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International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (21 papers in press) Regular Issues
Abstract: Based on internet of things (IoT) technology, this study constructed a monitoring and optimisation model for the interactive effect of classroom teaching. Centring on indicators such as the effective response rate, the level of follow-up inquiries, the ratio of teacher-student interaction pairs, and the number of group interaction rounds, a data mapping scheme and evaluation framework were designed. Through an empirical analysis of the classroom data of 162 undergraduate courses in a certain university, it was found that although the interaction frequency was high, the structure was unbalanced and student participation was uneven. After the introduction of the optimisation strategy, all four interaction quality indicators significantly improved, verifying the practical effectiveness of internet of things empowerment in enhancing the depth of classroom interaction and student participation. The research provides feasible paths and evaluation tool support for universities to promote data-driven teaching reform. Keywords: internet of things; IoT; classroom interaction; data-driven; teaching optimisation. DOI: 10.1504/IJICT.2026.10075837
Abstract: Driven by rapid urbanisation and dual-carbon targets, conventional cast-in-place construction struggles with high energy use, pollution, and low efficiency. Prefabricated concrete buildings improve productivity and sustainability, yet joint connections govern global load capacity, seismic behaviour, and durability. This study develops and validates a finite-element optimisation framework for prefabricated concrete joints. Concrete is modelled with the concrete damage plasticity (CDP) formulation and steel components with the von Mises yield criterion, including nonlinear material behaviour, contact interaction, and displacement-controlled loading (perfect bond assumed between sleeve grout and reinforcement). Parametric analyses evaluate concrete strength (C30/C40/C50), reinforcement ratio (1.0%/1.2%/1.5%), and bolt diameter (18/20/25 mm). Relative to C30, bearing capacity increases by 4.53% (C40) and 9.93% (C50); raising reinforcement to 1.2% and 1.5% improves capacity by 6.04% and 15.29%; and enlarging bolts to 20 and 25 mm increases capacity by 6.15% and 13.34%. Validation achieves 6%8% average error and R2 = 0.984. Keywords: intelligent construction; prefabricated concrete structure; node mechanical performance; finite element analysis; structural optimisation. DOI: 10.1504/IJICT.2026.10075880
Abstract: The traditional credit evaluation model has the disadvantages of difficult data mining and low evaluation accuracy. This study constructs an improved convolutional neural network and applies it to the marketing credit assessment technique used by e-commerce businesses. The results showed that the area under the accuracy-recall curve of this study, random forest, and decision tree were 0.83, 0.74, and 0.65, respectively. The area of the convolutional neural network under the curve of number of iterations was 0.65. The convolutional neural network completed the iteration when the iteration number was 198 times. The random forest model completed the iteration when the iteration number was 239 times. The decision tree model completed the iteration when the iteration number was 594. Thus the suggested method owns better accuracy and robustness. Keywords: neural network; marketing; credit evaluation; convolutional neural network; CNN; model. DOI: 10.1504/IJICT.2026.10075838
Abstract: Facing the problem that current methods to infer tourist behaviour and consumption intentions are hard to deeply mine the textual semantic information, this paper optimises convolutional neural networks-based vision transformer algorithm first, and then proposes improved inference tourist behaviour and consumption intentions based on Visual Transformer. In tourist behaviour detection branch, text-aware module is introduced to improve the extraction of tourist image features and enhance the expressive power of textual visual features. In consumption intention inference branch, parallel Transformer decoding is performed at both visual and linguistic levels, and semantic information is mined and integrated by positional encoding to realise accurate inference of consumption intentions. The experimental results show that the accuracy of visitor behaviour detection is 96.8%, and the accuracy of consumption intention inference is 94.2%. Compared with the baseline model, the model has high efficiency and is superior. Keywords: convolutional neural network; CNN; vision transformer; feature extraction; tourist behaviour detection; consumption intent inference. DOI: 10.1504/IJICT.2026.10075839
