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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology (36 papers in press) Regular Issues
Abstract: The real-time nature and high noise interactive environment of live streaming e-commerce make it difficult to capture the dynamic behavioral characteristics of customers, which reduces the accuracy of customer churn risk prediction. Therefore, a risk prediction method for customer churn in live streaming e-commerce based on Stacking ensemble learning is proposed. Firstly, detect and eliminate outliers in the data sample, and normalize the data features based on skewness. Secondly, extract customer characteristics of live streaming e-commerce from both time and behavioral dimensions. Finally, using the extracted customer features as input, a live streaming e-commerce customer churn risk prediction model based on Stacking ensemble learning is constructed. The experimental results show that the accuracy of the proposed method is 0.96 and the F1 value is 0.96. Therefore, it indicates that the research method provides accurate customer retention decision support for real-time high noise e-commerce live streaming scenarios. Keywords: stacking ensemble learning; live streaming e-commerce; customer churn; risk profile. DOI: 10.1504/IJCAT.2026.10077093
Abstract: In order to solve the problems of low F1 value and prediction accuracy, as well as long prediction completion time in traditional financial transaction risk prediction methods, an accurate prediction method of financial transaction risk based on multidimensional data mining is proposed. For multidimensional mining and dimensionality reduction of financial transaction data, the reduced data is input into the WOA-CNN-BiLSTM-Attention model, which initializes BiLSTM with WOA optimized parameters. CNN extracts data features through convolutional and pooling layers, and inputs the extracted feature data into BiLSTM to determine long-term dependencies of the data. Input the features processed by the attention mechanism into the fully connected layer, and output the prediction results through the activation function. The experimental results show that the maximum F1 value of the proposed method is 0.98, the maximum prediction accuracy is 97.23%, and the minimum prediction completion time is 0.78 s. Keywords: multidimensional data mining; financial transaction risk; accurate prediction; EWKM; WOA-CNN-BiLSTM-attention model. DOI: 10.1504/IJCAT.2026.10077094
Abstract: To address the limitations of traditional resource library recommendation methods such as low accuracy, prolonged response times and poor user satisfaction this study proposes a personalised recommendation approach for innovation and entrepreneurship resource libraries based on a multi-dimensional time series data algorithm. After obtaining multi-dimensional time series data recommended for the innovation and entrepreneurship resource library and determining the abnormal score of the data, the abnormal data is removed. Extracting spatial and temporal features of multi-dimensional temporal data through graph attention network, fusing the features to determine user and content feature vectors, predicting the correlation score between oral examination users and innovation and entrepreneurship resource library content, and recommending innovation and entrepreneurship resource libraries with high scores to users. The experimental results show that the accuracy of the proposed method varies between 92.38% and 97.89%, with an average response time of 0.76 and an average satisfaction rate of 97.96. Keywords: multi-dimensional time series data algorithm; innovation and entrepreneurship; resource library; personalised recommendation; graph attention network. DOI: 10.1504/IJCAT.2026.10077095
Abstract: In order to provide real-time feedback and personalized intervention for online English teaching, this paper proposes a facial detection based English online interactive learning behavior recognition method. Firstly, a multi-modal data fusion is used to construct an English online interactive learning behavior standard feature library, with composite constraint conditions and validation criteria set, ultimately forming an interpretable behavior discrimination system. Secondly, the curvature extremum coefficient algorithm was improved by introducing the principal curvature direction derivative and optimizing the preprocessing for outlier removal, which improved the accuracy and robustness of feature point localization. Finally, a functional partitioning strategy based on curvature stability and a dynamic weighted voting mechanism were proposed to achieve fine-grained recognition of learning behavior. The experiment shows that the maximum Jaccard similarity coefficient of this method is 0.92, the maximum Pearson correlation coefficient is 0.94, and the maximum Fowlkes Mallows index can reach 0.93. Keywords: English online interactive learning; learning behaviour; standard features; facial detection; behaviour recognition. DOI: 10.1504/IJCAT.2026.10077096
