Forthcoming Articles
International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (59 papers in press) Regular Issues
Abstract: While neural machine translation facilitates effective cross-lingual information transfer, existing lightweight architectures continue to encounter significant challenges in structural modelling precision and decoding stability. They struggle with long-range and syntactic dependencies, shallow attention limits fine-grained structure representation, and compressed architectures often cause semantic drift or repetition. To address these issues, we propose LDCIR-Trans, a lightweight structure-aware translation model. It introduces essential structural priors and a stable decoding mechanism while remaining compact. First, a dependency graph modelling (DGM) module explicitly constructs dependency graphs to supply syntactic cues and compensate for limited global modelling capacity. Second, a dependency-constrained iterative refinement (DCIR) mechanism guides decoding with structure-enhanced signals, enabling progressive correction and reducing semantic deviation. Finally, the lightweight structure-aware decoder (LSAD) employs parameter sharing and distribution calibration to improve representation and stability. Experiments show that LDCIR-Trans achieves high efficiency and significantly outperforms existing lightweight baselines. Keywords: neural machine translation; NMT; dependency graph modelling; DGM; gate-augmented iterative update; lightweight structure-aware decoder; LSAD. DOI: 10.1504/IJICT.2026.10077724
Abstract: Traditional course recommendation methods lack flexibility and perform poorly, as their quality relies heavily on historical datasets making them ill-suited for complex-attribute tasks. This study integrates collaborative filtering (CF) with deep learning, leveraging item-based recommendation timeliness to boost accuracy. Experimental outcomes indicate that the proposed algorithm achieves a mean square error of 0.572, whereas the mean square error for content-based recommendation algorithms and singular value decomposition methods increase by 0.076 and 0.099. The core new insight lies in the bidirectional empowerment fusion architecture of CF and deep learning (DL): integrating CF-derived course similarity as a priori information into the DL models input and attention mechanism, which not only solves CFs sensitivity to sparse data but also compensates for DLs poor interpretability and reliance on massive data. This provides a novel hybrid recommendation paradigm for addressing personalised course recommendation issues in higher vocational colleges. Keywords: deep learning; course recommendation; collaborative filtering; recurrent neural network; RNN; higher vocational colleges. DOI: 10.1504/IJICT.2026.10077725
Abstract: In the current era of deeply integrated global supply chains, new-quality productivity acts as a powerful engine, continuously driving the high-quality development of the transportation and logistics sectors. This study focuses on innovative productivity, technical productivity, and green productivity as core dimensions of new-quality productivity. By gathering panel data from 31 provinces through multiple sources, an empirical model is constructed to analyse the influence mechanisms of each productivity component on the evolution of railway logistics. The findings reveal that innovation-driven productivity encourages railway logistics enterprises to continually explore new service models. Meanwhile, digital and technical productivity, along with regional e-commerce development levels, exert a substantial impact on railway logistics and facilitate industrial synergy through optimised resource integration. The results not only offer a foundation for enterprises to refine investment strategies and enhance operational development, but also provide valuable insights for policymakers to formulate targeted industrial policies and promote the dynamic growth of new logistics business forms. Keywords: new quality productivity; railway logistics; factor synergy. DOI: 10.1504/IJICT.2026.10077726
Abstract: Complex occlusions and rapid movements in basketball make tracking difficult, but deep learning-based visual computing provides effective new solutions. This study proposes an object tracking method that integrates the YOLOv5 model with the simple online and realtime tracking (SORT) algorithm. To address the challenge of multi-source information fusion, a cross-modal transformer model was designed to achieve adaptive deep integration of visual and motion data. Experiments utilised the public SportsMOT dataset, featuring 240 HD clips from real games across diverse arenas, lighting, and tactics. Validation on datasets shows the algorithm achieved a recall of 0.97 and a precision of 97.2%, with the mean average precision improving by 15% over the baseline. The multiple object tracking accuracy and precision reached 98.1% and 96.2% respectively. The algorithm thus proves to be an efficient and accurate tracking solution, offering robust data for coaching analysis and strategy. Keywords: tracking method; basketball players; multi-source data; attention mechanism; DeepSORT algorithm. DOI: 10.1504/IJICT.2026.10077727
Abstract: To address the fragmentation in tourist need identification and the disconnect between a multi-model fusion analysis method is proposed. This approach uses a bidirectional long short-term memory (Bi-LSTM) network to extract semantics from review texts and a latent Dirichlet allocation (LDA) model to identify core topics. A spatiotemporal cube structure maps emotional labels to spatiotemporal coordinates, quantifying experiential differences and optimising tourist group segmentation. Experimental results showed that five themes from both positive and negative comments and five from negative comments were well-separated, effectively reflecting dimensional differences in tourist feedback. Multiple regression models indicated varied group preferences, with one group favouring architectural features (preference coefficient of 1.820) and another prioritising affordability (preference coefficient of 2.186). The overall prediction accuracy of the model is 0.82. The research results provide data-driven decision-making basis for precise service design and resource optimisation allocation in scenic spots. Keywords: scenic area management; multi-clustering; data mining; tourist behaviour; latent Dirichlet allocation; LDA. DOI: 10.1504/IJICT.2026.10077728
Abstract: It is a challenge in education to evaluate the actual learning progress of students accurately and dynamically. The existing evaluation methods mainly rely on static exam scores, so that they cannot reflect the competency development of students learning process in a timely manner. This paper proposes a novel intelligent evaluation model. Based on the meta-learning technology, the model can adapt to various course requirements and combine the evaluation experience of multiple virtual educational specialists. Experiments show that the system can realise the dynamic learning process and realise precise evaluation of the learning process of students. The results show that the accuracy rate is 89.2% and the error rate is only 7.8%. In the early stage, the model achieves a high accuracy of 80%. The model overcomes the lag effect of the existing evaluation method and helps educators to get timely information for education improvement. Keywords: meta-learning; knowledge distillation; outcome-based education; OBE; dynamic evaluation. DOI: 10.1504/IJICT.2026.10077765
Abstract: With the rapid growth of global tourism, Xiamen has drawn attention for its sustainable tourism development. This study applies GIS analysis, an improved CNN model, grey correlation analysis, and a Swin transformer to analyse the spatial distribution and influencing factors of Xiamens tourism industry. The enhanced CNN performs well in spatial analysis, accurately identifying tourism resource distribution. Results show the improved CNN achieves an accuracy of 0.953, recall of 0.947, F1-score of 0.950, MSE of 0.039, and MAE of 0.201. The values are all superior to those of the multilayer perceptron (MLP) and the long short-term memory network (LSTM). The Swin transformer also excels in predicting employment impact and resource efficiency, with accuracies of 0.882 (energy consumption), 0.856 (resource recovery), 0.874 (water use efficiency), and 0.863 (waste management). Its performance is also superior to that of the vision transformer (ViT) and data-efficient image transformers (DeiT) models. The findings indicate the improved CNN effectively captures spatial distribution patterns, while the Swin transformer reliably predicts employment and resource utilisation outcomes. This research provides a valuable basis for policymaking and sustainable development of Xiamen. Keywords: Xiamen’s tourism industry; XMTI; GIS spatial analysis; improved CNN model; Swin transformer model; grey correlation analysis; GCA. DOI: 10.1504/IJICT.2026.10077766
