Forthcoming Articles
International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (21 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: 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 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
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
Abstract: This study constructs a multi-source ESG rating system via entropy weight-TOPSIS and embeds ESG factors into a modified Z-score model. Using Chinese A-share data from 2015 to 2024, a 0.1 ESG score increase raises Z-score by 0.238, confirming risk mitigation, especially for non-state-owned and small-scale, high-competition firms where prediction errors decline by over 29%. To capture inter-firm risk transmission, an explicit-implicit hybrid association graph neural network integrates observable supply chain and equity linkages with attention-based latent correlations. It achieves the lowest MAE (0.81) and RMSE (2.07) against XGBoost, random forest, and transformer, with Diebold-Mariano significance (p < 0.01) and a Brier score of 0.063 (0.058 after temperature scaling). This research provides a dynamic early-warning tool for sustainable financial risk monitoring and differentiated ESG strategy allocation. Keywords: sustainable development; financial risk warning; multi-source data fusion; explicit-implicit association network; dynamic risk monitoring. DOI: 10.1504/IJICT.2026.10080962
Abstract: In view of the lack of adaptability and refined risk control of the DQN-based budget allocation model across cycles and contexts, this paper proposes a dynamic intelligent financial decision-making model integrating VaR and CVaR. The state space consists of 12-dimensional dynamic variables, which include macroeconomic cycles and market risk indices; the action space defines departmental budget proportions (minimum step 5%) and adjustable risk reserves. The dual-channel reward embeds penalties for risk overflow and budget deviation, balancing economic benefits (0.45) and risk control (0.35). The model is trained on 320 sets of eight-year enterprise financial data using MSE loss and Adam optimiser (learning rate 0.001, discount factor 0.95), achieving allocation accuracy of 92.1%, 87.5%, and 82.3% in stable, mild, and severe periods, and controlling risk overflow at 3.2%, 8.7%, and 14.5% respectively, outperforming traditional DQN. Cross-cycle adaptive budget optimisation is enabled as the loss converges to 0.08 within 200 rounds. Keywords: deep Q-network; dynamic budget allocation; financial risk control; intelligent decision-making; cross-cycle adaptation. DOI: 10.1504/IJICT.2026.10080963
Abstract: Traditional assessment methods relying on scales or single observations are difficult to accurately depict the true teaching level of teachers in complex classroom situations. Therefore, based on generative AI and multimodal data, a method for constructing TPACK knowledge integration portraits of university teachers was studied. A three-dimensional portrait model covering four dimensions of knowledge, behaviour, ability, and development was designed based on the TPACK theory, aiming to systematically depict the cognitive structure, practical performance, decision-making literacy, and growth trajectory of teachers. By extracting behavioural features from multimodal data such as teaching videos, design texts, and platform logs, and quantifying and clustering them, a standardised evaluation input is constructed. On this basis, a hybrid evaluation model combining generative AI (big language model) and discriminative model (gradient boosting decision tree) is used to achieve objective and efficient quantitative scoring. Through deep semantic understanding, teaching behaviours are inferred and integrated portraits are generated. And it has been proven that it has advantages in quantitative consistency, situational matching, and growth trajectory fitting, effectively verifying its scientific, accurate, and developmental nature. Keywords: generative AI; multimodal data; university teachers; TPACK; portrait construction. DOI: 10.1504/IJICT.2026.10080973
Abstract: Under a high proportion of grid-connected renewable energy, source-side photovoltaic/wind power fluctuates randomly while load-side flexible loads and storage exhibit heterogeneous response characteristics, making system regulation assessment face the dilemma of unknown distributions. This paper integrates Wasserstein distributionally robust optimisation with multi-agent simulation to construct a collaborative modelling framework for source-load heterogeneous resources, filling the gap between overly conservative robust optimisation and distribution-dependent stochastic programming. Experiments on public datasets show that distributed robust optimisation-driven multi-agent simulation reduces the regulation power mismatch rate from 18.7% to 9.3%, increases the confidence self-regulation elasticity coefficient by 42% (from 0.38 to 0.54), and maintains schedulable potential coverage deviation within 4.1% even when the sample size is only 30% of the full distribution. This provides a quantifiable simulation tool for scheduling heterogeneous resources under high uncertainty. Keywords: distributionally robust optimisation; multi-agent simulation; heterogeneous resources; Wasserstein ambiguity set; regulation capability. DOI: 10.1504/IJICT.2026.10080991
Abstract: This study proposes an English metaphor identification and paraphrase generation model that integrates a generative adversarial network with an optimised compression algorithm to address contextual dependence and semantic ambiguity. The model employs adversarial training to enhance metaphor representation, generation, and discrimination, combined with a bidirectional transformer encoder for deep semantic encoding, and BiLSTM with attention for feature fusion. Principal component analysis and extreme learning machine are further integrated to improve training and inference efficiency. Experiments on the VU Amsterdam metaphor dataset and metaphor usage finder dataset demonstrate a recognition accuracy of 92.03%, latency of 13.2 ms, and 97.11% agreement with human judgments. On the metaphor understanding challenge and contemporary news headlines datasets, the model achieves a lexical overlap ratio of 0.987 with a generation time of 100.4 ms, demonstrating high accuracy, efficiency, and paraphrase quality. Keywords: generative adversarial network; GAN; optimised compression algorithm; metaphor identification; paraphrase generation; principal component analysis; PCA. DOI: 10.1504/IJICT.2026.10080992
