International Journal of Networking and Virtual Organisations (12 papers in press)

Regular Issues

• Evaluation and analysis of classroom teaching quality of art design specialty based on DBT-SVM  
  by Junmei Guo 
  Abstract: Evaluating the quality of classroom teaching in higher education can improve teachers’ teaching, but the evaluating results are currently inaccurate. The study combines the Binary Tree Support Vector Machine (BT-SVM) and the Euclidean distance method to obtain the DBT-SVM (Distance Binary Tree Support Vector Machine, DBT-SVM) algorithm. Test the performance of DBT-SVM algorithm and compare it with one versus one (OVO) algorithm and one versus rest (OVR) algorithm. The results show that the accuracy of the DBT-SVM is 92.2% and the test time is 0.02s. It is superior to the traditional algorithms. In the empirical analysis of the evaluation model, the accuracy rate of the DBT-SVM algorithm model is 97.85%, which is superior to TW-SVM and traditional algorithm models. The results show that the performance of the optimized DBT-SVM algorithm has greatly improved the accuracy and test time of the traditional SVM algorithm.
  Keywords: Teaching quality evaluation; Binary tree; Euclidean distance method; Support vector machine.
  DOI: 10.1504/IJNVO.2023.10052698
   
  
• THE APPLICATION OF CLUSTERING ALGORITHMS IN A NEW MODEL OF KNITTED GARMENT TALENT TRAINING IN THE CONTEXT OF SUSTAINABLE DEVELOPMENT  
  by Jing Wang 
  Abstract: Under the concept of sustainable development, the innovation and development of the knitted garment industry is crucial. In order to enhance the core competitiveness of the knitted garment industry, the study proposes a talent training strategy for the knitted garment industry based on a clustering algorithm, and constructs a talent training model. The clustering algorithm showed a significant clustering effect, with a clustering accuracy of 93.66% in the real dataset. The knitwear talent development model obtained through the clustering analysis was applied in practice, and the application of talent development was able to significantly increase the proportion of elite talent in the company. The above results show that in the knitted garment industry under the concept of sustainable development, cluster analysis can effectively build a talent training program, which is of great value to the sustainable development of the knitted garment industry and the production industry.
  Keywords: sustainable development; clustering algorithm; knitwear; talent development.
  DOI: 10.1504/IJNVO.2023.10053338
   
  
• The Nexus Between Allied Policies of GST And FDI With Dependent Telecom Policies of Licensing and Universal Service in India.  
  by Pankaj Mishra, Netra Pal Singh, Ayesha Farooq 
  Abstract: The Indian telecom industry has witnessed sustained grievances from the operators on telecom and allied policies. This paper analyses if such behavior is due to gaps in policy formulation, gaps in implementation or are an ex-post rent seeking behavior. Using the conceptual framework from Lemke and Harris-Wai (2015), the author analyzed if there was adequate telecom stakeholder participation in FDI and GST policy formulation. The causal map of regulation from Coglianese (2012), was used to evaluate gaps in the implementation of FDI and GST for telecom. In FDI policy formulation, the stakeholders were included at the discretion of the DPIIT , and the process is mainly inter-ministerial. In GST policy formulation the operators and DoT were involved in all the stages. DoT did all required changes in the telecom policy that were triggered by FDI and GST policies. Stakeholder participation did not result in lower ex-post issues rather the provisions
  Keywords: Policy Formulation; Stakeholder Participation; rent seeking; telecommunications.
  DOI: 10.1504/IJNVO.2023.10054116
   
  
• Using Digital Health to Support Superior Preparedness to Enable Better Preparedness and Readiness to Combat Pandemics: A Scoping Review  
  by Nilmini Wickramasinghe, Rima Gibbings 
  Abstract: The COVID-19 pandemic that dominated the world in 2020 causing millions of deaths, severe illness, immense chaos and stress to healthcare systems and services not to mention detrimental effects to economies and the lives of all, has served to highlight key vulnerabilities in most countries; namely, those around not being prepared. Based on the worlds experiences through 2020, it has been noted by the WHO that a better state of preparedness would have enabled a better state of readiness to combat the COVID-19 pandemic and thus not only significantly reduce the loss of life but also the catastrophic impacts to the economy and the lives of all people. This paper performs a scoping review to identify and propose an appropriate approach of being better prepared for pandemics and other emergency scenarios.
  Keywords: clusters of pneumonia; vulnerable populations; comorbidities; missing data.
  
