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 Articles will not be published until the final proofs are validated by their authors.

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International Journal of Forensic Software Engineering (4 papers in press)

Regular Issues

  • CloudJS file encryption algorithm for cloud environment   Order a copy of this article
    by Sara Dharadhar, Rhea Gupta, Prathamesh Churi 
    Abstract: Cloud computing is an extremely popular technology in todays world and its popularity arises from its ability to store, process, manage, and retrieve data from anywhere at any time. Along with its many advantages, security and privacy have proven to be a major setback for this technology. Security concerns of cloud data include its protection from data breaches, hijacking of accounts, insider threat, malware injection, abuse of cloud services, DOS attacks, insufficient due diligence, insecure APIs, shared vulnerabilities, and data loss. Encryption algorithms that address these concerns have been reviewed along with the jumbling salting algorithm and its applications. The main goal of this paper is to propose a framework for cloud encryption using the CloudJS algorithm and to discuss the results (in standalone machine) for the same.
    Keywords: cloud computing; cloud security; data encryption; jumbling salting algorithm.
    DOI: 10.1504/IJFSE.2020.10034787
  • The South African software industry lacking project critical success factors: a project team perspective   Order a copy of this article
    by Robert Toyo Hans, Ernest Mnkandla 
    Abstract: Project success is a much-researched area in project management, with research on project success on two main fronts: project success criteria and project success factors. This research paper intended to identify and report on some of the success factors, which seem to be missing in the South African software industry. The identification of such factors may assist software organisations to address the problem and thus improve their project success rate. Defined methodologies, team inclusion in decision-making, proper communication in teams, standardised project management practices and standardised project management leadership were identified as project success factors missing in some of South African software organisations. Based on implications from the absence of these factors, recommendations have been made, including formalising and defining project methodologies, involving project teams in decision-making by project managers, improving communication levels in project teams, and standardising both project management practices and project leadership. This study will be of interest to those software development organisations seeking to improve their project success rate by paying attention to project success factors.
    Keywords: project success; success criteria; success factors; software; standardisation; communication.
    DOI: 10.1504/IJFSE.2020.10036204
  • Portfolio construction and weight optimisation using principal component analysis   Order a copy of this article
    by Kartikay Laddha, Vidhi Kapoor, Siba Panda 
    Abstract: Principal component analysis helps in constructing statistical risk factors by explaining the variance inherited from the dataset. It transforms the dataset into new independent components which are uncorrelated and are hence used to optimise portfolios for earning higher returns and more financial control. It helps uncover the underlying drivers hidden in the data by summarising huge feature sets using a few components. In this paper, the method of PCA is used to derive the principal components that capture most of the market volatility and are used to define the cash allocation strategy which helps outperforming the sectoral benchmark index (NIFTY IT) in terms of returns and Sharpe ratio. Our optimised portfolio provides a risk to reward ratio of 1.0605 in comparison to 0.4583 provided by the NIFTY IT index as the weights allocation in our portfolio is explained by the captured variability, which was obtained using the PCA technique. This methodology helps in creating portfolios with reduced dimensionality and variability of the dataset used for practical applications.
    Keywords: nifty; portfolio construction; principle component analysis; Sharpe ratio; weight optimisation.
    DOI: 10.1504/IJFSE.2021.10043971
  • Prediction and diagnosis of diabetes using machine learning classifiers   Order a copy of this article
    by Harsh Tyagi, Aditya Agarwal, Aakash Gupta, Kanak Goel, Anand Kumar Srivastava, Akhilesh Kumar Srivastava 
    Abstract: Major portion of diabetes in the world is of type 2 due to environmental conditions and lifestyle. If the diabetes is predicted at an early stage, it would really help in reducing its effects by the use of early medication. This article is based on machine learning model to predict diabetes based on diagnostic measurements. Machine learning can play an essential role in predicting presence/absence of diabetes mellitus (type 2 diabetes). The article presents the ML-based approach for prediction of the diabetes that makes use of algorithms like XGBoost, decision tree, random forest. In this, medical data of user is used as input and prediction of diabetes is done using mentioned algorithms. The output (prediction) will be the ensemble of the output of all three algorithms. That way, all the algorithms are used to make predictions and to establish a comparison between the accuracy obtained from these methods.
    Keywords: machine learning; diabetes; random forest; XGBoot; decision tree.
    DOI: 10.1504/IJFSE.2021.10044205