Abstract: Vocational skill assessment needs decisions that are accurate, explainable, and stable when rubrics change. To reduce hidden contradictions and cohort drift in evidence-driven assessment, this paper proposes a joint management approach that integrates a knowledge graph with rule-checked reasoning and distribution monitoring. First, indicator dependencies and evidence links are organised into a graph to keep decisions traceable. Then, explicit rules are validated and applied together with model outputs to prevent inconsistent judgements. Finally, evidence embeddings are analysed with manifold and density diagnostics to reveal sparse regions and guide targeted governance tests. Experiments on five cohorts and large-scale interaction logs show that decision accuracy increases by 3.4% points, rule-consistency improves from 88.1% to 96.7%, and uncovered rule paths decrease by 41% compared with strong baselines. The approach supports maintainable, audit-ready assessment at scale. Keywords: vocational skill assessment; knowledge graph; rule-based reasoning; evidence governance; embedding manifold; robustness monitoring. DOI: 10.1504/IJICT.2026.10077813
Abstract: To address the challenges of data silos and privacy in cross-institutional collaboration, this study introduces a secure data collaboration framework combining federated learning (FL) and differential privacy (DP). The framework enables collaborative model training by keeping data local while using client-side DP to counter privacy threats like membership inference attacks. An adaptive privacy budget allocation (APBA) strategy further optimises the utility-privacy balance. Evaluations on real educational datasets show the framework maintains strong privacy (attack success <5%), achieves a 95% F1 score - comparable to centralised training - and improves communication efficiency by ~40%. This work provides a technical foundation for building secure and efficient cross-institutional platforms in innovation and entrepreneurship education. Keywords: federated learning; FL; differential privacy; DP; data security; innovation and entrepreneurship education; collaboration mechanism; privacy protection. DOI: 10.1504/IJICT.2026.10077814
Abstract: This paper proposes a personalised English teaching knowledge recommendation system based on the MoE-RAG algorithm. The system integrates a mixture of experts (MoE) architecture with eight specialised sub-models and a sparse gated network to dynamically select the most relevant experts for each query. Combined with a retrieval-augmented generation (RAG) module, it retrieves relevant knowledge from multiple bases and fuses it with expert output via a transformer-based generator to produce personalised recommendations. This approach effectively addresses cold-start issues and enhances interpretability. Experiments on 10,000 interaction records from 500 students show that MoE-RAG significantly outperforms traditional models (e.g., collaborative filtering), achieving 87.5% accuracy, 90.2% precision, 84.1% recall, and an 87.0% F1-score. Through a real-time feedback and reinforcement learning mechanism, the system dynamically adjusts resources and optimises learning paths, demonstrating strong adaptability across different learning stages and improving student engagement, time optimisation, and satisfaction. This system promotes the intelligent, personalised development of English education. Keywords: MoE algorithm; RAG algorithm; personalised recommendation system; English teaching. DOI: 10.1504/IJICT.2026.10077815
Abstract: Accurate photovoltaic (PV) model parameter identification is crucial for reliable simulation and maximum power point tracking (MPPT). To address common metaheuristic shortcomings like sensitivity to initialisation and premature convergence, this study proposes a modified improved crested porcupine optimiser (MICPO) featuring a dynamic balancing framework. MICPO integrates chaotic reverse learning, optimal value-guided search, and polynomial differential learning to maintain a robust global-local search balance. Validated on single- and double-diode models, MICPO achieves state-of-the-art accuracy (e.g., RMSE of 9.8602E-04) with faster, more stable convergence. Its superior generalisation is further demonstrated on the CEC2017 benchmark and commercial PV modules under varying conditions. Results confirm MICPO as a highly accurate, efficient, and robust solution for practical PV parameter extraction. Keywords: PV cell parameter identification; single-diode and double-diode model; modified improved crested porcupine optimisation; MICPO; algorithm; dynamic balancing framework. DOI: 10.1504/IJICT.2026.10077856
Abstract: This study tackles inefficient resource allocation in concurrent English translation teaching platforms by proposing the C-GHM model. This model integrates a greedy heuristic task migration algorithm with multi-dimensional corpus features. It constructs a priority evaluation system (using vectors like task professionalism and syntactic complexity) and a simulated annealing optimisation layer for intelligent computing resource allocation. Experimental results show C-GHM significantly outperforms traditional algorithms: reducing average task completion time to 125.3 seconds, increasing throughput to 45.2 tasks/second, and optimising load imbalance to 0.15. It also excels in robustness, energy efficiency, and scalability tests. Its core contribution is a transferable, collaborative scheduling framework that synergistically combines greedy heuristics, corpus features, and simulated annealing, achieving superior performance in heterogeneous task environments, with potential applications beyond translation platforms. Keywords: greedy heuristic task migration algorithm; English translation teaching platform; resource scheduling optimisation; corpus-driven; multi-objective optimisation. DOI: 10.1504/IJICT.2026.10077857
Abstract: This paper proposes a GAN-LSTM model for predicting brand value fluctuations and managing risks in rural characteristic industries. The model integrates generative adversarial networks to enhance limited data samples and long short-term memory networks to capture long-term dependencies in time-series data, improving prediction accuracy and robustness. Analysing a decade of data from traditional agriculture, tourism, handicrafts, and local food across multiple provinces, the study reveals distinct fluctuation patterns. Prediction errors were minimal, with handicrafts at 4.00% and local food at 3.16%. The GAN-LSTM model outperformed traditional and basic LSTM methods, reducing average prediction errors by 15% and 8%, respectively. It also provides quantified risk assessments, achieving up to 92% prediction accuracy and 90% risk management effectiveness. The findings offer theoretical guidance and practical support for the sustainable development of rural industries. Keywords: GAN-LSTM model; fluctuations in brand value; risk management; rural characteristic industries. DOI: 10.1504/IJICT.2026.10077858
Abstract: To achieve dynamic attitude monitoring of shipboard equipment and overcome the limited generality of traditional single-view methods, this paper proposes a multi-feature-point, multi-view attitude measurement approach. The method is designed as a redundant supplement to existing attitude measurement systems, providing additional attitude information under complex operating conditions. An improved MobileNet-v2 network incorporating squeeze-and-excitation modules is employed for feature point extraction from equipment images, achieving a detection accuracy of 95.4% on the test set. Based on multi-view observations and known equipment geometry, the three-dimensional coordinates of feature points in the camera coordinate system are reconstructed, and the equipment attitude is estimated by solving the rotation matrix between the camera and body coordinate systems using singular value decomposition. Experimental validation on a small-scale multirotor UAV platform demonstrates root mean square errors of 0.708 in pitch, 0.833 in roll, and 0.593 in heading, indicating good potential for attitude monitoring of large-scale ship equipment. Keywords: shipboard dronesÍž attitude measurementÍž MobileNet-v2Íž multi-view. DOI: 10.1504/IJICT.2026.10077859
Abstract: Building energy efficiency management is crucial for sustainable development amid global energy challenges. This study integrates big data analytics and artificial intelligence to develop an intelligent scheduling system for building energy optimisation. Using long short-term memory (LSTM) networks, a deep learning model was trained on multi-source data including energy consumption, weather forecasts, and pedestrian flow, achieving over 95% prediction accuracy. The system dynamically adjusts building equipment operations based on predictive outcomes, reducing overall energy consumption by 20%. Experimental results demonstrate significant economic benefits and enhanced energy efficiency. The research also explores broader applications of AI in energy management, such as equipment failure prediction and performance evaluation. This work provides a novel technological pathway for green building development and supports global sustainability goals. Keywords: big data analytics; artificial intelligence; building energy efficiency; intelligent scheduling; deep learning. DOI: 10.1504/IJICT.2026.10077974