Abstract: To address the issues related to poor completion rates, poor planning quality, and long planning time in traditional planning methods for multi branch operation tasks, an intelligent planning method for unmanned aerial vehicle (UAV) inspection path of complex distribution network lines under multi branch operation tasks is proposed Build a multi branch task allocation objective function based on the modeling results of unmanned aerial vehicle inspection environment, and solve the objective function through an improved bidirectional ant colony algorithm to achieve multi branch task allocation The APF-RRT fusion algorithm conducts efficient and orderly search guided by potential field trends to determine the unmanned aerial vehicle inspection path for complex distribution network lines Test results indicate that the suggested method attains a peak completion rate of 99 32% for multi-branch tasks, while ensuring drones avoid danger areas entirely The trajectories remain efficient in length, and the minimum computation time is 1.03 s. Keywords: multi-branch operation tasks; complex distribution network lines; unmanned aerial vehicle; inspection path; intelligent planning; APF-RRT fusion algorithm. DOI: 10.1504/IJCAT.2026.10077097
Abstract: To address the challenges of cross-modal feature fusion and semantic association in online education, this study develops an intelligent resource recommendation method based on SA-BPNN multimodal fusion. The proposed approach first employs a self-attention mechanism to dynamically weight features from video, exercise, and behavioral modalities, establishing an "input-process-output" correlation model. A hybrid fusion layer is then applied to handle potential missing modalities, with BPNN optimizing the integrated feature representation. Furthermore, a structured teaching knowledge graph is constructed, and resource similarity is computed by combining user-resource rating matrices with semantic representations from the graph, ultimately generating a Top-N recommendation list. Test results demonstrate that the method maintains stable performance, with coverage remaining between 97.68% and 99.45%, and P/N measure consistently exceeding 0.8 Keywords: SA-BPNN multimodal fusion; online teaching resources; automatic recommendation; knowledge graph. DOI: 10.1504/IJCAT.2026.10077220
Abstract: In order to improve the accuracy and F1 score of Chinese named entity recognition, this paper proposes a Chinese named entity recognition method based on residual convolutional neural network and dynamic adaptive activation mechanism. Firstly, residual connections and Dynamic ReLU activation strategies are introduced to address the challenges of gradient vanishing and network degradation faced by deep networks. Subsequently, to enhance the ability of LSTM in short message fitting, residual connection module, Dynamic ReLU module, and attention regulation unit were added. Finally, by integrating nonlinear connections, residual units with adaptive activation characteristics and Dynamic ReLU, as well as attention mechanisms that dynamically adjust network depth, the method effectively alleviates the performance degradation problem in deep networks, thereby improving the efficiency of Chinese named entity recognition. Experimental results show that our method consistently achieves over 96% accuracy and an F1 score above 0.94. Keywords: residual convolutional neural network; Chinese named entity recognition; dynamic ReLU activation mechanism; feature vector. DOI: 10.1504/IJCAT.2026.10077221
Abstract: The offshore wind farm booster station has been in a high salt spray marine environment for a long time, which makes it difficult to detect abnormal temperature in the electrical cabinet. Therefore, a method for identifying temperature anomalies in electrical cabinets of offshore wind farm boosting stations based on infrared temperature measurement is proposed. Firstly, infrared temperature measurement technology is used to accurately monitor the surface temperature of the electrical cabinet. Secondly, by using deep learning architecture to achieve multidimensional feature analysis of infrared thermal images, the recognition accuracy of temperature anomaly areas can be improved. Finally, based on three-dimensional spatial operations and dynamic segmentation algorithms, the classification and identification of nonlinear features of the temperature field in the electrical cabinet are completed. The results show that the temperature identification error of the method proposed in this paper is relatively small, and the accuracy of temperature anomaly detection reaches 92.9%. Keywords: infrared temperature measurement; offshore wind farms; boosting station; electrical cabinet; temperature anomaly identification. DOI: 10.1504/IJCAT.2026.10077222
