International Journal of Computer Applications in Technology (40 papers in press)

Regular Issues

• Open Access

  Digital media video image data processing based on computer vision ( Free Full-text Access )

  by Xiawei Lu 
  Abstract: This paper describes the application direction of DMVI processing technology and the acquisition and post-processing of ultra-high-definition quality data, explores the application of DMVI processing technology in image analysis, proposes a method for obtaining ultra-high definition quality video data, and discusses the reconstruction of ultra-high-definition quality video. According to the research results, satisfaction with the introduction of the five-dimensional light field function algorithm and CV technology reached over 21%; at 4K resolution, the processing time of the five-dimensional light field was 1.05
  Keywords: digital media video image; image data processing; computer vision; ultra clear picture quality image.
  DOI: 10.1504/IJCAT.2025.10073493
   
  

• Open Access

  Research on image enhancement of smart home product layout scene based on virtual reality ( Free Full-text Access )

  by Zhongmei Liu 
  Abstract: To elevate the quality of image enhancement for smart home product layout scenes and expedite processing times, a study focused on virtual reality-based enhancement of these scenes has been undertaken. Initially, a virtual reality framework is employed to create an indoor environment for smart homes, with the ISGSA algorithm model utilized to generate this environment. Subsequently, the attributes of each constituent element are amalgamated and fed into a generator to produce a novel indoor scene. Ultimately, a conditional generative adversarial network is devised to formulate a composite loss function, integrating channel color loss, structural feature loss, and smoothness loss. This loss function is instrumental in achieving image enhancement. Experimental findings reveal that the proposed method attains an average information entropy of 8.846, with an image enhancement processing duration of merely 3.9 s.
  Keywords: virtual reality; smart home products; layout scene; image enhancement; ISGSA algorithm; attention learning module; channel colour loss.
  DOI: 10.1504/IJCAT.2025.10073932
   
  

• Open Access

  Mean-shift-based moving target tracking algorithm in complex industrial environments ( Free Full-text Access )

  by Zhongming Liao, Zhaosheng Xu, Xiuhong Xu, Azlan Ismail 
  Abstract: This paper proposes an improved moving target tracking algorithm (TTA) based on the mean-shift (MS) method, which is suitable for complex industrial environments. The improved algorithm introduces the YOLO (You Only Look Once) model for moving target detection and uses its results as tracking input. In addition, the algorithm also introduces a twin network (SN) to extract the deep features of the target for re-identification after occlusion. In order to further improve the tracking stability, a Kalman filter is introduced to predict the next motion state of the target. Stability analysis shows that the algorithm achieves the best multi-target tracking accuracy (MOTA) index in various complex environments, outperforming other tracking methods and showing good multi-target tracking stability. In summary, the algorithm successfully overcomes the limitations of the traditional MS method and provides a novel solution for moving target tracking in industrial environments. The algorithm has important practical value and provides a valuable reference for future research on moving target tracking in dynamic and complex environments.
  Keywords: moving target tracking; mean-shift algorithm; YOLO model; Siamese network; Kalman Filter.
  DOI: 10.1504/IJCAT.2025.10074104
   
  

• Open Access

  Research on multi-modal teaching resource association resource mining under MOOC ideological and political learning ( Free Full-text Access )

  by Hui Wang 
  Abstract: To overcome the limitations of current mining algorithms and improve the effectiveness of resource mining, this paper proposes a multimodal teaching resource association resource mining algorithm for MOOC ideological and political learning. Firstly, the features of text, image, and audio modalities are extracted using the bag of words model, VGG16 network, and Mel frequency cepstral coefficient method. Secondly, the feature vectors of each modality are concatenated and fused. Due to the high dimensionality after fusion, principal component analysis is used for dimensionality reduction. Finally, feature fusion, dimensionality reduction, and association rule mining are used to optimize the association of multimodal teaching resources, and dynamic association rules are introduced to adapt to the dynamic needs of students' learning process, thereby improving the effectiveness of MOOC ideological and political learning. The experimental results show that the mining results of the proposed algorithm have diversity and strong correlation with the target topic.
  Keywords: MOOC; ideological and political education; multimodal; teaching resources; resource mining; principal component analysis; association rules.
  DOI: 10.1504/IJCAT.2025.10074404
   
  

• Open Access

  Study on Road to Waterway model for medium to long-distance cargo transportation considering transportation efficiency ( Free Full-text Access )

  by Zengli Fang, Yali Liang, Gaoling Li, Xiang Tang 
  Abstract: This paper studies a "Road to Waterway" model for medium and long-distance cargo transportation with consideration of transport efficiency. First, addressing the time-sensitive requirements of high-value-added cargo transportation faced by multimodal operators, a "Road to Waterway" model for medium and long-distance transportation is developed. Second, through cost analysis that quantifies various expenses while establishing objective functions and constraints, the model ensures reasonable transportation mode selection, transit connections, and flow balance. Finally, employing genetic algorithms to generate initial solutions and maintain population diversity, combined with ant colony algorithm's positive feedback mechanism for optimal solution search, the model demonstrates significantly improved solving efficiency and time performance. Experimental results indicate a stable on-time arrival rate exceeding 97.7% and cost savings reaching 9.3%.
  Keywords: transportation efficiency; medium to long distance; freight transportation; ‘Road to Waterway’ model.
  DOI: 10.1504/IJCAT.2025.10074405
   
  

