Forthcoming Articles
International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (16 papers in press) Regular Issues
Abstract: The diversity and complexity of new media information bring great pressure to personalised information recommendation, so intelligent terminals need to be improved with the support of reliable recommendation algorithms. This paper proposes a time-aware (TA) multi-hop path recommendation inference model TACKG-TDPRec to improve the intelligent push effect of personalised information on new media platforms, and introduces the TA path diversity reasoning method, uses time information to improve the accuracy of recommendation results, and enhances personalised diversity rewards on the basis of personalised diversity rewards designed according to user needs. It can be seen that the AUC of the recommended model is as high as 97.94, which is much higher than other models of the same type. From the diversity comparison, it can be seen that TACKG-TDPRec model can adapt to various types of information recommendation needs, and the similarity of items is low, so it has strong practicability. Keywords: new media; personalisation; information; intelligent push. DOI: 10.1504/IJICT.2025.10073271
Abstract: This article presents a dynamic fuzzy neural network (DFNN) based intelligent classification approach for oil painting styles. When it comes to images like oil painting styles, which are high-dimensional, sophisticated and feature a lot of fuzzy elements, traditional oil painting style classification techniques still provide difficulties. DFNN teaches the deep features of oil painting images from data automatically by combining fuzzy logic and neural networks. Moreover, the dynamic learning mechanism of DFNN helps it to dynamically modify its structure and parameters in response to changes in the training data, hence preserving excellent classification accuracy in the face of new oil painting styles or style evolution. The testing results reveal that the technique greatly surpasses the conventional one in many respects, thereby offering fresh technical assistance for the automatic identification of oil paintings and other sectors. Keywords: oil painting style classification; dynamic fuzzy neural network; DFNN; intelligent classification; feature extraction; dynamic learning. DOI: 10.1504/IJICT.2025.10073282
Abstract: This study addresses the challenge of autonomous navigation for intelligent mobile robots (IMRs) operating in dynamic environments by proposing a navigation framework that integrates an improved Google cartographer algorithm with a hybrid path planning strategy. The enhanced cartographer algorithm incorporates a KD-tree-based keypoint extraction technique for point cloud data, effectively reducing the amount of data required for point cloud matching to 10%20% of the original volume. Furthermore, an adaptive loop closure detection mechanism is introduced, leading to a reduction of approximately 20% in mapping error. For path planning, a hybrid algorithm combining A* global planning with timed elastic band (TEB) local optimisation is developed. This approach dynamically adjusts the robots pose sequence and time intervals, achieving a 98% success rate in obstacle avoidance while increasing path length by only 5%10%. The planning cycle remains consistently within 100 ms. The proposed system demonstrates robust performance across practical scenarios, including warehouse logistics (with a 40% increase in handling efficiency) and medical delivery (achieving an 80% task completion rate). This research presents an efficient and scalable solution for autonomous navigation in complex dynamic environments, contributing both algorithmic innovation and significant engineering applicability. Keywords: robot operating system; ROS; simultaneous localisation and mapping; SLAM; cartographer algorithm; adaptive loop closure detection; hybrid path planning; dynamic obstacle avoidance. DOI: 10.1504/IJICT.2025.10073328
Abstract: With the increasing demand for tourism English learning, traditional learning mode analysis methods have limitations in capturing dynamic behaviour and personalised recommendations. This article proposes a tourism English learning mode mining framework that integrates temporal clustering and ensemble learning, aiming to extract multidimensional learning features from time series data and construct a high-precision prediction model. Firstly, the behaviour trajectory of learners is segmented using temporal clustering algorithm to identify their time distribution characteristics and knowledge mastery rhythm at different learning stages; secondly, an ensemble learning model is used to fuse multi-dimensional features of clustering results, achieving learning effect prediction and pattern classification. In addition, the study revealed the nonlinear correlation between contextualised vocabulary memory and listening and speaking ability development in tourism English learning, providing data-driven decision support for the development of adaptive learning systems. Keywords: temporal clustering; ensemble learning; attention mechanism; tourism English. DOI: 10.1504/IJICT.2025.10073371
