International Journal of Information and Communication Technology (13 papers in press)

Regular Issues

• Open Access

  Investigation of a music genre data classification method based on an improved ECAPA-TDNN algorithm ( Free Full-text Access )

  by Dongyan Li, Shuhan Du 
  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
   
  

• Open Access

  Fusing ConvLSTM and graph convolutional network for mapping the suitability of elderly-friendly tourist destinations ( Free Full-text Access )

  by Maodi Liu, Bo Liu 
  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
   
  

• Open Access

  Mobile terminal-assisted interactive English learning design to facilitate knowledge deepening ( Free Full-text Access )

  by Meixue Wang 
  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
   
  

• Open Access

  A fusion architecture of heterogeneous graph neural network and reinforcement learning for business innovation decision-making ( Free Full-text Access )

  by Fangyi Gu, Chenlu Jia 
  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
   
  

• Open Access

  Cross-modal sentiment analysis of new media content based on an enhanced question-answering framework ( Free Full-text Access )

  by Xirui Zhang 
  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
   
  

• Open Access

  EC-RLPA: a dynamic pricing law framework for smart connected vehicles integrating edge computing and reinforcement learning ( Free Full-text Access )

  by Bei Zhou 
  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
   
  

• Open Access

  Knowledge tracing in mathematical problem-solving processes: a spatio-temporal graph neural network approach ( Free Full-text Access )

  by Xia Li 
  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
   
  

• Open Access

  Integrated knowledge graph and reinforcement learning: a graph modelling approach for international trade behaviour analysis and economic trend prediction ( Free Full-text Access )

  by Xiaoxiao Yi, Yao Luo 
  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
   
  

• Open Access

  Early warning of college students' ideological public opinion based on TF-IDF and RFB neural network ( Free Full-text Access )

  by Guixue Tan 
  Abstract: To address the need for real-time early warning of college students' social media opinions, this study proposes a dynamic model integrating term frequency-inverse document frequency (TF-IDF) feature weighting and radial basis function (RBF) neural networks. A subset of 32,715 college-student comments from Tsinghua University's Weibo-100k dataset serves as training samples, with cross-domain validation performed using the ChnSentiCorp benchmark. The approach optimises text feature sparsity via TF-IDF and utilises the nonlinear classification capability of RBF networks for opinion risk categorisation. Experimental results demonstrate an F1-score of 89.7% on the test set - marking a 6.2% improvement over conventional long short-term memory networks - while reducing warning response latency to 12 ms. This confirms high accuracy and real-time performance, providing a lightweight solution for monitoring campus ideological dynamics.
  Keywords: public opinion early warning; TF-IDF features; radial basis neural network; college students' thought dynamics; social media analysis.
  DOI: 10.1504/IJICT.2026.10076066
   
  

• Open Access

  Optimal allocation model network of human resources in energy enterprises based on NSGA-II ( Free Full-text Access )

  by Zhenhua Fan 
  Abstract: Against the backdrop of the 'Dual Carbon' goal and the intelligent transformation of energy systems, energy enterprises face core human resource allocation challenges, including 60% fluctuations in demand between peak and valley, multi-objective optimisation, and complex skill matching. Traditional static methods lead to a 40%-50% labour cost ratio and an over 35% skill mismatch rate. This study proposes a dynamic optimisation model with a 'forecasting-optimisation-real-time adjustment' closed-loop framework, adopting ARIMA-GARCH (±7% error) and IE-NSGA-II for four-objective optimisation. Empirical tests on a provincial power grid show the model reduces labour costs and carbon emissions by 17.3% and 10.5%, respectively, while improving efficiency and satisfaction by 21.8% and 18.6%, respectively.
  Keywords: NSGA-II algorithm; energy firm; human resource allocation; dynamic optimisation; multi-objective decision-making; carbon emission constraints.
  DOI: 10.1504/IJICT.2026.10076333
   
  

• Open Access

  Multimodal deep learning for evidence assessment with algorithmic bias analysis in criminal law ( Free Full-text Access )

  by Huishan Li 
  Abstract: This study integrates multimodal deep learning techniques for evidence assessment to investigate algorithmic fairness in the criminal justice system. The proposed approach predicts criminal charges and evaluates bias related to age and ethnicity by analysing demographic data, online crime reports, and historical records. Convolutional and recurrent neural networks with fairness-aware regularisation are employed to balance equity and predictive accuracy. While algorithmic crime prediction can assist judicial decision-making, it often faces criticism for bias, limited transparency, and lack of interpretability. The primary objective of this research is to predict charge severity while ensuring fairness and transparency. Prior studies have emphasised deep learning applications in fairness-aware algorithms and legal decision prediction, as well as potential racial bias in tools like COMPAS. Using extensive government statistics and crime narratives, ConvLSTM and Bi-LSTM models achieved superior performance, with macro-average F1 scores up to 0.86, while fairness regularisation reduced demographic disparities.
  Keywords: algorithmic crime prediction; deep learning models; bi-LSTM/RNN; ConvLSTM architecture; neural network classifiers; risk assessment instruments; RAIs; algorithmic fairness; bias in criminal justice.
  DOI: 10.1504/IJICT.2026.10076181
   
  

• Open Access

  College students' career planning for the development of low-carbon renewable energy economy ( Free Full-text Access )

  by Yunwei Dong 
  Abstract: This study examines the impact of low-carbon renewable energy economic development on college students' career planning. Findings reveal rapid industry growth but a significant talent shortage, with only 15% of students considering careers in this sector due to limited awareness. The paper proposes enhancing industry promotion, improving relevant knowledge and skills, expanding internships and employment channels, and calls for governmental and societal support to foster sustainable industry growth and talent cultivation.
  Keywords: low carbon economy; employment; renewable energy; market research; career planning; survey research.
  DOI: 10.1504/IJICT.2026.10076117
   
  

• Open Access

  The synergy of educational resource allocation and teacher motivation based on NSGA-II model ( Free Full-text Access )

  by Xueyao Wang, Jin Xu 
  Abstract: This article proposes a collaborative optimisation model for educational resource allocation and teacher incentive mechanism based on NSGA II. By simulating various allocation and incentive strategies, the model quantitatively analysed their interactions. The results indicate a significant synergistic effect: optimising coordination can improve student performance and teacher efficiency. Once the incentive intensity reaches the threshold, the teaching quality and teacher participation significantly improve, while the turnover rate decreases. Research has shown that combining appropriate resource allocation with incentive design can effectively improve educational outcomes. This method provides a scientific basis for resource allocation, offers a new perspective for incentive mechanism design, and has significant practical application value.
  Keywords: NSGA-II model; educational resource allocation; teacher motivation; multi-objective optimisation; quality of teaching.
  DOI: 10.1504/IJICT.2026.10076180