International Journal of Information and Communication Technology (17 papers in press)

Regular Issues

• Open Access

  Application research of a LightGBM-based predictive evaluation model for college English learning behaviour ( Free Full-text Access )

  by Xinxin Yang 
  Abstract: In order to meet the demand for the digital modelling and intelligent analysis of students learning behaviour, a predictive evaluation framework is proposed, which combines feature engineering with LightGBM. Through the construction of tags and the extraction of training samples from college English course behaviour log, the construction strategy and interpretable output mechanism of the model are discussed. The results show that the proposed model achieves an F1-score of 0.889 on the test set, with an average response latency of less than 230 ms, and a performance fluctuation of less than 1.6% in 5 times cross validation, demonstrating robust deployment stability and generalisation.
  Keywords: learning behaviour prediction; LightGBM algorithm; educational data mining; Intelligent teaching analytics; learning behaviour modelling.
  DOI: 10.1504/IJICT.2027.10080149
   
  

• Open Access

  Comprehensive evaluation of key indexes of power engineering based on K-nearest neighbour algorithm ( Free Full-text Access )

  by Yulong Li, Wei Ni, Bo Chen 
  Abstract: To improve the accuracy of key indicator evaluation in power engineering, this study proposes a comprehensive evaluation method based on K-nearest neighbour (KNN) algorithm. Aiming at the limitations of traditional methods in processing nonlinear and high-dimensional data, an evaluation system including ten key indicators such as engineering cost, construction period, and equipment performance was constructed to conduct experiments on 50 power engineering project data in a certain region. The results show that compared with traditional methods, the KNN model has a 15% improvement in evaluation accuracy, a more reasonable allocation of indicator weights, and can effectively identify key factors affecting engineering quality and efficiency. It also exhibits strong robustness in dealing with nonlinear problems and is suitable for power engineering of different scales and types. This method provides a scientific basis for decision-making in power engineering and has promotional value.
  Keywords: KNN algorithm; power engineering; index analysis; comprehensive evaluation.
  DOI: 10.1504/IJICT.2026.10080314
   
  

• Open Access

  Artificial intelligence-enhanced Chinese academic writing instruction for cross-cultural communication: a framework for Sino-French and Sino-African contexts ( Free Full-text Access )

  by Yan Xiao, Yunbiao Xu, Yanxia Gong 
  Abstract: Recent advances in artificial intelligence (AI) have enabled automated support for academic writing; however, many platforms remain tool-centric, providing isolated corrections rather than process-aware instructional support. This paper proposes a novel AI-enhanced academic writing instruction platform designed from an ICT perspective, focusing on the specific rhetorical needs of Sino-French and Sino-African cross-cultural academic communication. The framework integrates a three-layer architecture: a multi-level linguistic feature extraction layer using fine-tuned transformer models, a learner modelling layer driven by longitudinal data analytics, and an adaptive feedback engine. To validate the system, a 16-week empirical study was conducted with 342 undergraduate students. Analysis of a corpus comprising 1,250 academic essays reveals that the platform significantly enhances discourse coherence (18.4% improvement) and lexical complexity (12.6% increase) compared to traditional automated writing evaluation tools. Statistical significance was confirmed via a paired-sample t-test (p < 0.01), and effect size calculations (Cohens d = 0.76) suggest a robust impact on student writing quality. The results demonstrate that embedding evaluation directly into the system operation enables a continuous feedback loop that fosters long-term linguistic development in specialised academic contexts.
  Keywords: artificial intelligence; academic writing instruction; learning analytics; intelligent educational systems; information and communication technologies; ICTs.
  DOI: 10.1504/IJICT.2026.10080315
   
  