Abstract: In order to overcome the problems of low signal peak signal-to-noise ratio, low accuracy, and long time consumption in traditional SF6/N2 decomposition gas concentration prediction methods, this study proposed a prediction method of SF6/N2 decomposition gas concentration based on infrared spectroscopy data. Collecting gas absorption spectrum data through infrared spectroscopy technology, optimizing variational mode decomposition parameters using classification particle swarm optimization algorithm, and achieving noise suppression and feature enhancement of infrared spectrum data signals. Combining the artificial fish swarm algorithm to optimize the initial weights and thresholds of the BP neural network, the processed infrared spectral data is used as input to build a pre built AFSA-BP prediction model, and relevant prediction results are obtained. The experimental results show that the PSNR value of the infrared spectral signal of the proposed method always remains above 47 dB, with the highest accuracy reaching 98.9% and the lowest time consumption being 0.76 s. Keywords: infrared spectroscopy data; SF6/N2; decompose gas; concentration prediction; artificial fish swarm algorithm; BP neural network. DOI: 10.1504/IJCAT.2026.10077223
Abstract: This paper proposes a novel two-stage framework for automated foul recognition in track and field. The first stage introduces a self-learning feature pyramid network (FPANet), which incorporates a multidimensional self-learning attention module (MSLM) and a channel separation extraction module (CSEM) to enhance human keypoint detection. This design addresses the underutilization of multi-resolution features and inter-keypoint feature coupling in complex motion scenarios, improving detection accuracy and robustness. The second stage employs an innovative Action Guided Masking Strategy to dynamically select informative keyframes while suppressing redundant and background frames. This forces the model to infer complete actions from partial observations and sharpens its ability to discern subtle foul movements. An auxiliary classifier further refines the encoded features for final classification. Experimental results confirm the framework's superior performance in both keypoint detection and foul recognition, showcasing its strong potential for intelligent sports analytics. Keywords: keypoint detection; foul move recognition; action-guided masking; FPANet; feature pyramid attention network. DOI: 10.1504/IJCAT.2026.10077357
Abstract: Accurate error labelling in English machine translation (MT) is crucial for enhancing translation quality and advancing natural language processing technologies. To address this need, this paper proposes a novel error labelling method based on a fuzzy neural network (FNN). The process begins by collecting MT data using the Scrapy framework and extracting a set of surface and grammatical features. These features are then normalised via Min-Max scaling and concatenated into fixed-order continuous vectors. The core of our method involves optimising the FNNs fuzzy rule connection weights and bias vectors with a Bat Algorithm, enabling the network to perform the final error labelling. Experimental results demonstrate the superior performance of our approach, which achieves a minimal average error omission rate of 0.19%, a high average Kappa coefficient of 0.95, and a fast average labelling time of only 1.5 seconds. Keywords: English machine translation; feature; fuzzy neural network; bat algorithm. DOI: 10.1504/IJCAT.2026.10077358
Abstract: To address the challenges of coupling high-frequency supply chain forecasting with low-altitude economic risk, a unified framework integrating machine learning and artificial intelligence is proposed. A Long Short-Term Memory (LSTM)-Transformer hybrid model is designed to capture multi-granularity temporal dependencies and mitigate the bullwhip effect. A Graph Attention Network (GAT)-driven risk coupling network is constructed, integrating AirSim simulations with real warehouse data for dynamic quantification. Results show that the forecast RMSE is reduced by 35.3% compared to the Autoregressive Integrated Moving Average (ARIMA) model, and the multi-granularity MAPE variance is only 0.123. Through a joint decision-making mechanism, this method provides an intelligent decision-making paradigm for multi-source heterogeneous scenarios. Keywords: supply chain demand forecasting; low-altitude economy; risk analysis; machine learning; artificial intelligence. DOI: 10.1504/IJCAT.2026.10077901
Abstract: This paper establishes a new supply chain management system based on artificial intelligence and blockchain technology (BT), and proposes a new supply chain solution on this basis. This paper defines the traditional centralised management mode as Supply Chain 1 and the blockchain distributed architecture as Supply Chain 2. Sample 1 consists of 500 sets of structured transaction data, while Sample 2 consists of 500 sets of unstructured sensor log data. The results showed that in sample 1, the information transmission times of supply chain 1 and supply chain 2 were 15.4 s and 3.3 s. In sample 2, when the sample size was 500, the information transmission times of SC 1 and SC 2 were 20.7 s and 4.2 s. It can be seen that SC 2 has a higher information transmission efficiency. This evaluation algorithm can be used in practice to diagnose supply chain information transmission bottlenecks and optimise resource allocation. Keywords: artificial intelligence; blockchain architecture; fresh supply chain; information ecological management. DOI: 10.1504/IJCAT.2026.10077983