  
• Research on government network public opinion monitoring algorithm under the background of sustainable smart government  
  by Shiwei Zhang 
  Abstract: It is very necessary for the government to strengthen the supervision of network information. Considering the problems of over fitting and gradient disappearance in the traditional bi directional long short-term memory (BiLSTM) network, the regularisation method is used to adjust the input weight of the model. At the same time, 333 function is used to replace tanh activation function to build a government network public opinion monitoring model of double-layer long short-term memory network (RLSTM). The model performance test results show that in dataset type 1, the public opinion prediction accuracy is 0.993, and in dataset type 2, the public opinion prediction accuracy is 0.982, and the prediction performance is the best. At the same time, the improved RLSTM model also has excellent performance in the test of model convergence effect and error performance. The research content is of great significance to strengthen the security supervision of network information.
  Keywords: smart government; RLSTM model; public opinion; monitoring.
  DOI: 10.1504/IJNVO.2023.10054645
   
  
• Construction of a GA-RBF-based early warning model for corporate financial risk in the context of sustainable development  
  by Jingjing Gong, Hongwen Han, Zhen Lv 
  Abstract: Financial risk indicators will have a negative impact on the development planning of enterprises, so the research introduces the theory of genetic algorithm. The result is that the overall performance of the model based on GA-RBF is superior to that of the model based on BF, CNN and RBF. GA-RBF model reaches a stable state when the number of training is 120, and the speed is significantly faster than the other three models. The error value of GA-RBF model is significantly lower than other model, and the error reduction speed is also faster. The time and memory of the four models increase with the increase of the number of samples, but the time and memory of GA-RBF model is less than the other three models. The highest prediction accuracy of GA-RBF model is 91.25%, and the highest prediction accuracy of RBF neural network is 64.5%.
  Keywords: sustainable development; GA-RBF; corporate finance; risk warning.
  DOI: 10.1504/IJNVO.2023.10054651
   
  
• Application analysis of data mining based on improved decision tree in English flipped classroom teaching  
  by Xianghong Yin 
  Abstract: This study aims to analyse the application of data mining based on improve decision in English flipped classroom teaching. The experimental results show that the improved C4.5 algorithm had a better performance than ID3 algorithm and C4.5 algorithm and CART algorithm. In terms of accuracy rate, the four algorithms can be ranked as improved C4.5 algorithm, C4.5 algorithm, ID3 algorithm, and CART algorithm from high to low. Moreover, the improved C4.5 algorithm had the lowest error rate among the four algorithms. Under the same number of training samples, the improved C4.5 algorithm takes the least time at the least memory cost. Therefore, the improved C4.5 algorithm is adopted to construct a data mining model for English flipped classroom teaching and promote the research on English flipped classroom teaching.
  Keywords: improved decision tree; data mining; English flipped classroom; C4.5 algorithm.
  DOI: 10.1504/IJNVO.2023.10055109
   
  
• The Triggers on Compulsive Online Shopping of Jeans  
  by D. Manimegalai, Senthilkumar S 
  Abstract: This study investigates the antecedents of compulsive online shopping for jeans and identifies the compulsive shoppers of jeans. This descriptive research collects data through an online survey; the sample size is 205
  Keywords: compulsive online shopping; internal and external triggers; online usage; buzz.
  DOI: 10.1504/IJNVO.2023.10055603
   