• Open Access

  Pose estimation technology of electronic components based on point cloud segmentation algorithm ( Free Full-text Access )

  by Wei Shen 
  Abstract: In actual manufacturing environments, electronic components often face occlusion problems, which makes it difficult for traditional point cloud segmentation methods to estimate the pose of objects accurately. To address this challenge, this paper introduces the multi-scale feature learning capability provided by PointNet++ to extract deep collective feature information in local areas of different scales and understand the overall morphology of components in a global context. According to experimental analysis, under the same occlusion level, PointNet++ outperforms the PointNet model, the RANSAC (Random Sample Consensus) algorithm, and the voxelisation method Point-Voxel CNN in terms of segmentation accuracy. The pose estimation method of electronic components studied in this paper is highly applicable in actual mechanical manufacturing environments, can process large-scale data, and meets real-time requirements. It provides the theoretical basis and technical support for solving the positioning and assembly problems of components in actual industrial production.
  Keywords: point cloud segmentation; pose estimation; PointNet++ Model; occlusion problems; mechanical manufacturing; random sample consensus.
  DOI: 10.1504/IJCAT.2025.10074466
   
  
• FPGA implementation and Multisim simulation of a new four-dimensional two-scroll hyperchaotic system with coexisting attractors  
  by Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, Khaled Benkouider, Aceng Sambas, Ciro Fabian Bermudez-Marquez, Samy Abdelwahab Safaan 
  Abstract: Field-programmable gate array (FPGA) design of a new four-dimensional two-scroll hyperchaotic system is investigated in this work. A detailed system modelling of the new system with a hyperchaotic attractor begins this work with phase plots, which is followed by a bifurcation study of the new system. Special dynamic properties such as multistability and symmetry are also investigated for the new system. Using Multisim software, a circuit model is designed and simulated for the new hyperchaotic system. FPGA design and Multisim simulation of the new system enable practical applications in science and engineering. The implementation of the FPGA design in this work is carried out by applying two numerical schemes, viz. Forward Euler and Trapezoidal methods. Experimental attractors observed in the oscilloscope show good match with the Matlab signal plots.The FPGA hardware resources are detailed for both numerical methods.
  Keywords: hyperchaos; bifurcation; symmetry; phase plots; hyperchaotic system;rnparameters; stability; multistability; circuit model; FPGA implementation.
  
  
• Improving hybrid-layer convolutional neural network system for lung cancer nodule classification using enhanced weight optimisation algorithm  
  by Vikul Pawar, P. Premchand 
  Abstract: In recent times, lung cancer is evolving as a highly life-threatening disease for human beings. According to the WHO, lung cancer disease is the second largest cause of deaths as compared to all other types of cancer. The prevailing available technology is striving to get more exposure in the field of medical science using Computer Assisted Diagnosis (CAD), where image processing is playing a crucial role for detecting the cancerous nodules in computer tomographic images. Augmenting the machine learning techniques with image processing algorithms is becoming a more comprehensive examination of cancer disease in proposed CAD systems. This paper is describes a heuristic approach for lung cancer nodule detection, and the proposed model predominantly consists of the following tasks, which are image enhancement, segmenting ROI (Region of Interest), features extraction, and nodule classification. In pre-processing, primarily the Adaptive Median Filter (AMF) filtering method is applied to eliminate the speckle noise from input CT images of Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): in the LIDC-IDRI dataset, the quality of input image is improved by applying Histogram Equalization (HE) technique with Contrast-Limited Adaptive (CLA) approach. Secondly, in the successive stage the Improved Level-Set (ILS) algorithm is used to segment the ROI. Furthermore, the third step of the projected work is applied to extract the definite learnable texture features and statistical features from the segmented ROI. The extracted features in the subsequent stage of classification are applied to Hybrid-Layer Convolutional Neural Network (HL-CNN) architecture to classify the lung cancer nodule as either benign or malignant. Principally this research is carried out by contributing to each stage of it, where the novel concept of the improved Hybrid-Layer Convolutional Neural Network (HL-CNN) is employed by optimising and selecting the optimal weight using the Enhanced Cat Swarm Optimisation (ECSO) algorithm. The experimental result of the proposed HL-CNN using the weight optimisation algorithm ECSO is achieved an accuracy of 93%, which is comparatively efficient with respect to existing models such as DBN, SVM, CNN, WOA, MFO, and CSO. Moreover, the proposed model conclusively gives a decision on the detected nodule as either benign or malignant.
  Keywords: Computer Assisted Diagnosis (CAD); Computer Vision; Cancer Diagnosis; Image Classification; Image Enhancement; Image Segmentation; Feature Extraction.
  
  
• Prediction model for total amount of coke oven gas generation based on FCM-RBF  
  by Lili Feng, Jun Peng, Zhaojun Huang 
  Abstract: The rational use of Coke Oven Gas (COG) is of great significance to improve the economic efficiency of enterprises. In this paper, a COG generation prediction model based on fuzzy C-mean clustering (FCM) and radial basis function (RBF) neural network is proposed to address the problems such as the difficulty of accurate modelling of COG generation process and the difficulty of real-time flow prediction. Firstly, the coke oven production process is analysed and correlation analysis is used to select the influencing factors. Secondly, the FCM is used to classify the working conditions of the coke oven, and the appropriate number of working conditions is selected through experiments. Finally, the prediction models under different working conditions are established separately by using RBF. The experiments were carried out using actual industrial production data, and the experimental results showed that the model could provide guidance reference for the dispatchers.
  Keywords: coking oven process; fuzzy C-means clustering; prediction model; radial basis function neural network.
  
