International Journal of Networking and Virtual Organisations (4 papers in press)

Regular Issues

• Tennis-assisted teaching assessment technology based on machine learning algorithm  
  by Miaomiao Li 
  Abstract: To excel in table tennis, players must understand both their strengths and weaknesses to develop effective strategies and improve their chances of winning. Previous studies were often limited by incomplete or inaccurate data. To address this, we developed The Intellectual Tactical System in Competitive Table Tennis, which utilises video analysis of competition matches to collect comprehensive data. We proposed a machine learning approach that combines feature selection with association rules to extract meaningful patterns. This study used matches featuring Yun-Ju Lin as case examples, employing the 3S theory speed, spin, and spot for data collection and analysis. By identifying key factors and match contexts, winning strategy models were constructed. The findings can assist Yun-Ju Lin in optimising his training and tactical planning. This approach may also serve as a valuable framework for elite players and coaches aiming to conduct in-depth strategic analysis.
  Keywords: tennis-assisted; Quasi-Experiment;teaching assessment; The Intellectual Tactical System.
  DOI: 10.1504/IJNVO.2025.10074356
   
  
• Selection of Cross-border E-commerce Import Mode based on AHP Algorithm  
  by Guozhang Li, Xue Wang, Yetong Wang 
  Abstract: In this paper, data intelligence and AHP algorithm were used to propose a new method for selecting import and export EC import modes, and simulation experiments were conducted to compare the differences in work efficiency and profit earning ability between import and export EC enterprises using this method and import and export EC enterprises not using this method. Finally, it was concluded that the capabilities of import and export EC enterprises using this method had increased by about 15.9% compared with those of enterprises not using this method. However, due to the lack of uniform standards for trade data in various regions, it still needs time to promote the intelligent development of import and export EC import mode selection.
  Keywords: Cross-border E-commerce; AHP Algorithm; Data Intelligence; Machine Learning.
  DOI: 10.1504/IJNVO.2025.10075187
   
  
• A Smart Intelligent Internet of Things Framework for Predicting Mental Health  
  by G. Sherlin Shobitha, V. Sudarshani Kataksham, T. Nagalaxmi, V. Spandana, G. Sreelatha, V. Radha 
  Abstract: A psychiatric disorder is a global concern affecting millions and burdening the healthcare system. Current diagnosis relies on subjective symptoms and isolated clinical examinations, leading to premature diagnosis and treatment, affecting millions of lives. The paper introduces a new Fossa-based Graph Neural Network (FbGNN) technique to enhance mental illness predictive accuracy. The study collected mental health data from various sources, including Reddit, Twitter, and discussion forums, and processed it using the Fossa optimization technique. The selected features were then used in a Graph Neural Network model to classify various mental health diseases. The data was then pre-processed, noise removed, and the model was applied to further refine the classification process. The FbGNN model outperformed traditional machine learning models in key performance metrics, with an accuracy rate of 98.87%, precision of 97.85%, recall of 98.60%, F1 Score of 98.22%, and minimal error rate of 1.13%.
  Keywords: Mental Health Data; Fossa Optimization; Graph Neural Network.
  DOI: 10.1504/IJNVO.2025.10075780
   
  
• Fraud Detection with Integrated Blockchain Technology in Secure Online Payment System  
  by Shivaprasad Sakharam More, Sufola Das Chagas Silva E. Araujo, Priyanka S. More, Pooja Bagane, Prashantkumar Gavali 
  Abstract: Integrating blockchain technology into secure online payment systems enhances cybersecurity by providing decentralised, transparent, and tamper-proof transaction records that significantly reduce fraud and improve data integrity. This study investigates the role of blockchain in strengthening online payment security while addressing challenges such as scalability and transaction speed. It introduces the SIEM-DAC technique, which combines real-time event monitoring with dynamic access control to prevent unauthorised actions. Also, the adaptive context-aware zero trust security policy automation (ACZT-SPA) approach dynamically adjusts security policies based on user behaviour and contextual information. The context-aware anomaly and intrusion detection response (CA-IDR) improves fraud detection by identifying abnormal transaction patterns. Findings show that the proposed blockchain-based framework achieves up to 90% threat detection, 95% system safety and data reliability, and 90% access control efficiency. These findings demonstrate the effectiveness of blockchain integration for secure online payment systems, with future work focusing on scalability and AI-driven fraud detection.
  Keywords: Security Information and Event Management; Dynamic Access Control; Security Policy Automation; Online Payment Systems; Adaptive Context Aware System; Intrusion Detection Response; Payment System Secur.
  DOI: 10.1504/IJNVO.2025.10075781