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International Journal of Information and Communication Technology
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International Journal of Information and Communication Technology (27 papers in press) Regular Issues
Abstract: As smart grids evolve, substation point cloud data is vital for management, maintenance, and monitoring. However, traditional recognition algorithms struggle with challenges such as noise, occlusion, viewpoint changes, and uneven density. This paper proposes a novel approach for substation equipment point cloud recognition. It first establishes a local coordinate system for the point cloud based on symmetry and spatial distribution, achieving translation and rotation invariance. A new attribute descriptor is then defined, considering form characteristics and viewpoint variations, and a template database of 20 electrical devices is created. By matching descriptor of a devices point cloud with those in the template database, the correct device is identified. The proposed method is compared with two other methods, achieving a 90% identification precision with an average time of 3.2 seconds per device. The method demonstrates robustness, maintaining over 70% accuracy even with noise, occlusion, and irregular point cloud density. Keywords: substation equipment; point cloud recognition; local coordinate system; attribute descriptor; matching descriptor. DOI: 10.1504/IJICT.2025.10071592
Abstract: Intending to the issue that the existing study do not fully exploit features, the random forest algorithm (IRF) is improved first. The splitting feature screening function is simplified based on the principle of infinitesimal equivalence, and the Gini coefficient value of the non-category attribute is introduced to improve the computational efficiency of the algorithm. Then, public health economic impact variables are selected, and spatial features are extracted using a residual convolutional neural network. Temporal features are extracted using a gate rate unit (GRU), and a self-attention mechanism is incorporated to enhance the spatial and temporal features. Finally, the IRF filter is used to select the most important spatio-temporal features of the early warning results and map them to the monitoring and early warning results through nonlinear transformation. The experimental outcome indicates that the accuracy of the proposed model has been improved by 5.07%-14.85%. Keywords: economic early warning; random forest; feature screening; residual convolutional neural network; GRU. DOI: 10.1504/IJICT.2025.10071629
Abstract: Time-series data analysis has grown even more crucial in many sectors as information technology and big data expand rapidly. This work proposes a recurrent neural network (RNN) optimisation model based on the linear constraint numerical method, namely, LSTM-LP optimiser, which combines the powerful time-series modelling capability of long short-term memory (LSTM) and the optimisation characteristics of linear programming (LP) optimisation features, and so effectively improves the training efficiency and stability of the model in resource-constrained environments. This helps to efficiently capture the temporal dependencies in time-series data and solve the noise and missing problems in the data. On two datasets, experimental results show the LSTM-LP optimiser beats the conventional model in several performance criteria. Future studies will investigate more effective optimisation techniques, increase the generalisation capacity of the model, and simplify the hyperparameter tweaking process to thus further promote the model in practical uses. Keywords: time-series data analysis; recurrent neural network; RNN; linear programming; LP; long short-term memory; LSTM; resource-constrained optimisation. DOI: 10.1504/IJICT.2025.10071630
Abstract: For the goal of ensuring the smooth progress of construction, it is urgent to design a real-time construction safety management method. First, the overall architecture of internet of things (IoT) real-time monitoring is constructed, which includes sensing layer, network layer, platform layer, and application layer; second, according to the accident causation theory, the construction risk monitoring index system is determined, and the key risk features are extracted. Subsequently, the improved ReliefF algorithm is used to select important construction risk features, and the hyperparameters of the support vector machine (SVM) are optimised by the particle swarm optimisation (PSO) algorithm, and important risk features are inputted into the PSO-SVM model to obtain final risk warning results. Application results of a construction project show that the data transmission delay of the system is less than 0.2 s, and the monitoring accuracy can reach 91.31%, showing excellent real-time and accuracy. Keywords: construction site safety management; monitoring and early warning; internet of things; IoT; feature selection; support vector machine; SVM; particle swarm optimisation; PSO. DOI: 10.1504/IJICT.2025.10071631
Abstract: Digital media contains a huge amount of emotional information that needs to be mined. To solve the problem that existing models ignore the features of multi-dimensional emotional words, firstly, multi-dimensional emotional words are expanded based on improved Word2vec, and then the digital media comments are input into the pre-trained model to generate a multi-dimensional text emotional word vector. The modified term frequency-inverse document frequency (TF-IDF) method is used to obtain the representation of multidimensional emotion subject words. Then the global features are obtained by using the hybrid model of convolutional neural network (CNN) and gated recurrent unit (GRU). Multi-dimensional attention mechanism is used to interact global features and multi-dimensional emotion features, and multi-dimensional emotion classification results are output by full connection layer. The results show that the Marco-F1 of the proposed model is 91.17%, which can accurately classify the emotions of digital media. Keywords: digital media visualisation; sentiment classification; Word2vec algorithm; TF-IDF method; multidimensional attention mechanism; convolutional neural network; CNN; gated recurrent unit; GRU. DOI: 10.1504/IJICT.2025.10071633
