International Journal of Information and Communication Technology (11 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

  Automatic graphic-text composition for graphic design using diffusion models and layout optimisation ( Free Full-text Access )

  by Ju Ling 
  Abstract: In graphic design practice, traditional manual iteration is time-consuming, while existing generative artificial intelligence tools often fail to accurately capture design semantics, layout rules, and user intentions, leading to outputs that deviate from professional requirements. This paper presents a graphic-text synthesis model based on diffusion models and layout optimisation. The model consists of three parts: an intent encoder that transforms natural language into design constraints, a layout generator with spatial optimisation to prevent overlap and boundary violations, and a diffusion-based style transfer module for consistent content filling. Results on the public Peking University layout dataset and Crello dataset show that the proposed model outperforms baselines, achieving an 8.2% improvement in F1-score and an area under the curve of 0.93. All metric improvements are statistically significant (p < 0.01). This approach reduces design iterations and offers a new direction for human-machine collaborative creation.
  Keywords: generative AI; graphic design; diffusion model; layout optimisation; text-image synthesis.
  DOI: 10.1504/IJICT.2026.10080869
   
  

• Open Access

  UrbanGreen-LiteFormer: a lightweight vision model for disease and pest recognition of urban landscape plants in complex urban green-space environments ( Free Full-text Access )

  by Na Li, Xianxian Yu 
  Abstract: The visual context of landscape plant management in urban settings is complex and not like controlled crop datasets. Leaves are often found near roads, buildings, walls, soil, shadows, vehicles, people and other vegetation, and small areas of the image are filled with disease spots and pests. Moreover, healthy leaves are polytypic, which induce a degradation in the performance of the conventional CNN, and complete Transformer models are prohibitively expensive to deploy in the field. This paper proposes UrbanGreen-LiteFormer, a lightweight hybrid vision model for disease and pest recognition in urban green spaces. The ULPDP-12640 dataset contains 12,640 images collected from 42 urban scenes in Nanjing, China, covering 18 disease, pest, and healthy categories. The model integrates an urban texture stem, multi-scale pest-disease block, background-suppressed efficient attention, and edge-calibrated channel mixer. Across five runs, it achieved 92.41% accuracy and 92.05% macro-F1 with 4.26 million parameters and 0.93 GFLOPs, supporting efficient Jetson Nano deployment with calibrated INT8 quantisation for urban pest control.
  Keywords: urban landscape plants; plant disease recognition; pest recognition; lightweight deep learning; hybrid vision model; edge deployment.
  DOI: 10.1504/IJICT.2026.10080898
   
  

• Open Access

  A secure and scalable blockchain framework for enhancing transaction transparency and operational efficiency in banking systems ( Free Full-text Access )

  by Yuan Yuan, Cheng Ju 
  Abstract: The banking industry is going digital very quickly, so it needs frameworks that make sure data is safe, operations run smoothly, and transactions are managed in a clear way. Blockchain technology offers decentralised trust, but combining it with traditional banking systems is hard because of issues like scalability, interoperability, and privacy. Traditional banking systems depend on centralised transaction verification, which raises the chances of single-point failure, data tampering, and delays in processing. Limited real-time auditing and traceability make it even harder to follow the rules and make customers feel safe. This study suggests the Hybrid Consensus driven adaptive blockchain framework (HCABF) as a way to make banking systems safer. The framework combines delegated proof of trust with adaptive byzantine fault tolerance to find a balance between security and scalability. HCABF achieves 23% higher throughput, 22%28% lower latency, 91%94% consensus efficiency and 94% fraud detection accuracy than CAM, CEM and BAM.
  Keywords: blockchain; banking systems; hybrid consensus; transparency; scalability; security.
  DOI: 10.1504/IJICT.2026.10080926
   
  

• Open Access

  Deep learning-driven modelling of acoustic logging signals and identification of complex reservoir fractures ( Free Full-text Access )

  by Jingping Wang, Quanxi Bao 
  Abstract: To address the challenges in fracture identification within complex reservoirs, including traditional methods inefficiency and dependence on precise parameters, and conventional models need for extensive labelled data with limited generalisation, this paper proposes an integrated deep learning method for acoustic logging signal modelling and identification. By constructing a conditional generative adversarial network, high-fidelity acoustic logging signals are efficiently synthesised, achieving a correlation coefficient of 0.92 between simulated and real waveforms and reducing the root mean square error by 30%. Using this generated data for training enhancement, the fracture identification model reaches 94.5% accuracy and 89.2% recall on the test set, significantly outperforming the unaugmented baseline. This approach reduces reliance on scarce and costly labelled data in well logging interpretation and provides a new tool for intelligent reservoir evaluation.
  Keywords: acoustic logging; fracture identification; data augmentation; complex reservoirs.
  DOI: 10.1504/IJICT.2026.10080927
   
  

• Open Access

  Optimisation of English speech recognition using contrastive learning and feature reconstruction ( Free Full-text Access )

  by Jing Ren 
  Abstract: Speech recognition still faces challenges such as insufficient feature discriminability, severe noise interference, and low training efficiency in complex noise and multi accent scenarios. The existing English speech recognition methods mainly suffer from fuzzy feature discrimination boundaries, coarse-grained noise robustness modelling, limited cross accent generalisation ability, and high dependence on annotated data and slow convergence speed during model training. This study proposes a model integrating enhanced contrastive training, feature reconstruction, and parameter optimisation. It introduces a momentum contrast hybrid algorithm to reduce dependence on negative sample quality in traditional contrastive learning, combined with feature reconstruction for better information integrity. A deep neural network-ideal ratio mask module optimises noise suppression. These components are fused for global parameter optimisation. On benchmark datasets, it achieves a feature reconstruction error as low as 0.113, a heterogeneous-to-homogeneous distance ratio up to 3.56, and a maximum SNR improvement of 16.2 dB. Furthermore, it attains a recognition accuracy of 98.23%, a word error rate of 1.03%, and a short-term objective intelligibility score of 0.962. This provides robust technical support for real-time, high-accuracy applications like intelligent assistants and real-time translation.
  Keywords: feature comparison training; FCT; feature reconstruction; FR; supervised contrastive learning; SCL; algorithm optimisation; English speech recognition.
  DOI: 10.1504/IJICT.2026.10080677
   