  
• Hie-Graph-YOLOv9: a hierarchical YOLOv9 model with graph-based SE attention mechanism for vehicle detection in complex background
  by T. Selvamuthukumar, K. Vijayalakshmi, P. Dhanalakshmi, R. Abinaya 
  Abstract: Advanced vehicle detection algorithms are key to Intelligent Transportation Systems (ITS), enabling real-time traffic analysis, congestion and security management. Existing models like YOLOv9 face challenges in feature selection and learning, especially in dynamic or cluttered environments. To address these limitations, this research proposes Hie-Graph-YOLOv9 which is an extended version of YOLOv9 based on improving the feature selecting, feature learning and loss function by incorporating Hiera Transformers, Graph-based GAN-SE attention mechanism and Geometric-based Weighted Smooth L1 loss function. Hiera Transformers, integrated into the backbone network across four stages, refine multi-scale feature learning, ensuring robust representation of fine-grained and global patterns. The Graph-based GAN-SE, embedded in the bottleneck module, emphasises critical regions of feature maps, enhancing detection accuracy. Additionally, a Geometric-based Weighted Smooth L1 loss function is employed for bounding box regression, improving convergence speed and training stability. Experimental evaluations demonstrate the superiority of Hie-Graph-YOLOv9, achieving an AP (0.5) of 79.5%, improvement of faster convergence by 120 Epochs and an increased inference speed of 41.95 FPS, outperforming state-of-the-art models. This work offers a significant step forward in vehicle detection under complex real-world conditions.
  Keywords: object detection; YOLO; vehicle; Hiera; graph; squeeze and excitation.
  
  
• Hie-Graph-YOLOv9: A Hierarchical YOLOv9 model with Graph-based SE attention mechanism for vehicle detection in complex background  
  by T. Selvamuthukumar, K. Vijayalakshmi, P. Dhanalakshmi, R. Abinaya 
  Abstract: Advanced vehicle detection algorithms are key to intelligent transportation systems (ITS), enabling real-time traffic analysis, congestion and security management. The proposed Hie-Graph-YOLOv9 method is an extended version of YOLOv9 based on improving the feature selecting, feature learning and loss function. In this YOLO architecture, we induced Hiera Transformers in the backbone network in four stages for improving the feature learning. We also introduced the Graph based GAN-SE attention mechanism in the bottleneck module for giving attention to essential feature map regions and utilized Geometric based Weighted Smooth L1 loss function for bounding box prediction for faster convergence, training stability and improved accuracy.
  Keywords: object detection; YOLO; vehicle;Hiera; graph; Squeeze and Excitation.
  DOI: 10.1504/IJCAT.2025.10072853
   
  
• Graphic design optimisation mechanism based on deep learning in smart cities  
  by Yinan Chen 
  Abstract: This article focuses on the background of smart cities, analyzes and optimizes urban graphic design based on deep learning, and proposes the improved UNet model based on the coordinate attention(CA-IUN). First, we improve the model based on UNet. The improved UNet model (IUN) replaces some traditional convolutions in the encoding and decoding stages with dilated convolutions. Then, transposed convolution is used for upsampling, replacing traditional linear interpolation. We also design multi scale fusion using phantom convolution and SENet. CA-IUN adds coordinate attention module to the encoder and decoder of IUN to focus on the specific positions of features. In addition, this article combines perceptual loss and smooth L1 loss function to train the network. Finally, experiments are shown that CA-IUN outperforms other models in optimizing graphic design, indicating that CA-IUN can effectively achieve more refined and efficient graphic design optimization in smart cities.
  Keywords: deep learning; graphic design optimisation; smart cities.
  DOI: 10.1504/IJCAT.2025.10073305
   
  
• Adaptive constraint multi-objective evolutionary computation industrial economic optimisation in smart city  
  by Yao Lv, Zimeng Guo 
  Abstract: This paper introduces an adaptive constraint multi-objective evolutionary algorithm for smart city industrial economics (ACMEA-SCIE). ACMEA-SCIE employs a dual reproduction strategy, evolving two complementary populations: a main population for exploring diverse industrial configurations and an archive population for preserving high-quality solutions. Additionally, a dynamic fitness allocation function adaptively balances objective optimization and constraint handling, while an innovative archive update mechanism maintains solution diversity. The algorithm's performance was evaluated on three benchmark sets: smart city resource allocation, industrial ecosystem optimization, and dynamic urban industrial planning. Experimental results demonstrate ACMEA-SCIE's superior performance compared to state-of-the-art algorithms, achieving significant improvements in both inverted generational distance and hypervolume metrics. Additional analyses, including convergence performance and solution distribution, further validate ACMEA-SCIE's effectiveness. The proposed algorithm shows remarkable adaptability across various problem types, enhanced constraint handling, and improved multi-objective balancing.
  Keywords: smart city; industrial economic; evolutionary computation; multi-objective optimisation.
  DOI: 10.1504/IJCAT.2025.10073306
   
  
• Sports injury prediction based on sensor information fusion and neural network  
  by Ying Song 
  Abstract: A sensor information fusion method for sports injury prediction is proposed in this paper. The hole effect is eliminated by employing the accumulation of multi-frame differences. On this basis, accurate motion regions are determined by fusion sensors to monitor motion in different scenes. Non-stationary signals of monitoring results are analyzed by wavelet analysis method to obtain motion injury characteristics. Machine learning algorithms can be trained on this sensor data to develop predictive models for sports injuries. Sensor information fusion and wavelet radial basis function neural network are combined to obtain the wavelet eigenvector of all sensors. A radial basis function neural network will output a value when the data sent to it matches a certain risk level to achieve sports injury prediction. The results reveal that the proposed model performs well in prediction accuracy and running time, which can provide real-time feedback to athletes and coaches.
  Keywords: sports injury; sensor information fusion; RBF; wavelet; Neural network.
  DOI: 10.1504/IJCAT.2025.10073307
   