Abstract: Accurately attributing Chinese second language grammatical errors is crucial for optimising teaching strategies. However, traditional methods are prone to being disturbed by confounding factors such as the learners level, making it difficult to distinguish between superficial correlation and true causation. To address this, this paper introduces the framework of counterfactual causal inference for the first time. By simulating correction interventions on specific grammatical points, it aims to identify the root causes of the errors. Experiments based on a large-scale public Chinese proficiency test dynamic composition corpus show that this method achieves an accuracy rate of 87.5% in error attribution, an improvement of 8.2% over the best baseline model; its causal effect ranking quality reaches 0.92, significantly outperforming traditional correlation analysis. This method provides interpretable and verifiable causal insights for Chinese second language teaching, and can directly serve the construction of personalised learning paths. Keywords: second language acquisition; attribution of grammatical errors; dual machine learning; DML. DOI: 10.1504/IJICT.2026.10077934
Abstract: Social network data has become a vital resource driving product innovation and design. Current research struggles to fully uncover users emotional needs toward products when dealing with unstructured, high-dimensional social data, resulting in subpar product quality. To address this, this paper first employs a multi-scale attention network to analyse product emotional needs, capturing users emotional demands. Subsequently, a spatial cross-reconstruction module is designed within the generative adversarial network to obtain more refined features. Simultaneously, a semantic correlation attention module is designed for mapping emotional needs to product images. This extracts attribute and word encodings as semantic representations to guide image generation, enhancing semantic consistency between emotional needs and visual content. Experimental results demonstrate that the proposed method achieves 92.71% accuracy in emotional need recognition and an FID of 11.88 for product images, outperforming state-of-the-art methods and delivering outstanding performance in innovative product design tasks. Keywords: innovative product design; social network; deep learning; emotional needs analysis; generative adversarial network; GAN. DOI: 10.1504/IJICT.2026.10077935
Abstract: Aiming at the problem of prediction deviation caused by ignoring deep semantic information in online marketing effect perception, this study proposes an innovative framework that deeply integrates neural networks and multi-level semantic mining. Traditional methods mostly rely on shallow interaction features, making it difficult to capture complex intentions in texts. Our model achieves deep understanding and alignment of user preferences and product connotations through collaborative fine-tuning of pre-trained language models and graph neural networks. Experiments on public datasets show that, compared with mainstream baseline models, this framework has increased the area under the receiver operating characteristic curve for click-through rate prediction by 2.1% and the ranking metric normalised discounted cumulative gain @10 by 4.7%. All improvements are statistically significant (p < 0.01). This research provides an effective approach for building a more precise and interpretable intelligent marketing system. Keywords: online marketing; deep neural networks; DNNs; semantic mining; effect perception; recommendation systems. DOI: 10.1504/IJICT.2026.10077936
Abstract: As the scale of university graduates has continued to expand, the evaluation of employability has become a critical issue in higher education management and talent cultivation. This study aimed to develop a scientific, quantitative, and multidimensional method for assessing graduate employability. An employability indicator system was constructed using the analytic hierarchy process (AHP). The results indicated that professional competence had the highest weight (0.55). Within this dimension, technical operation and experimental skills, the application of theoretical knowledge, and the quality of project experience contributed most significantly to employment competitiveness. A back propagation neural network (BPNN) model was further applied to train and predict the sample data. The results demonstrated a high level of consistency between the predicted values and the actual values. The absolute error ranged from 0.03 to 0.11, the relative error remained below 2.12%, and the overall accuracy reached 0.926. Universities should strengthen professional practice and innovation capacity development and given to enhancing students professional qualities to improve overall employment competitiveness. The main contribution of this study provides a decision-making reference for higher education management, career guidance, and policy formulation. Keywords: back propagation neural network; BPNN; neural network model; university graduates; employability. DOI: 10.1504/IJICT.2026.10077937
Abstract: To address the shortcomings of neural machine translation in handling complex sentences and terminology, this paper proposes a translation quality improvement model based on the quantum-optimised osprey optimisation algorithm (QOOA). This model integrates quantum computing and metaheuristic algorithms, enhancing population diversity through qubit encoding, dynamically adjusting individual positions using a quantum rotation gate strategy to balance global exploration and local exploitation, and constructing a multi-objective fitness function that combines semantic similarity and syntactic complexity. Experiments on the WMT2018 English-Chinese dataset show that, compared to the baseline model, this method improves the BLEU score by 3.2 percentage points and reduces the TER by 12.7%, significantly reducing translation confusion. The results demonstrate that QOOA effectively improves translation quality, especially in long sentences and technical texts. Keywords: quantum optimisation; osprey algorithm; machine translation; parameter optimisation; BLEU index; meta-heuristic algorithm. DOI: 10.1504/IJICT.2026.10077980
Abstract: As the belt and road project continues to proliferate, the ChinaLaos Railway appears as a connector between infrastructure and a place of cultural exchange. This paper examines the architecture of an interactive three-dimensional (3D) book that can be fuelled with Artificial Intelligence and shows the geography, ethnic culture, and transport development along the railway. The study improves visual representation, multimedia incorporation by using AIGC image generation and layout, and interactive design tools. A four-dimensional framework is created, which is called railway culture, spatial structure, interactive experience and AI generation mechanism to maximise visual narratives and dynamic content. The results show that AI is a way of enhancing design efficiency and creative behaviours as it is a new avenue of merging cultural communication with technology using conventional paper-based media. Keywords: China-Laos Railway; three-dimensional book design; artificial intelligence; AI; interactive narrative; cultural communication; visual expression. DOI: 10.1504/IJICT.2026.10077981
Abstract: Combining personalised expression with high-fidelity geometric reconstruction has been a challenging task. To generate realistic virtual humans, this paper proposes an effective new framework. By combining users emotional preferences with implicit neural radiance fields, personalised virtual human bodies are generated. This method encodes multi-modal user input into structured conditional variables, and then guides the conditional neural radiance field model to generate facial images with emotional expressiveness. The innovative learnable user-specific embeddings can capture individual expression styles. Additionally, the attention-based fusion module ensures precise alignment between emotional semantics and facial details. Through experiments on standard datasets, the proposed method achieved a frechet inception distance score of 15.38 and an emotion recognition accuracy of 0.892, significantly outperforming three baseline approaches. These results demonstrate its substantial advantages in emotional accuracy, identity preservation, and overall visual quality. Keywords: virtual human generation; implicit neural radiance fields; affective computing; conditional generation. DOI: 10.1504/IJICT.2026.10078000
