International Journal of Computer Applications in Technology (30 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
   
  

• Open Access

  Cloud computing based construction and empirical evaluation of the security risk early warning evaluation system of digital economy ( Free Full-text Access )

  by Yanan Wu 
  Abstract: Traditional static risk assessment methods struggle to meet real-time processing demands for large-scale, multi-source heterogeneous data, showing sluggish responsiveness to emergencies and abnormal transactions. These approaches often suffer from poor early-warning accuracy and frequent false or missed alerts. To address these challenges, this study proposes a cloud-based security risk warning evaluation system for the digital economy. The system first establishes a multi-level risk indicator framework, utilizing fuzzy hierarchical analysis and information entropy to calculate weighted metrics that integrate qualitative and quantitative indicators. It then employs grey prediction algorithms for short-term risk trend forecasting. Through a cloud computing distributed architecture, the system achieves real-time collection, processing, and risk assessment of multi-source heterogeneous data, ensuring instant precision in warnings. Experimental results demonstrate that this method consistently outperforms existing approaches in both warning accuracy and Recall metrics, with significantly reduced average response time while maintaining reasonable control over false alarm rates and resource consumption. This research provides a practical technical solution for digital economy security risk management, offering both theoretical value and practical significance.
  Keywords: risk early warning; evaluation system construction; digital economy; economic security.
  DOI: 10.1504/IJCAT.2025.10075047
   
  

• Open Access

  DAFPN: a lightweight multi-objective framework for small object detection in remote sensing images ( Free Full-text Access )

  by Wanqi Ren 
  Abstract: Remote sensing object detection faces persistent challenges in accurately identifying small-scale targets embedded in high-resolution, cluttered scenes. Conventional detectors often suffer from feature dilution, scale variance, and high computational cost, limiting their applicability in real-time or edge-based remote sensing scenarios. To address these issues, we propose DAFPN, a lightweight Dynamic Attention-guided Feature Pyramid Network that integrates asymmetric multi-scale fusion and dual-branch attention, consisting of spatial and channel-wise attentions, into a unified architecture optimized via multi-objective constrained learning aimed at simultaneously maximizing detection accuracy, attention alignment, and architectural compactness. On DOTA-v2.0, our method improves mAP@0.75 by 4.5% and mAP@0.5 by 3.8% over YOLOv8, while achieving similar gains on FAIR1M, DIOR, and RSSOD. The results confirm DAFPN’s robustness under variable input resolutions and dense object distributions, highlighting its practical value for deployment in real-time and resource-constrained remote sensing applications.
  Keywords: remote sensing; small object detection; multi-objective optimization; feature pyramid networks; dynamic attention; lightweight detection; aerial imagery; real-time inference.
  DOI: 10.1504/IJCAT.2026.10075464
   
  

• Open Access

  Anomaly detection and pattern recognition methods for high-dimensional data ( Free Full-text Access )

  by Shengqi Wang 
  Abstract: High-dimensional data has become increasingly prevalent in a wide range of fields, including cybersecurity, finance, healthcare and industrial monitoring. However, the sparsity, redundancy and complex inter-feature relationships inherent in such data significantly complicate anomaly detection and pattern recognition tasks. Traditional machine learning methods often suffer from poor scalability and limited generalisation in high-dimensional settings. To address these limitations, this paper proposes a novel deep learning framework specifically designed for high-dimensional anomaly detection and pattern recognition. The proposed model introduces three key innovations. First, a hierarchical representation module is developed to extract multilevel semantic features by integrating adaptive kernel transformations with semantic-preserving aggregation strategies. This design improves the models ability to capture both global patterns and local anomalies. Second, a dual-branch attention mechanism is introduced to jointly learn feature-level and instance-level relevance, enhancing the models robustness to noise and irrelevant dimensions. Third, an interpretable anomaly scoring strategy is constructed based on prototype deviation in latent space, offering transparency and actionable insights for decision support. Extensive experiments are conducted on multiple real-world high-dimensional data sets. Results demonstrate that the proposed method consistently outperforms existing approaches in terms of accuracy, robustness and interpretability.
  Keywords: high-dimensional data; anomaly detection; hierarchical representation learning; attention mechanism.
  DOI: 10.1504/IJCAT.2026.10075605
   
