International Journal of Web and Grid Services (10 papers in press)

Regular Issues

• Bi-Phase LSTM: A LSTM-Based Autoencoder Architecture for Dynamic Social Network Prediction  
  by Hui Lin, Yi-Cheng Chen 
  Abstract: In recent years, social networks have grown in popularity, with most people actively engaging on these platforms. These networks hold valuable insights into users’ values and interests, allowing us to analyse relationships between connected individuals and even predict potential friendships. However, social networks are dynamic, and their structure evolves over time. To account for this, we employed a dual approach using a bi-phase LSTM autoencoder and a bi-phase LSTM predictor. These tools capture the changing characteristics of social networks and predict future graph structures. We rigorously tested our model on three datasets and compared its performance with other models. The bi-phase LSTM consistently delivered strong results across all datasets. Additionally, the model’s hyperparameters were fine-tuned to improve predictive accuracy, demonstrating its reliability in forecasting the evolution of social network structures.
  Keywords: feature extraction; autoencoder; decoder; long short-term memory; dynamic social network.
  DOI: 10.1504/IJWGS.2025.10068502
   
  
• Adaptive Proportional-Derivative Adjuster for Notch Filtering in Wearable ECG Monitoring  
  by Chao-Ting Chu, Jiawei Chang 
  Abstract: This paper presents an adaptive proportional-derivative adjuster (APDA) integrated into a notch filter for processing ECG signals in wearable smart clothing. The APDA filter dynamically adjusts parameters such as gain and cutoff frequency in real time to mitigate environmental interference, including 60 Hz noise and electromyographic signals common in workplaces and fitness centres. Unlike traditional fixed-parameter filters, this adaptive method minimises noise and preserves ECG clarity under varying conditions. A notable contribution of this study is optimising the APDA algorithm for resource-constrained microcontrollers, reducing both memory requirements and processing power. This streamlined computation lowers complexity and extends battery life, making it suitable for continuous, long-term monitoring. Experimental results from a smart clothing prototype validate the filters effectiveness in suppressing noise while maintaining signal integrity, offering an energy-efficient and cost-effective solution for wearable healthcare applications. This advancement addresses the limitations of conventional designs and shows promising potential for future clinical applications.
  Keywords: adaptive filtering; proportional-derivative control; wearable ECG monitoring; real-time noise suppression; sustainable healthcare technology.
  DOI: 10.1504/IJWGS.2025.10069918
   
  
• Improved Simplified Swarm Optimisation for Bipartite Graph Convolutional Network  
  by Zhenyao Liu, Wei-Chang Yeh 
  Abstract: Bipartite graphs have been widely applied in data mining to represent data relationships, such as in e-commerce recommendation systems. Graph neural networks (GNNs), with their powerful ability to process structured data and explore higher-order information, have become the state-of-the-art method for recommendation problems. Recommendation systems increasingly rely on graph structures to represent relationships between users and items, like user click behaviours and purchase records. Through graph convolutional networks (GCNs), these structures capture connections between users and items, integrating structural information (e.g., user-item links) with node features (e.g., user preferences and item attributes) for accurate recommendations. This study combines improved simplified swarm optimisation (iSSO) with bipartite graph convolutional networks and eye-tracking technology to explore user preference behaviour, called iSSO-BGCN. We construct a node-feature bipartite graph, using iSSOs optimisation capabilities and natural gradient descent to train the model. Trials validate its ability to deliver precise recommendations.
  Keywords: Graph Neural Networks; Graph Convolutional Networks; Improved Simplified Swarm Optimization; Bipartite Graph; Recommendation System.
  DOI: 10.1504/IJWGS.2025.10070494
   