Abstract: In todays globalised and technology-driven world, improving spoken English is increasingly important. However, traditional automatic speech recognition (ASR) systems often produce outputs with grammatical errors, poor word choices, and pronunciation ambiguities, hindering effective communication. To address this, we propose MTG-ERR, a novel multimodal transformer-GCN framework that integrates acoustic and textual information for real-time and accurate spoken English error correction. The model uses a transformer-based acoustic encoder to capture temporal speech features and a GCN-based module with dependency syntactic trees to model grammatical structures. A dynamic fusion mechanism effectively combines both modalities, significantly enhancing error correction. Experiments on the L2-ARCTIC and LibriSpeech corpora show our framework outperforms baseline models, achieving a 92.7% F1-score in grammatical error correction. Ablation studies confirm that incorporating grammatical information improves performance on long, complex sentences by 12.1% in F1-score. With an average response latency under 320 ms, the system meets real-time interactive requirements. This research provides valuable insights for developing robust spoken language assistance systems, with significant potential for educational and commercial applications. Keywords: oral error correction; multimodal learning; transformer; graph convolutional networks; GCNs; real-time systems; grammatical dependency analysis. DOI: 10.1504/IJICT.2026.10078001
Abstract: This paper proposes a federated learning framework integrated with adaptive graph convolution for accurate and privacy-preserving carbon emission calculation in cross-regional power grids. It addresses data silos and privacy concerns by training models locally, avoiding raw data transfer. The adaptive graph convolution component automatically captures the dynamic spatial dependencies and carbon flow effects between grid regions. Validated on a Chinese grid dataset, the method reduces calculation errors by 22.3% and 14.7% compared to centralised and traditional distributed approaches, respectively, while demonstrating strong robustness against grid topology and operational fluctuations. Keywords: federated learning; adaptive graph convolutional networks; grid carbon emissions; collaborative computing; privacy protection. DOI: 10.1504/IJICT.2026.10078002
Abstract: This study proposes a scenariobased stochastic optimisation framework for the optimal placement and sizing of energy storage systems (ESS) in distribution networks. The model integrates ICTenabled data acquisition and communication infrastructures to process realtime load and renewable energy data. A complete mixedinteger linear programming (MILP) formulation is developed, incorporating power balance, ESS dynamics, and network operational constraints across multiple uncertainty scenarios. The proposed method is validated on a real distribution network case study, demonstrating operational cost reductions, improved grid stability, and enhanced renewable energy utilisation compared with deterministic approaches. Keywords: energy storage systems; ESS; distribution networks; stochastic optimisation; mixed-integer linear programming; MILP. DOI: 10.1504/IJICT.2026.10078003
Abstract: With the rapid growth of data-intensive applications, achieving low-latency and reliable content retrieval in complex networks has become a major challenge. Information-centric networking (ICN) leverages content naming and pervasive in-network caching to enable retrieval from multiple replicas, making replica selection crucial for performance. However, selection is complicated by replica capacity limits, bursty workloads, and dynamic path variations. To address these issues, we propose a replica selection strategy that integrates the multi-armed bandit (MAB) framework with dynamic redundancy control. By modelling selection as an MAB problem, the strategy incorporates path variability, service heterogeneity, and blocking risk into decision-making, enabling adaptive exploration and exploitation. An additional load-aware redundancy mechanism adjusts redundancy levels to curb exploration overhead and suppress tail latency. Simulations on a real-world topology show that the method significantly reduces latency and improves robustness. Compared with nearest-replica routing, it reduces average latency by 32.09% and P99 tail latency by 45.76%. Keywords: information-centric networking; ICN; multi-armed bandits; MAB; adaptive redundancy; in-network cache; replica selection. DOI: 10.1504/IJICT.2026.10078035
Abstract: Japanese kana writing is fundamental to learning the Japanese language, and its standardisation has a significant impact on language learning outcomes. To address the inefficiency and subjectivity of traditional manual evaluation, this study proposes an intelligent evaluation model that integrates a convolutional long short-term memory (ConvLSTM) network with a conditional random field (CRF). First, the model utilises the ConvLSTM to efficiently extract spatiotemporal features of handwriting traces. Second, the CRF layer optimises sequence annotation to achieve automatic quantitative evaluation of kana writing accuracy, fluency, and structural standardisation. Finally, a self-constructed dataset containing 2,000 handwriting trace samples from five common hiragana and five katakana categories was used for evaluation experiments. The results show that the model achieved a 98.2% accuracy rate in kana character recognition, a Pearson correlation coefficient of 0.91 between its writing style score and expert evaluations, and a 91.2% accuracy rate in kana stroke regularity assessment, significantly outperforming the single LSTM and CNN-CRF models. Keywords: writing trajectory evaluation; ConvLSTM; CRF; Japanese kana; intelligent evaluation; sequence labelling. DOI: 10.1504/IJICT.2026.10078036
Abstract: Tourism route planning has traditionally emphasised shortest-path optimisation, often over-looking the importance of enhancing tourists overall experiences. Many travellers rely on user-generated content to guide their journeys, yet manually searching and adjusting routes in real-time can be inefficient and inaccurate. This study focuses on building a high-quality database to support model training for tourism route optimisation and dynamic adjustments. By leveraging graph theory and the Floyd-Warshall algorithm, the proposed approach integrates various tourism-related data factors to enhance route planning accuracy based on personalised preferences. The high-quality dataset, sourced from travel agencies and user-generated data, ensures the algorithms adaptability in real-world scenarios. The model is tested on an online tourism platform, with its effectiveness evaluated through a framework grounded in tourism theories and user behaviour research. The results demonstrate significant improvements in both route planning accuracy and the efficiency of real-time adjustments when travellers modify their plans mid-journey. Keywords: database establishment; machine learning; tourism route planning adjustment. DOI: 10.1504/IJICT.2026.10078037
Abstract: Addressing the issue of traditional customer segmentation relying on static data and struggling to respond to behavioural changes in real-time, a real-time customer segmentation framework based on big data analysis and clustering analysis is proposed. The data comes from e-commerce websites and includes user activities, transactions, and demographic information. Preprocessing involves data cleaning, normalisation, and TF-IDF feature extraction. The key features include transaction frequency, interest in product categories, and page dwell time. The proposed model is an adaptive k-nearest neighbour (k-NN) logistic regression based on clonal selection (CS-AK-LR), integrating adaptive K-means clustering (AK) and logistic regression (LR) for customer clustering and value classification prediction. The clonal selection algorithm (CS) optimises the hyperparameters of AK and LR. The segmentation detection rate of this method reaches 96.21%, and the error rate is reduced by 1.03% compared to existing methods. Combining big data with real-time clustering analysis can effectively enhance the speed and accuracy of marketing responses. Keywords: consumer segmentation; clonal selection-based adaptive K-logistic regression; CS-AK-LR; marketing strategy; big data; cluster analysis. DOI: 10.1504/IJICT.2026.10078097