  

• Open Access

  An extraction method of pop music singing beats based on audio features ( Free Full-text Access )

  by Zhuo Kong, Guofeng Liu 
  Abstract: In the analysis process of popular music singing audio, factors such as environmental noise interference and complex instrument accompaniment seriously affect the accuracy of audio feature extraction, resulting in the performance of traditional music beat extraction methods being difficult to meet practical needs. Therefore, this study innovatively proposes a popular music singing beat extraction method based on multi feature fusion. Performing preprocessing operations such as discretization, denoising, and normalization on the original singing audio signal effectively improves signal quality. Through joint time-frequency domain analysis, comprehensively extract the time-frequency characteristics of music signals. Adopting a feature fusion strategy, combined with beat cycle analysis and inter beat distance calculation, high-precision beat detection is achieved. Experimental data shows that the missed detection rate and false detection rate of this method are as low as 2.1% and 2.5%, respectively, significantly better than traditional methods, providing reliable technical support for pop music performance analysis.
  Keywords: audio features; pop music; singing rhythm; intelligent extraction model.
  DOI: 10.1504/IJCAT.2026.10075713
   
  

• Open Access

  Study on accurate prediction method for daily tourist flow in tourist attractions based on feature recursive elimination ( Free Full-text Access )

  by Xiaoyan Wang 
  Abstract: To reduce peak lag deviation (PLD), enhance flow mutation responsiveness (FMR), and optimize hotspot overlap rate (HOR), this paper proposes a feature recursive elimination-based method for accurate daily tourist flow prediction in attractions. Firstly, integrate multidimensional historical data through data dimensionality reduction processing to reduce data complexity. Then, extract the daily average tourist volume and traffic fluctuation features, and use feature recursive elimination method combined with random forest to streamline the feature dataset and improve feature effectiveness. Finally, utilizing evolutionary strategies to optimize the BP network, overcoming its limitations, and achieving accurate prediction. The experiment shows that for weekdays, the PLD, FMR, and HOR of this method are 2.34%, 94.56%, and 92.34%, respectively. For holidays, the PLD, FMR, and HOR of this method are 5.62%, 83.21%, and 81.23%, respectively. The numerical results are superior to existing methods.
  Keywords: tourist attractions; daily tourist flow; prediction methods; feature extraction; recursive elimination; evolutionary strategy; BP network.
  DOI: 10.1504/IJCAT.2026.10075714
   
  
• 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
   
  
• 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
   
  
• A theoretical framework for integrating federated learning and transfer learning: advancing optimisation in decentralised systems  
  by Mohammed Abdul Wajeed, Annavarapu Chandra Sekhara Rao 
  Abstract: Federated Learning (FL) has transformed decentralised model training by enabling collaborative learning while protecting data privacy. Key challenges include non-iid data distributions, slow convergence and limited understanding of combining FL with other paradigms. This research introduces a theoretical framework establishing foundations for incorporating Transfer Learning (TL) into FL to address these issues. The Federated Transfer Optimisation (FTO) framework expands FL optimisation theories by introducing transfer-invariant initialisation metrics for efficient use of pre-trained models. We introduce a Transfer Learning Augmented Loss (TLAL) function combining global objectives and local transfer dynamics to control knowledge retention during fine-tuning. The framework presents adaptive task-alignment kernels to balance global and client-specific objectives in heterogeneous scenarios. Experimental evaluations on text classification data sets show FTO achieves better accuracy, reduced communication overhead and faster convergence compared to existing FL methods. This study provides a principled basis for integrating TL, enabling efficient learning systems for privacy-sensitive applications.
  Keywords: federated learning; transfer learning; federated transfer optimisation; distributed optimisation; adaptive task-alignment kernels; transfer learning augmented loss; TLAL; integrate federated transfer learning; text classification.
  DOI: 10.1504/IJCAT.2025.10074663
   
