International Journal of Web Engineering and Technology (9 papers in press)

Regular Issues

• Service Recommendation Method based on Text View and Interaction View  
  by Shuaijia Lin, Ting Yu, Yaqi Wang, Jie Xu, Fangying Cheng, Tian Liang 
  Abstract: With the increasing prosperity of web service-sharing platforms, more and more software developers are reusing web services when developing applications. Existing web service recommendation systems often face two challenges. Firstly, developers discover services by inputting requirements, but the user's input is arbitrary and it cannot fully reflect the user's intention. Secondly, the application-service interaction records are too sparse, making it particularly difficult to find services that meet the requirements. To address the above challenges, in this paper, we propose a service recommendation method based on text and interaction views (SRTI). Firstly, SRTI employs graph neural network to deeply mine the features of applications and services. Secondly, SRT uses transformer and fully connected neural networks to deeply mine the matching degree between candidate services and requirements. Finally, we integrate the above two to obtain the final service list. Extensive experiments on real-world datasets have shown that SRTI outperforms several state-of-the-art methods.
  Keywords: service recommendation; text view; interaction view; application; recommendation algorithm.
  DOI: 10.1504/IJWET.2024.10064249
   
  
• Clustrosearch: A Novel, Intent-Aware, and Anomalous Reducing Meta-Search Engine Optimisation Algorithm based on User Scoring System and Clustering  
  by Parsa Parsafar 
  Abstract: This paper introduces Clustrosearch, a novel meta-search engine optimization algorithm integrating a machine learning-based user scoring system to enhance search result accuracy and efficiency. In response to the shortcomings of traditional search engines in delivering tailored content, Clustrosearch innovatively addresses these challenges by prioritizing user-centric information retrieval. Unlike existing approaches, it focuses on optimizing result relevance without explicit intent awareness, making it versatile across various search scenarios. Clustrosearch incorporates advanced anomaly reduction techniques to minimize the impact of outlier results, thereby enhancing the overall quality of search outcomes. This approach is evaluated through comprehensive benchmarking against established meta-search algorithms, demonstrating its capability to significantly reduce irrelevant results and improve retrieval precision. This research underscores its scalability and effectiveness in enhancing user satisfaction through advanced information filtering techniques.
  Keywords: Search Engine Optimization (SEO); Meta-Search Engine Optimization; Optimization; Linear Search; Rank-Biased Overlap (RBO).
  DOI: 10.1504/IJWET.2025.10068922
   
  
• A Sanda Action Recognition Using CNN-LSTM Network Model  
  by Jingying Ouyang, Jisheng Zhang, Yuxin Zhao, Shenghai Chen 
  Abstract: This study presents an action recognition algorithm based on convolutional long short-term memory (CNN-LSTM) to enhance the movement analysis precision. The model takes the joint position as the recognition node, and combines with the cylindrical coordinate system with random sliding window to effectively capture the angle and position information of the action frame. The algorithm groups joints, selects root nodes, and extracts local features through a multi-stream network, with classification completed in the pooling and fully connected layers. The proposed model achieves an average accuracy of 98.89%, with a recognition time of 0.61s and a minimal deviation of 0.035 in Sanda movements, demonstrating superior performance in action recognition.
  Keywords: Action recognition; Convolution; Long short-term memory network; Joint; Sanda.
  DOI: 10.1504/IJWET.2025.10068998
   
  
• Optimisation Algorithm for Distributed Economic Dispatching Problems in Smart Grid  
  by Yan Li, Zhibang Ruan 
  Abstract: The development of smart grids faces many problems. The urgent problem is the dispatch of smart grids. To address this, a smart grid economic dispatch model based on a second-order consistency algorithm is proposed. The model transforms dispatch into an unconstrained optimisation problem using convex optimisation theory and integrates a multi-agent consistency algorithm with the internal penalty function method. According to the experimental results, when the training set was 900, the energy saving efficiency of the traditional dispatch model, the dispatch model based on the first-order consistency algorithm, and the second-order consistency algorithm during peak electricity consumption periods were 0.38, 0.55, and 0.64, respectively. During the low power consumption period, when the training set size was 900, the energy saving efficiency of the three models was 0.32, 0.41, and 0.49. The proposed method can effectively solve the power grid dispatch issues, providing a certain reference for relevant research.
  Keywords: Smart grid; Economic dispatch; Distributed second-order consistency algorithm; Internal penalty function method.
  DOI: 10.1504/IJWET.2025.10069681
   