Abstract: The complex sea ice and marine environment in the polar region significantly affects marine safety operations. How to accurately simulate the complex polar environment is a key concern at home and abroad. The greenhouse effect leads to ice melting, with the expanding area of broken ice posing new challenges to ice navigation. This paper reviews the principle of discrete element method (DEM), special features of ship navigation in broken ice areas, and the progress of DEM applications in broken ice research. Based on this foundation, it discusses the existing challenges and key research applying DEM to broken ice studies. Keywords: discrete element method; DEM; broken ice areas; computational fluid dynamics; ship navigation; review. DOI: 10.1504/IJICT.2026.10078099
Abstract: Government agencies struggle to track and respond to public sentiment on social media platforms like Weibo. This case study describes the design and development of a monitoring system for an anonymous municipal government in China, leveraging deep learning to analyse sentiment and emerging topics. The case details the system architecture, implementation challenges, and how the outputs can be used for targeted public communication. To achieve effective management of social public opinion, this article uses deep learning and clustering algorithms to process public opinion information on the Weibo platform and establishes a Weibo public opinion analysis system. Focusing on user blog posts and comments, we first use distributed crawlers to obtain data, and then complete preprocessing through cleaning and word segmentation. Emotion analysis is implemented to obtain sentiment polarity and probability, and to explore potential themes using a latent Dirichlet allocation topic model. The experimental results show that the established model has high accuracy in emotion classification. Using real Weibo data, the emotional value change curve of netizens is plotted to determine the impact of topics on netizens emotions. The system supports targeted public opinion intervention for governmental use. Keywords: Weibo; public opinion; analysis. DOI: 10.1504/IJICT.2026.10078157
Abstract: This study proposes a framework for suppressing the spread of fake news on social networks based on multimodal sentiment analysis. This study employs the BERT model to extract contextual semantic vectors from news texts. These are then fused with the output of a bidirectional long short-term memory (BiLSTM) network through feature concatenation, enabling simultaneous capture of local context and global long-range dependencies. Emoticon sentiment features are then extracted through autoencoders and deeply integrated to accurately identify user sentiment inclinations. The studys core innovations are: 1) a multi-tiered fake news detection and suppression architecture; 2) deep fusion of text and emoticon features through multimodal sentiment analysis; 3) dual-strategy dissemination suppression combining detection + sentiment immunity. Experimental results demonstrate that the fake news detection model achieves an accuracy of up to 89.4%. The proposed model can provide effective solutions for building a timely and accurate false news prevention and control system. Keywords: fake news; multi-modal data; sentiment analysis; dissemination suppression; BERT model. DOI: 10.1504/IJICT.2026.10078158
Abstract: The demands of customers for spiritual culture are successfully met by cultural and creative products, which are a significant carrier of museum culture. Customers may develop a closer relationship with museums through the creative and cultural products original design, which can greatly increase museums social awareness. This paper suggests using the KANO model to innovate the design of museum cultural products from the perspective of consumer demand, given the issues of significance homogenisation, exorbitant prices, and a lack of functional development of current museum cultural products. The KANO model is utilised to analyse and prioritise consumers demands for museum cultural products. This analysis employs a series of metrics to assess consumer needs and determine the most pressing issues within the field. The application of the KANO model in this particular context facilitates the generation of innovative concepts in the domain of product design and development. Keywords: KANO model; cultural and creative products of museums; product design; consumer demand. DOI: 10.1504/IJICT.2026.10078159 Abstract: This study proposes a collaborative management framework for tourist destination dynamic carrying capacity based on multi-agent deep reinforcement learning (MADRL) and spatio-temporal graph neural network (STGNN). A multi-dimensional topological model is constructed to characterise the spatio-temporal correlation of passenger flow, resources, environment, and service. A STGNN module embedded with spatio-temporal attention is designed to capture dynamic evolution features. A hierarchical MADRL structure realises global coordination. Experiments show that the framework reduces MAE to 0.037, shortens response delay to within 8.2 s, and improves carrying capacity utilisation to 92.6%. It outperforms traditional models in prediction, response, and multi-objective balance, providing an effective method for intelligent and sustainable tourism management. Keywords: tourist destination; dynamic bearing capacity; multi-agent deep reinforcement learning; MADRL; spatio-temporal graph neural network. DOI: 10.1504/IJICT.2026.10078160
Abstract: This paper constructs an intelligent English translation teaching model based on a multi-strategy bee colony algorithm, capable of realising personalised teaching by dynamically adjusting content and learning paths. Experimental data indicate that the model significantly enhances student performance; average scores in large classes increased by over 13 points (approximately 19.6%), demonstrating strong scalability. Compared to traditional algorithms like PSO and GAE, the model achieves stability within just 18 iterations, significantly optimising error rates compared to previous fluctuations. Furthermore, it drastically reduces task completion time - handling 60-word tasks in under one hour, whereas traditional neural models require over nine. While senior students exhibit rapid short-term gains and juniors show stable long-term improvement, the model ultimately validates itself as a highly efficient, precise, and personalised solution for modernising translation teaching. Keywords: multi-strategy swarm algorithm; intelligent translation teaching; personalised learning paths; teaching optimisation model; improvement of translation ability. DOI: 10.1504/IJICT.2026.10077623
Abstract: AI enhances classroom behaviour analysis, yet participation assessment remains subjective, single-dimensional, and lacks real-time quantitative support. This study proposes a multimodal framework integrating video, audio, eye tracking, and positioning to capture facial expressions, body posture, speech emotion, and semantic content. A dual-stream ResNet, TCN, and pretrained transformer are employed to extract visual, acoustic, and textual features, with cross-modal alignment achieved via timestamp synchronisation and learnable positional encoding. A multi-head self-attention-based fusion module and multi-task evaluation head quantify participation frequency, interaction depth, emotional engagement, and knowledge feedback. Experiments on 192 students across 90 classes achieve 91.2% accuracy, over 90% F1-score, and over 93% consistency with teacher ratings, significantly outperforming baseline methods. Keywords: multimodal perception; classroom participation; English teaching; deep learning. DOI: 10.1504/IJICT.2026.10077624
Abstract: Faced with the lag challenge of traditional macroeconomic indicators in monitoring regional economic fluctuations, this study proposes a multi-modal public opinion knowledge graph construction method that integrates agenda setting and signalling theory. Through cross-modal coding, spatio-temporal fusion and graph structure learning, the massive heterogeneous graph-text public opinion data is transformed into a structured dynamic knowledge graph. Experiments based on the constructed Yangtze River Delta Regional Economy - Multi-modal Public Opinion dataset show that method achieves 78.3% accuracy in predicting the direction of regional quarterly gross domestic product growth rate, which is significantly better than a variety of frontier baseline models (p < 0.01), and performs well in the task of graph link prediction. This study constructs a theory-computational bridge connecting micro social perception and macro economy, and provides an interpretable new paradigm for intelligent research and judgement of regional economy. Keywords: regional economic fluctuations; multi-modal public opinion; knowledge graph; signal theory; prediction accuracy. DOI: 10.1504/IJICT.2026.10077698