  
• Evaluation of ultra-large-scale English translation mechanism based on Bi-LSTM  
  by Yafei Bi 
  Abstract: How to effectively extract and utilize syntactic features in the model is an issue worthy of further study in the current translation quality estimation task. This paper introduces a Bi-directional Long Short-Term Memory (Bi-LSTM) based English translation mechanism evaluation model aimed at providing fast and accurate feedback to enhance machine translation systems. The proposed model incorporates the following strategies. Firstly, we utilize the Skip-gram model and the Continuous Bag of Words (CBOW) model of the Word2vec to preprocess text data before feature extraction. Second, we utilize three types of translation feature to promote the performance of translation evaluation, including word prediction feature, word-embedding feature, and syntactic structure feature. Third, we design a English translation mechanism evaluation model based on the Bi-LSTM model by fusing the three types of extracted features. The results of the experiment demonstrate that the approach suggested in this paper exhibits favorable evaluation performance.
  Keywords: machine learning; English translation; evaluation model; neural network; feature extraction.
  DOI: 10.1504/IJCAT.2025.10073308
   
  
• A large-scale high-definition music performance strategy based on the combination of reality and metaverse  
  by Minglong Wang, Manqi Kongshi, Daohua Pan 
  Abstract: In this paper, we explore the intersection of the Metaverse, music generation, deep learning, and performance strategy. Deep learning techniques have shown promise in generating music, and can be applied to create personalized soundscapes for users in the Metaverse. However, creating music with deep learning is a complex process that requires careful consideration of performance strategy. Factors such as data quality, model selection, and training methodology can significantly influence the quality of generated music.In this paper, we propose a method for large-scale high-definition music generation and dance performance by combining Metaverse and deep learning techniques. First, we use the transformer model to generate polyphonic music. Then, we use the VAE to encode dance movements. Finally, we use a joint attention mechanism to map music to dance performances. Experimental results and comparative analysis show the effectiveness of the proposed method.
  Keywords: reality and metaverse; deep learning; music generation; large-scale creation.
  DOI: 10.1504/IJCAT.2025.10073494
   
  
• Preschool education video image optimisation mechanism based on deep evolutionary learning in smart city  
  by Junqing Fan 
  Abstract: As an important stage of basic education, the richness and quality of teaching resources in preschool education directly affect the growth and development of children. In order to better optimize the processing of preschool education video image and improve their clarity, this paper proposes a deep evolutionary learning method based on the improved whale optimization algorithm and bi-directional long-short term memory (IWOA-BiLSTM). BiLSTM utilizes the temporal information between adjacent frames of preschool education video image to preserve the time series output in the feature map of preschool education video image. This can fully learn the information between adjacent frames of preschool education video image, making the optimized image contain richer information. IWOA is used to optimize the key parameters of BiLSTM and improve its optimization performance. Finally, experiments show that IWOA-BiLSTM can effectively optimize preschool education video image in smart city.
  Keywords: deep learning; image optimisation; evolutionary algorithms; preschool education video; smart city.
  DOI: 10.1504/IJCAT.2025.10073495
   
  
• Digital media art design mechanism based on reinforcement learning in smart city  
  by Xin He 
  Abstract: Digital media art has emerged as a pivotal domain that intersects technology, culture, and urban life, transforming public spaces and offering novel forms of interaction and expression. In this paper, we presents a novel framework for 3D face reconstruction in digital media art design, leveraging Generative Adversarial Networks (GANs) and Reinforcement Learning (RL). We train and evaluate our model with rigorous experiments based on public dataset, comparing its performance against several state-of-the-art methods. Our proposed model demonstrates superior performance in two metrics. Additionally, we conduct convergence analysis and robustness to input noise experiments to further validate our approach. The results highlight the effectiveness of our method in producing high-quality, realistic, and robust 3D face reconstructions, underscoring its potential for enhancing digital media art installations in smart cities.
  Keywords: digital media; smart city; reinforcement learning.
  DOI: 10.1504/IJCAT.2025.10073496
   
  
• Dual-phase temporal attention framework for energy-aware music recommendation  
  by Long Tang 
  Abstract: Personalized music recommendation systems build preference models based on users' listening history to suggest music aligned with their interests. As music streaming data volumes increase exponentially, energy consumption has become a critical concern in processing these recommendations. This paper introduces a novel energy-conscious approach to music recommendation. First, we propose a sequential preference framework that captures both enduring and recent user preferences using temporal attention networks. Second, we develop a cascaded decomposition technique to address data sparsity and imbalance challenges in large-scale music interaction datasets. Finally, we implement an energy-aware computation strategy that optimizes resource utilization during recommendation processing. Our experimental results demonstrate that the proposed framework outperforms baseline methods across multiple evaluation metrics while reducing energy consumption by up to 25%. Ablation studies confirm each component's effectiveness in enhancing recommendation quality and energy efficiency.
  Keywords: music recommendation; temporal attention; energy-aware computation; sequential preference.
  DOI: 10.1504/IJCAT.2025.10073545
   