Abstract: Current simulation models that focus on marine biological communities have not yet taken into account the effects of comfort on the behaviour of single organisms. This shortcoming makes it difficult for researchers to develop accurate simulations of how groups behave when faced with adaptive behaviour in complicated environments. A Multi-Agent System based on Reinforcement Learning (RL) is proposed in this paper, which uses Environmental Perception Mechanisms and Comfort Functions to optimise decisions made by individual agents in complex marine environments via Q-Learning and Deep Q-Networks (DQN). The proposed approach employs a collaborative strategy that combines local and global comfort to simulate collective actions. Experimental results indicate that a comfort-driven decision-making model would enable a collective of individuals to converge within 240 seconds, with 85% of the individuals congregating in areas they perceived as having the highest comfort levels. This work opens up new avenues for simulating the behaviours of marine biological communities and demonstrates the potential of a comfort-driven approach to studying the adaptive behaviour of groups. Keywords: marine species group simulation; comfort-driven mechanism; multi-agent system; reinforcement learning; deep Q-network. DOI: 10.1504/IJCAT.2026.10078256
Abstract: The rapid growth of the low-altitude economy and complex airspace operations pose challenges for efficient and adaptive resource allocation. Traditional methods struggle with multi-dimensional coupling among resources, missions, and management under uncertainty. This paper proposes a Collective Intelligence-based Spatio-Allocation and Administration (CISAA) framework that integrates swarm intelligence with hierarchical dynamic optimization to enable collaborative decision-making among UAVs, infrastructure units, and management systems. Through multi-layer coordination, CISAA optimizes task scheduling, airspace utilization, and engineering progress. Experiments in simulated low-altitude scenarios show that CISAA achieves higher resource efficiency, task completion, and system stability compared with heuristic and centralized approaches, offering a novel path toward intelligent and sustainable low-altitude airspace management. Keywords: low-altitude airspace management; collective intelligence optimisation; multi-agent coordination; engineering resource allocation; adaptive scheduling. DOI: 10.1504/IJCAT.2026.10078501
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 utilised 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 colour 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
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
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. Owing 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 optimise 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
Abstract: Enterprise Resource Planning (ERP) stands out as a viable solution, recognising its potential to elevate organisational efficiency. Organisations continue to face challenges in selecting the right ERP systems and often struggle with integrating ERP systems and Artificial Intelligence (AI) technologies. This integration issue leads to inefficient decision-making due to less responsive ERP systems. Additionally, managing the collected data and transforming it into actionable insights for informed decision-making through ERP systems remains a difficult task. Thus, the study focuses on enhancing decision-making using an intelligent ERP system. The proposed method implements a dataset obtained by surveying ERP consultants across different industry verticals worldwide. This dataset is then applied to a decision tree to generate rules, and other machine learning algorithms are tested for their performance. The results show excellent performance of decision trees, while Linear SVM, Efficient Logistic Regression, Efficient Linear SVM, and SVM Kernel are compared with this performance. The deployment shows matched cases across industry verticals and geographic regions, achieving a high accuracy of 81.5%. Additionally, the Speed of Operation, Flexibility, and Cost Efficiency are significantly improved, highlighting the importance of the intelligent ERP system in enhancing organisational efficiency. Keywords: business benefits; intelligent resources planning; decision tree; ERP selection; intelligent systems; artificial intelligence. DOI: 10.1504/IJCAT.2026.10077269
