Forthcoming and Online First 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 Articles will not be published until the final proofs are validated by their authors.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

  • Harnessing deep learning for quality engineering and technology: innovations in process optimisation, defect detection, and predictive quality control   Order a copy of this article
    by Pratik Patel, Swagata Sarkar, N. Ashok Kumar, Tanvi Jaydeep Patel 
    Abstract: If you are in charge of water sources, you need to be able to guess how streams will flow. We can learn a lot from this study about how well complicated deep learning models can guess when the Gilgit River Basin’s water level will be high and low every month. CNN-LSTM, CNN-BiLSTM, CNN-GRU, CNN-BiGRU, LSTM, BiLSTM, and GRU were all employed. Each of the final four is a combination of these. The model did well for our study based on its RMSE, MAE, NSE, and R2 marks. There’s a problem. R2 tells you how strong a link is. Simple models like LSTM and GRU did not do as well with that data. But the mix models did a lot better. CNN-BiGRU and CNN-BiLSTM did the best most of the time. It was taught with an R2 of 0.962 and tested with an R2 of 0.929. It got 144.1%, which was good enough for second place. CNN can help you find things in space. Now, things have a better chance of going well.
    Keywords: long short-term memory; LSTM; gated recurrent unit; GRU; background; CNN-Bi LSTM.
    DOI: 10.1504/IJQET.2025.10070208
     

 

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