  
• Adaptability analysis of artificial intelligence and evolutionary computation in modelling and prediction of complex economic systems  
  by Na Tao 
  Abstract: The rapid advancements in artificial intelligence (AI) and evolutionary computation (EC) have paved the way for innovative solutions to complex economic modeling and prediction challenges. In this paper, we present a novel approach that integrates Deep Belief Networks (DBNs) with Particle Swarm Optimization (PSO) to enhance the accuracy and robustness of exchange rate predictions in the Forex market. The proposed hybrid DBN-PSO model leverages the deep learning capabilities of DBNs to capture intricate data patterns, while PSO optimizes the hyperparameters to achieve optimal performance. Extensive experiments on historical Forex data demonstrate that the DBN-PSO model significantly outperforms compared models in terms of four metrics. Visual analyses further illustrate the close alignment between predicted and actual exchange rates, underscoring the model's predictive accuracy and reliability. This research contributes to the advancement of economic forecasting by providing a robust and efficient tool for modeling and predicting complex economic systems.
  Keywords: artificial intelligence; evolutionary computation; complex economic systems; adaptability analysis.
  DOI: 10.1504/IJCAT.2025.10073633
   
  
• Deep learning-powered automatic assessment mechanism in enhancing spoken English fluency  
  by Chunyan Xu 
  Abstract: Spoken English is essential for individuals who wish to work or study in an English-speaking environment. It is the primary means of communication for many professions, including business, education, and healthcare. To improve the efficiency of spoken English learning, an end-to-end automatic English assessment method based on deep learning is designed. At the input level, the words are represented as a sequence tensor, where each position corresponds to the pre-trained word vector and the high-level information is obtained using a bidirectional long short-term memory (LSTM) network. The attention mechanism is integrated into the network in the acoustic model layer to improve the method's efficiency. In the output layer, the expression of words is connected with the spoken English expression, and the Softmax function is used to predict the grades. Simulation results show that the proposed method performs better than traditional LSTM and gate recurrent unit.
  Keywords: spoken English; automatic assessment; deep learning; LSTM.
  DOI: 10.1504/IJCAT.2025.10073634
   
  
• Building a tourism decision support system based on big data  
  by Li Fu, Yi Yao 
  Abstract: This paper studies the construction of a tourism decision support system based on Big Data (BD) technology and deep learning models. Apache Kafka is a pipeline for real-time data streams to stream data from different sources to the processing system. Apache Flink is a stream processing engine to processes and analyses the real-time incoming data streams and identifies emergencies. The Long Short-Term Memory (LSTM) network model receives data streams from Flink and performs time series prediction based on the users historical data and real-time information. The output prediction results are used for travel recommendations through a collaborative filtering algorithm. The research results show that compared with the rules-based and collaborative filtering systems, the retention rate of the system implemented in this paper is higher than the other two systems. This study enhances tourism decision support systems personalisation and real-time response capabilities.
  Keywords: tourism decision support system; big data technology; deep learning models; real-time response; personalised recommendations.
  DOI: 10.1504/IJCAT.2025.10073933
   
  
• Virtual reality data visualisation design based on model predictive control in metaverse  
  by Tiankuo Yu, Lei Ding, Xiaocheng Zhou, Gaofeng Han 
  Abstract: In response to the problem of slow data updates caused by a large amount of static data display and neglect of real-time dynamic interaction in visual design, this study developed a framework based on MPC (Model Predictive Control) to address the limitations of static display and promote real-time interaction. In the article, a data acquisition and processing module is constructed, combined with linear regression and LSTM (Long Short Term Memory) models, optimized and integrated into a VR (Virtual Reality) system. Multiple interaction methods are designed, and reinforcement learning is introduced to improve prediction performance, data display effectiveness, and multi-user synchronization accuracy. The results showed that the average accuracy of the method reached 93.17%, with response delay, frame rate, and update frequency of 6.97 milliseconds, 101 frames per second, and 67 hertz, respectively. These results demonstrate the effectiveness of the framework in VR applications.
  Keywords: data visualisation design; model predictive control; virtual reality; art design; system architecture design.
  DOI: 10.1504/IJCAT.2025.10073937
   
  
• Design and research of IIoT intelligent automatic production line security monitoring system based on digital twin  
  by Mengjia Lian, Lanqing Li, Shiyu Wang, Chunxiao Wang, Mingshi Li 
  Abstract: The paper proposes a security monitoring method of intelligent automatic production line to address the issues such as the inability to proactively predict instrument failures and inconvenient daily maintenance, and establishes a security monitoring architecture of intelligent automatic production line. The architecture specifically includes four parts: the physical model of the production line, the virtual model of the production line, the twin data of the production line and the digital twin service platform. Furthermore, the twin data of the production line are effectively analysed based on the fault hybrid prediction method, which can predict the possible faults and existing security risks that the production line is running. The intelligent automatic production line security monitoring method based on digital twins has the ability to predict and maintain the possible faults in the production line while ensuring normal production and processing, which can improve the stability of the production line.
  Keywords: industrial internet of things; intelligent automatic production line; security monitoring; failure prediction.
  DOI: 10.1504/IJCAT.2025.10073941
   
  
• Application of neural network technology in English speech recognition and its impact on English speaking teaching  
  by Fengxiang Zhang, Feifei Wang 
  Abstract: In order to improve the accuracy of English speech recognition and promote the improvement of pronunciation accuracy in English oral teaching, this paper studies the application of neural network technology in English speech recognition and its impact on oral teaching. Using Mel frequency cepstral coefficients to extract audio features of English speech signals, taking the extracted audio features as input, and based on the English speech recognition results, a BP neural network is used to construct an English speech recognition model, which outputs the English speech recognition results with the minimum cumulative residual. Analyse the impact of this technology on English oral teaching from four aspects: improving pronunciation accuracy, achieving personalised learning, enhancing interactivity and expanding learning resources. The experimental results show that the accuracy of the English speech recognition method proposed in this paper always remains above 92%, which can improve the accuracy of English oral pronunciation.
  Keywords: neural network technology; English speech recognition; English speaking teaching; Mel frequency cepstral coefficient.
  DOI: 10.1504/IJCAT.2025.10074406
   