Abstract: The fast search random tree (RRT) algorithm is an algorithm used for path planning, which aims to reduce the search distance of UAV during flight and find the optimal path. Search for viable paths in the environment by building a random tree. The basic steps of UAV RRT algorithm include initialisation, generating random node, finding nearest tree node, extending tree and generating path. The improved algorithm can accelerate the search speed of the whole search space, and can optimise the multi-dimensional environment, and has good adaptability to the uncertain environment. However, the traditional RRT algorithm is not guaranteed to find the optimal path. By improving the centralised RRT algorithm, the flight path of UAV can be optimised, and then the advantages and disadvantages of each can be compared. First, build space model and drone model space. Secondly, the artificial potential field algorithm, pruning algorithm and area limit algorithm are improved to improve the search efficiency. Keywords: unmanned aerial vehicle; UAV; RRT algorithm; flight path; improve. DOI: 10.1504/IJICT.2025.10071663
Abstract: Distribution network management has emerged as a crucial aspect of socio-economic survival and a driver of global technological change. This study proposes a machine learning (ML) lifecycle management framework that targets performance maximization, system resilience enhancement, and the reduction of environmental pollution. Moreover, the latest ideas of data science, through advanced ML models are used in such areas as predictive maintenance, demand forecasting, fault detection, and energy flow optimization, which are key challenges addressed in this study. So, it results in lower operational costs, improved network reliability, and support of sustainability goals. The results showed that data science and network engineering should be combined in training programs to stimulate the sustainable development of new technologies. Most importantly, the research is a call for policy support and industry collaboration to speed up the use of smart systems in the formation of efficient, resilient distribution networks. Keywords: machine learning; sustainable management; distribution networks; resilience; environmental protection. DOI: 10.1504/IJICT.2025.10071717
Abstract: In the ceramic packaging industry, there is an ever-growing and escalating demand for unique and culturally resonant visual effects. However, traditional prediction methods encounter difficulties when attempting to seamlessly blend multimodal data sources like images, text, and profound cultural insights. This frequently results in inaccurate visual effect forecasts and may even cause potential cultural misinterpretations. To surmount these constraints, this paper introduces the Visual Multimodal Inference and Synthesis for Intelligent Ceramic Packaging (VISIC). It constructs a hierarchical multimodal feature fusion network, refines the Light-GAN, and incorporates a cultural compliance verification module. Specifically, the model employs advanced algorithms to more effectively manage data. Experiments demonstrate that VISIC improves multimodal feature extraction accuracy by 5.08% and attains a peak prediction success rate of 82.6%, significantly enhancing the prediction capabilities for ceramic packaging visual effects. Keywords: multimodal data pre-processing; feature engineering; ceramic packaging; cultural symbol knowledge graph. DOI: 10.1504/IJICT.2025.10071718
Abstract: The assimilation of artificial intelligence (AI) and machine-learning techniques within digital modelling has provided a new perspective on design in sculpture besides offering capabilities that can be harnessed for an artistic agenda with an unsurpassed level of efficacy and speed. This study is focused on digital modelling using virtual reality (VR) technology in the field of sculptures, and it aims to improve the algorithms of fusion programs using deep learning techniques. In this research, we have improved our ability to create digital sculptures more accurately and flexibly by taking advantage of techniques such as neural networks, adversarial models, and reinforcement learning. The proposed AI-VR model outperforms traditional and baseline models, achieving 92% model fidelity, 45 frames per second (FPS) rendering speed, and a user interaction score of 90. This framework paves the way for the future of AI-powered artistic innovation. Keywords: artificial intelligence; digital modeling; virtual reality; sculpture design; deep learning. DOI: 10.1504/IJICT.2025.10071753