Abstract: The report of the 20th National Congress of the Communist Party of China proposed building a strong sports nation and promoting green development, thereby guiding the high-quality development of Chinas ice and snow economy. National-level ski tourism resorts in Jilin Province attract many tourists but face intense competition and diverse demands. This study develops an evaluation indicator system via grounded theory and applies IPA analysis to identify key factors affecting tourist satisfaction. Results show common problems include inadequate ski trail facilities, inconsistent coaching services, lack of price transparency, and insufficient health protection. Individual issues involve catering and information retrieval. However, performance in tourist interaction, equipment rental, and transportation is excellent. This study proposes systematic optimisation strategies to support the high-quality development of ski tourism in Jilin Province, contributing the Jilin sample to Chinas ice and snow industry. Keywords: Jilin Province; national-level ski tourism resort; tourist satisfaction; grounded theory; IPA analysis. DOI: 10.1504/IJICT.2026.10075840
Abstract: To address the critical challenges of insufficient diagnostic granularity and limited interpretability in spoken English assessment, this research proposes an intelligent framework that synergistically integrating knowledge graph and deep learning technologies. We construct a structured oral knowledge graph using multidimensional error annotations from the Speechocean762 corpus and phoneme-level pronunciation data from L2-Arctic, and design a knowledge graph-enhanced multi-task learning model to achieve cross-dimensional joint optimisation. Experimental results show 12.3% reduction in pronunciation error rate and 14.7% improvement in grammatical diagnostic F1-score compared to mainstream baselines, with overall diagnostic accuracy reaching 86.2%. Ablation studies confirm the knowledge graphs pivotal role in error-path reasoning, while the meta-relation learner significantly enhances few-shot adaptation capability (31.2% F1-score gain). This framework provides interpretable diagnostic support for adaptive language learning systems, reducing error-correction cycles by 40.5% in real-world educational applications. Keywords: knowledge graph fusion; spoken English diagnosis; multi-task learning; fine-grained error analysis. DOI: 10.1504/IJICT.2026.10075841
Abstract: The contradiction between course resource overload and learners personalised needs in online education platforms is becoming increasingly prominent. Addressing the common issues of weak interpretability and poor dynamic adaptability in existing recommendation methods, this paper proposes a knowledge graph-based adaptive course recommendation model. By constructing a hierarchical knowledge graph to precisely represent the course knowledge system and integrating deep knowledge tracking with reinforcement learning techniques, the model dynamically perceives learners knowledge states and evolving interests, enabling real-time adjustment of recommendation paths. Experiments on the publicly available china university massive open online course dataset demonstrate that compared to mainstream baseline models, our model achieves up to 8.7% higher performance on key metrics such as normalised discounted cumulative gain@10 and hit rate @10. This validates its effectiveness and superiority in delivering precise, explainable personalised recommendations. Keywords: knowledge graph; adaptive recommendation; massive open online courses; MOOCs; personalised learning. DOI: 10.1504/IJICT.2026.10075842
Abstract: This paper presents a cognitive-semantic guided generative adversarial network for automatically generating interactive environment layouts that optimise both visual realism and user experience. By computationally operationalising cognitive load theory, our framework integrates a novel interaction-aware discriminator and a semantic consistency loss, enabling the generator to produce layouts that minimise navigational cognitive load. Validated on the Stanford 2D-3D-Semantics dataset, our model significantly outperforms state-of-the-art methods in functional metrics, achieving an 85.2% navigation success rate, a 13.4% higher mean intersection over union than graph-based methods (68.7% versus 55.2%), and a substantially lower cognitive load score of 0.65. Ablation studies and user evaluations involving 45 participants confirm the necessity of each component and demonstrate a strong preference for the generated environments. This work aims to establish between cognitive theory and generative artificial intelligence for human-centric design. Keywords: cognitive load theory; generative adversarial networks; interactive environment design; semantic scene understanding; human navigation simulation. DOI: 10.1504/IJICT.2026.10075843