Abstract: Automating packaging layout design requires balancing aesthetic appeal, semantic constraints, and personalised needs. This paper presents a human-computer collaborative reinforcement learning for packaging design framework that generates packaging layouts by modelling the task as a Markov decision process. Our approach incorporates a dual-stream policy network for global composition planning and local element adjustment, alongside a joint reward function that integrates aesthetic, semantic, and human-preference signals. Training follows a two-stage strategy: pre-training with automated rewards, then fine-tuning via online human feedback. Experiments on a dataset of 10,000 packaging samples show that design framework outperforms existing methods in layout rationality, aesthetic score, rule compliance, and brand prominence. In user studies, professional designers rated our layouts higher for visual appeal and information clarity. This work offers a practical human-in-the-loop solution for automated creative design under complex constraints. Keywords: packaging design; visual layout; human machine collaboration; deep reinforcement learning; DRL; computational aesthetics. DOI: 10.1504/IJICT.2026.10077718
Abstract: To address voltage unbalance induced by three-phase load discrepancies in rural areas, this paper proposes a voltage compensation technology utilising hybrid photovoltaic-energy storage (PV-ESS) inverters. Data analysis from 12 typical distribution stations indicates an average three-phase load unbalance of 18.7% and a maximum phase-voltage deviation of 7.2%, contributing to a 35% rise in user-side equipment failure rates. The study employs a collaborative PV-ESS control strategy that dynamically modulates inverter output by monitoring three-phase currents alongside real-time active and reactive power. A three-month pilot verification involving 500 households in a distribution area demonstrated that voltage unbalance dropped from 15.3% to 2.1%, the power factor improved from 0.82 to 0.96, line losses decreased by 12.8%, and the user-side voltage compliance rate rose from 92.1% to 98.7%. Through optimised charge-discharge strategies, the technology achieves a PV self-consumption rate exceeding 85%, effectively mitigating heavy loads on distribution transformers. This study provides a quantifiable technical solution for rural grid voltage regulation, with empirical data validating its significant compensation efficacy. Keywords: three-phase load imbalance; PV-ESS inverter; voltage unbalance compensation; data analysis; distribution network optimisation. DOI: 10.1504/IJICT.2026.10077723
Abstract: Due to the number of individuals who rely on cloud computing, there are massive issues with sharing data over the cloud. To enhance the security and flexibility of data sharing, this paper proposes a cloud-based data attribute-based sharing method grounded in fine-grained dynamic access control. This approach first introduces a dynamic revocation mechanism to ensure granularity in permission management. Subsequently, it combines attribute encryption with a weight exchange mechanism to further enhance data access control. Finally, experimental validation confirms the method's performance. The findings indicate that the proposed solution is more accurate by 5.2%, more inclined to detect relevant information by 6.3% and it is 7.8% better than the conventional methods. The model is highly scalable and efficient in dealing with large scale data, and it satisfies security requirements in data exchange in the cloud. Keywords: cloud data sharing; fine-grained dynamic access control; attribute-based encryption; ABE; data privacy protection. DOI: 10.1504/IJICT.2026.10077717
Abstract: This study proposes a novel English translation method using an adaptive label smoothing algorithm to address issues of rigid and non-diverse outputs. It employs three modules: a grammar feature capture module (redefined as a 'multi-modal syntactic structure modeller'), a component attention-enhanced encoder, and an adaptive label smoothing decoder. These create an interaction mechanism of syntactic feature, attention, and loss weight flows to better represent sentence structures and generate varied translations. Experimental results show the model's repetition rate reduced to 0.09, beating diverse beam search (0.12). It achieved a syntactic tree similarity score of 0.87, outperforming a grammar-enhanced model by 0.09. With ten candidate outputs, it showed higher diversity in sentence length and n-gram differences (8.60 and 0.92) compared to baseline methods. The model also demonstrated efficient convergence, with loss dropping to 0.98 after 50 epochs. The method enhances translation accuracy, semantic fidelity, and output diversity under a clear evaluation framework. Keywords: English translation; label smoothing algorithm; constituent attention; grammar feature capture; mixture of experts model. DOI: 10.1504/IJICT.2026.10077697
Abstract: To address security and real-time challenges in 5G networks, this paper proposes a dual-modal system integrating AES-GCM encryption with an LSTM anomaly detection model, effectively balancing data confidentiality with anomaly identification. Experimentally, the system achieves optimal response speeds with encryption and decryption times of 18.4 ms and 21.7 ms, respectively. It demonstrates superior detection capabilities, attaining 98.1% accuracy - surpassing standalone models - while restricting missed detections to 1.0% and false positives to 0.5%. Additionally, the system excels in high-bandwidth environments, with encryption delays dropping significantly to 0.04 ms. In terms of resource efficiency, the hybrid model optimises CPU scheduling and lowers energy consumption to 130 J, compared to 165 J for traditional AES-GCM. Conclusively, this dual-modal architecture provides a robust, quantitatively validated solution that significantly enhances security, efficiency, and real-time processing in 5G scenarios. Keywords: 5G communication; AES-GCM; LSTM; anomaly detection; encryption algorithm optimisation. DOI: 10.1504/IJICT.2026.10077622
Abstract: Student mental health issues are becoming increasingly severe, yet traditional scale assessments suffer from limitations such as high subjectivity and delayed feedback. To address this challenge, this paper proposes an intelligent evaluation framework multi-branch adaptive social-emotional fusion network that integrates social-emotional analysis with multi-branch neural networks. This framework continuously and seamlessly integrates multidimensional digital footprints generated by students in campus life (e.g., text, voice, behavioural patterns) to enable dynamic psychological risk assessment. In this work, digital footprints refer to passively collected multimodal data from students' daily digital activities, including text messages, voice recordings, smartphone sensor logs, and social interaction records. Experiments on the public studentlife dataset demonstrate that the proposed method achieves a key evaluation metric area under the receiver operating characteristic curve of 0.927, surpassing mainstream unimodal models by over 3.2%. It also achieves an overall accuracy of 89.7% and passes statistical significance tests. This confirms the effectiveness and feasibility of utilising multi-source socio-emotional signals for early, objective intervention. Keywords: student mental health; social-emotional analysis; multi-branch neural networks; intelligent assessment; digital footprint. DOI: 10.1504/IJICT.2026.10077722
Abstract: With the widespread use of encryption in data storage and transmission, efficient password recovery is critical for legitimate access restoration and digital forensics. Traditional CPU-based solutions are inefficient for modern cryptographic algorithms such as AES-256 and SHA-512 due to limited serial computing capability, while GPU-based acceleration suffers from memory bandwidth and latency bottlenecks under complex control logic. To address these issues, this paper proposes a high-performance password recovery system for autonomous and controllable platforms. An intelligent decryption heterogeneous acceleration architecture (IDHAA) is designed to improve resource utilisation and coordination efficiency through fine-grained task decomposition and dynamic scheduling across heterogeneous computing units. Furthermore, a heuristic dynamic optimisation search (HDOS) algorithm is introduced to reduce blind traversal in large password spaces by adaptively optimising search strategies based on structural features and feedback information. Experimental results demonstrate significant improvements in recovery efficiency, success rate and system scalability. Keywords: password recovery; autonomous and controllable platforms; GPU acceleration; parallel computing; encryption; algorithm optimisation. DOI: 10.1504/IJICT.2026.10077713