Abstract: Currently, when multimodal models handle high-level semantic concepts such as ideological literacy value orientation, they often encounter the problem of fine value features being lost due to modality gaps, and are unable to effectively distinguish between factual statements and value judgements. This paper proposes a multimodal semantic representation method called literacy-aligned multimodal semantic representation based on difference-aware contrastive learning. By dynamically adjusting the contrast intensity of the text-image pairs and combining hierarchical regularisation, it constructs a progressive representation structure from the knowledge layer to the value layer in the shared space. Experiments on two public educational multimodal benchmarks show that this method improves the accuracy of text-image matching from 75.8% to 83.2%, and reduces the modality gap ratio from 1.02 to 0.87, significantly enhancing the discriminative ability for cross-modal value orientation. Keywords: multimodal representation; contrastive learning; value orientation; semantic alignment. DOI: 10.1504/IJICT.2026.10080993
Abstract: In this study, an adaptive teaching strategy optimisation model of artificial intelligence (AI) based on cognitive diagnosis (CD) and cognitive perception reinforcement learning (CA-RL) is proposed to solve the problem of weak correlation between cognitive assessment and subsequent teaching decisions. First, learner cognitive states are constructed from item-knowledge attribute mappings and learning behaviour sequences. Second, the cognitive state is introduced into the reinforcement learning (RL) process as a structured decision input. Finally, a diagnosisdecision dual-loop mechanism is designed to coordinate cognitive state updating and teaching strategy optimisation. Experiments are conducted on the EdNet dataset and a simulated teaching environment. Results show that accuracy increases by 5.1%, the stability index rises from 0.62 to 0.71, cognitive fluctuation decreases from 0.084 to 0.039, average mastery reaches 0.38, target achievement time decreases to 27 steps, redundant teaching drops to 0.18, and cumulative return reaches 189.2. CA-RL supports stable and interpretable adaptive teaching optimisation. Keywords: adaptive teaching; cognitive diagnosis; CD; reinforcement learning; RL; cognitive-aware reinforcement learning; CA-RL; intelligent education system. DOI: 10.1504/IJICT.2026.10081072
Abstract: Intangible cultural heritage faces communication difficulties in the digital age. Traditional static display is difficult to attract audiences. Augmented reality technology provides new possibilities, but existing applications have shortcomings in tracking stability and immersion. This study develops a mobile augmented reality application for Xiangtan paper shadow puppetry, improves the accuracy of virtual content superposition by improving the image recognition algorithm, and designs a multi-layer screen to create a three-dimensional sense of depth, so that users can perceive the scene level on mobile devices. Experimental results show that the success rate of image recognition is 94.2% after optimisation, which is 15.7% higher than that of the traditional method. The user usability score was 82.1, which exceeded the good standard by 14 points. This study provides a reusable technical scheme and empirical basis for the digital protection of intangible cultural heritage. Keywords: intangible cultural heritage; mobile augmented reality; storytelling game; paper shadow puppetry; user experience evaluation. DOI: 10.1504/IJICT.2026.10081137
Abstract: Existing vocal melody generation models often neglect long-term structural modelling, causing pitch and rhythm discontinuities in phrases. Specifically, in segments over eight bars, tonality and motif development frequently become incoherent. This article propose cross-attention diffusion for vocal melody, which uses a piano roll representation and embeds a structural condition interaction module at the denoising networks bottleneck and decoder front-end to adaptively guide generation from global melodic priors. Evaluated on the musical instrument digital interface and audio edited for synchronous tracks and organisation and lakh musical instrument digital interface datasets, our model achieves a pitch accuracy rate of 0.856, a rhythm consistency rate of 84.32%, and a mean opinion score of 4.01, outperforming the optimal baseline polyffusion by 0.037, 3.87 percentage points, and 0.33, respectively. This approach effectively fills the structural controllability gap in diffusion-based symbolic music generation and provides a new technical path for intelligent vocal melody creation. Keywords: diffusion model; cross-attention; vocal melody; symbolic music; piano roll. DOI: 10.1504/IJICT.2026.10081138
Abstract: The distribution characteristics of geotechnical fractures significantly impact engineering stability. However, geotechnical fractures exhibit complex morphology with uneven roughness, and existing research cannot match fracture features across different scales, leading to biased results in morphological transformation. To address this, this paper first performs preprocessing on the original geotechnical images. Subsequently, a twin network architecture is employed, where both normal rock images and fracture images serve as network inputs. An enhanced ResNet network extracts multi scale features from the images. A designed cross-correlation matching module determines the spatial relationships between corresponding feature maps. Concurrently, precise spatial feature alignment is achieved through global and local deformation offset calculation modules. Subsequently, precise morphological transformations are achieved through a global homomorphic deformation module and a local image grid deformation module. Simulation results demonstrate that the proposed method reduces deformation prediction errors by at least 27.78%, validating its effectiveness. Keywords: geotechnical engineering fracture; deep morphological transformation; ResNet-50-based simulation modelling; feature extraction; twin network. DOI: 10.1504/IJICT.2026.10081139
