusion; feature decoupling; improved active learning.
  DOI: 10.1504/IJICT.2026.10079190
   
  

• Open Access

  Practical application of social science text translation based on the computer-assisted translation platform YiCAT ( Free Full-text Access )

  by Ru Shen 
  Abstract: With increasing demand for international dissemination of social science works, computer-assisted translation tools face severe challenges when dealing with culturally-loaded words and abstract concepts. Existing general platforms lack terminology consistency and contextual adaptability. Therefore, based on the Yi Computer-Assisted Translation platform, this study constructed an enhanced workflow integrating a dynamic terminology database and a context verification mechanism. Experimental results on the public United Nations parallel corpus subset show that compared to the original yi computer-assisted translation baseline, this workflow increased translation accuracy by 12% and improved key term consistency by 18%; compared to pure manual translation, it increased translation efficiency by approximately 35% while maintaining 90% semantic fidelity. The results indicate that the proposed enhanced solution improves social science translation quality and human-computer collaboration efficiency, providing a reusable technical path for related field practices.
  Keywords: computer-assisted translation; CAT; social science texts; human-computer collaboration; quality assessment.
  DOI: 10.1504/IJICT.2026.10079271