Title: Harnessing deep learning for quality engineering and technology: innovations in process optimisation, defect detection, and predictive quality control

Authors: Pratik Patel; Swagata Sarkar; N. Ashokkumar; Tanvi Jaydeep Patel

Addresses: School of Cyber Security and Digital Forensics, National Forensic Sciences University, Sector 9, Gandhinagar-382007, Gujarat, India ' Department of Artificial intelligence and Data Science, Sri Sairam Engineering College, West Tambaram, Chennai, India ' Department of Electronics and Communication Engineering, Mohan Babu University, Tirupati, Andhra Pradesh 517102, India ' Department of Computer Science, Atmanand Saraswati Science College, Kapodra Patiya, Surat, Gujarat 395006, India

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 is 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.2024.147893

International Journal of Quality Engineering and Technology, 2024 Vol.10 No.4, pp.336 - 350

Received: 03 Jan 2025
Accepted: 21 Feb 2025
Published online: 07 Aug 2025 *

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