Abstract: To improve the accuracy of automatic piano music transcription in complex environments, a recognition system applicable to practical scenarios such as music education assistance and intelligent performance analysis was developed. First, audio features were extracted using Log-Mel spectrograms, combined with data augmentation and adaptive pitch normalisation to enhance model robustness. Second, a state-action modelling mechanism integrating a Transformer encoder with a multidimensional action space was constructed to precisely represent note content, rhythmic positions, and dynamics information. Finally, a primary policy and an auxiliary rhythm policy based on proximal policy optimisation (PPO) were designed, and a multidimensional reward function along with imitation learning signals were introduced to jointly optimise the note prediction strategy. Comparative experiments indicated that incorporating the multidimensional action structure and boundary auxiliary strategy significantly improved recognition accuracy. The proposed method achieves high-precision piano audio transcription with strong structural continuity. Keywords: piano transcription; deep reinforcement learning; DRL; multidimensional action space; music sequence modelling; proximal policy optimisation; PPO. DOI: 10.1504/IJICT.2026.10075844
Abstract: In the new media communication environment, digital art faces severe infringement challenges diversified forms, fast spread, and cross-platform supervision difficulties. Traditional copyright protection struggles to address these effectively. This study focuses on the copyright security issues of NFT digital artworks in new media communication, and proposes a comprehensive protection framework that integrates blockchain-based rights confirmation, smart contract authorisation, and multi-level monitoring and traceability mechanisms. The experimental results show that: 1) in terms of infringement detection, the integrated method is significantly superior to traditional methods in scenarios of Joint Photographic Experts Group (JPEG) compression and central cropping, and its F1-score reaches 94.4%; 2) in terms of blockchain-based evidence storage, for a sample size of one thousand, the evidence integrity reaches 97.9%, the traceability accuracy reaches 96.7%, and the anti-tampering rate reaches 97.5%. The study has positive significance for promoting the healthy development of the digital art industry. Keywords: non-fungible token; NFT; digital works of art; copyright protection; blockchain; smart contracts; new media communication. DOI: 10.1504/IJICT.2026.10075845
Abstract: With the continuous improvement of sports training and competitive levels, athletes demands for motion recognition and motion monitoring during training are increasing day by day. Based on a multi-node sensor platform and the internet of things environment, this study constructed an action data acquisition system and ensured high-quality data input through pre-processing and feature extraction. In terms of model construction and optimisation, the performance of LSTM, CNN, SVM and the fusion model was compared and analysed. The results show that the fusion model is significantly superior to the single model in terms of recognition accuracy, system delay, stability and energy consumption, especially in the recognition of complex actions such as rotation and bending, the accuracy exceeds 95%. Further three-dimensional surface analysis shows that the fusion model still maintains a latency of less than 120 milliseconds and a stability index higher than 0.85 in a high-load environment, demonstrating good robustness. Keywords: machine learning; internet of things; IoT; action recognition; athlete training; stability. DOI: 10.1504/IJICT.2026.10075846
Abstract: Cigarette leaf blend formulation design is a core component in determining product sensory quality. This study proposes a multi-objective optimization method based on sensory-chemical correlations and machine learning. First, key chemical components of leaf blend samples are systematically collected to construct an initial dataset. Subsequently, multivariate statistical methods such as partial least squares regression are employed to identify the key chemical indicators driving sensory quality. Based on this, a machine learning model based on deep learning is established to accurately predict the key chemical indicators and sensory quality scores of the formulation. Finally, sensory quality, key chemical indicators, and raw material costs are set as optimization objectives to construct a multi-objective optimization model. The experimental results show that the multi-objective optimization model constructed by this method generates 152 Pareto optimal solutions, improving sensory quality by 12%, reducing raw material costs by 19%, and increasing chemical stability by 55%. Keywords: cigarette leaf blend formulation; sensory-chemical correlation; machine learning; multi-objective optimization. DOI: 10.1504/IJICT.2026.10075847