Abstract: This paper addresses the prevalent issue of 'over-correction' in current AI English writing tools - where correct personalised expressions are often misidentified as errors - by proposing an innovative adversarial generative error correction system. This system mimics the 'teacher-student interaction' mechanism: one network attempts to modify sentences, while another network judges the necessity of such modifications, thereby achieving more precise error correction. For instance, a system might incorrectly 'correct' a stylistically chosen active-voice sentence (e.g., 'our team analysed the data') into a passive construction ('the data was analysed by our team'), thereby altering the author's intended emphasis. Another common over-correction involves replacing a correctly used but less frequent disciplinary term with a more common, yet less precise, synonym. In public dataset evaluations, the system achieves an 89.5% correction accuracy - a significant improvement over traditional rule-based methods (approximately 70.2%) - while maintaining an over-correction rate of only 12.1%, substantially lower than that of a general-purpose large model (approximately 35.7%). This demonstrates the advantages of adversarial generation methods in understanding writing intent and context, providing an effective pathway for developing smarter, more human-like writing assistance tools. Keywords: English writing assistance; adversarial generative networks; grammar correction; overcorrection. DOI: 10.1504/IJICT.2026.10077721
Abstract: Traditional methods face challenges such as weak feature extraction, content distortion, and unstable training in the style transfer of Liao, Jin, and Yuan ceramic patterns. This study proposes a style transfer model based on an improved VGG16 and a cyclic consistency adversarial network, integrating multi-channel input, spatial attention, and a multi-scale feature pyramid, with a three-scale discriminator and structural similarity constraints. Experiments show that in Liao-to-Jin transfer, style accuracy reached 89.7%, content retention 91.2%, SSIM 0.801, and PSNR 27.9 dB. In Jin-to-Yuan and Yuan-to-Liao transfers, average accuracy and retention were 88.4% and 90.5%, with SSIM 0.792 and PSNR 27.5 dB. The three-scale discriminator improved style fidelity by 6.1%, and expert ratings averaged 8.7-9.1. While the method preserves semantic content and enhances stability for flat painted patterns, performance decreases for engraved designs with strong 3D features (e.g., Yaozhou kiln), with a success rate of 78.3%. Keywords: ceramic decoration; Liao, Jin, Yuan; style transfer; VGG16; CycleGAN. DOI: 10.1504/IJICT.2026.10077621
Abstract: According to past researches, lause-level risk identification is formulated as a classification task across multiple publicly available legal datasets, including commercial contracts, terms of service, and compliance clauses. To better capture long-range dependencies and structural patterns in legal text, this paper introduces an intelligent risk analysis framework for civil and commercial contract clauses with proposed relative position enhanced bidirectional encoder representations from transformers. It is a strong language model architecture owing to its transformer model, which captures long distance dependences efficiently. Meanwhile, compared with traditional position embedding, the relative embedding has better performance on understanding local context, improving model's robustness. Experimental results show that the proposed model consistently outperforms CNN-based, RNN-based, and standard transformer baselines across five datasets. The enhanced model achieves 2-3.5 percentage points improvement in macro F1-score over baseline models. These findings demonstrate that integrating relative positional information effectively enhances the detection and classification of risky contractual clauses. Keywords: contract risk analysis; clause classification; bidirectional encoder representations from transformers; BERT; relative position embeddings; legal text mining. DOI: 10.1504/IJICT.2026.10077712
Abstract: Amid digital music's growth, challenges like multi-track interference and noise robustness persist. Traditional single-domain analysis struggles with harmonic and transient details. We propose a computer music sound signal separation model (CMSSM-TFCFS), which includes an encoder with time-frequency cross-domain feature selection, a residual temporal convolution-based separator for long-term dependencies, and a decoder. It is jointly trained with an acoustic parameter synthesis model (APSM-NV) that uses a multilayer LSTM to predict clean acoustic features and a transformer-based vocoder for waveform generation. On a self-built dataset, the separation model achieves a signal-to-distortion ratio of 16.6 dB and a scale-invariant signal-to-noise ratio of 16.9 dB, improving baselines by 6.4% and 10.5%. By dynamically integrating time-frequency features and enabling end-to-end optimisation, this work offers a new paradigm for complex music signal processing, advancing support for music production and audio restoration, and promoting progress in digital music processing. Keywords: time-frequency cross-domain characteristics; sound signal processing; computer music; residual time convolution; neural vocoder; attention mechanism. DOI: 10.1504/IJICT.2026.10077696
Abstract: Aiming at the challenges of insufficient accuracy in complex damage repair and time-consuming and inefficient parameter adjustment in traditional methods in the digital protection of murals, this paper proposes a multi-scale image restoration algorithm based on Bayesian optimisation. By constructing a multi-scale feature fusion network to capture context information and introducing Bayesian optimisation to the hyperparameters and loss weights of the automated repair model, the adaptive and efficient repair process has been achieved. Experiments on the Dunhuang Mural dataset and public damage benchmark show that, compared with mainstream deep restoration methods, this algorithm improves the structural similarity index by an average of 2.5% and the peak signal-to-noise ratio by 0.8 dB, significantly enhancing the visual fidelity and detail restoration ability of the restoration results. It provides reliable technical support for the digital archiving and virtual restoration of large-scale murals. Keywords: digital protection of murals; image restoration; Bayesian optimisation; multi-scale network. DOI: 10.1504/IJICT.2026.10077716
Abstract: Lexical ambiguity is a core challenge in machine translation, such as translating 'apple' as either 'fruit' or 'Apple Inc.' depending on context. While existing neural machine translation models produce fluent output, they often exhibit bias in selecting specialised terminology and low-frequency words. To address this issue, this study innovatively combines statistical patterns from large-scale corpora with the probabilistic modelling capabilities of neural networks to construct a lexical selection optimisation framework. Experiments on the publicly available workshop on machine translation English-German translation dataset demonstrate that this approach improves the bilingual evaluation understudy score from 31.2 to 33.3 while significantly reducing the translation error rate from 52.1% to 49.8%. This confirms that integrating statistical prior knowledge effectively enhances machine translation accuracy and lexical consistency. Keywords: machine translation; lexical choice corpus statistics; probabilistic modelling. DOI: 10.1504/IJICT.2026.10077720
Abstract: Most of the existing knowledge tracking methods ignore the dynamic influence of psychological states on the learning process, resulting in limited accuracy of personalised prediction. To this end, this paper proposes a psychomotivation-driven personalised knowledge tracking graph neural network. By integrating motivational factors such as concentration and curiosity and constructing a student-knowledge heterogeneous graph, it can simulate the learning process more accurately. Experiments on the assistments2012 and ednet public datasets show that psychomotivation-driven personalised knowledge tracking graph neural network has an average improvement of 1.2% in the area under the roc curve metric and 2.1% in the prediction accuracy compared to the optimal baseline model, and the improvement is statistically significant. This study provides an effective approach for achieving fine-grained learning state assessment that integrates cognition and emotion. Keywords: knowledge tracking; psychological motivation; graph neural network; GNN; personalised learning. DOI: 10.1504/IJICT.2026.10077711
