International Journal of Information and Communication Technology (15 papers in press)

Regular Issues

• 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

  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

  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
   
  

• Open Access

  Intelligent assessment and teaching optimisation of college Russian translation based on transformer multimodal representation learning ( Free Full-text Access )

  by Hongxue Yin, Yongping Wang 
  Abstract: This study develops transformer-based multimodal intelligent evaluation model for college Russian translation instruction, tackling frequent pragmatic failures and context deficiency that lead to lagged feedback. It leverages XLM-RoBERTa to capture Russian's intricate morphological and syntactic features, adopts ViT for global visual context of accompanying images, and uses multi-head cross-attention (MCA) to deeply integrate and calibrate textual-visual semantics. Experiments on the improved multimodal corpus based on Wikipedia-based image text (WIT) show that the model's Pearson correlation coefficient to gauge the consistency of scoring is as high as 0.835, and the error diagnosis reaches 89.4%. In identifying high-order pragmatic inconsistency errors, compared with the ResNet+XLM-R (naive fusion) baseline model, its F1-score significantly improved to 0.87 (p < 0.01), with statistical significance verified. Highly consistent with expert scores in real teaching, the model proves valuable for accurate teacher feedback and cultivating students' text-image integrated translation thinking.
  Keywords: multimodal representation learning; Russian translation teaching; translation intelligence evaluation; XLM-RoBERTa; vision transformer.
  DOI: 10.1504/IJICT.2026.10078897
   
  

• Open Access

  Recurrent neural network-based analysis of influencing factors on sports event communication effectiveness from the intelligent media perspective ( Free Full-text Access )

  by Hongwei Zhang, Minghua Jiang 
  Abstract: To address the issues of insufficient real-time performance and accuracy in the communication of sports competitions, this study proposes a dynamic LSTM for sports event communication effectiveness (DLSTM-SEC) model based on dynamic long short-term memory (LSTM) to optimise the communication effect of sports events. The model combines compressed sensing feature selection and the momentum-improved adaptive moment estimation (Adam) algorithm to achieve efficient capture and rapid response to key events. Experimental results show that the DLSTM-SEC model achieves a test accuracy of 91.1% with an average loss value below 0.35. In terms of communication delay, 95% of the latency is controlled within 250 ms, and the 99th-percentile delay is no more than 450 ms. Under abnormal load conditions, the packet loss recovery rate remains above 92%. The results demonstrate that the model has stable real-time communication capability and data adaptability in complex dynamic scenarios, and can support the dynamic optimisation and efficient operation of sports event communication in an intelligent media environment. This study aims to provide a reliable competition communication tool for sports event organisers, intelligent media platforms, and audiences to improve real-time information acquisition and user experience, and realise more efficient event content push and interactive feedback.
  Keywords: long short-term memory; LSTM; Adam algorithm; sports events; intelligent media communication; interactive feedback.
  DOI: 10.1504/IJICT.2026.10079116
   
  

• Open Access

  Adaptive crawling strategies and quality-aware cleansing for Korean teaching resources ( Free Full-text Access )

  by Jihong Quan, Jiyue Quan 
  Abstract: This study addresses the critical challenge of automatically harvesting high-quality Korean language teaching resources from the open web, where existing methods focus on topical relevance rather than pedagogical suitability. The study proposes a novel quality-aware adaptive crawling and cleansing framework. It integrates a real-time linguistic quality assessment module, powered by universal dependencies parsing, with an adaptive crawling strategy driven by a contextual bandit algorithm. Experimental results demonstrate that quality-aware adaptive crawling and cleansing framework significantly outperforms current state-of-the-art methods. It achieves a high-quality page acquisition rate of 7.47 pages per hour (a 39% improvement), a pedagogical precision of 0.892, and a top-ranking accuracy of 0.915. The framework successfully bridges linguistic theory and web mining, offering an effective solution for building structured, high-quality pedagogical resource repositories.
  Keywords: adaptive web crawling; quality assessment; universal dependencies; resource cleansing.
  DOI: 10.1504/IJICT.2026.10078898
   
  