Abstract: Track and field sports skill recognition is a key technology in intelligent sports training, but traditional methods suffer from issues such as information redundancy and poor recognition performance. To address this, this paper first proposes an adaptive selection mechanism for multimodal sensor data based on mutual information, filtering out sensor combinations that provide maximum information correlation. Then, a convolutional neural network (CNN) is combined with a long short-term memory network (LSTM) for multimodal sensor feature extraction, and a recurrent matrix-based multimodal feature fusion method is proposed. Finally, the fused feature vector is input into a fully connected layer, and the softmax function is used to calculate the score for each category of athletics skill from the output classification layer. The experimental results show that the Macro_F1 of the proposed method is improved by at least 4.01% compared to baseline methods, demonstrating good recognition performance. Keywords: track and field sports skill recognition; mutual information; multimodal sensor; convolutional neural network; CNN; graph attention network. DOI: 10.1504/IJICT.2026.10075848
Abstract: The tobacco wire guide system is a key component in cigarette production equipment. This paper proposes a tobacco wire guide PID control optimisation model based on convolutional neural network (CNN-PID). The tobacco wire guide speed, guide position, guide temperature, ambient temperature, ambient humidity, and PID parameters at the previous moment are selected as model dependent variables. After normalisation, they are input into the lightweight convolutional neural network. After model parameter adjustment, the predicted proportional gain, integral gain and derivative gain are finally output. After training, the R2-score values of CNN-PID on proportional gain, integral gain and derivative gain are 0.992, 0.984, and 0.982, respectively. In addition, the R2-score values of the CNN-PID model are better than those of traditional PID control, BP neural network PID, and PSO optimised PID. Keywords: PID control optimisation; convolutional neural network; CNN; intelligent control algorithm. DOI: 10.1504/IJICT.2026.10075849
Abstract: A multi-instrument polyphonic automatic transcription method integrating bidirectional gated recurrent units and an improved Deeplabv3+ network is proposed to enhance transcription accuracy under complex audio conditions. A pre-separation module first performs source separation and denoising. Frequency-harmonic composite features are then extracted, and temporal dependencies are modelled using a gated recurrent network, followed by lightweight decoding for note onset localisation and instrument classification. Experiments show that the proposed model achieves 92.8%, 91.5%, and 92.1% accuracy, recall, and F1 on the training set, and 91.2%, 88.7%, and 89.9% on the test set, surpassing baseline methods. In mixed-instrument scenarios, the model attains an average F1 of 83.65% and 88.3% note recognition accuracy, improving piano-violin transcription by 7%. The method offers high precision and robustness for polyphonic transcription, providing a practical foundation for intelligent music analysis and automatic orchestration. Keywords: multi-instrument polyphonic auto-transcription; DeepLabv3+ network; bi-directional gated loop unit; audio feature extraction; preamplifier separation. DOI: 10.1504/IJICT.2026.10075866
Abstract: Accurate assessment of professional spoken English necessitates capturing nuanced linguistic accuracy and non-verbal paralinguistic cues in cross-cultural communication settings. To address limitations of unimodal approaches and static fusion methods, we propose Multimodal Feature Fusion based Professional English Assessment (MFF-PEA), an adaptive framework integrating speech, facial expressions, and gestural dynamics. The core innovation lies in a cross-modal dynamic fusion (CMDF) mechanism that employs learnable attention gates to weight modalities based on contextual relevance. For joint optimisation, a hybrid loss function combines regression loss for absolute scoring and pairwise ranking loss for proficiency discrimination. Rigorous evaluations on multi-domain professional datasets confirm MFF-PEAs significant superiority over state-of-the-art baselines, exhibiting stronger predictive consistency and lower assessment errors. Comprehensive ablation studies validate each architectural components necessity, while cross-domain tests in business, medical, and legal scenarios demonstrate transferable robustness. This work establishes a context-sensitive paradigm for automated multimodal language assessment. Keywords: professional oral English assessment; multimodal fusion; dynamic attention; ranking loss; cross-domain evaluation. DOI: 10.1504/IJICT.2026.10075867
Abstract: Logistics cost optimisation involves multiple complex constraints, and traditional optimisation methods face challenges when addressing transportation cost optimisation problems under these multi-constraint conditions. To this end, this paper models the problem using a cigarette transportation model as an example, designing a multi-objective optimisation function and multi-constraint conditions. Subsequently, a hierarchical decision network based on proximal policy optimisation is proposed. To address dynamic variations in both cigarette quantities and truck availability, a representation enhancement module incorporating spatio-temporal graph attention is introduced. This module utilises real-time data updates and dynamic graph modelling to ensure timely representation and intelligently resolves potential conflicts among multiple objectives, thereby achieving global optimisation over time. Experimental results show that the proposed method reduces transportation costs by 44.32%, significantly enhancing the efficiency of cigarette logistics. Keywords: logistics and transportation costs; multi-objective optimisation; reinforcement learning; proximal policy; graph attention network. DOI: 10.1504/IJICT.2026.10075868
