Forthcoming Articles
International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (19 papers in press) Regular Issues
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
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
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 ARITAs 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
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 103, 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
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
Abstract: Accurately predicting an athletes 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
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
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
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
Abstract: The long-term safety, operational stability and economic efficiency of grid-connected wind power generation systems are of great significance to the accurate and continuous monitoring of wind turbine blade operating conditions. This paper proposed a new wind turbine blade acoustic monitoring scheme based on adaptive noise reduction, ulti-domain feature enhancement, and a two-stage fault detection method for non-contact blade condition assessment. The system directly addresses the critical technical gap of insufficient early fault capture capability and poor anti-interference performance of existing monitoring methods in complex and dynamic wind farm operating environments. Rigorous experimental validation shows that the system achieves 99.2% fault recognition accuracy, 47.6% improvement in anti-noise performance, 0.8-second early fault detection delay, and maintains a low false alarm rate of 1.2% under varying field operating conditions. Keywords: wind turbine blade; acoustic monitoring; fault detection; signal processing. DOI: 10.1504/IJICT.2026.10080614
Abstract: In the context of the increasing importance of interactive mobile English translation teaching and aiming at the real-time and accuracy challenges of quality evaluation, this paper proposes an innovative model based on particle swarm optimisation-neural network (PSO-NN). The model dynamically adjusts the hyperparameters of the LSTM-attention network through adaptive multi-scale particle swarm optimisation (AMPSO), combines attention mechanism to enhance time series feature extraction, and adopts a unified preprocessing process to process multi-source data. The experimental verification results show that the F1-score of the model reaches 0.895, the accuracy rate is 89.5%, and the AUC is 0.951 in the performance comparison, which is significantly better than the baseline. In the robustness test, ΔF1 of the model only decreases by 4.1% under noisy data. The practical test shows that the inference time is 50.1 ms, the memory occupation is 130.5 MB, and the efficiency and real-time performance are considered. This model effectively improves the evaluation accuracy and anti-interference ability, and its innovation lies in the collaborative optimisation of AMPSO's multi-scale mutation strategy and neural network components, which provides a reliable tool for mobile teaching. Keywords: particle swarm optimisation-neural network; PSO-NN; interactive; English translation; quality of teaching; evaluation model. DOI: 10.1504/IJICT.2026.10080661
Abstract: In the process of agricultural digital transformation, field sensor and remote sensing data are often mixed with interfering factors such as soil heterogeneity and management differences, disconnecting the causal chain between digital investment and new quality productivity. This paper proposes a dual causal inference framework, the digital transformation driven new quality productive forces analysis framework, integrating propensity score weighting and double machine learning to isolate the net effect of digitalisation from mixed data. Experiments on two public datasets with over 67,000 samples show that adopting digitalisation increases the new quality productivity index by 21.6% (95% confidence interval 17.8%-25.4%). The framework improves causal entropy from 0.42 to 0.67 and reduces neighbourhood confounding degree from 0.31 to 0.13, indicating a residual confounding reduction of approximately 40%-58%. This method fills the causal attribution gap and provides a quantifiable path for distinguishing correlation from causation. Keywords: causal inference; digital agriculture; new quality productive forces; double machine learning; DML. DOI: 10.1504/IJICT.2026.10080615
Abstract: With the rapid application of transformative technologies such as federated learning and generative AI in consumer terminals and digital commerce, there is a growing demand for decision-support frameworks that are intelligent, interpretable, and privacy-protective. In the e-commerce field, consumer satisfaction is increasingly dependent on data-driven product evaluation mechanisms rather than isolated design attributes. This study proposes a grey multi-criteria decision-making (G-MCDM) framework integrated with entropy weighting to evaluate consumer satisfaction with various types of cosmetic packaging, including bottles, jars, hoses, and aerosol cans. The model integrates entropy-based objective weighting methods and grey system theory, effectively addressing the uncertainty, heterogeneity, and information incompleteness present in large-scale consumer feedback data. Empirical analysis based on e-commerce platform data shows that tube packaging achieves the highest overall consumer satisfaction among all tested packaging forms. Beyond the specific application scenario, this framework can serve as an interpretable decision-making module, embedded in the federated learning architecture to support decentralised consumer data analysis and generative AI-assisted packaging design optimisation. This research improves the transparent and scalable evaluation mechanism suitable for AI-enabled consumer scenarios, and clarifies the core role of hybrid decision intelligence models in supporting the next-generation digital e-commerce ecosystem. Keywords: federated learning applications; privacy-preserving computation; intelligent packaging design; grey multi-criteria decision making; hybrid decision intelligence. DOI: 10.1504/IJICT.2026.10080635