• Open Access

  Dynamic optimisation algorithm for online learning intervention based on cognitive-emotional multimodal fusion ( Free Full-text Access )

  by Na Tian 
  Abstract: Aiming at the problem that online learning systems are difficult to perceive and optimise learners' internal states in real time, this paper proposes a dynamic optimisation algorithm based on cognitive-emotional load model and multimodal fusion. The algorithm constructed a theoretical framework of cognition and emotion collaborative computing, and used a hierarchical deep reinforcement learning architecture to realise the continuous space optimisation of intervention strategies. The dataset constructed in the simulation environment, compared with a variety of cutting-edge baselines, the algorithm can significantly improve the standardised learning benefit to 85.7, and reduce the incidence of harmful cognitive emotional overload events to 9.3%. This research provides a feasible path with both theory and technology for the construction of 'state adaptation' intelligent education system.
  Keywords: cognitive emotional load; multi-modal fusion; deep reinforcement learning; DRL; online learning intervention.
  DOI: 10.1504/IJICT.2026.10079056
   
  

• Open Access

  Keyword association mining and topic evolution analysis algorithm for knowledge graph in the energy field ( Free Full-text Access )

  by Wenke Li, Xinping Miao, Qingbo Kong, Yue Tian, Ruoyan Dong 
  Abstract: With the rapid growth of energy literature data, accurately mining semantic associations between keywords and dynamically tracking topic evolution patterns is a key challenge. This paper proposes an algorithm for keyword association mining and topic evolution analysis for knowledge graphs in the energy field. The model integrates topic modelling, graph neural networks and time series analysis. It combines the topic probability distribution from BERTopic with the knowledge graph topology via a topic- graph coupling mechanism, uses a graph attention network to optimise association weights. Test results show the model outperforms baseline models like LDA and BERTopic in accuracy (91.2%), F1-score (0.892) and topic consistency (0.848). It also excels in robustness (F1-score drops 7.2% with 20% noise), interpretability (expert score 4.5/5) and generalisation (performance degradation 6.3%). These results verify the model's efficiency and reliability for practical energy knowledge analysis, providing support for energy policy evaluation and technology trend prediction.
  Keywords: knowledge graph of energy field; keyword association mining; theme evolution analysis; graph neural network; GNN; dynamic topic model; DTM.
  DOI: 10.1504/IJICT.2026.10078952
   
  

• Open Access

  Dynamic optimisation of visitor diversion in smart scenic areas using deep reinforcement learning ( Free Full-text Access )

  by Qi Ma, Qinglin Wan 
  Abstract: Peak-time congestion in smart scenic areas often concentrates at a few hotspots and spreads quickly, raising safety pressure and degrading visitor experience. To address dynamic diversion under non-stationary demand, this paper proposes a constrained deep reinforcement learning framework for real-time guidance. First, a graph-based encoder captures spatial spillover among attractions and corridors. Then, a spatiotemporal attention module anticipates short-horizon surges and stabilises decisions. Finally, constraint-aware learning keeps recommendations within safety margins while balancing waiting, load equity, and throughput. Experiments on calibrated peak-demand scenarios show that the proposed method reduces average waiting time from 29.2 to 24.6 minutes and cuts safety violation rate from 2.0% to 1.2% compared with a vanilla learning baseline. relative to rule-based control, waiting drops from 38.6 to 24.6 minutes and near-violation time decreases from 41.2 to 14.8 minutes. The framework delivers robust improvements with steadier operating behaviour under diverse demand regimes.
  Keywords: smart scenic area; visitor diversion; crowd management; constrained deep reinforcement learning.
  DOI: 10.1504/IJICT.2026.10078955
   
  

