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 Articles will not be published until the final proofs are validated by their authors.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 (One paper in press)

Regular Issues

  • Chronic Obstructive Pulmonary Disease Prediction: Analysis for Smart Healthcare System 5.0 for Smart Cities in South Asia   Order a copy of this article
    by Rohit Rastogi, Prabhinav Mishra, Rayush Jain, Mohd Shahjahan 
    Abstract: Chronic obstructive pulmonary disease (COPD) could be a dynamic respiratory condition characterised by wind current restriction and respiratory side effects. Early discovery and mediation are pivotal for overseeing COPD effectively and progressing quiet results. In this term paper, we display a novel approach to COPD expectation utilising machine learning (ML) strategies coordinates into a web-based framework. Our proposed framework permits clients to transfer voice notes, which are at that point analysed by the ML show to anticipate the probability of COPD nearness. The ML demonstration is prepared on a dataset consisting of voice recordings from people with and without COPD, consolidating highlights such as hack designs, respiratory sounds, and vocal characteristics. Different ML calculations are investigated to recognise the foremost compelling approach for COPD forecast. The web-based interface gives a user-friendly stage for people to survey their COPD hazard helpfully and secretly. Through thorough assessment and approval, our framework illustrates promising precision and unwavering quality in foreseeing COPD nearness based on voice recordings. Execution of such a framework has the potential to revolutionise early location techniques for COPD, empowering convenient mediations and making strides persistent results.
    Keywords: Chronic Obstructive Pulmonary Disease; COPD; Prediction System; Machine Learning; Web-Based Interface; Voice Notes; Respiratory Symptoms; Early Detection; Intervention; Accuracy.
    DOI: 10.1504/IJQET.2025.10075809
     

 

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