  
• Enhancing Environmental Education through Virtual Reality: a Case Study in Primary Marine Ecology Learning  
  by Hsuan-Che Yang 
  Abstract: This study uses virtual reality technology to create digital teaching materials for primary school students on marine ecology and environmental education. These materials enhance learning motivation while conveying information about endangered marine organisms. Through an immersive VR diving experience, students can observe the habits and threats faced by these creatures. The study includes four games that focus on ecological issues and pollution along Taiwans east and west coasts, developed in collaboration with an elementary school in Danshui using the ADDIE instructional design model. A pre-test was conducted before introducing the VR system to evaluate progress in two classes. Results showed that the class using the VR system improved more in the post-test than the one receiving the traditional way. Although the improvement was not statistically significant, feedback from both students and teachers indicated a positive response, suggesting that this approach benefits students with weaker foundational knowledge.
  Keywords: Virtual Reality; Meta Quest 2; Environmental Education; Marine Ecology; Instructional Design.
  DOI: 10.1504/IJWGS.2025.10071216
   
  
• Optimality and Scalability of Semantic Web Service Composition with Hierarchical Parameter Relationship
  by Jung-Woon Yoo 
  Abstract: Semantic web service composition considers semantics for finding better solutions than syntactic web service composition. This paper focuses on hierarchical relationships among parameters of web services. A comprehensive mathematical model for semantic web service composition, into which hierarchical parameter relationships are incorporated, is presented as a general mathematical formulation. Experimental results demonstrate that the mathematical model for semantic composition finds hidden and better solutions that syntactic composition cannot find. The optimality of the solutions is empirically verified through extensive experiments. Furthermore, the scalability of the model is tested by comprehensive experiments to explore the impacts of eight key factors on web service composition. The mathematical model and the provided data sets are expected to serve as benchmarking tools for performance evaluation of heuristic algorithms for semantic web service composition. Finally, a web application is presented to visualize the semantic web service composition process, which is developed using the Django framework.
  Keywords: AI Planning; Web Service Composition; Semantics; Parameter Hierarchy; Mathematical Modeling.
  
  
• Web-based traditional craft Kansei retrieval method using a machine learning model and feature extraction  
  by Naho Kuriya, Kaisei Komoto, Tomoyuki Ishida 
  Abstract: In a previous study, we conducted a Kansei analysis of traditional crafts using the semantic differential method and correlated visual features with physical features extracted via visual pattern image coding (VPIC) from images of craftwork. However, associating the VPIC features with visual attributes is labour-intensive. Therefore, this study proposes a new Kansei retrieval method for traditional crafts. This method utilises a machine learning model trained on the previous study's data to predict Kansei words based on features extracted from colour histograms and colour moments of craft images. To evaluate the proposed method, we conducted a questionnaire survey with 52 university students. Approximately 90% of the participants responded positively to the operability, visibility, relevance, and effectiveness of the entire system, and the functionality of the Kansei word retrieval function, while approximately 20% responded negatively to the functionality of the Kansei word prediction function.
  Keywords: web application; Kansei retrieval; Kansei prediction; machine learning model; feature extraction.
  DOI: 10.1504/IJWGS.2025.10070264
   
  
• Optimality and scalability of semantic web service composition with hierarchical parameter relationship  
  by John Jung-Woon Yoo, Jaffer Sadiq Mughal, Fariborz Tayyari 
  Abstract: Semantic web service composition considers semantics for finding better solutions than syntactic web service composition. This paper focuses on hierarchical relationships among parameters of web services. A comprehensive mathematical model for semantic web service composition, into which hierarchical parameter relationships are incorporated, is presented as a general mathematical formulation. Experimental results demonstrate that the mathematical model for semantic composition finds hidden and better solutions that syntactic composition cannot find. The optimality of the solutions is empirically verified through extensive experiments. Furthermore, the scalability of the model is tested by comprehensive experiments to explore the impacts of eight key factors on web service composition. The mathematical model and the provided datasets are expected to serve as benchmarking tools for performance evaluation of heuristic algorithms for semantic web service composition. Finally, a web application is presented to visualise the semantic web service composition process, which is developed using the Django framework.
  Keywords: AI planning; web service composition; semantics; parameter hierarchy; mathematical modelling.
  DOI: 10.1504/IJWGS.2025.10072054
   
