Forthcoming Articles
International Journal of Information and Communication Technology
Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.
Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.
Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.
Online First articles are also listed here. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.
Articles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.Register for our alerting service, which notifies you by email when new issues are published online.
International Journal of Information and Communication Technology (22 papers in press) Regular Issues
Abstract: To address the issue of low accuracy in music genre data classification, this study proposes an improved model based on the integrated Res2Net module extended context-aware parallel aggregation-time delay neural network (ECAPA-TDNN). The study adopts the pro-WGAN strategy to balance the fma_cedium dataset, and generates feature data through nested pro-WGAN loops for undersampling schools with more samples and schools with fewer samples. The results show that the improved ECAPA-TDNN model has a classification accuracy of 89%, which is 11.6% higher than the original ECAPA-TDNN model. The training time is only 32.22 seconds, and data balancing processing significantly improves classification performance. The results provide an efficient solution for music type classification. Keywords: ECAPA-TDNN algorithm; music genre; data classification method; ResNet architecture; BiLSTM; pro-WGAN; multifeature fusion. DOI: 10.1504/IJICT.2026.10076436
Abstract: With the increasing aging of the population, the selection of elderly-friendly residential places has become an urgent problem. In this paper, we propose a model CSGNet, which combines ConvLSTM and graph convolutional network (GCN), to construct a suitability map of elderly-friendly residential areas. The model combines the advantages of ConvLSTM and GCN and is able to effectively capture the temporal evolution patterns and spatial adjacencies in heterogeneous data from multiple sources. In the scheme, dynamic features are first processed using ConvLSTM; then, potential connections at the spatial level are modelled by GCN; finally, CSGNet makes suitability maps. The experimental results demonstrate that CSGNet surpasses other comparative models in predicting suitability scores and spatial distribution, exhibiting superior accuracy and spatial-temporal feature fusion capabilities, thereby offering an effective solution for assessing elderly-friendly residential areas. Keywords: elderly-friendly residential locations; ConvLSTM; graph convolutional network; GCN; suitability map. DOI: 10.1504/IJICT.2026.10076451
Abstract: This study designs and evaluates a mobile-assisted language learning system that integrates knowledge graphs with multimodal interaction including touch, voice, and augmented reality to support deep knowledge comprehension. A controlled experiment compared an experimental group using the proposed system against control groups using either a Bayesian knowledge-tracing tutor or a conventional multimodal application. Results demonstrated that the experimental group achieved significantly higher post test scores, with a learning gain improvement of nearly 40%. Cognitive load measurements showed a significant reduction, supported by a greatly increased effective interaction ratio. Over 85% of interactions were meaningful, contributing to enhanced knowledge structure formation. These outcomes confirm that tightly coupling knowledge-aware scaffolding with multimodal mobile interaction improves learning efficiency, reduces cognitive burden, and supports integrated knowledge framework development. This research provides theoretical and practical implications for designing intelligent, cognitive-friendly mobile learning systems. Keywords: mobile learning; knowledge graph; multimodal interaction; cognitive load; experimental study. DOI: 10.1504/IJICT.2026.10076452
Abstract: Business innovation decision-making hinges on the integration of insights from complex, multi-source heterogeneous data, which can be naturally represented using heterogeneous graphs. Traditional approaches often fall short in capturing the rich semantics and dynamic nature of such structures. This paper proposes a novel framework for business innovation decision-making by integrating heterogeneous graph neural networks with reinforcement learning. The model employs meta-path-enhanced Heterogeneous graph neural networks to perceive environments and learn rich node and graph representations. A dual-level attention mechanism (node and semantic levels) adaptively fuses heterogeneous information. A hybrid reward function combining immediate returns and long-term innovation potential mitigates reward sparsity and promotes sustainable optimisation. Extensive experiments on real and simulated business graphs show the framework outperforms state-of-the-art methods, achieving a 20% higher cumulative return, 85% accuracy in innovation pathway identification, and significantly improved strategic adaptability in dynamic pricing. Keywords: heterogeneous graph neural networks; reinforcement learning; business innovation decision-making; meta-paths; reward function design. DOI: 10.1504/IJICT.2026.10076453