  
• Multi-dimensional data mining of English online teaching platform based on improved decision tree  
  by Jingping Du 
  Abstract: In order to improve the acceleration ratio and mining accuracy of data mining, this paper proposes a new multi-dimensional data mining method for English online teaching platforms based on improved decision tree. Firstly, information granule technology is introduced for data reconstruction, utilizing neighborhood data relationships to improve clustering accuracy. Secondly, constructing an association rule mapping structure, using association matrix and difference coefficient matrix to present the relationships between datasets, introducing mining factors and relative errors to improve subsequent mining accuracy. Finally, the improved C4.5 decision tree algorithm is adopted, combined with principal component analysis to reduce dimensionality, and features are filtered through information gain rate to improve data mining accuracy and efficiency. The experimental results show that the mining performance of our method is significantly improved, with a data mining acceleration ratio maintained above 0.9 and a data mining accuracy maintained above 98.54%.
  Keywords: improve decision tree; English online teaching platform; multidimensional data; data mining.
  DOI: 10.1504/IJCAT.2025.10075111
   
  
• An enhancement processing for smoke environment images of firefighting robots based on improved homomorphic filtering  
  by Lei Zhang, Baochen Yang, Wenlian Guo 
  Abstract: In order to improve the image processing effect and enhance the usability of images, this paper designs a smoke environment image enhancement processing method for fire extinguishing robots based on improved homomorphic filtering. Using fire extinguishing robots as carriers, high-speed cameras are installed to capture image information of smoke environments. Homomorphic filtering is used to eliminate interference information in the images, and the homomorphic filtering results are improved through a total variation model. Through smoothing processing, the edges of objects are well preserved. Extract global feature values of images using Retinex algorithm. Finally, the extracted image feature values are weighted and fused to construct an image enhancement model, which completes the image enhancement process through model calculations. The experimental results show that this method can effectively enhance the information in smoke environment images, with an average contrast gain of nearly 30% and a natural preservation of around 98%.
  Keywords: firefighting robot; smoke environment image; image enhancement; homomorphic filtering; total variational model; Retinex algorithm; feature extraction.
  DOI: 10.1504/IJCAT.2025.10075218
   
  
• Integrating security within DevOps for continuous protection: securing software development through unified practices  
  by Bahaa Eddine Elbaghazaoui, Tarik El Moudden, Salma El Omari, Soukaina Nai, Imane Moustati, Khalid Benabbes 
  Abstract: DevSecOps integrates security into the DevOps pipeline, embedding it as a core part of the software development lifecycle. This paper examines its evolution from traditional DevOps, emphasizing principles such as Security as Code, Shift-Left Security, and Continuous Monitoring, which together enable proactive vulnerability management and resilient delivery. It explores challenges including cultural resistance, skill gaps, and the complexity of tool integration, while outlining practical solutions such as automating security checks, fostering a security-first culture, and leveraging metrics to track progress. Future trends shaping DevSecOps are also discussed, including AI-driven threat detection, Zero Trust Architecture, and Compliance-as-Code to streamline regulatory adherence. By addressing these aspects, organizations can achieve secure, agile, and adaptive software delivery. The paper contributes an actionable, stage-wise adoption view that couples culture, process, and CI/CD gate placement, illustrated with a small-business example and concrete outcome metrics to demonstrate practicality and measurable impact.
  Keywords: DevSecOps; shift-left security; AI; artificial intelligence; zero trust architecture; compliance-as-code.
  DOI: 10.1504/IJCAT.2026.10075793
   
  
• 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 user's 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 in 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
   
  
• 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 lines 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 lines. 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
   
  
• 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 Model Predictive Control (MPC) 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 Long-Short-Term Memory (LSTM) models, optimised and integrated into a Virtual Reality (VR) system. Multiple interaction methods are designed, and reinforcement learning is introduced to improve prediction performance, data display effectiveness and multi-user synchronisation 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
   
  
• Industrial phased array ultrasonic imaging data processing and defect recognition technology based on deep learning  
  by Dawen Yao, Peiwen Meng, Jinggang Xu, Shuqi Li 
  Abstract: This paper innovatively applies deep learning technology and uses a deep Convolutional Neural Network (CNN) to automatically extract key features from ultrasonic imaging data and perform defect recognition. The ultrasonic imaging data is denoised, normalised and data augmented, and a deep CNN model is constructed. Image features are automatically extracted through multi-layer convolution and pooling layers. The model is trained and optimised using the backpropagation algorithm and cross-entropy loss function. The trained model is used to realise real-time defect detection and precise positioning of new ultrasonic images. The defect classification and positioning model comparison experiment is compared with different CNN architectures such as Residual Network (ResNet), CBAM-CNN (Convolutional Block Attention Module CNN) and Hybrid CNN. The accuracy of the proposed method reaches 93.10%, and the detection speed is 832 images per second, which is significantly better than the detection precision and efficiency of other models.
  Keywords: deep learning; industrial phased array ultrasonic imaging; defect recognition; deep convolutional neural network; data denoising; augmentation.
  DOI: 10.1504/IJCAT.2025.10074854
   