  
• Does remote work promote shared leadership in public organisations?  
  by Iman Ashmawy  
  Abstract: With the outbreak of the COVID-19 pandemic and the imposed lockdowns, various public organisations adopted remote work for safety measures. However, many studies warn that remote work might make the employees feel isolated and make it difficult for the leadership to observe and evaluate employee performance. These challenges were preceded by an increased emphasis on informal, lateral, or collective styles of leadership, such as shared leadership. Therefore, this paper seeks to investigate the extent to which remote work promotes shared leadership. By disseminating a questionnaire to N = 90 employees working at a central public organisation (CPO) and statistically analysing it, the findings reveal surprising conclusions that highlight a positive correlation between remote work and shared leadership, including its components of bottom-up leadership, collective assumption of responsibility, and reciprocal interdependence, respectively.
  Keywords: remote work; shared leadership; collective assumption of responsibility; reciprocal interdependence; bottom-up leadership.
  DOI: 10.1504/IJNVO.2023.10056236
   
  
• An actor-network model for developing data sovereignty: evidence from Indonesia  
  by Musfiah Saidah, Purwadi Purwadi 
  Abstract: This study aimed to understand and form an actor-network model in realising data sovereignty based on various evidence of problems in Indonesia. In addition, this study used the concept of data sovereignty to understand information processing practices, laws, and the needs of a country. This study used a qualitative approach using multi-methods, which looked at the heterogeneity of qualitative methods using case study research strategies and action research with soft system methodology (SSM). The findings of this study offered an actor-network model in realising data sovereignty based on evidence-based problems in Indonesia. A precise classification between private and public data is required to implement data sovereignty. The realisation of data sovereignty required the support of various stakeholders: the government, parliament, the private sector, academia, the community, and non-human actors. Data sovereignty is oriented towards economic value and data security. Then, good regulation to ensure data security is the primary pretension.
  Keywords: actor-network model; actor-network; data sovereignty; communication applications; government; privacy; Indonesia; data; sovereignty; communication.
  DOI: 10.1504/IJNVO.2023.10056429
   
  
• An Efficient Optimal Load Balancing Algorithm For Distributed File Systems in Cloud Environment  
  by Manjula H. Nebagiri, Latha P. H 
  Abstract: Efficient operations in distributed environments can be obtained by load balancing (LB). LB has turned out to be a vital and interesting research area with respect to the cloud owing to the swift augmentation of cloud computing, and the more services together with better outcomes demand of the clients. The work has developed a framework named an efficient optimal LB (EOLB) for distributed files system to beat the challenges faced in LB. LB was done by means of the framework centred on node distribution together with task distribution. Centred upon the data aspects as well as cloud servers, say CPU in addition to memory usage, together with disk IO occupancy rate, etc. it renders task distribution. Experimental analysis exhibits that the framework attains a better response rate of 74.68 ms, and a processing time (PT) of 0.43 ms, in addition, remains to be efficient when weighed with the prevailing methods.
  Keywords: cloud computing; virtual environment; distributed cloud computing; load balancing; improved K-means clustering; IKMC; modified cockroach swarm optimisation; MCSO.
  DOI: 10.1504/IJNVO.2023.10056519
   
  
• Architectural Framework For Multiplayer Cooperative Cloud Gaming To Optimize Quality Of Service  
  by Nimmagadda Srilakshmi, Naresh Kannan 
  Abstract: The gaming industry is getting more attraction from cloud services providing gaming applications for cooperative multiplayer gaming. Real-time services like cloud gaming are possible by performing necessary process-intensive tasks within the cloud. In this paper, an architectural model for supporting cooperative gaming towards multiplayer is proposed to improve the quality of service in terms of bandwidth and latency compared to existing architectural models. According to this study, the time it takes to change a video has grown by 95% compared to how well Ad hoc mobile cloudlets and the cloud do at sharing videos. Although P-frames, I-frames, and B-frames account for 23%, 78%, and 94% of the resource consumption, density in intra-stream P-frames is also considered. Similarly, resource and CPU use based on skewness are effective compared to mobile devices’ 95% faster video sharing and 77% shorter link delays.
  Keywords: cloud gaming; quality of service; cooperative multiplayer gaming; social networking.
  DOI: 10.1504/IJNVO.2023.10056762