Forthcoming and Online First Articles

International Journal of Networking and Virtual Organisations

International Journal of Networking and Virtual Organisations (IJNVO)

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International Journal of Networking and Virtual Organisations (6 papers in press)

Regular Issues

  • Evaluation and analysis of classroom teaching quality of art design specialty based on DBT-SVM   Order a copy of this article
    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
     
  • Evolutionary Algorithms and Artificial Intelligence in Drug Discovery: Opportunities, Tools, and Prospects   Order a copy of this article
    by MOOLCHAND SHARMA, Suman Deswal 
    Abstract: The drug design process is lengthy, complex, and dependent on several factors. De-veloping a medicine can take ten to fifteen years, from discovery to commercializa-tion. Machine learning (ML) refers to a set of tools that can assist you in learning more and making better decisions for well-defined questions with a large amount of data. The opportunities to use ML occur throughout the drug development process. Examples include target identification and validation, identification of alternative targets, and biomarker identification. Some approaches have produced accurate predictions and insights, while others have not. But to deal with high-dimensional data, we need soft-computing methods to find the best solution, which could be a new drug. This article provides a detailed overview of various ML, evolutionary algo-rithms, and soft computing techniques surveyed and analyzed for de novo drug de-sign, emphasizing the computational aspects.
    Keywords: Drug Design; Machine Learning; Evolutionary Algorithms ;Target Identification; Soft Computing; Artificial Intelligence; Deep Neural Networks.
    DOI: 10.1504/IJNVO.2022.10052699
     
  • Can a Location-Based Game Make Players Mindful during the Gameplay A Case Study of Pok   Order a copy of this article
    by Aung Pyae 
    Abstract: Recently,
    Keywords: mindfulness; digital games; user experiences; human-computer interaction; HCI; location-based games; LGBs; game user experience.
    DOI: 10.1504/IJNVO.2022.10053337
     
  • THE APPLICATION OF CLUSTERING ALGORITHMS IN A NEW MODEL OF KNITTED GARMENT TALENT TRAINING IN THE CONTEXT OF SUSTAINABLE DEVELOPMENT   Order a copy of this article
    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
     
  • Governing Business Networks: An Analysis of Micro-Governance Functions in an Agribusiness Network   Order a copy of this article
    by Mathäus Marcelo Freitag Dallagnol, André L. B. Zuliani, Gabriele Girardi, Douglas Wegner 
    Abstract: This study investigates how governance occurs in collaborative networks based on practices of collaborative arrangements aiming to reduces gaps of how networks are governed, analysing the influence of contextual factors, functions, and practices to obtain certain results. A qualitative case study of a Brazilian agribusiness network that has worked collaboratively for over 20 years. Based on in-depth interviews and complementary data. To analyse the data, we used the content analysis technique. Results indicate that micro-governance functions and practices produce outcomes that influence the network’s development, coordination activities, and performance. This implies that members believe that joining the network provides strategic, financial, relational, and social benefits that would be difficult to achieve on their own. Moreover, contextual factors regarding the relationships after the network formation, environmental factors and previous relations influence the organisation of micro-governance. Thus, the observed high commitment and incentive to engage in this network, certain functions are not used.
    Keywords: inter-organisational relations; network governance; micro-governance; agribusiness; collaboration.
    DOI: 10.1504/IJNVO.2022.10053517
     
  • Defect Prediction in Software Using Spiderhunt-Based Deep Convolutional Neural Network Classifier   Order a copy of this article
    by Prashanthi M, Chandramohan M 
    Abstract: In this research, the defects in the software are predicted using the deep CNN classifier by effectively optimizing the classifier using spiderhunt optimization. The effective communication and hunting characteristics of the spiderhunt are employed for tuning the classifier that boosts the classifier performance. The proposed spiderhunt optimization not only optimizes the classifier but also plays a significant role in the feature selection for the extraction of necessary features that helps in defect prediction. The proposed spiderhunt optimization achieved an improvement rate of 1.009 %, 1.083 %, 0.578 %, and 1.01 % in terms of accuracy, precision, recall, and f-measure and is proved to be quite efficient compared to state of art methods.
    Keywords: Deep Learning; spiderhunt optimization; software defect prediction; Software Engineering; Quality.
    DOI: 10.1504/IJNVO.2022.10053585