Abstract: Generative artificial intelligence (AI) has rapidly advanced and brought a new revolution in various domains including language learning and grammar correction. The research focuses on the role of generative AI chatbots in the process of English language learning through real-time grammar and spelling correction. By utilising deep learning models, such as transformer based architectures, AI-powered chatbots deliver tailored feedback, individualised learning experiences, and grammar suggestions that are put in context. The results of the research indicate the efficiency of generative AI chatbots in enhancing the accuracy of writing, together with their use in educational settings and the engagement and retention of students. Furthermore, the report looks at the different issues related to the automation of grammar checking using AI, such as the training data, excessive reliance on automation, and supervision by humans. The experimental results supported by case studies indicate that AI chatbots are of great help in self-directed learning and linguistic accuracy. Therefore generative AI can possibly change the ways in which grammar is taught, especially in the spheres of English language learning, through the provision of intelligent and real-time help. Keywords: generative AI; chatbots; grammar correction; spelling correction; English learning. DOI: 10.1504/IJICT.2025.10071754
Abstract: In order to improve the appearance design effect of art exhibits, this paper uses the improved StyleGAN architecture to generate high-fidelity three-dimensional objects. This paper maps the image to the feature space through VAE, and then reconstructs the three-dimensional properties of the shape and surface from the well-decoupled latent vectors through the improved StyleGAN architecture. The VA2T (Visual-audio-to tactile, VA2T) algorithm directly generates tangential friction force and normal force data in the time domain, serving multi-dimensional data-driven tactile rendering. Combined with the experimental analysis, the VA2T algorithm based on time series force tactile data proposed has certain effects. In addition, combined with experimental analysis, it can be seen that the model proposed in this paper has a certain effect in the design of art exhibits, which can effectively improve the design effect of art exhibits and enhance the actual experience of visitors. Keywords: computer; visual rendering; art; exhibits; appearance design. DOI: 10.1504/IJICT.2025.10071755
Abstract: This study introduces an advanced network intrusion detection system (NIDS) to protect Wi-Fi-based wireless sensor networks (WSNs) using the Aegean Wi-Fi intrusion dataset (AWID). The dataset, which contains multiple classes of attacks, including flooding, injection, and impersonation, is used to train and evaluate the proposed model. The approach employs a robust feature selection process to optimise dataset quality, starting with 130 features, which are narrowed down to 90 relevant ones and further refined to 13 key features critical for detecting security breaches. The data is pre-processed using the standard scaler function, followed by the implementation of a hybrid convolutional neural network (CNN)-based model. The models performance is compared with other deep learning methods, including deep neural networks (DNN-5, DNN-3) and long-short-term memory (LSTM) networks, using evaluation metrics such as precision, recall, and F1-score. Our CNN model achieves an impressive accuracy of 98% and a low loss of 0.08, with minimal false alarm rates. This research significantly enhances intrusion detection accuracy while reducing false alarms, strengthening the cybersecurity posture of Wi-Fi-supported WSNs in the face of evolving cyber threats. Keywords: cyber security; network security; intrusion detection; machine learning; deep learning; convolutional neural network; CNN. DOI: 10.1504/IJICT.2025.10071739
Abstract: The study examines state-of-the-art artificial intelligence (AI) methodologies aimed at developing sports image classification as it affects multimedia management as well as recommendation algorithms and sport data analysis capabilities. The sports industry is witnessing unprecedented growth, fuelled by advancements in technology, and the exponential rise of digital content. The vast quantity of sports-related media requires critical management for improved accessibility for user engagement capabilities. AI brings transformative automation capabilities through its ability to tackle these sorts of tasks. Deep learning applications show outstanding performance for resolving intricate classification challenges. This research developed a sports image classification framework using deep neural networks (DNNs) and analysed two pre-trained models ResNet-50 and MobileNet for performance comparisons. The DNN model demonstrated outstanding performance metrics through 98% accuracy which matched its precision and recall and F1-scores. DNN proved the most suitable solution when compared to pre-trained models ResNet-50 and MobileNet. Keywords: artificial intelligence; sports classification; game; deep neural network; DNN; feature extraction. DOI: 10.1504/IJICT.2025.10071786
Abstract: In this paper, a novel approach is given to evaluate the effect of chorus size on the quality of art music performance using the hesitant bipolar fuzzy multi-criteria decision-making (HBFS-MCDM) framework. The method accounts for positive and negative expert evaluations under conditions of hesitancy concerning five performance criteria: tonal balance, articulation precision, dynamic range, audience cohesion, and emotional Expression. To aggregate, expert conductors and musicologists evaluated a hesitant bipolar fuzzy number, and then a weighted decision model was proposed. Results show limited evidence of balanced and consistent performance among all evaluated dimensions across various ensemble sizes. The HBFS-MCDM approach offers a robust and expressive tool for supporting decisions in professional chorals and for actionable insights on ensemble configuration. Keywords: choral performance evaluation; chorus size; hesitant bipolar fuzzy sets; multi-criteria decision-making; MCDM; art music analysis; subjective judgment modelling. DOI: 10.1504/IJICT.2025.10071787
