Forthcoming and Online First Articles

International Journal of Data Science

International Journal of Data Science (IJDS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Data Science (5 papers in press)

Regular Issues

  • Tree-based Methods for Analytics of Online Shoppers' Purchasing Intentions   Order a copy of this article
    by Lu Xiong, Xi Chen, Jingsai Liang, Xintong Cao, Pengyu Zhu, Minyuan Zhao 
    Abstract: The recent speedy growth of e-commerce and big data has accumulated vast amounts of data about online shopping behavior. Analyzing these data can help online retailers gain competitive advantages. We propose four tree-based methods for analytics of online shoppers'; purchasing intentions. After exploring data through various visualization techniques, we conduct feature engineering to improve the model's accuracy. AUC is the primary measurement to evaluate models. To make the conclusion more statistically robust, k-fold cross-validation is applied to obtain the statistics of AUCs, such as the average and standard deviation. By analyzing the global and local feature importance of each model, the most critical predictor PageValues is found. Furthermore, we do sensitivity analysis for PageValues concerning the target variable Revenue to examine the relationship. Our findings support the decision on how to improve sales. The interpretation of the models and the explanation of their business implications make this paper unique.
    Keywords: Online Shopping Data Analytics; Feature Engineering; Decision Tree; Random Forest; Stochastic Gradient Boosting; XGBoost; Feature Importance; Sensitivity Analysis.
    DOI: 10.1504/IJDS.2024.10058603
  • Comparing the Impact of COVID-19 on Three States: A Data-Driven Approach   Order a copy of this article
    by Kevin Shao, Qin Shao 
    Abstract: COVID-19 was the first coronavirus pandemic in human history. The 50 states of the United States gradually took necessary steps to try to flatten the curve and save lives. However, the states of Florida, Michigan, and Ohio implemented rather different public health emergency policies. One interesting question is whether these three states had different COVID-19 outcomes. This study aims to provide insight into one of the most important and fundamental topics for making public health policy: how to effectively handle life-threatening infectious diseases while minimizing overall disruption of society. The scientific question is whether these three states had different outcomes as measured by various risk metrics. The raw data of case count, death count, and senior death count suggest that Florida was the worst affected state, having the largest mean counts. To compare these three states objectively, three severity rates that take population size into account are proposed and their log odds data are analyzed. Both linear and multivariate models are applied to the log odds of the three severity rates. Contrary to visual inspection of the count data, only the result of one hypothesis test is statistically significant from the linear model, and none are significant from the multivariate model, at the significance level of 0.05. For the significant result, the estimates of the model parameters are in favor of Florida and Ohio.
    Keywords: COVID-19; Population Infection Rate; Case Fatality Rate; Senior Fatality Rate; Log Odds; Statistical Models; Statistical Hypothesis Testing; State of Florida; State of Michigan; State of Ohio.
    DOI: 10.1504/IJDS.2024.10059625
  • The evaluation of college students’ entrepreneurship education performance using the t-test method   Order a copy of this article
    by Naiqi Chen, Yumei Wu 
    Abstract: This paper briefly introduced evaluation methods for entrepreneurship education performance, conducted a questionnaire survey with college students at Zhejiang Normal University, and compared the differences between the entrepreneurship education performance of students who participated in entrepreneurship education courses and those who did not use the T-test method. The results showed that students who participated in the entrepreneurship education course had higher overall entrepreneurship levels, but the entrepreneurship education course did not play a significant role in the dimension of entrepreneurial behaviour, which involves entrepreneurial practice, but only improved the performance in determining the direction of entrepreneurship quickly and developing a plan; after the entrepreneurship course, there was a need to focus more on the teaching of entrepreneurship practice.
    Keywords: entrepreneurship education; T-test method; analytical hierarchy process; college students.
    DOI: 10.1504/IJDS.2024.10059670
  • Research on reconstruction algorithm of finite element deformation model based on digital twin   Order a copy of this article
    by Jing Xu, Li Wei, Jun Wang 
    Abstract: To ensure the integrity of the digital twins in the virtual-reality symbiosis stage, the problem that the finite element deformation and failure models can only be displayed in the software and cannot be exported needs to be solved. In this paper, a finite element reconstruction technique is developed through the biomimetic study of natural objects. First, the deformation data in the finite element is obtained by the hexagonal method and slice method through the study of mathematicao principles and development based on Visual Studio, then the model format that can be recognised by the 3D printing equipment is reconstructed and smoothed and optimised, and finally the modified model in the finite element is presented by using prototyping 3D printing technology rapidly. The innovation of the method and the development of the reconstruction algorithm solved the problem that digital twins can not accurately perceive the transient deformation of virtual reality, which has strong application value and practical significance.
    Keywords: digital twin;finite element analysis ; model reconstruction.
    DOI: 10.1504/IJDS.2024.10061691
  • An Innovative Research on Encryption and Protection of E-commerce with Big Data Analysis   Order a copy of this article
    by Yifu Shu, Wenda Wang 
    Abstract: In the e-commerce sector, protecting data privacy is crucial. This study introduces the symmetric balanced funnel P^5 model as a method for addressing data protection challenges. The P^5 model organises data protection into five levels, each tailored to the specific security needs of different types of e-commerce data. It employs encryption algorithms like DES, AES, and RSA, with increasing encryption strength across the levels to ensure adequate protection. This approach not only safeguards user confidentiality and commercial interests but also provides balanced protection for data exchanged between parties. By offering focused protection for various categories of sensitive data, the P^5 model enhances overall data security in e-commerce. This study offers a new and comprehensive strategy for ensuring data privacy in e-commerce environments.
    Keywords: Big Data Security; E-commerce Data; Symmetric Balanced Funnel P^5; Multilevel Encryption; Data Privacy; Cybersecurity.
    DOI: 10.1504/IJDS.2024.10064485