Abstract: As new media grows quickly, sentiment analysis is very important for comprehending what it says. But standard ways of analysing sentiment cannot fully capture multimodal sentiment elements. This research introduces a cross-modal sentiment analysis model for new media content, utilising an upgraded question-answering framework known as EQA-CMSA, to tackle this issue. By creating a new cross-modal fusion mechanism and using an improved question-answering framework, this approach makes sentiment analysis more accurate. First, the input multimodal data is pre-processed, and features are taken out. Then, modal alignment successfully combines several types of modal information. Finally, adaptive weighted fusion strategies are used to weigh the modal information. The experimental results show that the EQA-CMSA model performs better than other multimodal sentiment analysis models, with an accuracy rate of 82.4%. It also exceeds existing models in other indicators and has good sentiment classification performance. Keywords: enhanced question-answering framework; new media content; cross-modal sentiment analysis. DOI: 10.1504/IJICT.2026.10076454
Abstract: Targeting regulatory compliance challenges in smart connected vehicle (SCV) dynamic pricing, this study proposes edge-cloud reinforcement learning pricing architecture (EC-RLPA) a collaborative edge computing and reinforcement learning pricing architecture. The framework processes HighD highway trajectory data via edge nodes to extract real-time traffic states, while fusing New York taxi demand patterns to generate dynamic prices in the cloud through a multi-agent proximal policy optimisation (MAPPO) algorithm. Notably, we innovatively embed geofencing (OpenStreetMap) and price elasticity constraints, converting regulatory requirements [e.g., European Union Digital Services Act (EU DSA Act)] into reinforcement learning reward functions. Experiments show that the system improves pricing law to 92.3% and reduces decision latency by 67% in a simulation environment, while reducing raw data transfer by 80%. This research provides critical technical support for implementing intelligent transportation policies with real-time compliance assurance. Keywords: edge computing; reinforcement learning; dynamic pricing; regulatory compliance; smart connected cars. DOI: 10.1504/IJICT.2026.10076455
Abstract: A novel modelling method incorporating spatio-temporal graph neural networks is proposed for the knowledge tracking problem in the process of mathematical problem solving. In the spatial dimension, the complex relationships among three types of nodes, namely, students, exercises and knowledge points, are integrated through a heterogeneous graph structure, and a knowledge graph is dynamically constructed to capture the implicit associations among concepts; in the temporal dimension, a dual time encoder and a memory gate mechanism are introduced to differentiate the semantic differences between long and short time intervals, and a temporal sequential convolution network is used to model the cumulative evolution characteristics of the problem solving sequences. It is experimentally demonstrated that the prediction accuracy of this method is improved by 5.3%6.7%, which significantly optimises the ability to track the dynamic changes of students cognitive state and provides more robust decision support for personalised learning path recommendation. Keywords: mathematical problem-solving process; knowledge tracing; spatio-temporal graph neural network; STGNN. DOI: 10.1504/IJICT.2026.10076456
Abstract: In this paper, a unified framework of knowledge graphs (KG) and reinforcement learning (RL) is suggested to understand the behaviour of international trade and forecast economic trends. The framework builds a dynamic knowledge graph of key entities in international trade and their historical interactions. The unstructured trade reports and economic bulletins are analysed by means of natural language processing and combined with the structured data of the World Bank and the World Trade Organization. The graph neural network encoder pulls out relational representations of the knowledge graph and inputs the information into a deep reinforcement learning agent to maximise prediction of trade policies and produce strategic advice. The reward component takes into consideration such economic indicators as the GDP influence, change in trade balance, and geopolitical risk. Simulation on historical trade conditions show better prediction using simulation as opposed to the econometric model used in the past. Keywords: knowledge graph; reinforcement learning; international trade analysis; economic trend prediction; graph neural networks; GNNs. DOI: 10.1504/IJICT.2026.10076457
Abstract: To address the challenges of slow dynamic market response and insufficient multi-source information integration in sports sponsorship valuation, this study proposes a dynamic evaluation model based on neural-symbolic fusion learning. Traditional methods struggle to capture real-time fluctuations in brand value during sporting events, while single-source data analysis fails to comprehensively measure sponsorship effectiveness. This approach integrates real-time perception from neural networks with domain knowledge from symbolic systems to construct an intelligent evaluation framework that simultaneously processes media exposure, social media sentiment, and brand health metrics. Experiments on the Nielsen sports sponsorship dataset demonstrate that this model reduces prediction errors by 32% compared to mainstream evaluation methods and captures value fluctuations at critical event milestones with greater precision. This research provides a next-generation evaluation tool for sponsors investment decisions and event organisers rights management. The ongoing digital transformation within the sports industry, characterised by datafication and real-time analytics, further underscores the timeliness and relevance of developing advanced evaluation tools like the one proposed in this study. Keywords: neural symbolic learning; sports sponsorship; dynamic evaluation; multi-source data. DOI: 10.1504/IJICT.2026.10076508