Abstract: An augmented reality-based mobile real-time assistance system is developed to enhance the effectiveness and interactivity of sports training. The proposed system integrates motion capture, image recognition, and mobile computing technologies to provide athletes with immediate feedback and visual guidance during exercise. Using a smartphone's camera and built-in sensors, the system tracks body movements, analyses posture accuracy, and overlays virtual training cues on the real-world scene. A real-time data processing module based on lightweight computer vision algorithms ensures low latency and high stability in dynamic environments. Experimental results demonstrate that the proposed system improves training efficiency, reduces technical errors, and enhances user engagement compared with traditional video-based training. This research provides a practical reference for the application of augmented reality and intelligent interaction technologies in the sports training domain. Keywords: augmented reality; sports training; real-time assistance; mobile computing; computer vision. DOI: 10.1504/IJICT.2026.10077715
Abstract: With the rapid development of artificial intelligence, generative adversarial networks have been widely applied to music generation. However, existing methods still face limitations in multi-style control, temporal coherence, and sound quality enhancement. To address these issues, this study proposes a multi-style music generation and sound quality enhancement approach based on an improved deep convolutional generative adversarial network. The method integrates a multi-condition control mechanism, a temporal structure generator, and an adaptive instance normalisation module to improve melody coherence, style controllability, and audio quality. Experimental results show that the proposed method achieves a style classification accuracy of 89.4% and a cross-section coherence of 0.83, with precision and recall of 0.74 and 0.69, respectively. For popular music styles, the generation accuracy approaches 98%, note diversity exceeds 95%, rhythm consistency reaches 0.98, and cross-bar coherence reaches 0.97. These results demonstrate the effectiveness and robustness of the proposed method. Keywords: multi-style music generation; generate adversarial networks; deep convolutional networks; sound quality enhancement; temporal structure generator; TSG; adaptive instance normalisation. DOI: 10.1504/IJICT.2026.10077620
Abstract: A new abstract has been updated. The specific content is as follows: As power communication networks grow more intelligent and complex, traditional management models struggle with information silos, weak knowledge links, and low decision efficiency when handling massive heterogeneous data across equipment lifecycles. To address this, this paper proposes a knowledge graph-based intelligent management method for power communication equipment's full lifecycle. It organises multi-source data from procurement, installation, operation, maintenance, and decommissioning stages, constructs a domain ontology, and uses NLP and entity-relationship extraction to fuse unstructured knowledge. A graph database-stored lifecycle knowledge graph supports state tracing, fault diagnosis, and maintenance recommendations. Experiments show the method integrates equipment chain information, boosts data efficiency, and enhances O&M decision intelligence, supporting reliable power communication system operations. Keywords: knowledge graph; power communication equipment; full life cycle management; intelligent operation and maintenance; graph database. DOI: 10.1504/IJICT.2026.10077695
Abstract: Foreign language anxiety is a key psychological barrier to language acquisition. In this study, we propose a dynamic mitigation framework based on multi-modal conditional generative adversarial networks, which uses cross-modal transformers to fuse speech, text, and facial features in real time to identify anxiety states, and conditionalise them to generate text rewriting, speech adjustment, and visual guidance feedback. Experiments show that the anxiety recognition accuracy of the system reaches 85.3%, and the naturalness score of the generated feedback is significantly better than the baseline (mean opinion score 4.2). Longitudinal studies found that participants who used the system had an average 30% decrease in state anxiety scores and a 25% increase in spoken fluency. This study provides an effective paradigm for developing personalised and adaptive emotion regulation systems. Keywords: adversarial networks; dynamic mitigation; affective computing; conditional generation. DOI: 10.1504/IJICT.2026.10077719
Abstract: Visualisations and their captions are central to data analysis, yet most models still process images and text separately and lose important cross-modal cues. To address this limitation, this paper proposes a fusion framework that jointly represents visualised elements and descriptive language within a shared transformer architecture. First, charts are decomposed into typed visual elements and encoded structurally; then, captions and analytic comments are contextualised and softly grounded to these elements; finally, a gated fusion module and multimodal transformer layers integrate both streams for retrieval, question answering and summary generation. Experiments on three figure-caption benchmarks show that the proposed model outperforms strong visual-only, text-only and dual-encoder baselines, raising top-10 accuracy by up to 2.6 percentage points and BLEU-4 scores by up to 2.8 points while reducing variance across runs. Keywords: visualised data; text embeddings; multimodal fusion; transformer models; figure understanding; information retrieval. DOI: 10.1504/IJICT.2026.10077710
Abstract: Predicting the spread of information in sports social media remains challenging due to the complex interplay between dynamic propagation processes and confounding contextual factors. To move beyond purely correlation-driven approaches, this paper proposes a novel twin-stream temporal transformer architecture integrated with a causal inference module. This model concurrently encodes sequences of match events and social media propagation states, while a counterfactual reasoning component adjusts for potential confounders. Evaluated on a real-world dataset from professional soccer, our framework achieves a root mean square error of 0.412, a mean absolute error of 0.298, and an area under the curve of 0.891, outperforming existing benchmarks by 6.3% to 14.7% across key metrics. The model not only enhances predictive accuracy but also quantifies the causal effect of specific match events, offering both robust forecasting and explainable insights into the drivers of social media engagement. Keywords: social media influence prediction; temporal transformer; causal inference; sports analytics; information diffusion. DOI: 10.1504/IJICT.2026.10077714
Abstract: This paper examines how US tariff changes have affected consumer inflation over the past four decades using linear and quadratic regression models. Combining headline CPI with sub-indices for automobiles and apparel, and applying a 10% trimmed-sample procedure, the analysis tests for both average and nonlinear effects. Once GDP growth, interest rates and unemployment are controlled for, tariffs have little explanatory power for aggregate CPI. Sector-level regressions tell a different story: in autos and apparel small tariff changes barely move prices, but beyond a threshold further increases are associated with sharply higher inflation, consistent with convex, state-dependent pass-through. The findings suggest that tariffs do not operate as a broad engine of US inflation; they mainly reallocate price pressure toward import-intensive consumer goods, with the strength of the response shaped by supply-chain flexibility and market structure. Keywords: tariffs; inflation; consumer prices index; trade policy. DOI: 10.1504/IJICT.2025.10077420
Abstract: In the context of the continuous development of the energy market and increasing competition within the electricity sector, an effective daily fund scheduling model has become crucial for the management and operation of power enterprises. This paper proposes an optimal daily fund scheduling model based on the autoregressive integrated moving average (ARIMA) model, aiming to optimise the fund utilisation and daily operational decisions of power companies. Initially, we establish the ARIMA model, utilising historical sales data for training and validation to forecast future electricity sales volumes. Subsequently, we adjust the electricity sales revenue data to account for the impact of holidays. By comparing the deviation rates before and after the adjustments, we demonstrate that the adjusted model exhibits higher accuracy and stability. Finally, we propose an intra-month adjustment strategy to further refine the daily fund scheduling model, enhancing its adaptability to market changes and holiday effects. Empirical results indicate that the proposed ARIMA-based optimal daily fund scheduling model offers significant advantages in forecasting accuracy and decision-making effectiveness. This model can serve as a valuable reference for the fund management and daily operations of power enterprises. Keywords: autoregressive integrated moving average; ARIMA; daily fund scheduling; monthly day count adjustment. DOI: 10.1504/IJICT.2026.10077694 |
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