• Open Access

  Large language model-driven student career knowledge graph and interpretable adaptive reasoning ( Free Full-text Access )

  by Tong Shen 
  Abstract: In response to challenges in temporal modelling, heterogeneous data fusion, and recommendation transparency in student career development, this paper proposes a temporal knowledge graph method based on large language models and interpretable reasoning. The approach designs a dynamic graph with time intervals to capture skill evolution, builds a self-validating extraction pipeline to automatically extract temporal information from unstructured resumes, and integrates symbolic logic with vector matching for interpretable reasoning. Experiments on CareerHop demonstrate strong recommendation accuracy with area under the curve reaching 0.842, an 11.7% improvement over graph neural networks, and temporal extraction accuracy reaching 0.957, a 57% increase over rule-based baselines. This technical approach addresses limitations of static representations in capturing ability growth and provides an accurate, transparent solution for high-stakes career decisions.
  Keywords: temporal knowledge graph; explainable recommendation; large language model; LLM; person-job matching.
  DOI: 10.1504/IJICT.2026.10079189
   
  

• Open Access

  Deep learning-based multimodal vocal emotion recognition ( Free Full-text Access )

  by Lei Zhang 
  Abstract: In the field of intelligent interaction and mental health screening, accurately identifying the emotions in singing is crucial. However, traditional methods rely solely on voice features, which are prone to misjudgement when recognising complex emotions (such as 'sarcastic' or 'mixed emotions'). To address this, this paper proposes a multimodal deep learning model that integrates singing and lyrics text, achieving deep collaboration of the two types of information through a cross-modal attention mechanism. Experiments show that the sentiment recognition accuracy of the model proposed in this paper reaches 81.3%, which is significantly higher than that of the model using only voice (accuracy 72.1%) and the early fusion method (accuracy 78.5%). Its comprehensive discrimination ability is also superior to the comparison baseline. This confirms that multimodal fusion can more comprehensively capture emotional cues, providing a reliable solution for achieving more refined human-computer interaction.
  Keywords: vocal emotion recognition; multimodal learning; attention mechanism; deep learning.
  DOI: 10.1504/IJICT.2026.10078954
   
  

• Open Access

  A multimodal generative AI dialogue system for real-time intervention in English learning anxiety ( Free Full-text Access )

  by Qingbin Huang 
  Abstract: English learning anxiety significantly impairs learners' cognitive performance and language acquisition, yet existing interventions lack real-time responsiveness and personalisation. This paper introduces multimodal generative artificial intelligence for anxiety intervention in conversation, a multimodal generative artificial intelligence dialogue system that continuously perceives a learner's anxiety level through audio, video, and text, and generates adaptive supportive responses to alleviate anxiety in real time. The system integrates a cross-modal transformer with bidirectional long short-term memory for anxiety perception, a conditional variational autoencoder for generating empathetic responses, and deep reinforcement learning to optimise when to intervene. A new English learning anxiety corpus comprising 120 real learners is constructed for training and evaluation. Experiments demonstrate that MAGIC significantly reduces self-reported anxiety (Δa = 0.31, p < 0.01) compared to baseline methods, confirming its effectiveness in providing timely and personalised emotional support.
  Keywords: multimodal perception; generative dialogue system; English learning anxiety; ELA; real-time intervention; cognitive load theory; CLT.
  DOI: 10.1504/IJICT.2026.10078953
   
  

• Open Access

  A big data-driven personalised learning framework for education with adaptive content delivery ( Free Full-text Access )

  by Yilin Yuan, Kamisah Binti Osman, Yunlei Liao 
  Abstract: The increasing demand for sustainable and technology-enabled education underscores the necessity of adaptive learning systems to meet diverse learner needs. Traditional static curricula struggle to support dynamic knowledge domains and personalised learning paths. This study presents a big data-driven personalised learning framework that integrates educational datasets and learner analytics from wearable-enabled environments to dynamically adjust content delivery. Experiments using real educational data and simulated interaction logs show that the proposed framework outperforms conventional static approaches, with a 32.6% improvement in learning gain, a 22% increase in quiz accuracy, and a 50% rise in learner engagement time. Comparative assessments against four existing adaptive models verify its superior effectiveness and robustness. The findings highlight the value of big data analytics and intelligent models in aligning academic learning with evolving industry skills. This framework offers a scalable, sustainable solution for modern education, supporting personalised, adaptive learning tailored to future skill requirements.
  Keywords: big data; personalised learning; adaptive content; educational data analytics; intelligent education.
  DOI: 10.1504/IJICT.2026.10079117