Forthcoming and Online First Articles

International Journal of Forensic Software Engineering

International Journal of Forensic Software Engineering (IJFSE)

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 Articles will not be published until the final proofs are validated by their authors.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.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Forensic Software Engineering (3 papers in press)

Regular Issues

  • Integration of multiple comments based on aspect using text summarisation technique   Order a copy of this article
    by Nimesh Yadav 
    Abstract: This research paper focuses on integrating multiple comments into one salience comment for better understanding. This paper concentrated on most cared aspects from the multiple comments. The most cared aspects considered here are price, screen, battery, colour, and then assigning these aspects as our keyword for defining the set of explicit comments. After defining the explicit comment set, this paper used the text summarisation technique to summarise the comments based on aspects. Aspect-based multiple comment integration (AMCI) framework gives two unique features, first it integrates multiple comments into one salience comment, and second salient comments include low redundancy with summarised comments. The goal of this study is to improve the accuracy of the current state of the art models, and we have used ROUGE scores to compare proposed model with other models. Compared to state-of-the-art (SOTA) models, ours performs better on some ROUGE metrics, as evidenced by automatic evaluation findings.
    Keywords: multiple comment integration; text summarisation; text analysis; performances; brand-level multiple comment integration; product-level multiple comment integration.
    DOI: 10.1504/IJFSE.2022.10054296
     
  • Business forecasting time series models of Hindustan Unilever Limited stock price   Order a copy of this article
    by Suresh Akella 
    Abstract: In this research, business to consumer (B2C) enterprise the supply chain to customer happened effectively despite the COVID-19 pandemic. The importance of this business is of importance to the customers as most of the products belong to the daily used household goods. A major reflection of the performance of a listed company is its stock price. The daily needs at home are covered by fast moving consumer goods (FMCG). Hindustan Unilever Limited (HUL), stock price is taken to see the projected trends. Bombay Stock Exchange, BSE, provides past data on daily, intraday, monthly, yearly, etc. However, projections, and forecasts are required to be done by analysts, to assist the investors in this stock. In this study, a forecast is done using a numerical model, autoregressive integrated moving average (ARIMA). The time series of Hindustan Unilever is first seen and transformed to avoid differencing and Integration. The resulting simplified method is evaluated using MS Excel, to calculate forecasted values with assumed coefficients.
    Keywords: COVID-19; Hindustan Unilever; time series; ARIMA; household goods; retail market; FMCG; forecast; prediction; digital marketing; B2C.
    DOI: 10.1504/IJFSE.2022.10055572
     
  • Integration of blended learning approaches in modern classrooms: a quantitative study   Order a copy of this article
    by Muhammad Mujtaba Asad, Pireh Mahar, Antonio-José Moreno-Guerrero, Prathamesh Churi 
    Abstract: This quantitative research paper investigates the integration of blended learning practices during the current pandemic, focusing on challenges and benefits in the academic context. Conducted at the Public School of Sukkur, Pakistan, the study employs a quantitative research approach with a survey research design, distributing 78 questionnaires among upper secondary grade students. Utilising descriptive data analysis, the results are examined based on gender. The findings highlight that blended learning, amid the pandemic, facilitated learning processes and increased interaction among student-teacher and peer groups. Students expressed a preference for continued involvement in blended learning. The research sheds light on the impact of the pandemic on Pakistans education system, emphasising the need for educators, parents, and students to adapt to a blended instructional approach. The study underscores the importance of clear instruction and online engagement, serving as a valuable resource for future research on parental and student perspectives in the context of pandemic-induced blended learning.
    Keywords: blended learning; educational technology; pandemic; interactive learning.
    DOI: 10.1504/IJFSE.2023.10061645