Abstract: To address the significant challenges such as insufficient personalisation and poor dynamic adaptability in vocational education resource matching, this paper proposes an e-commerce professional education resource matching system based on collaborative multi-agent quality-learning. The system achieves dynamic and highly precise resource recommendations by enabling multiple intelligent agents to collaboratively perceive students knowledge states, learning behaviours, and environmental contexts. Experiments on the publicly available educational datasets xuetangx and massive open online course demonstrate that compared to traditional widely-adopted collaborative filtering and recent deep learning-based recommendation methods, this system achieves a 12.5% and 7.3% improvement in recommendation accuracy, respectively, while increasing resource utilisation by 18.2%. This approach significantly enhances learning completion rates and overall user satisfaction. This research provides a novel technical pathway and valuable practical reference for personalised resource adaptation in vocational education. Keywords: multi-agent Q-learning; vocational education; resource allocation; e-commerce major. DOI: 10.1504/IJICT.2026.10076511
Abstract: To address issues such as data sparsity, cold-start problems, and lack of interpretability in existing career recommendation methods, this paper proposes a career planning recommendation approach that integrates knowledge graphs with reinforcement learning. The method first constructs a unified knowledge graph that fuses multi-source information including users, positions, and skills. It then designs a hierarchical reinforcement learning inference mechanism that generates recommendations through multi-hop path exploration by agents within the graph, while simultaneously providing interpretable reasoning paths. Experiments on the public dataset career knowledge graph 15K demonstrate that our method achieves precision@10 of 0.782 and normalised discounted cumulative gain @10 of 0.815. Compared to the optimal baseline model, this represents significant improvements of approximately 6.3% and 5.1%, respectively. Notably, our approach exhibits enhanced robustness and interpretability, particularly in cold-start scenarios. Keywords: knowledge graph; career recommendation; reinforcement learning; explainability. DOI: 10.1504/IJICT.2026.10076512
Abstract: Recently, in order to save energy, there is increasing concern on reducing the weight of electric box-type trucks. Traditional design methods implement finite element analysis (FEA) repeatedly for large truck structures, which is extremely time-consuming. To solve this, we propose a framework that combines a ResNet152-based deep learning model with particle swarm optimisation. The deep learning model predicts key structural metrics, such as weight, maximum stress, maximum displacement, and energy absorption, while particle swarm optimisation searches the design space and tries to find the optimal weight under specific mechanical constraints. We evaluate our proposed method on a generated truck frame dataset, achieving an average R2 of 0.993. Compared with the baseline models, our method achieves the most reduction in weights and the least computational cost, proving its better reliability and efficiency than other optimisation methods. Keywords: electric trucks; lightweight design; deep learning; surrogate modelling; particle swarm optimisation. DOI: 10.1504/IJICT.2026.10076513
Abstract: This study examines the intervention effect of online CBT on depressive symptoms in college students. 48 students with obvious depressive symptoms at a Nanchang-based university were randomly divided into online and control groups. The intervention group received eight online interventions over six weeks using communication tools such as WeChat and artificial intelligence tools. After intervention, the HAMA-14 and BDI scores of the intervention group were significantly lower than those of the control group, and the improvement effect in sleep time, sleep efficiency, and total score of the emotion regulation difficulty scale was better than that of the control group (p < 0.05). The total effective rate of the intervention group reached 91.67%, significantly higher than the control groups 37.50% (p < 0.05). Online cognitive behavioural therapy can effectively relieve college students depressive symptoms, enhance sleep quality and emotion regulation capacity, yet exerts no notable impact on the dimension of recognising their own emotional responses. Keywords: online cognitive behavioural therapy; depressive symptoms; emotion regulation; psychological health intervention. DOI: 10.1504/IJICT.2026.10076514