Abstract: In regional economic planning, accurate forecasting and efficient resource allocation are vital for informed decision-making by both government and private sectors. The article titled Dynamic path transformer network for regional economic forecasting and resource allocation presents a novel deep learning-based approach that leverages transformer architecture to enhance forecasting precision and optimise the allocation of resources. Central to this study is the dynamic path transformer network (DPTN), which effectively captures complex spatial-temporal economic data through attention mechanisms that dynamically weigh economic indicators. This design allows the model to adapt to changing economic conditions and deliver more accurate predictions than traditional statistical or machine learning models. The study benchmarks DPTN against conventional approaches and demonstrates its superior performance in predictive accuracy and resource management. Moreover, the paper explores the broader implications for policy formulation and strategic planning, while also addressing key challenges such as data limitations, computational demands, and interpretability. Keywords: economic forecasting; deep learning; transformer network; resource allocation; spatial-temporal analysis. DOI: 10.1504/IJICT.2025.10071796
Abstract: The integration of artificial intelligence (AI) in the design of rural landscape strategies is transforming traditional practices of sustainable development and spatial planning. This research paper thoroughly analyses how AI-based models such as machine learning algorithms, geographic information systems (GIS), and deep learning techniques facilitate decision-making in rural land-use planning, environmental conservation, and resource management. These technologies are increasingly employed to analyse large datasets, predict land-use changes, and optimise strategic interventions. AI fosters more efficient and adaptive planning by handling complex policy decisions with data-driven insights. The study evaluates the effectiveness, challenges, and future possibilities of AI-based systems, emphasising outcomes such as environmental stability, community well-being, and drought mitigation. A collaborative approach involving AI experts, environmental planners, and policymakers is essential for ethically and contextually relevant implementation. Keywords: artificial intelligence; rural landscape; strategy design; machine learning; sustainable planning; geographic information systems; GIS. DOI: 10.1504/IJICT.2025.10071797
Abstract: In this paper, an early warning system of cigarette process quality combined with intelligent sensing technology is proposed to improve the quality of cigarette process production. A PCA multi-block modeling algorithm based on autoencoder feature extraction is proposed to extract autoencoder features from each sub-block, and the statistics of all sub-blocks are fused by Bayesian inference to make the monitoring results more intuitive. Compared with the traditional PCA and AE-PCA detection methods, the AE-MPCA algorithm proposed in this paper improves the abnormality detection accuracy of the drum leaf drying production process, and realizes the accurate alarm of quality abnormalities, thus providing technical support for the early warning of subsequent cigarette process quality. In the subsequent process of cigarette process quality control, the application scope of intelligent sensing technology can be further improved to promote the effect of cigarette process quality control. Keywords: intelligent perception; cigarettes; process quality; early warning. DOI: 10.1504/IJICT.2025.10071852
Abstract: Deep learning and digital image processing powered by artificial intelligence are now influencing art education. With AI, artists can now experiment with new styles and effects, thanks to CNNs, GANs and NST. Tasks such as edge detection, segmentation and super-resolution give rise to helpful approaches in creative learning. AI-assisted art is represented by platforms such as DeepDream and RunwayML. While AI offers fast and original feedback to improve learning, many are worried about who should get credit for the results, ethics and the loss of traditional abilities. We must deal with problems such as dataset bias, copyright and having too much trust in AI. By being careful with AI, it can connect rather than conflict with conventional art, while aiming for ethics, diverse sets and blended ways of teaching. Keywords: AI in art education; deep learning in creativity; digital image processing; generative adversarial networks; GANs; neural style transfer; NST; ethical AI in art. DOI: 10.1504/IJICT.2025.10071872
Abstract: The integration of deep learning algorithms in art classification has revolutionised the way artistic styles are identified and analysed. This study explores the application of neural networks particularly convolutional neural networks (CNNs), generative adversarial networks (GANs) and vision transformers (ViTs) in distinguishing and classifying various forms of art, including abstract, realism, impressionism, and digital art. By leveraging large datasets, these models can identify stylistic features with high accuracy. The paper compares the performance of different models and highlights the challenges of training on heterogeneous art databases, such as data imbalance and complex feature extraction. Results show the effectiveness of hybrid architectures like CNN + ViT, and potential future applications include museum curation, style transfer, and computational creativity. This research underlines the evolving role of AI in bridging technology and art. Keywords: deep learning; art classification; neural networks; style recognition; computational creativity.