Abstract: A chain fault risk assessment method based on transformer-federation migration learning algorithm is proposed. Firstly, this paper describes the chain fault evolution path of AC-DC hybrid grid, constructs fault simulation model, and clarifies the selection principle of the initial fault set of the accident chain. Secondly, the chain fault probability assessment model based on the accident chain is established according to the key indicators of the accident chain, the weighted fuzzy C-mean clustering algorithm is used to cluster and analyse the correlation indicator values, and the fault set feature extraction module is obtained according to the transformer model. Finally, a multimodal transformer architecture based on federated learning cooperative work is designed to realise the accurate estimation. The experimental results show that the proposed method for AC-DC hybrid grids has a good generalisation capability and can quickly and accurately determine the fault set feature extraction module. Keywords: AC-DC hybrid grid; accident chain; transformer model; federated migration learning; fault analysis; risk assessment. DOI: 10.1504/IJICT.2025.10073372
Abstract: To address the issues of high computational resource consumption and low clustering efficiency in big data clustering, this paper first proposes the density deviation sampling improvement algorithm (EDDS). Then, each cluster node independently performs clustering on a subset of the big data to generate initial local clustering results. Next, using the EDDS algorithm on each node, representative data subsets are extracted, and these subsets are aggregated into a sample set that reflects the characteristics of the entire big dataset. Finally, further clustering analysis is performed on this sample set. By integrating the local clustering information from each node using the clustering results, a comprehensive clustering result for the entire big dataset is output. Experimental results demonstrate that, compared to traditional clustering methods, the suggested approach effectively combines the efficiency of parallel processing with the accuracy of integrated analysis. Keywords: big data clustering; distributed computing; density deviation sampling; node sampling; two-stage clustering. DOI: 10.1504/IJICT.2025.10073373
Abstract: As educational informatisation progresses, optimising personalised learning paths has become a focal point. Static learning paths cannot meet learners diverse and dynamic needs. We present a dynamic personalised learning path optimisation approach using knowledge graphs. By leveraging knowledge graphs association and representation, it analyses learner characteristics and learning resource attributes. Then, it builds a precise learning path model and monitors learners real-time status. This allows dynamic adjustment of learning path node sequences and content presentation to fit individual learner differences. Experiments show it boosts learning efficiency, cuts learning time and error rates, and improves knowledge understanding. This study offers fresh ideas for personalised learning path optimisation, holding theoretical and practical importance. It can boost educational informatisation and aid in the personalised allocation and efficient use of educational resources. Keywords: knowledge graph; personalised learning path; dynamic optimisation; educational informatisation; personalised allocation of resources. DOI: 10.1504/IJICT.2025.10073374
Abstract: Due to the huge amount of information interested by users on e-commerce platforms, it is difficult to predict consumers purchasing behaviour. To this end, this paper first forms a session graph based on consumers session sequences. Meanwhile, modelling inter-item multivariate relationships and inter-session cross-information through graph convolutional networks. Then the users intention representation is generated through comparative learning. Then we construct a behavioural model of user consumption based on attention network. Finally, this paper calculates the ratings of users with purchasing behaviours on the target items, and obtain several items with high ratings to generate a recommendation list to predict e-commerce consumers behaviours. Experiments are conducted on two public datasets, and the results show that the accuracy of the proposed model is improved by at least 3.31% and 5.21% respectively, which effectively improves the accuracy of e-commerce consumer behaviour prediction. Keywords: e-commerce consumer behaviour prediction; collaborative filtering; graph convolutional network; GCN; attention network; comparative learning. DOI: 10.1504/IJICT.2025.10073375
