Forthcoming Articles

International Journal of Quality Engineering and Technology

International Journal of Quality Engineering and Technology (IJQET)

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 Articles will not be published until the final proofs are validated by their authors.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 Quality Engineering and Technology (2 papers in press)

Regular Issues

  • A robust optimisation model for a dual-objective software reliability growth model with multiple fault types   Order a copy of this article
    by Mohammadreza Namdar, Rassoul Noorossana 
    Abstract: Reliability and development costs are two significant criteria that highly dependent on software release time and test termination time. Simultaneous optimisation of both criteria is a substantial challenge in current projects. Studies in the literature usually suffer from some of the following drawbacks; 1) considering a single objective function (reliability or development costs); 2) not considering the uncertainty of the parameters; 3) considering one type of software fault, whereas multiple fault types with different debugging costs can occur in real condition; 4) ignoring the discounted rate in cost optimisation. This paper presents a dual-objective robust optimisation method for joint optimisation of the development discounted costs and reliability considering the various fault types with different debugging costs to cover the mentioned weaknesses. Eventually, it analyses the model performance through a case study. The findings prove the distinguished role of uncertainty and the interest rate on both objective functions.
    Keywords: robust optimisation; software reliability; multiple fault types; dual-objective mathematical model; cost optimisation.
    DOI: 10.1504/IJQET.2025.10071173
     
  • High-Quality Liquor Prediction through Machine Learning and PCA Advancements   Order a copy of this article
    by Rupa Rani, Aman Sanger 
    Abstract: Machine learning is increasingly applied in diverse industries, including healthcare, astronomy, and hospitality. In the growing liquor industry, predicting high-quality liquor using machine learning can significantly enhance production efficiency and reduce costs. This study proposes a model that combines random forest (RF) with principal component analysis (PCA) to predict liquor quality based on chemical properties, grape growth conditions, and production processes. The model achieves a high accuracy of 91.68%, outperforming traditional methods by effectively capturing non-linear feature interactions. PCA is used to reduce dimensionality and balance the dataset, making the prediction process more efficient. The proposed RFPCA model provides deeper insights into the variables influencing liquor quality and demonstrates better performance compared to previous studies. This approach not only supports quality assessment but also helps liquor manufacturers maintain consistency and control over production. Additionally, demographic analysis assists in identifying ingredients that affect the quality, ensuring improved consumer satisfaction.
    Keywords: Machine Learning; High-Quality Liquor; RFPCA (Random Forest Principal Component Analysis); Decision Tree Algorithm; SVM; Logistic Regression.
    DOI: 10.1504/IJQET.2025.10073093
     

 

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