Abstract: Demand for tactical optimisation and decision support in sports competitions is growing; traditional rule-based methods suffer from poor adaptability and latency. This study builds a DRL-based decision model for Wushu Sanda, trained on multi-source data and validated in simulation. The agent learns policies via interaction to optimise tactical choices in dynamic contexts. Compared with rule-based and classical RL baselines (Q-learning, SARSA), our model achieves higher decision accuracy, larger cumulative reward, and faster convergence. It adapts to diverse scenarios and supports real-time tactical adjustment. We also identify challenges in data quality, computational cost, and cross-sport generalisation. The findings highlight DRLs practicality for competitive decision-making and outline directions for improving interpretability, sample efficiency, and deployment in live matches. Keywords: deep reinforcement learning; DRL; sports competition; decision optimisation. DOI: 10.1504/IJICT.2026.10075905
Abstract: This study addresses limitations in current dance action style transfer methods, such as weak spatiotemporal coupling and poor generalisation. It proposes a novel approach using improved adaptive instance normalisation (I Ada IN) with a joint-limb-global layered normalisation structure to enhance style decoupling. The method incorporates a spatiotemporal transformer and inverse kinematics correction to improve stability and style fidelity in long sequences. Experiments show significant gains: a 43% higher style detail retention rate (0.89 vs. 0.62), a 27% improvement in structural similarity (0.94), and a 50% reduction in joint motion error (4.3 mm) over the original Ada IN. With a frame rate of 120 and processing time of 8ms per frame, the model meets real-time performance standards. This method achieves high-fidelity style transfer, accurate content preservation, and stable cross-domain generalisation through innovative hierarchical feature fusion and spatiotemporal modelling strategies, providing feasible technical support and application prospects for virtual dance teaching, intelligent choreography systems, and the digital protection of intangible cultural heritage. Keywords: dance action style transfer; improved adaptive instance normalisation; Ada IN algorithm; multi-feature fusion; feature extraction; digital art. DOI: 10.1504/IJICT.2026.10075875
Abstract: At present, all the positioning methods for the trajectory of table tennis have limitations such as low accuracy and large deviation. Therefore, this study utilises the extrusion and excitation network to optimise YOLOv5, introduces the graph convolutional network to improve the hybrid algorithm of semi-global matching and census transformation, and combines the two to construct an intelligent positioning and recognition model for table tennis. The results show that the research model has an accuracy rate of 97.6%, an precision rate of 98.6%, a recall rate of 96.8%, and a specificity of 97.2%. The average error of the recall rate is 0.59%, and the overlap degree of trajectory positioning is 0.93. In conclusion, the research model not only ensures the reliability of the table tennis positioning and recognition results, but also improves the recognition efficiency and result quality, making significant contributions to the development of table tennis. Keywords: YOLOv5; squeeze-and-excitation network; semi-global matching and census; SGCM; graph neural network; GNN; positioning and recognition; ping-pong balls. DOI: 10.1504/IJICT.2026.10075877
Abstract: The objective of this study is to explore how personalised AI-based physical education (PE) tools can enhance learning outcomes and physical fitness among college students. The research investigates the potential of AI to make PE more adaptive and data-driven through innovative motion-sensing and analytics-based game applications. Seventy-two students were divided into twelve groups, with half using AI-enhanced mobile apps that provided real-time feedback and guidance. Over eight weeks, the AI-enhanced group demonstrated significant improvements in core, upper-body, and lower-body strength (p < 0.01). The AI systems adapt continuously, offering immediate corrective feedback to improve performance. These results suggest that AI can effectively personalise physical training, promote independent exercise, and increase engagement in physical activity, contributing to sustainable fitness development. Keywords: artificial intelligence; personalised physical education; recommendation system; motion analysis; fitness training; data-driven learning. DOI: 10.1504/IJICT.2026.10075909 |
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