Abstract: To address the challenge that existing machine translation systems struggle to adaptively handle texts of varying complexity, this paper proposes an adaptive multi-engine optimisation framework combining cognitive load theory and reinforcement learning. The framework quantifies the lexical, syntactic and semantic complexity of the source text as the cognitive load state, and uses the proximal strategy optimisation algorithm to learn the decision strategy of dynamically selecting the optimal translation engine. Experiments on the Workshop on Machine Translation 2019 English-German translation real dataset show that the proposed method achieves 45.2 in Bilingual Evaluation Understudy index, which is 2.1 points significantly higher than the current advanced baseline model, and the translation edit rate is also significantly optimised. All improvements are statistically significant (p < 0.05). This study provides a solution with both theoretical basis and practical value for building an intelligent and robust adaptive translation system. Keywords: adaptive machine translation; reinforcement learning; cognitive load theory; multi-engine optimisation. DOI: 10.1504/IJICT.2026.10076515
Abstract: To address urgent climate challenges, reducing carbon emissions is a core goal of global energy transformation. Renewable energy, such as wind and solar, is increasingly replacing fossil fuels. Smart grids facilitate this shift by enabling efficient distribution and large-scale integration of renewables. However, their growing share raises critical questions about optimal dispatch and emission reduction. This study employs big data analytics and machine learning to investigate carbon emission prediction and management through renewable energy and smart grid synergy. A data-driven prediction model was developed using decade-long energy consumption and emission data from Heilongjiang Province, China. Simulations show that introducing 30% renewable energy reduces carbon emissions by approximately 12%. With smart grid optimised scheduling, emissions decrease a further 8%, demonstrating its vital role in advancing low-carbon energy systems. Keywords: renewable energy; smart grids; carbon emissions; data analysis. DOI: 10.1504/IJICT.2026.10076516
Abstract: Electric vehicles (EVs) are gaining rapid popularity, necessitating the development of advanced and environmentally sustainable charging infrastructures. This study presents an AI-driven optimisation framework for designing a smart charging control device installed alongside distribution transformers. The proposed system leverages artificial intelligence algorithms to predict energy demand, analyse user behaviour and dynamically adjust charging schedules in real time to improve efficiency. By minimising operational costs, reducing grid congestion, and enhancing energy utilisation, the system maximises overall efficiency and sustainability. Integrating reinforcement learning and predictive analytics further enables adaptive responses to the evolving needs of EV users. Additionally, the system promotes the use of renewable energy sources such as solar and wind power to minimise environmental impact. Experimental results confirm the systems effectiveness in stabilising the grid, optimising energy distribution, and lowering consumer charging costs, demonstrating its scalability and eco-friendly potential within existing infrastructure. Keywords: smart charging; artificial intelligence; optimisation; electric vehicles; grid stability; renewable energy. DOI: 10.1504/IJICT.2026.10076517
Abstract: Global energy demand surge and worsening environmental issues make optimising corporate energy management key to boosting efficiency, cutting costs and achieving sustainability. This study proposes a multi-level, modular decision support system (DSS) architecture for enterprise energy management optimisation, based on big data analysis. It integrates deep learning, reinforcement learning and digital twins, using genetic algorithms (GA) for global search (e.g., multi-energy allocation) and particle swarm optimisation (PSO) for faster-convergent local refinement. Lighting systems account for 47% of production auxiliary energy; office devices take 63.9% of administrative consumption. A 500+-device factory saw energy utilisation rise from 82% to 92% via the system. Analysis shows production-linked consumption (52.7% of total) has core equipment contributing 88.3%. Keywords: big data analytics; energy management optimisation; decision support system; DSS; reinforcement learning; digital twin technology; particle swarm optimisation; PSO. DOI: 10.1504/IJICT.2026.10076518
Abstract: This study aims to use advanced machine learning models and big data analytics (BDA) to investigate online marketplace client loyalty. To capture nonlinear correlations and identify significant loyalty determinants, the suggested framework uses algorithms like LSTM, XGBoost, support vector machines, and random forests, rather than typical statistical techniques. The study delves into the elements that influence consumer engagement, the frequency of purchases, and the likelihood of repurchases within the fresh food platform and pet-related e-commerce sectors. A ten-phase framework covering foundation, data collection, preprocessing, AI development, pilot study, validation, and evaluation is employed to ensure methodological rigour. When compared to more traditional methods of consumer loyalty prediction, LSTM performs better on measures including accuracy, precision, recall, F1 score, and area under the curve (AUC). The findings highlight the role of personalised recommendations, delivery services, and mobile engagement in shaping loyalty, while offering practical strategies for sustainable growth in vertical e-commerce markets. Keywords: e-commerce; consumer loyalty; big data analytics; BDA; machine learning; LSTM; AI marketing; recommendation systems; vertical e-commerce; customer retention; predictive modelling. DOI: 10.1504/IJICT.2026.10076519