Abstract: The optimisation of enterprise talent structure faces challenges of high-dimensional and multiple constraints. Existing meta-heuristic methods mostly focus on a single objective and are unable to balance skills matching, generational balance, and strategic resilience. This paper proposes a person-job optimisation model based on particle swarm optimisation, which objectively assigns weights using the entropy weight method and introduces a penalty function to handle multi-objective conflicts. On a real human resource dataset from an enterprise, the area under the curve of person-job optimisation model based on particle swarm optimisation reached 0.94, which was 14.6% higher than that of traditional particle swarm optimisation; the talent-job matching degree increased from the initial 0.51 to 0.88, and the generational coupling coefficient reached 0.82. This model fills the gap in methods for the collaborative optimisation of multi-dimensional talent structures under the requirements of new quality productivity. Keywords: new quality productive forces; optimisation of talent structure; particle swarm optimisation match degree. DOI: 10.1504/IJICT.2026.10080619
Abstract: High penetration of photovoltaic power poses a dual challenge to asynchronous power grids: insufficient inertia support and output fluctuations. Traditional strategies struggle to coordinate the complementary characteristics of photovoltaic, energy storage, and load resources on a millisecond-to-second timescale. This paper proposes a source-load-storage coordinated frequency regulation strategy: first, photovoltaic fluctuations are isolated using adaptive variational modal decomposition; then, model predictive control is employed to uniformly allocate the load to energy storage, photovoltaic derating, and shiftable loads. Simulation results on an enhanced 39-node IEEE system demonstrate that the peak frequency deviation is reduced from 0.32 Hz to 0.12 Hz, representing a 62.5% improvement; the fluctuation rate of photovoltaic grid-connected power decreases from 15.8% to 12.4%; and the average daily charge-discharge cycles of energy storage are reduced from 12 to 7. This strategy leverages the high inertia characteristics of asynchronous grids to create a time window for slow-responding loads. Keywords: high-inertia asynchronous grid; source-load-storage coordination; model predictive control; variational modal decomposition; frequency damping. DOI: 10.1504/IJICT.2026.10080616
Abstract: Existing design tools can recommend harmonious colour palettes yet fail to quantify the emotions a scheme evokes, leaving designers to rely on intuition rather than measurable feedback. This paper introduces a multimodal deep neural framework that continuously measures the affective qualities of design colours. The model fuses hierarchical visual features from design images with semantic intentions extracted from designer descriptions via a cross-attention mechanism. A perceptual colour-difference regularisation loss ensures physically plausible predictions, enforcing smooth emotional transitions consistent with human perception. Tested on three datasets, our method achieves a root-mean-square error of 0.0873 for emotion regression, a relative gain of 16.2% over the strongest baseline, and yields ratings statistically indistinguishable from those of professional designers. The framework bridges colour physics and design cognition, enabling emotion-aware design assistance. Keywords: colour emotion quantification; multimodal deep learning; design cognition; physical constraints; affective computing. DOI: 10.1504/IJICT.2026.10080617
Abstract: There is a systematic deviation between the disciplinary levels of university regulations and judicial judgements, but existing research lacks a quantitative identification method for the deviation patterns. This paper proposes a deviation degree analysis model based on hierarchical clustering. By constructing 15 indicators such as absolute deviation degree, relative deviation degree, and proportional deviation degree to form a multi-dimensional feature vector, a cluster analysis is conducted on the publicly available administrative judgement data (N = 658). The results show that the deviation patterns can be classified into five types, among which the upward deviation type accounts for 28.3%, with a silhouette coefficient of 0.76. Compared with the K-means clustering method, the method improves the intra-class compactness by 12.4%. This model provides a quantifiable analysis tool for the standardisation of university disciplinary discretion. Keywords: school disciplinary punishment hierarchy; judicial judgement deviation; hierarchical clustering; proportionality; quantitative discretion. DOI: 10.1504/IJICT.2026.10080620
Abstract: The sinking efficiency of dual-motor driven vibration pile hammers is significantly affected by soil nonlinear hysteretic characteristics, causing synchronisation difficulties. To address this issue, this paper presents a simulation study that establishes an electromechanical coupling dynamic equation based on the Bouc-Wen model and proposes a nonlinear synchronisation control algorithm. The simulation results show that soil nonlinearity causes periodic phase difference fluctuations of ±8°, with synchronisation time prolonged by 32.5% compared to the linear model. The nonlinear synchronisation control algorithm reduces synchronisation time to 3.2 seconds under soft soil conditions, converges the phase difference to within ±2°, and achieves 26% improvement over master-slave control. The sinking displacement decreases non-linearly with increasing soil stiffness, reducing by 41.2% when stiffness increases from 5 MN/m to 20 MN/m. This study reveals the quantitative relationship between soil nonlinearity parameters and synchronisation performance, providing a theoretical basis for intelligent control. Keywords: vibrating pile hammer; soil nonlinearity; dual-machine synchronisation; Bouc-Wen model. DOI: 10.1504/IJICT.2026.10080618
Abstract: Vocal fold fatigue can cause organic lesions for a long time, and monitoring its evolution process is of great significance. The existing research mode single response is slow, static modelling is difficult to tolerate dynamic, poor interpretability and weak generalisation. This paper proposes physics-informed joint simulation and identification framework. The framework uses mathematical modelling of fatigue changes, simultaneous analysis of sound and vibration signals, and introduces the principle of sound to improve the authenticity of the signal, so as to realise the mutual optimisation of generation and recognition. Experimental results show that the signal simulation correlation coefficient of the framework is 0.92, the fatigue prediction determination coefficient is 0.86, and the F1 score is 0.89, which is significantly improved compared with the variational recurrent neural network. Keywords: vocal fold fatigue; timing simulation; multi-modal fusion; physical information deep learning; generative adversarial network. DOI: 10.1504/IJICT.2026.10080621 |
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