  
• Evaluation of sound perception using a wireless sensor network for individuals with normal hearing  
  by Xinfei Shen, Wei Wei 
  Abstract: Tone perception depends on reliable frequency cues, yet wireless-sensing implants often convey imprecise pitch because of electrode length, channel limits, and speech-coding strategies. To enhance robustness in wireless sensor network (WSN) sound-source localisation, we introduce a linear-programming sequential localisation algorithm (LPSBL). The method models sequential signal arrival-time constraints across nodes as a linear program and embeds relaxation to compensate for measurement errors, preventing localisation failure under noise. We also examined pitch outcomes in children using hearing technologies. Average tone-perception scores for normal-hearing children with unilateral WSN hearing aids remained at chance, whereas children fitted bimodally (implant + acoustic aid) showed modest pitch recognition that was nevertheless low overall. These findings indicate that, while LPSBL strengthens WSN localisation robustness, bimodal assistance yields only limited improvements in pitch perception, underscoring the need for refined acoustic-electric processing and targeted training.
  Keywords: teaching effect; normal hearing people; music perception; wireless sound sensor network.
  DOI: 10.1504/IJCAT.2025.10074445
   
  
• A monitoring and early warning of respiratory infectious disease symptoms based on multi-source information data fusion  
  by Shengcong Tao, Yirong Guo 
  Abstract: An oversight and alert methodology grounded in multi-source information data amalgamation is proposed to address the issues of elevated root mean square error and suboptimal alert efficacy in respiratory infectious disease symptom monitoring. First, manifestation data characteristics are delineated through time series analysis, and Support Vector Machines (SVM) are employed for feature extraction. Wavelet transformation technology is utilised to eliminate noise and rectify missing data. Subsequently, data level, feature level and decision level are progressively integrated to consolidate multi-source data characteristics, while Markov chain models are amalgamated to determine alert zones. The experimental results demonstrate that the proposed method achieves optimal performance in the root mean square error test of multi-source respiratory infectious disease symptom data fusion, with a minimum error of 0.11%. In the absolute accuracy value test for symptom monitoring and warning, the highest accuracy is observed to approach 100%.
  Keywords: data fusion; time series definition; SVM; decision level fusion; Markov chain.
  DOI: 10.1504/IJCAT.2025.10074468
   
  
• Advancing financial security: integrating AI and blockchain for cloud network protection in supply chain financing  
  by Weishuang Xu, Daming Li 
  Abstract: In view of the cloud network security issues in supply chain finance, the integrated application of Artificial Intelligence (AI) and Blockchain Technology (BT) is discussed to meet the challenges of data collection and integration efficiency in enterprise supply chain finance. AI has the ability to analyse massive data and accurately identify patterns, so it can strengthen the security of cloud networks and improve risk identification and control capabilities. At the same time, the application of BT in corporate supply chain systems demonstrates its potential for real-time monitoring and management of financing processes. This study aims to verify the feasibility of applying BT supported by the Internet of Things (IoT) in the field of supply chain finance to improve risk management and security. Research results show that the combination of AI and BT can significantly improve risk identification capabilities (0.99) and safety performance (1.22), thus providing a new solution for the safety management of supply chain finance.
  Keywords: corporate supply chain; risk identification and control; internet of things blockchain; electricity system trading.
  DOI: 10.1504/IJCAT.2025.10069456
   
  
• Development of intelligent data collection and management system based on internet of things big data crawler technology  
  by Jin Chen, Yao Li, Xia Hua, Long Lu 
  Abstract: This paper used web crawler technology to develop an intelligent data collection and management system. This paper first analysed the basic principles of system design and the structural requirements of the system according to the system requirements. Then, it evaluated the overall results of the intelligent data collection and management system. The process and principle of information collection using the IoT Big Data (BD) crawler technology were introduced in detail. Finally, the information collection effect of the system was verified by experiments. The experimental results showed that the data acquisition accuracy of the system was high, accounting for more than 90%. The system had high usability and efficiency; users were satisfied, and data collection and management could be done well. Existing research is often limited by the dispersion and heterogeneity of data sources when collecting data. Data in the IoT environment usually has problems with diverse formats and complex protocols.
  Keywords: intelligent data collection; data management; big data of the internet of things; big data crawler technology.
  DOI: 10.1504/IJCAT.2025.10069544
   
  
• Visual evaluation of tourism ecological and environmental protection green management in the context of sustainable development  
  by Kun Zheng, Lijing Zhang 
  Abstract: The tourism industry has seen growth in recent years but has been impacted by the spread of COVID-19. Tourism not only supports societal development and economic growth but also affects local ecosystems. Tourists' behaviours can harm the environment due to regional differences, leading to ecological damage. With the increasing scale of tourism and improved economic conditions, the environmental impact is becoming more apparent. Researchers are focusing on new development models that balance tourism growth and environmental protection. One solution is promoting a green, low-carbon lifestyle among tourists and integrating ecofriendly practices into industry development. By using sustainable development and visualisation technology, a new green management model has been proposed. Experimental analysis shows that this model improves performance by 52.3% compared to traditional methods, helping protect the ecological environment while fostering sustainable tourism growth.
  Keywords: tourism ecology; green management; sustainable development; visualisation analysis.
  DOI: 10.1504/IJCAT.2025.10069655
   