• Open Access

  Modelling and predicting energy consumption patterns using generative adversarial networks for effective carbon management ( Free Full-text Access )

  by Yongting Liu, Baotong Li 
  Abstract: Effective carbon management requires accurate prediction of energy consumption patterns, yet conventional models struggle with complex temporal dependencies. This study proposes a novel integrated framework combining transformer-based generative adversarial networks with Bayesian optimisation (BO-TransGAN) for energy forecasting and carbon optimisation. Using 2,000 hourly records of energy use and emissions, data were pre-processed via imputation, outlier removal, and normalisation. The TransGAN captures nonlinear temporal dependencies through adversarial learning, while Bobcat optimisation tunes hyperparameters for enhanced convergence and stability. BO-TransGAN achieves 0.987 accuracy, 0.995 R2, minimal losses, and low training (2.12s) and run times (7.35s). It generates realistic synthetic sequences and provides actionable insights for reducing carbon emissions, offering a scalable tool for sustainable energy planning and real-time carbon management.
  Keywords: carbon management; generative adversarial networks; GANs; energy consumption prediction; sustainable energy planning; energy-carbon pattern modelling.
  DOI: 10.1504/IJICT.2026.10080316
   
  

• Open Access

  GAN-based reconstruction for the automatic generation of business English emails and its potential ( Free Full-text Access )

  by Xingxin Liu, Hui Ye 
  Abstract: Traditional business English emails generated by artificial intelligence are often rigid and lack flexibility, making it difficult to meet complex and variable communication needs. To address this issue, this paper explores the application potential of generative adversarial networks (GANs) in this field. It designs a new conditional generative adversarial network architecture, enabling the model to learn and imitate the appropriate style and accurate content of professional business letters. Experiments on an open business email dataset show that, compared with mainstream generation models, the method proposed in this paper improves the practicality of generated emails by 15.7% in human evaluation and increases the accuracy of key terms by 8.2%. This demonstrates that generative adversarial network technology can effectively reconstruct the quality and adaptability of automatically generated business emails, providing a new path for enhancing the professionalism of intelligent communication tools.
  Keywords: generative adversarial network; GAN; business English email; natural language generation; style control.
  DOI: 10.1504/IJICT.2026.10080359
   
  

• Open Access

  Design and development of a student information management platform based on support vector machines and data mining ( Free Full-text Access )

  by Yuzhen Jin 
  Abstract: This paper proposes a student information management platform that integrates support vector machine (SVM) modelling with data mining to improve academic early warning and personalised guidance. The platform adopts a four-layer architecture covering data, processing, application, and presentation functions. Multi-source student data, including academic records, behavioural information, and consumption data, are cleaned through missing value imputation, outlier correction, and feature selection to form model-ready datasets. An SVM model with an RBF kernel is developed for predicting course-failure risk and performance fluctuations, with parameters optimised by grid search (C = 10, = 0.1). Using 5,000 undergraduate records, the model is compared with decision tree, KNN, and logistic regression methods. Results show that the SVM model achieves 89.6% accuracy, outperforming decision tree and KNN models. The platform also maintains acceptable response and prediction times under 500 concurrent users.
  Keywords: intelligent management of student information; support vector machine; SVM; educational data mining; EDM; academic risk early warning; platform design.
  DOI: 10.1504/IJICT.2026.10080360
   
  

• Open Access

  Intelligent human-computer collaborative scoring system for English writing using large-scale model representation learning and knowledge reasoning ( Free Full-text Access )

  by Lulu Hao 
  Abstract: Traditional manual English writing scoring is inefficient and subjective, while existing automated systems lack semantic understanding and instructional interpretability. This study proposes an intelligent human computer collaborative scoring system integrating large-scale model representation learning with knowledge reasoning. The system combines representation, reasoning, and collaboration through a fine-grained feature learning algorithm and a domain knowledge map, enabling multidimensional evaluation and traceable scoring decisions. Experimental results on 8,800 English compositions demonstrate that the proposed system achieves 91.7% scoring accuracy (36.8% higher than traditional models), 72% lexical error detection coverage with a 5.2% omission rate, and a human-computer semantic consistency coefficient (SCC) of 43% in evaluating high-level dimensions like semantic coherence and argumentation logic.
  Keywords: large model representation learning; knowledge reasoning; English writing scoring; human-computer collaborative; explainable artificial intelligence.
  DOI: 10.1504/IJICT.2026.10080361
   
  