Abstract: This research introduces an AI-driven framework designed to automate the generation of sports highlights, optimising content creation for digital platforms. The framework utilises advanced deep learning techniques, including Spatial-Temporal Long Short-Term Memory (ST-LSTM) networks and convolutional neural networks (CNN), to address key challenges in sports classification, event detection, and player tracking. By incorporating multimodal data (audio and visual cues), the model achieves high accuracy rates, with 93% for goals, 90% for substitutions, and 86% for cards. However, further work is necessary to achieve 100% prediction accuracy for officially sanctioned events. The study also explores the integration of audio features to improve detection, particularly for dynamic events with strong audio cues, while acknowledging challenges with weak or ambiguous audio cues. Additionally, the research develops smart cropping techniques, automatic player detection, and an innovative multimodal game summarisation system aimed at enhancing sports content creation efficiency and engagement on digital platforms. Keywords: artificial intelligence; sports highlight generation; ST-LSTM; audio-visual fusion; automated journalism; multimodal analytics. DOI: 10.1504/IJCAT.2026.10077838 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. Unmanned aerial vehicle non-stop inspection and obstacle avoidance route planning based on reinforcement learning cuckoo search algorithm by Xiaoyong Han, Weiling Lv Abstract: In order to improve the success rate and coverage of obstacle avoidance routes for unmanned aerial vehicle (UAV) during non-stop inspections, a UAV non-stop inspection obstacle avoidance route planning method based on reinforcement learning cuckoo search algorithm is proposed. Firstly, establish a mathematical model that comprehensively considers environmental information, inspection task requirements, and drone performance constraints. Secondly, build a drone non-stop obstacle avoidance inspection route planning model that integrates capability limitations and operational requirements. Finally, the cuckoo search algorithm is used to solve the obstacle avoidance route planning for drone inspection, and reinforcement learning is used to optimize the search mode. Improved algorithm combined with Q-learning, enhancing global search efficiency through three strategic actions to improve solving efficiency and stability. Test results show that the proposed method consistently maintains a success rate of over 97% in obstacle avoidance during drone inspections, and the route coverage rate remains consistently above 95%. Keywords: reinforcement learning; cuckoo search algorithm; unmanned aerial vehicle; non-stop inspection; obstacle avoidance route planning. DOI: 10.1504/IJCAT.2025.10077513 Personalised recommendation methods for information resources on English online learning platforms by Jing Liang Abstract: In this paper, a new personalised recommendation methods for information resources on English online learning platforms is proposed. Design ER storage scale and keyword storage scale, and complete the construction of the English online education platform database. Based on data from English online education platforms, overlapping community partitioning is achieved through Bayesian non negative matrix decomposition, and user interest labels are automatically annotated to achieve user group modeling. Combining user interest preference features and cognitive level features, personalised English resource recommendations are completed. Through experiments, the results show that the proposed method has a recommendation accuracy of up to 95.4%, a recall rate of up to 94.1%, and a recommendation time consistently below 0.91s, demonstrating good application effectiveness. Keywords: English; online learning platform; information resources; personalised recommendation. DOI: 10.1504/IJCAT.2025.10077514 Slope stability assessment based on a combined weight-cloud model by Decheng Zhang, Jielu He, Yunjun Yang Abstract: To evaluate the stability of slopes accurately and reduce the risk of landslide accidents during slope treatment, this study focuses on four aspects: slope morphology, rock mass characteristics, geological structure, and triggering factors. Based on these factors and the intrinsic relationships among indicators, the study refines them into 12 secondary indicators to establish a risk assessment system for slope instability. This research aims to address limitations in previous studies, where the combined influence of subjective judgment and objective data was not fully integrated, and uncertainties in slope evaluation indicators were not effectively managed. To improve the accuracy of weighting, a combination of subjective weighting and objective weighting methods is adopted. The cloud model is used to solve the uncertainty and imprecision of the impact evaluation index, and the slope stability grade is determined according to the principle of maximum certainty. Finally, the method is applied to an engineering example, and the slope stability grade obtained is consistent with the actual slope stability grade, demonstrating that this method can accurately evaluate slope stability. Keywords: slope instability; stability evaluation; combined weighting; cloud