  
• Construction of intelligent tourism public service platform featuring communication big data and internet of things  
  by Shuo Liang, Yiran Wang, Lili Liu 
  Abstract: Tourism has evolved into a modern service industry and strategic pillar, driven by advancements in information and communication technology. This transformation has ushered in the era of intelligent tourism. This study introduces the background of intelligent tourism, summarising academic research on tourism public service platforms, big data (BD), and the Internet of Things (IoT). An algorithm model is proposed to establish a theoretical framework for constructing an intelligent tourism platform characterised by BD and IoT. Factor analysis identifies key elements for platform development, followed by simulation experiments to validate its effectiveness. Results demonstrate that the proposed platform increases user satisfaction by 20% compared to traditional systems. In the BD era, with growing socioeconomic and technological progress, integrating information technology across industries has become critical. Developing intelligent tourism platforms represents a significant theoretical and practical challenge for the tourism industry's advancement.
  Keywords: intelligent tourism; big data; internet of things; communication technology; wireless communication.
  DOI: 10.1504/IJCAT.2025.10071195
   
  
• A lightweight authentication architecture for local communication of power terminal based on quantum key  
  by Chao Chen, Jin Qian, Shaojie Luo 
  Abstract: With the acceleration of new power systems, there has been increasing emphasis on the need for secure power infrastructure. The implementation of 5G technology in power systems has leveraged wireless communication for the transmission of critical data. While this advancement has enhanced network accessibility, it has concurrently heightened security vulnerabilities. At present, the scheme to bolster the secure communication capabilities of power terminals through a fusion of quantum cryptography and lightweight identification technology is grappling with practical deployment challenges. To address this predicament, we introduce a novel quantum key-based lightweight authentication architecture for local communication networks. By leveraging a secure encryption chip in conjunction with lightweight authentication key technology, the proposed architecture facilitates efficient and secure connectivity among a multitude of terminals in local communication settings. A comprehensive security analysis shows that the proposed solution has strong security capabilities to meet the requirements of new power systems.
  Keywords: local communication; quantum cryptography; secure chip; lightweight authentication key technology; authentication.
  DOI: 10.1504/IJCAT.2025.10070796
   
  
• Research on image encryption algorithm based on logistic chaotic system  
  by Qing Lu, Te Zhang, Junxiang Wan, Siyuan Xu 
  Abstract: In the recently proposed image encryption algorithm (Zhang and Zhang, 2020) based on the logistic chaotic system, chaotic sequences and plaintext images are first generated through a key for XOR operations. Subsequently, pixel and block chaos are applied according to the chaotic sequence, finally resulting in the ciphertext image. Results show that the correlation between plaintext and ciphertext images in this algorithm is low, and no ciphertext feedback mechanism exists. Consequently, this algorithm is vulnerable to ciphertext selection attacks. The algorithm is improved, and the relevant test analysis of the improved algorithm is carried out, demonstrating better results than the algorithm proposed by Zhang and others.
  Keywords: chaotic system; image encryption; chosen-ciphertext attack.
  DOI: 10.1504/IJCAT.2025.10072127
   
  
• Immersive human-computer interaction system combining AI and augmented reality technologies  
  by Hui Wang, Ling Qin, Xinming Gu 
  Abstract: Integrating Artificial Intelligence (AI) and Augmented Reality (AR) technologies can enhance user experience. Firstly, the Simultaneous Localisation and Mapping (SLAM) model is used to complete scene modelling. Then, the Generative Adversarial Network (GAN) model is utilised to generate virtual objects or scenes. The long short-term memory (LSTM) model is used to analyse user action data and predict user behaviour. Finally, the k-means clustering algorithm is adopted to analyse user preferences. The LSTM model predicts the acceleration data [1.5, 1.2, 1.1] at time t=10. The k-means clustering method divides the six users into two groups - the users in category 1 like high-intensity exercises, and the users in category 2 like low-intensity exercises.
  Keywords: immersive human-computer interaction system; simultaneous localisation and mapping model; long short-term memory; generative adversarial network; k-means clustering algorithm.
  DOI: 10.1504/IJCAT.2025.10073931
   
  
• Design of library information recommendation system integrating transfer learning and population intelligence optimisation  
  by Fang He 
  Abstract: Traditional classification and recommendation methods encounter limitations due to high dimensionality, insufficient annotation and the heterogeneous nature of user interests. To address these challenges, this study proposes a novel framework for user interest classification and book recommendation, named Transfer Convolutional Adaptive Support Vector Machine (T-CASVM). This framework integrates deep transfer learning with Particle Swarm Optimisation (PSO). It utilises a deep transfer convolutional neural network with shared weights to extract features from both source and target domains, thereby mitigating distributional discrepancies by simultaneously optimising classification loss and domain loss. Furthermore, PSO is employed to refine the classifier, improving both accuracy and computational efficiency. The framework calculates cosine similarity between the target user and others to provide personalised book recommendations. Experimental results on the public Book-Crossing data set show that T-CASVM outperforms traditional methods, achieving over 0.79 in precision index concerning the user interest classification task.
  Keywords: swarm intelligence; PSO; transfer learning; recommendation system.
  DOI: 10.1504/IJCAT.2025.10073345
   