• Open Access

  Real-time capture and simulation system of gymnastics motion based on 3D vision transformer and multi-objective optimisation ( Free Full-text Access )

  by Qichang Xu 
  Abstract: Existing gymnastics motion capture and simulation methods struggle with high-dynamic movements, rapid joint changes, and unnatural simulations. To address these pain points, we propose an end-to-end intelligent gymnastics movement analysis framework integrating deep learning spatiotemporal representation with 3D deformable transformers. We introduce a 3D deformable attention mechanism for limb tracking and a modal feature fusion strategy combining RGB and bone pose data. Additionally, a multi-objective optimisation framework with heuristic rules and parallel computing improves simulation authenticity and efficiency. Experimental results demonstrate high accuracy: the average 3D coordinate error for difficult churning is 35.6 mm, key pose recognition is 78%, and swivel tracking accuracy reaches 91.2%. The real-time tracking delay is as low as 12.3 ms, significantly outperforming traditional methods.
  Keywords: 3D vision transformer; multi-objective optimisation; gymnastics motion capture; action simulation; real-time performance.
  DOI: 10.1504/IJICT.2026.10080363
   
  

• Open Access

  Construction of cross-media semantic relevance model for college English teaching based on output-driven hypothesis and two-way attention feature learning ( Free Full-text Access )

  by Liping Deng 
  Abstract: With the wide application of multimedia technology in English teaching, how to effectively correlate the semantic information of cross-media data (such as text, audio and images) has become a key challenge. This paper aims to construct a cross-media semantic association model for college English teaching (ODBA-CMSA) based on output-driven hypothesis and bidirectional attention feature learning to improve the accuracy and interpretability of grading. The model optimises semantic representation through multi-modal embedding, bi-directional attention mechanism, and output-driven loss. The test results show that the model is significantly better than the baseline in accuracy rate (89.5%) and F1-score (89.0%), and its robustness (accuracy rate only decreases by 5.1% under noise) and practicability (user satisfaction 4.4 points) are outstanding, which verifies its effectiveness in complex teaching environment. This study not only provides an efficient semantic association solution, but also enhances the transparency of the model through interpretability design, which provides a new idea for the development of intelligent teaching system.
  Keywords: output drive; two-way attention; cross-media; college English; semantic association.
  DOI: 10.1504/IJICT.2026.10080438
   
  

• Open Access

  Neural ordinary differential equation waveform modelling for multidimensional analysis of singing technique ( Free Full-text Access )

  by Yilin Zhao 
  Abstract: The precise analysis of singing techniques is of vital importance for intelligent vocal education. The existing methods process singing by segmentation, which destroys the characteristics of continuous changes of techniques, resulting in poor recognition of dynamic techniques such as vibrato. To address this issue, this paper proposes a neural ordinary differential equations waveform modelling network. This framework treats singing as a continuous process and simulates the physical laws of vocal cord vibration, enabling the model to more naturally capture the subtle changes in techniques. Experiments show that the comprehensive recognition accuracy of this method reaches 0.874, and the comprehensive evaluation index reaches 0.932. Compared with the current optimal method, it has improved by 4.2% and 3.1% respectively. The recognition of trills has also improved by 12.3%. This research provides high-precision and interpretable technical support for intelligent vocal education.
  Keywords: neural ordinary differential equations; singing technique; physics-informed machine learning; continuous-time dynamic modelling.
  DOI: 10.1504/IJICT.2026.10080439
   
  

• Open Access

  Cyber-physical system-based adaptive PID power regulation for cooling tower pumps ( Free Full-text Access )

  by Anhui Ma 
  Abstract: This paper presents an information and communication technology (ICT) enabled adaptive PID power regulation framework for cooling tower pumps. A cyber-physical system (CPS) architecture integrates real-time sensor data through industrial IoT communication links, enabling bidirectional data exchange between field devices and a remote adaptive tuner. The tuner dynamically updates PID gains using streaming operational data under practical network constraints, including time-varying delay and packet loss. Unlike conventional methods, the proposed framework embeds physical knowledge into networked control loops, ensuring reliable performance in non-ideal communication environments. Experimental results demonstrate that compared to state-of-the-art benchmarks, the ICT-based control reduces system response time by 18.5% under variable cooling loads and lowers total pump power consumption by 12.3%. Furthermore, statistical significance analysis confirms the robustness of the proposed approach against both extreme environmental disturbances and adverse network conditions.
  Keywords: cyber-physical system; CPS; adaptive PID; cooling tower pump; industrial IoT.
  DOI: 10.1504/IJICT.2026.10080440
   