  
• QMADM-S: data-driven algorithms for enhancing cloud service decision-making through imputation  
  by P. Navya, Sanjaya Kumar Panda, Rashmi Ranjan Rout 
  Abstract: With the rapid advancement of cloud computing, users encounter difficulties in selecting the best cloud service providers (CSPs). These providers offer numerous services, evaluated based on key quality of service (QoS) attributes such as availability, latency, reliability, response time, and throughput. Multi-attribute decision-making (MADM) techniques are widely used to assess CSP performance. However, in real-world applications, unavailable QoS values often result in incomplete decision matrices, potentially affecting the accuracy of CSP rankings. This study evaluates the impact of three imputation techniques, min, max, and mean, on handling unavailable performance data across different levels of unavailability in seven QoS MADM with Spearman's rank correlation coefficient (QMADM-S) algorithms to assess ranking consistency. The analysis uses the QoS for web services (QWS) dataset and sensitivity analysis to identify the most effective imputation technique. Simulation results indicate that the mean imputation technique maintains ranking stability better than the other imputation techniques.
  Keywords: cloud service provider; CSP; multi-attribute decision-making; quality of service; QoS; unavailable performance measure value; imputation technique; spearman's rank correlation coefficient; sensitivity analysis.
  DOI: 10.1504/IJWGS.2025.10071617
   
  
• Semantic search for Japanese sentences based on sentence embedding  
  by Yoshihiro Adachi, Minoru Uehara 
  Abstract: Research is progressing in semantic search, in which the meaning of a sentence is represented as a numerical vector called an embedding and sentences are searched for based on their similarity. The ability to process queries combined using logical operators is essential for semantic search. We developed an appropriate technique for processing queries containing logical operators with reference to fuzzy set operations. In this technique, AND and OR between queries take the minimum and maximum similarity of the search results, respectively. A NOT operation on a query subtracts the similarity to the query from 1 and scales the result to obtain the desired search results. We devised an example-based semantic search method that obtains search results that match the user's intention as closely as possible based on the positive and negative example sentences that should and should not be included in the search results, respectively, as specified by the user.
  Keywords: semantic search; digital document; sentence embedding; query processing; logical operators; example-based search; dimensionality reduction.
  DOI: 10.1504/IJWGS.2025.10072055
   
  
• A multi-objective GA approach for optimising gateway placement in a 5G-enabled vehicular delay-tolerant smart city transportation network  
  by Ronild Hako, Orjola Jaupi, Evjola Spaho 
  Abstract: In this paper a multi-objective genetic algorithm is applied to optimise the gateway placement in a bus transportation network within a system of systems (SoS) framework, where an integrated architecture based on 5G and vehicular delay-tolerant network (VDTN) is used. The algorithm's objectives are to minimise the number of gateways, load balance the traffic of different gateways, maximise the radio coverage and ensure the coverage of all bus stops. Different simulations are conducted considering: changing the number of bus lines, the radius of gateways, and considering some mandatory coordinates of gateway nodes. Simulations conducted on Tirana's bus network (21 lines, 267 stops) demonstrate that a 500 m gateway radius achieves a balanced solution with 67 gateways covering 47.4 km2 (0.71 km2/gateway) and a load standard deviation of 1.71. Reducing the network to 15 bus lines (165 stops) improved load balancing, achieving 17 gateways at 1,000 m radius (2.98 km2/gateway) with a deviation of 2.51. Enforcing mandatory gateway locations reduced the required gateways by 2-12%, depending on the tested scenario. The results show the ability of algorithm to adapt to different network configurations and constraints and to offer fast solutions for improving network performance and efficiency.
  Keywords: multi-objective genetic algorithm; node placement; 5G; vehicular delay-tolerant network; VDTN; system of systems; SoS; smart city.
  DOI: 10.1504/IJWGS.2025.10071873