Abstract: As the digital age moves quickly, automatic recognition and correction of English text has become a significant job in the field of natural language processing (NLP). Most traditional ways of correcting text use simple statistical models and manual procedures, which do not work well with complicated grammatical, spelling, and semantic mistakes. This paper suggests an English text recognition and correction framework called MT-Tec, which is based on the improved transformer model and the masked embedding technique. MT-Tec can find and fix spelling mistakes, grammar mistakes, and vocabulary mistakes through multilevel context modelling and accurate error correction mechanisms. The MT-Tec framework works very well with many kinds of text errors and text qualities, and it is especially good at handling low-quality text. In general, the MT-Tec framework can be quite helpful for automatic proofreading, revising text, and learning a new language. Keywords: English text recognition and correction; improved transformer; masked embedding; natural language processing; NLP. DOI: 10.1504/IJICT.2025.10073376
Abstract: To address sentiment inaccuracies in civic education contexts, this study proposes a data-driven analytics framework integrating domain-adaptive feature engineering with hierarchical modelling. We construct Chinese social media corpora (Weibo/WeChat) through keyword-filtered crawling and interaction-weighted prioritisation, reducing noise by 42%. A hybrid feature space combines TF-IDF lexical patterns, syntactic POS distributions, Word2Vec/BERT embeddings, and HowNet-derived sentiment features. The core classification employs a Bi-LSTM model with attention mechanisms, dynamically weighting sentiment-bearing terms while compensating for category imbalance via class-weighted cross-entropy loss. Crucially, ideological semantics are mapped through logistic regression classifiers trained on annotated civic categories. Experimental results demonstrate: 1) attention weights effectively localise civic sentiment triggers; 2) domain feature fusion improves classification robustness; 3) semantic mapping achieves 89.2% accuracy in civic topic identification. This methodology enables real-time Kafka-based opinion monitoring while preserving interpretability for educational governance. Keywords: sentiment analysis; TF-IDF; social media monitoring; Bi-LSTM. DOI: 10.1504/IJICT.2025.10073437
Abstract: To address resource fragmentation and energy inefficiency in cold chain logistics, we propose sensor-trust integrated resource synergy (STIRS) - a blockchain-secured framework integrating internet of things (IoT) sensing with thermodynamic optimisation. The architecture establishes a dynamic trust model that quantifies hardware reliability metrics (battery decay, signal strength, calibration cycles) into adaptive credit weights via Sensor Health Index (SHI). Combined with mixed-integer programming and lightweight Byzantine consensus (0.48s latency), it enables real-time co-scheduling with: 1) energy consumption modelling incorporating ambient temperature sensitivity (dT/dt) and door-opening penalties (0.8-1.2 kWh/event); 2) General Data Protection Regulation (GDPR)-compliant homomorphic encryption. Validation using public United States Department of Agriculture-Agricultural Research Service (USDA-ARS) and commercial Taobao datasets (38,700 orders) demonstrates statistically significant improvements: 15-18% energy reduction, 23.7% resource utilisation gain, and 40% decrease in temperature deviation versus four benchmarks. Keywords: cold chain logistics; resource sharing model; IoT sensing technology; dynamic trust assessment; energy consumption optimisation. DOI: 10.1504/IJICT.2025.10073438
Abstract: To address critical bottlenecks in production-oriented approach (POA) English speaking instruction including high feedback delays, inefficient contextual task generation, and suboptimal resource allocation this study proposes an AI-augmented POA framework. We develop a dual-engine architecture integrating dynamic task generation, multimodal resource recommendation, and multidimensional assessment to optimise POAs drive-facilitate-evaluate closed loop. In a 12-week quasi-experiment with 120 computer science graduates, the experimental group (AI-POA) demonstrated significantly higher oral proficiency gains versus traditional POA controls (36.1% vs. 19.2%, p < 0.001), with content elaboration increasing by 22.6%. The framework reduced instructor feedback time per task from 8.2 to 0.3 minutes (27-fold improvement) and lowered cognitive load (NASA-TLX: 42 vs. 65, p < 0.001). Task acceptance reached 92% through cognitive-contextual difficulty adaptation. This work establishes an AI-POA synergy that enhances pedagogical outcomes while substantially alleviating instructor workload. Keywords: production-oriented approach; POA; AI collaboration; English speaking teaching; multimodal assessment; adaptive learning. DOI: 10.1504/IJICT.2025.10073439