  

• Open Access

  Impact of personalised learning system based on generative AI on students' higher-order thinking ability and reconstruction of teachers' roles ( Free Full-text Access )

  by Duo Zhang, Mingqiang Wu, Shuping Liu, Qiuyun Song 
  Abstract: To investigate the impact of a generative artificial intelligence (AI) personalised learning system on students' higher-order thinking and teacher role reconstruction, this paper proposes a generative AI-based personalised learning system via DKT and PPO (GAP-DP). That integrates a dynamic concept graph and a metacognitive strategy prompt library. The system addresses the limitations of existing approaches in precise diagnosis, dynamic planning, and human-AI collaboration. It employs a graph-sequence dual-channel to diagnose cognitive states and uses hierarchical rewards to guide instructional pathways aimed at enhancing analysis, evaluation, and creativity. Simulation experiments show that training loss converges to 0.082 and instructional action exploration utilisation reaches 96%. In real classroom settings, diagnostic accuracy for knowledge modules attains 87.7%, students' higher-order thinking skills assessment score increases to 4.25, and teachers' role reconstruction perception score reaches 4.43. By deeply integrating precise diagnosis with metacognitive guidance, GAP-DP effectively empowers students' higher-order thinking development and facilitates teachers' transformation into thinking guides and human-AI collaborators.
  Keywords: generative AI; personalised learning system; deep knowledge tracking; proximal strategy optimisation; higher-order thinking ability; HOTA; teacher role reconstruction; TRR.
  DOI: 10.1504/IJICT.2026.10080794
   
  

• Open Access

  Predicting athletes' physical condition using multimodal machine learning models ( Free Full-text Access )

  by Chenlei Huang 
  Abstract: Accurately predicting an athlete's physical condition is key to optimising training loads and preventing sports injuries; however, monomodal data alone struggles to capture the complex interplay between physiological and motor functions. To address this challenge, this paper proposes a method for predicting physical condition based on a multimodal machine learning model. By integrating physiological signals collected from wearable sensors with biomechanical time-series data obtained from optical motion capture, the method constructs a cross-modal attention fusion mechanism to dynamically integrate heterogeneous features. Experimental results on public multimodal sports datasets demonstrate that the proposed model achieves an area under the curve of 0.94, representing an 8.2% improvement over single-modal baseline models, with an accuracy of 0.91, significantly outperforming traditional fusion methods. The study validates the critical role of multimodal information complementarity in fitness assessment and provides a more interpretable and robust technical pathway for intelligent sports monitoring.
  Keywords: physical condition prediction; multimodal fusion; sports biomechanics.
  DOI: 10.1504/IJICT.2026.10080692
   
  

• Open Access

  Risk perception and traceability in higher education quality management using dynamic knowledge graphs and link prediction ( Free Full-text Access )

  by Hua Zhong 
  Abstract: Higher education management risks are complex and dynamic, making accurate root-cause tracing difficult. This paper proposes the academic risk intelligence and traceability agent (ARITA) model, integrating dynamic knowledge graphs and link-prediction to create a closed-loop framework for risk perception and traceability. Using over 500,000 multi-source data records, we constructed a temporal knowledge graph with over 100,000 entities. ARITA utilises dynamic graph construction, attention-enhanced risk perception, and reinforcement learning-based intelligent agent traceability. Experiments demonstrate ARITA's superiority over the AttBiTi model, achieving an area under the curve (AUC) of 0.952 and an F1-score of 0.891 in risk link prediction. Furthermore, it attains an 85% success rate in tracing risk root causes, effectively restoring conduction paths with an average length of 3.5 hops. This research offers an intelligent, data-driven decision-making paradigm for higher education quality management.
  Keywords: dynamic knowledge graph; link prediction; risk traceability; ARITA model; higher education management.
  DOI: 10.1504/IJICT.2026.10080662
   
  

• Open Access

  Quantum computing-driven portfolio optimisation framework for the intelligent economy ( Free Full-text Access )

  by Leihua Ai 
  Abstract: Portfolio optimisation in intelligent economic systems faces the challenge of portfolio explosion caused by expanding asset scales, making it difficult for classical solvers to obtain high-quality solutions within a limited timeframe. This paper proposes a hybrid quantum-classical optimisation framework that combines the global search capabilities of quantum annealing with the constraint expression capabilities of variational quantum algorithms, while reducing quantum resource requirements through asset graph partitioning. In experiments covering 20 to 120 real-market assets, the hybrid framework achieved an area under the curve of 0.716 for a portfolio of 120 assets, representing an approximately 29% improvement over classical genetic algorithms; it maintained an area under the curve of 0.921 even under a noise level of 10^-3, demonstrating greater robustness than pure quantum annealing methods. The research confirms that the hybrid paradigm is a viable approach for solving large-scale combinatorial optimisation problems on current noisy quantum devices.
  Keywords: quantum computing; portfolio optimisation; smart economy; hybrid quantum framework; asset selection.
  DOI: 10.1504/IJICT.2026.10080663