Abstract: Procurement budget forecasting for low-value consumables is critical for corporate cost control. Addressing the limitations of traditional statistical methods in handling demand fluctuations, this study proposes a hybrid machine learning model integrating seasonal decomposition with random forest. Validated using public supply chain datasets, this model reduces the mean absolute percentage error of budget forecasts to 12.3%; significantly outperforming the autoregressive integrated moving average model (18.5%) and linear regression methods (16.2%). Experimental results demonstrate that by integrating temporal characteristics of historical procurement data with external influencing factors, the model achieves a coefficient of determination of 0.89 on the test set. The weighted mean absolute percentage error metric is reduced by approximately 35% compared to baseline methods, providing enterprises with a more precise budget forecasting tool for procurement decision-making. Keywords: low-value consumables procurement; budget forecasting; machine learning; random forest model; WMAPE.
Abstract: To address the issue of insufficient personalised adaptability in dynamic emotional interventions, this paper proposes a value-oriented meta-adaptive reinforcement learning framework. By integrating meta-learning and reinforcement learning, the framework constructs a dual-level learning architecture capable of rapidly adapting to individual emotional dynamics with minimal interactions. The model employs a multi-objective reward function to synergistically optimise intervention effectiveness, user engagement, and safety. Experiments on public datasets such as Emotional Support Conversation dataset and Distress Analysis Interview Corpus Wizard of Oz demonstrate that our approach achieves an emotional state improvement rate of 0.78 and a user satisfaction score of 0.82. These results represent significant improvements over traditional deep reinforcement learning and meta-learning baseline models, providing an effective computational paradigm for addressing adaptability challenges in personalised psychological interventions. Keywords: meta-adaptive reinforcement learning; value-based learning; affective computing; psychological intervention.
Abstract: Generative artificial intelligence technology provides new insights for art image inpainting and semantic reconstruction. To address the problem of semantically incorrect restoration content in existing research, this paper first optimises generative adversarial network by combining gated convolution with spectral normalisation. Based on this, an image inpainting and semantic reconstruction method is built by integrating text and art image features. The text disentanglement module of the suggested method can obtain key textual features that help restoration. A cross-modal attention module is designed to ensure restored results are as consistent as possible with text semantics. A dual channel reconstruction module is also designed to enhance the networks ability to predict image structure and text semantics. Experimental results show that the frechet inception distance (FID) of the proposed method is 3.01, which can restore realistic art images satisfying textual semantics. Keywords: image restoration; semantic reconstruction; generative adversarial networks; multimodal features; cross-modal attention.
Abstract: Delivering temperature-sensitive goods to remote mountain regions is difficult due to sparse infrastructure and challenging terrain. Unmanned aerial vehicles (UAVs) offer a practical option for fast, last-mile transport in such areas, but ensuring cold-chain reliability during flight remains a problem. We present a UAV-based cold chain logistics framework that integrates a hybrid path-planning approach, combining genetic algorithms with simulated annealing to optimise routes for both energy efficiency and terrain constraints. Onboard IoT sensors continuously record temperature and humidity, allowing real-time intervention if cargo conditions drift from required ranges. Tests with four real-world datasets showed up to a 14% reduction in energy use, faster delivery times, and improved temperature stability compared to baseline planners. These results suggest that combining intelligent routing with in-flight environmental monitoring can make UAV cold-chain delivery more reliable in difficult environments. Keywords: UAV logistics; cold chain delivery; genetic algorithm; simulated annealing; internet of things; IoT. |
Open Access
Keep up-to-date
-
Our Blog -
Join us on Bluesky -
Follow us on X -
Visit us on Facebook - Our Newsletter (subscribe for free)
- RSS Feeds
- New issue alerts
- Inderscience is a member of publishing organisations including:
-
-