model. DOI: 10.1504/IJCAT.2025.10078410 Personalized recommendation of multi-objective tourism routes based on improved shuffled frog leaping algorithm by Weina Pei, Fengjuan Tian Abstract: To address the limitations of low accuracy, long computation time, and low user satisfaction associated with traditional multi-objective tourism route personalised recommendation methods, this study proposes a novel personalised recommendation method based on an improved shuffled frog leaping algorithm. Tourist attraction data were compiled using the Scrapy framework. The compiled data were then clustered via spectral clustering and combined with relevant data to achieve regional division of attractions using an ARMA model. A personalised recommendation model for multi-objective tourism routes was constructed based on the divided attraction regions, incorporating multiple constraint conditions. An improved shuffled frog leaping algorithm was employed to solve the recommendation model, yielding the optimal personalised recommendation scheme. Experimental results demonstrate that the proposed method achieves a maximum recommendation accuracy of 97.4%, a minimum recommendation time of 0.98 s, and a high user satisfaction score of 9.67. Keywords: improved shuffled frog leaping algorithm; multi-objective; tourism routes; personalised recommendation; spectral clustering algorithm; regional division of tourist attractions. DOI: 10.1504/IJCAT.2025.10078500 Multimodal legal information extraction based on gated graph neural network by Lizhi Yuan Abstract: To augment the precision and reduce the extraction duration of legal information, a multimodal extraction technique utilising a gated graph neural network is introduced. Initially, multimodal legal data is subjected to binary transformation and dimensionality reduction processes. Following this, intricate textual and visual details from legal documents are captured by a bidirectional gated recurrent unit. An attention mechanism is employed to assess the correlation between legal text and images, with single-mode relationships being modelled through an intra-modal attention module, resulting in a unified multimodal representation. By integrating legal text and image features through a gated neural network enhanced with dropout, efficient multimodal extraction is achieved. Experimental results demonstrate that the accuracy and efficiency of multimodal legal information extraction are significantly improved by this method, with an extraction precision consistently maintained above 90%. Keywords: gated graph neural network; multimodal method; legal information extraction; text features; image features. DOI: 10.1504/IJCAT.2025.10076482 Study on high-dimensional biomedical data mining method based on K-means clustering algorithm by Octavia Panum Abstract: To address the issue of incomplete data mining caused by missing values, this study proposes a high-dimensional biomedical data mining method based on the K-means clustering algorithm. The methodology comprises three main steps: First, missing data values are imputed using the FCMSI algorithm. Second, linear discriminant analysis is employed for feature extraction, where generalised eigenvalues are calculated based on the inverse matrix of the intra-class divergence matrix, enabling dimensionality reduction through projection. Finally, the K-means algorithm is applied for data mining, incorporating probability weights to reselect clustering centres and avoid local optima. Experimental results demonstrate that the proposed method achieves lower mean square error and logarithmic loss while producing more comprehensive data mining outcomes. Keywords: K-means clustering algorithm; data mining; FCMSI algorithm; linear discriminant analysis; missing value filling. DOI: 10.1504/IJCAT.2025.10076481 Precision marketing method for online information of new e-commerce products based on user tags by Min Li, Liyang Sun Abstract: To improve marketing effectiveness for new e-commerce products, a precise online information marketing method based on user tags is proposed. First, e-commerce user features are extracted through a hierarchical attention model. Second, user feature data are processed using the rotated forest and AdaBoost algorithm to establish user labels. Then, features of online information for new e-commerce products are extracted through information entropy and information gain calculations using the decision tree algorithm. Finally, precise marketing is achieved through semantic similarity between user tags and product features, along with implicit ratings from neighbouring users. Experiments show the Hamming distance of this method remains below 0.30, with adjusted Rand coefficient values ranging from 0.73 to 0.92, while the highest click-through rate for new products reaches 0.758. Keywords: new e-commerce products; online information; precision marketing; user tags; feature extraction; semantic similarity. DOI: 10.1504/IJCAT.2025.10076477 A fault diagnosis of electrical equipment based on improved BP neural network