Abstract: This study proposes a novel probabilistic inference framework leveraging knowledge graphs (KG) to address sparsity and implicitness challenges in historical event causality. Key innovations include: a dynamic event embedding (DEE) model incorporating a temporal decay factor to capture the dynamic weakening of causal strength over time, and a causal graph neural network (CauGNN) utilising directional propagation and cross-event attention for modelling causal transmission between discontinuous events. Evaluated on the event-centric knowledge graph (EventKG) dataset spanning centuries, the method achieves 89.2% causal inference accuracy - a significant 12.7% improvement over state-of-the-art approaches and a low temporal prediction deviation of 5.2 years. This work establishes a mathematical model for historical causal decay, shifts computational historiography toward quantitative causal reasoning, and provides verifiable tools for historical analysis, education (via the HistVis platform), and societal risk extrapolation. Keywords: causal inference; knowledge graph; dynamic event embedding; DEE; historical event analysis. DOI: 10.1504/IJICT.2025.10073440
Abstract: Addressing the dual challenges of textual vulnerability to noise and inefficient cross-modal interaction in Chinese multimodal sentiment analysis, this paper introduces a novel framework enhanced by a cross-modal text enhancement module (CTEM). The CTEM adaptively recalibrates semantic representations of Chinese text through contextual refinement. Concurrently, a cross-modal attention mechanism directs visual and acoustic feature extraction, enabling synergistic fusion across modalities. Evaluated on the Chinese single and multimodal sentiment (CH-SIMS) benchmark (featuring unaligned video segments and dual sentiment labels), our model achieves 83.2% accuracy surpassing mainstream baselines by up to 3.2% with a 0.029 F1-score gain. Ablation studies confirm the critical contributions of both the CTEM representation refinement and cross-modal interaction design. This work establishes a robust paradigm for decoding nuanced sentiment in linguistically complex Chinese multimedia content. Keywords: cross-modal text information enhancement; multimodal sentiment analysis; Chinese semantic understanding; feature fusion; attention mechanism. DOI: 10.1504/IJICT.2025.10073441
Abstract: Serverless computing has become a mainstream cloud paradigm due to its elasticity in resource utilisation. However, it introduces high arrival rates and time-varying resource demands, making centralised network-aware scheduling approaches a bottleneck, especially in large resource pools. To address this, we propose a distributed, network-aware scheduling scheme where multiple agents make concurrent decisions. While this design improves scalability, it also brings challenges such as decentralised resource modelling, notification overhead, and decision contention. To mitigate these issues, we carefully design the DFaR placement algorithm and the MA migration algorithm. Simulations show that, compared to centralised approaches, DFaR achieves 23 orders of magnitude higher throughput with modest losses in scheduling objectives, and MA adapts to future resource fluctuations with performance comparable to prediction-based strategies. Keywords: serverless computing; distributed scheduling; scheduling speed; network awareness. DOI: 10.1504/IJICT.2025.10073516
Abstract: This study proposes SiamAttn-3D + spatio-temporal scoring module (ST-ScoreNet), an end-to-end framework for objective tennis movement assessment. The SiamAttn-3D tracker employs 3D spatio-temporal attention to achieve robust joint localisation (84.6% success rate at >160 km/h racket speeds), overcoming motion blur and occlusion challenges. Joint trajectories feed into ST-ScoreNet, which integrates graph convolutions and bidirectional gated recurrent unit (GRUs) to model biomechanical constraints and temporal dynamics. Evaluated on the Tennis-ITF dataset, the system attains a 92.3% F1-score in stroke assessment (= 0.89 vs. coach ratings) a 6.9% improvement over state-of-the-art methods. Real-time processing at 23 frames per second (FPS) enables instantaneous feedback, reducing hardware costs by 83% compared to sensor-based solutions. Limitations include sensitivity to weather degradation and athlete anthropometrics, with federated learning proposed for future personalisation. Keywords: twin networks; posture evaluation; tennis motion analysis; spatio-temporal modelling. DOI: 10.1504/IJICT.2025.10073532 |
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