  
• Optimisation of network learning platform based on machine learning algorithm  
  by Jin Zhang 
  Abstract: The purpose of this study is to optimise online learning platforms through deep learning and address issues related to personalised user experience and resource allocation. A comprehensive optimisation framework is proposed, comprising three modules: user behaviour analysis, personalised recommendation and resource optimisation scheduling. First, a recommendation mechanism is developed by integrating Neural Collaborative Filtering (NCF), the Transformer model and Content-Based Filtering (CBF) techniques. Accordingly, a user behaviour prediction and personalised recommendation model based on a fused NCF-CBF-Transformer algorithm (NCF-CBF-T) is constructed. This model enhances the personalised recommendation system by leveraging multi-level technology integration. Specifically, the Transformer model captures temporal dependencies in user behaviour sequences and dynamically models long-term user interest evolution through the multi-head self-attention mechanism. This study contributes to the theoretical advancement of deep learning applications in educational technology and provides practical experimental references for optimising online learning platforms.
  Keywords: network learning platform; machine learning; personalised recommendation system; transformer; user behaviour prediction; resource optimisation scheduling.
  DOI: 10.1504/IJCAT.2025.10073632
   
  
• Innovative methods for intellectual property protection in the automotive industry driven by artificial intelligence and deep learning  
  by Yahong Xu, Kewei Ji, Yakun Xu, Mengyao Chen 
  Abstract: The rapid advancement of Artificial Intelligence (AI) in the automotive sector has introduced complex challenges to Intellectual Property (IP) protection, particularly due to the proliferation of deep learning technologies. This study adopts an innovative interdisciplinary approach - encompassing law, technology and economics - to comprehensively address these multifaceted issues. A hybrid model combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) with an attention mechanism (CNN-RNN-AM) is developed. The CNN-RNN-AM model integrates the convolutional and sequential processing capabilities of CNN and RNN with the adaptive focus provided by attention mechanisms. Fine-tuned algorithms for convolutional and recurrent operations, as well as attention-based optimisation, enhance the model's capacity for data analysis and feature extraction. A multimodal data fusion strategy is employed to integrate diverse sources, including patent documentation. The neural architecture is optimised using residual connections and bi-directional memory networks, thereby improving feature representation and model robustness.
  Keywords: artificial intelligence; automotive industry; deep learning; intellectual property; attention mechanism.
  DOI: 10.1504/IJCAT.2025.10073673
   
  
• Back propagation neural network in artificial intelligence for intellectual property protection in the automotive industry  
  by Xiaolong Liang, Kewei Ji, Tiantian Qu, Huiting Wang, Qikun Ao 
  Abstract: With the rapid development of the New Energy Vehicles (NEVs) industry, how to scientifically evaluate patent value and improve intellectual property protection efficiency has become an important issue. This study focuses on intellectual property protection in the automotive industry, using NEVs as the subject, and constructs a patent value evaluation index system. The eXtreme Gradient Boosting (XGBoost) algorithm is employed to select feature indicators, and a high-precision patent value evaluation model, XGBoost and SAM-Based Back Propagation Neural Network (XSA-BPNN), is developed. The experimental results show that the XGBoost algorithm performs the best in feature selection. Its Mean Squared Error (MSE) is 0.127, Mean Absolute Error (MAE) is 0.198, explained variance is 0.438 and R² is 0.413. These values significantly outperform the Random Forest and Gradient Boosting Tree algorithms. Finally, the XSA-BPNN model achieves an average absolute error of only 0.0484 in patent value evaluation, significantly outperforming the comparison models. This indicates that the proposed XSA-BPNN model enhances the accuracy of patent value evaluation by precisely capturing and utilising key features. This study provides technical support for optimising patent transactions and management processes, as well as improving the scientific approach to intellectual property protection.
  Keywords: automotive industry; patent value evaluation; XGBoost; BPNN; intellectual property protection.
  DOI: 10.1504/IJCAT.2025.10073674
   
  
• A layout system design for ship engine room and equipment by artificial intelligence  
  by Meijing Song, Karia Noorliza 
  Abstract: This study aims at the problems of traditional intelligent algorithms, such as being prone to getting trapped in local optima, having slow convergence speeds, and insufficient accuracy when solving complex optimisation problems. It systematically compares and analyses the advantages and limitations of the genetic algorithm (GA), particle swarm optimisation (PSO) algorithm, and ant colony algorithm (ACA). Moreover, a GA-PSO-ACA that combines the advantages of the three is proposed. Taking the layout design of ship engine room equipment as the research object, under the consideration of multiple constraints, such as no overlap of equipment, balanced weight distribution, operation space, and safety distance, three traditional algorithms and the GA-PSO-ACA are respectively used for simulation optimisation. The results show that the optimal objective function value of the GPA algorithm is 160, which is significantly better than that of the GA (220), the PSO algorithm (225), and the ACA (218).
  Keywords: ship design; genetic algorithm; particle swarm optimisation; ant colony algorithm; hybrid algorithm.
  DOI: 10.1504/IJCAT.2025.10073304
   
  
• Tourism data mining and analysis methods for smart tourism  
  by Jiajun Chen, Guanxi Chen 
  Abstract: Smart tourism has shortcomings in real-time and information processing efficiency, and traditional data processing makes it difficult to cope with the rapid flow and complex analysis requirements of large-scale real-time data. This paper uses a distributed streaming data mining method based on Apache Flink. First, Flink is used to integrate scenic area sensors and multi-source data streams; then, the Long-Short-Term Memory (LSTM) algorithm is used to predict traffic trends; next, streaming K-means is used to mine tourist behaviour patterns; finally, dynamic optimisation schemes are generated through Frequent Pattern-Stream (FP-Stream). Studies have shown that this method achieves a low latency of 0.38 seconds and a high throughput of 5600 records/second when processing millions of data in a 20-node cluster through efficient parallel processing of distributed architecture and real-time analysis of streaming algorithms, providing real-time and precise technical support for smart tourism.
  Keywords: smart tourism; distributed stream data mining; traffic trend prediction; tourist behaviour patterns; dynamic optimisation schemes.
  DOI: 10.1504/IJCAT.2025.10073663