  

• Open Access

  Exposure dose reconstruction-guided imaging screening of tuberculosis close contacts using IoT-based localisation ( Free Full-text Access )

  by Yue Yan 
  Abstract: In screening close contacts of tuberculosis patients, current approaches classify contacts as close or general based solely on exposure duration, ignoring environmental ventilation, contact distance, and spatial distribution. Many contacts with subclinical lesions are missed while low-risk individuals undergo unnecessary imaging. This paper proposes the Exposure weighted lesion profiling framework, which uses internet of things positioning data to reconstruct individualised exposure doses and guide computed tomography screening. A prospective cohort of 214 close contacts showed that the area under the curve for predicting subclinical lesions using ventilation adjusted exposure doses was 0.91, superior to 0.68 of traditional binary classification. Prioritising screening of the top 20% of contacts by this framework increased lesion detection rate from 8.2% to 23.6% and reduced number needed to screen to detect one positive case from 12.6 to 5.9. This framework fills the gap from binary contact classification to continuous dose-response models.
  Keywords: contact investigation; exposure dose; subclinical tuberculosis; internet of things geolocation.
  DOI: 10.1504/IJICT.2026.10080479
   
  

• Open Access

  Personalised recommendation algorithm and platform design of engineering ethics case teaching resources incorporating big data analysis technology ( Free Full-text Access )

  by Weifeng Liang 
  Abstract: As engineering ethics teaching faces the contradiction between overload of case resources and increasing demand for individualisation, this study aims to develop a system that can provide accurate and explainable recommendations. By constructing the KGDP-RM model, which integrates knowledge graphs with differential privacy protection, we employ graph neural networks to learn case semantic association, and attention mechanism and Bayesian network are introduced to realise dynamic preference capture and causal explanation. The accuracy rate of the model on the EdNet dataset reaches 0.821, the F1-value is 0.813, representing an improvement of 0.038 over the best-performing baseline. Its performance degrades by only 0.024 in a 10% noise environment. The user relevance and satisfaction scores reach 4.3 and 4.1 points respectively. In addition, the ablation test confirms that the knowledge graph module contributes the most. Overall, the multi-technology integration framework proposed in this study effectively solves the problems of sparsity, privacy security and interpretability in teaching resource recommendation.
  Keywords: engineering ethics case teaching; personalised recommendations; knowledge graph; differential privacy; interpretability.
  DOI: 10.1504/IJICT.2026.10080173
   
  

• Open Access

  Refining cross-modal retrieval via two stage graph hashing with latent subspace alignment ( Free Full-text Access )

  by Yuhang Fang, Chaoye Li, Tao Liao, Zhaohui Liao 
  Abstract: Cross-modal hashing facilitates efficient retrieval across diverse modalities by mapping shared semantics into a compact binary space. Despite significant progress, conventional approaches frequently encounter two bottlenecks: 1) a preoccupation with Euclidean geometry that overlooks intrinsic graph structures, making them susceptible to data noise/outliers; 2) a reliance on a single latent subspace which fails to capture the complexity of multi-modal distributions. To circumvent these limitations, we introduce refining crossing modal retrieval via two stage graph hashing (RCRT). In this initial phase, collective matrix factorisation is integrated with a customised graph convolutional network (GCN) to extract modality-specific latent representations. By merging Euclidean features with non-Euclidean structural priors, this stage robustly mitigates the impact of anomalous data points. Subsequently, a global approximation strategy aligns these heterogeneous subspaces to distil high-level common semantics, followed by the derivation of discrete hash codes that preserve semantic consistency. In the second phase, a linear hash function is optimised via a local similarity preservation mechanism, ensuring the fine-grained topology is maintained within the Hamming space. Across multiple benchmark datasets, RCRT demonstrates superior performance over all current SOTA methods.
  Keywords: discrete optimisation; crossing modal retrieval hashing; graph convolutional.
  DOI: 10.1504/IJICT.2026.10080279
   