Abstract: When virtual power plants integrate dispersed distributed energy sources, they encounter the contradiction of differences in resource response speed and mismatch between the multiple time scales of scheduling instructions. Existing control strategies either fix the resource grouping or separate the day-ahead planning from the intraday correction, resulting in cumulative tracking deviations. This paper proposes partition-hierarchy collaborative, which solves the problem of insufficient coordination between spatial dispersion and time coupling through dynamic partition aggregation and three-level nested optimisation. Simulation results show that the average regulation error of the partition-hierarchy collaborative strategy drops to 0.144 MW, a 16.4% reduction compared to the fixed partition scheme; the defined regulation credibility index reaches 0.87, an increase of 22.5% compared to the scheme without intraday correction. Experiments prove that the collaborative mechanism of dynamic partitioning and rolling correction can simultaneously improve the tracking accuracy and response reliability of the virtual power plant. Keywords: virtual power plant; multi-time scale; synergistic regulation; dynamic zoning. DOI: 10.1504/IJICT.2026.10081140
Abstract: Millions of Internets of Things devices in water treatment plants and power grids generate massive streams of sensor data. A delay of only a few seconds in detecting an attack can cause environmental disasters or halt production. However, existing methods either upload all private data to a central cloud, which consumes high bandwidth and raises privacy concerns, or ignore the physical connections among devices, resulting in frequent missed detections. To overcome these limitations, this paper proposes a federated self-distillation graph neural network that preserves data locally on each device, models device relationships as graphs, and shares both model parameters and soft labels without exposing raw data. Experiments on the real-world Secure Water Treatment dataset demonstrate that our method achieves an F1 score of 0.937, outperforms the best baseline by 5.2%, reduces communication rounds by 74 percent, and detects attacks within 22 seconds. Keywords: internet of things; IoT; edge anomaly detection; federated learning; graph neural network; GNN; self‑distillation. DOI: 10.1504/IJICT.2026.10081141
Abstract: Detecting academic anomalies in higher education is crucial for early intervention in students academic difficulties. Existing methods struggle to simultaneously capture the dynamic evolution of learning behaviour over time and the complex relationships among various entities, such as students, courses, and instructors. To address this, this paper introduces a framework that integrates an adaptive time-series encoding module with a heterogeneous graph attention network. Experiments on real-world university data demonstrate that the proposed method achieves an area under the curve of 0.913, representing a 6.2% improvement over the best baseline; its precision is 0.847 and recall is 0.835, both outperforming the comparison models. The method does not require predefined fixed time windows or simplified relationship types, and can directly learn discriminative features from raw trajectories and heterogeneous networks. Keywords: academic performance monitoring; adaptive time-series modeling; heterogeneous graph attention. DOI: 10.1504/IJICT.2026.10081142
Abstract: In online learning, learners behavioural sequences are highly dynamic. Existing prediction models often treat behaviours at different time points as independent features, ignoring temporal dependencies, which leads to delayed risk identification and poor interpretability. This paper proposes the interpretable prediction and intervention framework for online learning. By learning longterm dependency patterns in behavioural trajectories, the framework provides early warning three weeks before a risk occurs. Experiments show that the method raises the risk identification recall rate to 89.1%, which is 7.2 percentage points higher than the baseline bidirectional long shortterm memory model. The opening rate of intervention push messages reaches 73.8%, significantly above the typical level in similar studies (less than 50%). This research fills the gap of interpretable temporal attribution in online learning warning, enabling teachers to trace back to which week and which behaviour pattern triggered the alert, and thus formulate targeted intervention strategies. Keywords: online learning; time series prediction; explainable artificial intelligence; educational intervention. DOI: 10.1504/IJICT.2026.10081143
Abstract: Government procurement continues to grow in scale, while bid-rigging grows more covert and organizedposing a persistent challenge for audit supervision. Existing methods rely largely on manual rule screening or single-source mining, which fail to reveal implicit links among bidders across data sources, leading to high miss rates and weak interpretability. This paper proposes a bid-rigging risk identification mechanism built on multi-source knowledge graphs. We first integrate heterogeneous audit data through a cross-source entity alignment method to construct a procurement audit knowledge graph. We then design an association-aware heterogeneous graph representation model that aggregates structural links via relation-level attention, jointly encodes quotation and temporal behavior features, and fuses them through a gating mechanism. Finally, a gang-level scoring and joint optimization algorithm enables end-to-end inference from individual links to collusion risk. Experiments show our method attains an F1 of 0.913, exceeding the best baseline by 6.2 points. Keywords: government procurement audit; knowledge graph; bid-rigging; graph representation learning; risk identification. DOI: 10.1504/IJICT.2026.10081144 |
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