Abstract: The distribution of teaching data varies among database courses, and traditional methods are often difficult to deal with such domain differences effectively, for this reason, this paper firstly utilises BERT model for embedding learning of teaching feedback text, and then extracts local and global features of the text through convolutional neural network (CNN) and long short-term memory (LSTM) network respectively, and enhances the text features through the attention mechanism. On this basis, the domain adaptive transfer learning algorithm is adopted to achieve the characteristic distribution migration alignment of the text source topic and objective topic, and minimise the scoring difference between different classifiers through consistency constraints, so as to assess the teaching quality more accurately. Simulation results show that the classification accuracy of the offered method is 94.39%, which demonstrates a substantial enhancement over the benchmark method. Keywords: database curriculum; teaching quality evaluation; BERT model; domain adaptation; transfer learning.
Abstract: This paper proposes an automatic registration method for power equipment images using contour centreline main orientation features. Addressing low accuracy caused by scale/viewpoint variations in multi-modal images (infrared and visible), the method detects contour corner points as feature points, assigns scale/viewpoint-invariant orientation features through centreline analysis, and employs improved scale-invariant feature transform (SIFT) descriptors with connecting-line consistency matching to determine transformation parameters. Experimental results demonstrate registration errors of 2.742 and 2.543 in scenarios with subtle and significant viewpoint differences respectively, outperforming traditional SIFT, speeded-up robust features (SURF) and partial intensity invariant feature descriptor (PIIFD) methods. This approach effectively resolves complex-scenario registration challenges, providing an effective solution for intelligent monitoring of power equipment. Keywords: infrared and visible image; image registration; contour centreline; feature matching. DOI: 10.1504/IJICT.2025.10071589
Abstract: Aiming at the communication efficiency bottleneck in the internet of things and edge computing scenarios, this paper proposes a communication efficiency improvement scheme based on adaptive improved federated learning. By constructing an ARMA bandwidth prediction model enhanced by wavelet transform, the client network environment is predicted, and the improved Sketch compression algorithm is adopted to dynamically adapt to the real-time bandwidth conditions, thus the communication efficiency optimisation in the internet of things and edge computing scenarios is achieved. Experiments show that the accuracy of the proposed method researches 95%, the average uplink communication time is 0.5 seconds, and the communication efficiency exceeds 1.7. It provides key technical support for real-time federated learning deployment in 5G edge computing environment. Keywords: federated learning; wavelet transform; ARMA; Sketch; communication efficiency. DOI: 10.1504/IJICT.2025.10071524
Abstract: Intending to the issue that existing adaptive content recommendation methods for distance education ignore the dynamic uncertainty of learners' cognitive level, the top-down approach is first used to construct the distance education KG (DEKG), and the TransR model is utilised to vectorise the representation of the DEKG. Secondly, based on fuzzy logic, the cognitive level of the learners is determined, and the matching degree and cognitive level are combined to calculate the similarity of knowledge points. Then, the degree of learner preference was measured using fuzzy logic to represent the knowledge point similarity as a vector over the content labels. Subsequently, a corresponding rating prediction formula is designed to realise more effective and accurate mining of distance education content that meets learners' characteristics for recommendation. The experimental results show that the proposed method improves the recall and F1 by at least 3.21%. Keywords: adaptive content recommendation; fuzzy logic; knowledge graph; knowledge point similarity; rating prediction. DOI: 10.1504/IJICT.2025.10071553