  

• Open Access

  Characteristics of bel canto resonance based on multi-scale features ( Free Full-text Access )

  by Xuemei Li 
  Abstract: Traditional studies on bel canto resonance rely mainly on subjective auditory evaluation, lacking standardised quantitative methods. It is difficult to accurately capture the internal correlations and variation patterns of multi-scale resonance characteristics, it also makes the optimisation of bel canto teaching and singing skills lack of scientific quantitative basis. To address this issue, this paper proposes a dedicated multi-scale feature extraction algorithm and an attention-based feature fusion algorithm. Combined with acoustic experiments on soprano singing signals, we conduct a systematic quantitative study. Results show that this method can effectively extract multi-scale features of bel canto resonance. The recognition rate of the fused features for bel canto vocal types is significantly higher than that of the single feature, and the average recognition rate of all vowels is more than 90%, which can accurately reveal the characteristic distribution of bel canto resonance.
  Keywords: bel canto; resonance characteristics; multiscale feature; acoustic quantitative analysis.
  DOI: 10.1504/IJICT.2026.10080152
   
  

• Open Access

  An attention-enhanced knowledge graph approach for personalised English vocabulary recommendation ( Free Full-text Access )

  by Kang Gao 
  Abstract: Personalised English vocabulary recommendation remains difficult because learner behaviour changes quickly while lexical relations are highly structured. To address this issue, this study proposes an attention-enhanced knowledge graph recommendation model for vocabulary learning. First, learner interaction sequences are encoded to capture recent review rhythm, repeated errors, and short-term preference shifts. Then, lexical relations are organised through a vocabulary knowledge graph to preserve semantic, derivational, and pedagogical connections among words. Finally, both sides are fused in a personalised scoring stage to generate the next-word recommendation list. Experimental results show that the proposed method achieved 0.742 Precision, 0.694 Recall, 0.781 NDCG, and 0.726 MRR on Dataset A, and remained consistently superior on Dataset B. The model shows strong ranking accuracy, stable recommendation behaviour, and good educational suitability.
  Keywords: personalised vocabulary recommendation; knowledge graph; attention mechanism; English learning.
  DOI: 10.1504/IJICT.2026.10080151
   
  

• Open Access

  A communication-efficient distributed sensor network framework for real-time tourist flow monitoring and control ( Free Full-text Access )

  by Jingjing Yan 
  Abstract: With the vigorous development of the global experience economy, the problem of overloading tourist traffic in popular scenic areas has become increasingly prominent, posing a huge threat to public safety. The traditional centralised cloud computing monitoring architecture, when dealing with massive sensing data, has bottlenecks such as communication congestion, high end-to-end latency, and high energy consumption at the edge nodes. This study proposes a communication-efficient distributed sensor network framework (CEDF), adopting a three-layer collaborative architecture of edge-fog-cloud, and downloading the feature extraction task to the edge nodes, while introducing an event-triggered communication protocol based on adaptive dynamic thresholds and a distributed state consensus algorithm. In a real scenic area with an area of approximately 25 square kilometres, 500 heterogeneous sensor nodes were deployed for on-site verification. The results showed that compared with the traditional continuous periodic transmission (CPT) and standard edge clustering (SEC) architectures, CEDF reduced the total network data communication overhead by over 90%, compressed the end-to-end closed-loop control delay to within 30 milliseconds, and increased the average tourist circulation efficiency by 34%. This research provides an engineering paradigm for the large-scale deployment of smart tourism systems.
  Keywords: distributed sensor network; event-triggered communication; real-time tourist flow monitoring; edge computing; smart tourism; congestion control; standard edge clustering; SEC; continuous periodic transmission; CPT.
  DOI: 10.1504/IJICT.2026.10080257