  
• Industrial Design Pattern Optimisation Research based on 3D Deep Learning algorithm  
  by Yao Wang 
  Abstract: The appearance design of industrial products is mainly done manually, which is time-consuming and inconsistent. To improve efficiency and quality, an intelligent design model based on Generative Adversarial Networks (GAN) was developed. The model converts 3D input data into a scene diagram to represent component relationships and hierarchical structures, making it suitable for complex product layouts. By incorporating the variational autoencoder (VAE) principle, the richness of design information is enhanced. GraphVAE is utilised to learn graph features and generate new structures that meet industrial design needs. Experimental results show that with 1000 test samples, the average information entropy of the proposed model, GoogLeNet, and Fast R-CNN is 35.79, 26.17, and 31.25, respectively, indicating high information richness and similarity to input data. This method optimises the design process, enhances output quality, and reduces computational costs, though its efficiency is lower, making it suitable for less time-sensitive applications.
  Keywords: Keywords: Industrial products; Appearance design; GAN; Artificial intelligence.
  DOI: 10.1504/IJWET.2025.10069713
   
  
• Sentiment Analysis using Deep Learning Algorithms Based on Chat Records and Product Reviews  
  by Haili Lu, Lin He 
  Abstract: With globalisation and the popularity of the internet, consumer evaluation and product feedback are no longer limited to traditional channels, but expressed through online chat and product reviews. These comments contain a wealth of emotional information and viewpoints, which are of great value to the enterprise. In response to the difficulty of traditional sentiment analysis models in handling complex emotional expressions and semantic information, a method combining support vector machines with bidirectional long short-term memory networks is proposed. The experimental results show that the average classification error of this model is less than 2.4% on the LAMAZON and Yelp datasets, which is superior to other schemes. In contrast, the fitting degree of this model is 99.7%, ranking the highest among all algorithms, and the accuracy of emotion classification exceeds 90%. Therefore, the model combining SVM and BiLSTM performs well in sentiment analysis tasks with high accuracy, providing valuable decision support for enterprises.
  Keywords: Support vector machine; Long short-term memory network algorithm; Sentimental analysis techniques; Chat records; Product reviews.
  DOI: 10.1504/IJWET.2025.10069715
   
  
• Assessment of Efficient Machine Learning Algorithms for Enhancing Road Safety and Predicting Accident Severity  
  by Akshi Bharadwaj, Sudesh Kumar, Pawan Singh 
  Abstract: In response to the pressing need for road safety enhancement, this study explores the implementation of Machine Learning (ML) methods to forecast the severity of accidents. The research aims to uncover the intricate factors underlying accidents and provide actionable insights for proactive measures. Utilising algorithms including Random Forest, Support Vector Classification (SVC), XGBoost, Balanced Bagging, and Voting Classifier, the study evaluates performance using standard metrics such as F1 score, recall, precision, and accuracy. Notably, the Soft Voting Classifier, comprising XGBoost, Balanced Bagging, and Gradient Boosting, emerges as the leading model with an accuracy rate of 85.7%, demonstrating its efficacy in accident severity prediction.
  Keywords: Accident predictions; Machine learning; ITS; XGBoost; Random Forest.
  DOI: 10.1504/IJWET.2025.10069725
   
  
• Enhancing User Intimacy: a Parallel Mining Algorithm Utilising UISFW  
  by Ruiping Kong, Ruimin Pan 
  Abstract: Abstract: User intimacy is vital for gauging the emotional bonds and interactions between users and brands, products, or services, crucial for enhancing user experience, brand value, marketing efficacy, and fostering innovative enterprise growth. Existing algorithms for user intimacy mining struggle with accuracy and potential relationship identification, limiting practical usability. Proposing a refined method, user intimacy calculation integrates genetic algorithms, enhancing relationship analysis. Through this approach, a parallel mining model employing Mad Reduce demonstrates promising results. Achieving P@n values of 90.28% and 90.89%, and NDCG values of 82.88% and 82.73% across datasets, the model effectively computes user intimacy and uncovers valuable insights for user relationship identification, benefiting product design and information recommendations.
  Keywords: Keywords: User intimacy; Feature assignment; Genetic algorithm; Parallel mining; MapReduce.
  DOI: 10.1504/IJWET.2026.10070867
   
  
• Performance and Reliability Analysis of Web Service Composition Using Probabilistic Model Checking  
  by Khadidja Salah Mansour, Youcef Hammal 
  Abstract: Design and implementation of composite software systems by integrating loosely coupled individual Web services are still an important issue within the SOA topic even though they still face significant complexities related to compatibility challenges which arise from their independent development processes. Our key aim is hence to provide an in-depth formal analysis of compatibility issues encountered in composite service and propose a comprehensive methodology to effectively address these challenges. We propose a formalisation and verification approach to tackle the crucial problem of services integration that always hinders reliable composition of distributed services. This paper focuses on ensuring functional and non-functional correctness of composite services described using WS-BPEL. These descriptions are first translated into communicating automata and then checked against intended specifications defined by WS-CDL. In order to enable probabilistic model checking of the composite service using the PRISM tool, the external choreography properties are formalised into formulae of PCTL.
  Keywords: Service Web composition; PTA; Model checking; CDL; BPEL; PCTL;SOA.
  DOI: 10.1504/IJWET.2026.10071686