by Xiaoli Xing, Jin Huang Abstract: This study proposes a fault diagnosis method based on an improved BP neural network. Initially, signals from electrical equipment are collected and pre-processed to enhance data clarity and computational speed. Subsequently, spectral analysis techniques are utilised to isolate crucial feature data. Genetic Algorithms (GA) are then employed to optimise the initial weights and biases of the Back Propagation (BP) neural network, with fitness criteria and genetic operators implemented to accelerate network refinement. Finally, a BP neural network model is established to train the network, enabling recognition of complex correlations between fault patterns and features, and yielding accurate fault identification results. Experimental results demonstrate the proposed method maintains throughput above 45 Mbit/s while keeping dropout rates consistently below 0.3. Keywords: improved BP neural network; electrical equipment; fault diagnosis; insulation failure. DOI: 10.1504/IJCAT.2025.10076480 Self-organised prediction method for potential conflicting vehicles at intersections considering vehicle intrusion by Lingmin Yang Abstract: To improve the accuracy of predicting advance time and spatial prediction, this study proposes a self-organising prediction method for potential conflict vehicles at intersections during vehicle intrusion. First, analyse the self-organising network system in vehicles and utilise short-range wireless communication technology between vehicles and roadside units to achieve information transmission. Second, vehicle status information is collected through communication between vehicles and RSU, as well as inter-vehicle communication. Finally, the circular danger range theory is introduced to predict potential conflict vehicles at intersections. Analyse the parameters of the circular danger range and the motion state of vehicles, then calculate the approaching time of danger to accurately predict potential conflict vehicles. Experimental results demonstrate that this method maintains a long lead time (7.7 s) in high traffic scenarios, with prediction accuracy ranging between 0.933 and 0.979 under different traffic flows. Keywords: intersection; invading vehicles; potential conflicts; conflict prediction; self-organising network; circular danger range. DOI: 10.1504/IJCAT.2025.10076478 Comprehensive management method of financial data based on knowledge graph by Yang Yang Abstract: To avoid financial data loss and enhance the read and write speed of financial data, a comprehensive financial data management method based on the knowledge graph is proposed. Firstly, complex features are automatically extracted from time-series data using CNN. Secondly, a distributed data storage architecture model is constructed, combined with a joint statistical strategy of multiple nonlinear components, to reconstruct the high-dimensional feature domain of financial time-series data and complete association rule mining. Finally, based on the mining results, a knowledge graph system is constructed, and intelligent protocols are deployed to ensure security and achieve comprehensive management of financial data. The experimental results indicate that the maximum amount of financial data loss achieved by this method is only 0.15 GB, while the read and write speed of financial data updates remains stable at 10 GB/s or above, which is 3 GB/s higher than that of existing methods. Keywords: knowledge graph; financial data; integrated management; high-dimensional feature domain. DOI: 10.1504/IJCAT.2026.10077225 An intelligent mining method for network marketing potential user based on random forest algorithm by Xiuming Yu Abstract: In this paper, an intelligent mining method for network marketing potential users based on random forest algorithm is proposed. Utilise web crawling technology to collect user behaviour data for network marketing. Cluster the collected data using the Literal Fuzzy C-Means (LFCM) algorithm to obtain clustered user behaviour data. Input the data from the user behaviour dataset into the Latent Dirichlet Allocation (LDA) model to obtain the theme extraction results of network marketing user behaviour documents. Combine the extracted topics with the random forest algorithm to achieve intelligent mining of potential users in network marketing. The experimental results show that the proposed method achieves a maximum accuracy of 98.45% and a maximum recall of 99.14%, with a processing time varying between 0.23 s and 0.71 s. This approach can be widely applied to potential user mining in network marketing. Keywords: random forest algorithm; network marketing; potential user; intelligent mining method; LFCM algorithm; LDA model. DOI: 10.1504/IJCAT.2026.10077224 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 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, emphasising 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, organisations 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 |
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