  
• 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
   
  
• Research on intelligent data collection and quality evaluation of computer science and art education systems based on systemic multi-information fusion approach  
  by Wang Ziming 
  Abstract: This paper presents an intelligent system framework tailored to systematically evaluate student learning outcomes and teaching quality in computer science education using a multi-methodological approach. By leveraging multi-information fusion technology in conjunction with advanced data collection techniques, the framework integrates tools such as the Internet of Things (IoT), big data analytics and machine learning to enhance the accuracy and precision of data acquisition. Addressing the specific demands of computer science education, this research proposes a comprehensive, multi-dimensional evaluation model designed to holistically assess both student learning effectiveness and instructional performance. The study seeks to provide structured, objective methodologies to evaluate and improve the quality of computer science education through in-depth systemic analysis.
  Keywords: multi-information fusion technology; systemic data collection; big data analytics; systemic teaching evaluation; computer science education systems.
  DOI: 10.1504/IJCAT.2025.10074664
   
  
• KNN strategies for addressing class overlap in IoT security  
  by Yassine El Yamani, Youssef Baddi, Najib El Kamoun 
  Abstract: This paper presents a hybrid model for IoT botnet detection that combines Convolutional Neural Networks (CNN), k-Nearest Neighbours (KNN) and a dynamic resampling strategy. The main contribution is to address the challenge of class overlap, where different classes, such as malicious and benign traffic, share similar features, leading to misclassification. CNN is used for deep feature extraction, while KNN improves classification by focusing on local decision boundaries. Dynamic resampling adjusts the class distribution during training, improving the representation of minority classes. Experiments on the N-BaIoT data set, especially the Philips B120N10 Baby Monitor, demonstrate significant improvements for overlapping classes like Gafgyt TCP and Gafgyt UDP. The proposed model reaches 99.94% accuracy and 99.93% precision, recall and F1-score. Furthermore, comparisons with other machine learning models, such as Logistic Regression, SVM, Random Forest and Naive Bayes, confirm that KNN achieves the best results for challenging classes. These findings show that integrating KNN and dynamic resampling with CNN is a robust and scalable solution for IoT botnet detection in real-world settings.
  Keywords: IoT security; botnet detection; KNN; CNN; class overlap; resampling.
  DOI: 10.1504/IJCAT.2025.10075435
   
  
• Design of deep learning-assisted practical teaching system based on multi-level semantic feature extraction and text matching modelling  
  by Li Peng, Zhenglong Wang, Jing Xiao, Huan Ning 
  Abstract: This study presents the A-BRUNet model for multi-level feature extraction, designed to achieve high-precision text matching and classification by seamlessly integrating bidirectional encoder representations from transformers (BERT), bidirectional gated recurrent unit (Bi-GRU), convolutional neural networks (CNN), and an attention mechanism. The model first utilises BERT to extract word vectors enriched with contextual semantic depth, followed by Bi-GRU to capture global semantic relationships. CNN, equipped with multi-scale convolutional kernels, identifies local salient features, while the attention mechanism assigns adaptive weights to different feature layers, refining the overall semantic representation. Experimental results on the Quora question pairs (QQP) and Microsoft Research Paraphrase Corpus (MRPC) datasets demonstrate that A-BRUNet significantly outperforms existing models in both accuracy and F1-score for text matching tasks. Furthermore, in a limited-sample test using randomly selected datasets, the proposed model consistently exhibits robust performance, highlighting its adaptability and generalisability in small-sample scenarios. These findings establish A-BRUNet as a technical foundation and research benchmark for optimising future intelligent teaching frameworks.
  Keywords: multi-level semantic analysis; text matching; construction of teaching system.
  DOI: 10.1504/IJCAT.2025.10075194
   
  
• 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. The impact of this technology on English oral teaching is analysed 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