Abstract: With the increasing share of renewable energy, its volatility poses challenges to grid dispatching, making wind power prediction crucial. Existing methods mainly include point forecasting and probabilistic forecasting, but the former struggles to capture fluctuations, while the latter lacks reasonable scenario generation for grid integration. Additionally, current approaches fail to fully utilise wind farm spatial structures and environmental factors, limiting prediction accuracy and generalisation. To address this, this paper proposes a scenario generation model (SLEP) based on structural attention LSTM and environmental correction. SLEP integrates temporal wind power characteristics, turbine spatial structures, and environmental factors, built upon TimeGAN. SA-LSTM combines a graph convolutional network (GCN) with LSTM to capture spatiotemporal wind power features, while the environmental correction module (ERM) employs cross-attention to embed environmental variables, improving sample adaptability. Experiments show that SLEP outperforms existing methods in accuracy, scenario diversity, and environmental adaptability, providing reliable support for grid dispatching. Keywords: wind power forecasting; deep generative model; structural attention LSTM; environmental correction; scenario generation. DOI: 10.1504/IJICT.2025.10071554
Abstract: Digital media today plays a vital role in visual aesthetics and bringing them into play can impact user engagement and be crucial for personalised recommendations of content. Using AI, the task to classify and differentiate between human selfies and animated images, which are hard because of the subtle stylistic changes and the complex feature presentations in both categories. In this research study, we proposed an advanced framework that utilises vision transformers (ViT) and high-resolution networks (HRNet) for classification. With the help of an online dataset, the proposed models not only learn high level representations but also representational contextual dependencies well, classifying test data with 99% accuracy for ViT and 97% for HRNet at a level better than 10% of what traditional convolutional neural network (CNN) based models can achieve. The results leading for automatically content moderation, provide a solid base of using advanced vision models into multimedia and digital content processing. Keywords: vision transformers; ViT; artificial intelligence; deep learning; visual aesthetics; convolutional neural networks; CNNs; feature extraction; classification. DOI: 10.1504/IJICT.2025.10071591
Abstract: The combination of artificial intelligence (AI) and deep learning (DL) within the field of human resource management (HRM) is revolutionising the conventional HR techniques, enhancing productivity, decision-making, and staff satisfaction. The focus of this research paper is on the examination of the core components and techniques of AI and DL in HRM, which are to be highlighted via their use in the areas of hiring, employment participation, performance management, and workforce analytics. Furthermore, it aims to delve into the constraints one might face in the integration of AI-powered HR solutions such as ethical issues, biases in algorithms, security of data, and the necessity of HR workers being re-4skilled. Through the robust examination of the progress and challenges, the research is likely to yield both a descriptive overview of the influence of AI & DL on HRM and recommendations on the future axes of research and best practices to leverage AI in the workforce. Keywords: artificial intelligence; AI; deep learning; DL; human resource management; HRM; talent acquisition; workforce analytics. DOI: 10.1504/IJICT.2025.10071590
Abstract: The growing diversity and intricacy of commercial art software raise substantive difficulties for artists, designers, and corporations in the identification of the most appropriate solution for their needs. The paper being discussed proposes a new methodology for the appraisal and selection of commercial art software using dynamic decision-making algorithms. This approach counts on the development of user preferences and priorities, as a hybrid approach combining multi-criteria decision analysis (MCDA) with dynamically adjusted weighting schemes. The resulting process of this methodology takes in many qualitative and quantitative factors, such as user-friendliness, performance ability, feature sets, price, and compatibility overall. Case studies and simulations illustrated the ability of the adopted procedure to guide the user in making the right choice while being ready to apply the changes to the conditions at any moment. The findings emphasised the advantages of dynamic decision-making algorithms over conventional static evaluation models which were flexible and user-oriented mechanisms for the selection of the software in the creative industry. The findings of this research will be a reliable tool for decision-making in the creative field as well as, and the available art software will be utilised more productively and satisfyingly. Keywords: dynamic decision making; professional art software; multi criteria evaluation; adaptive algorithms; software selection framework. DOI: 10.1504/IJICT.2025.10071525 |
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