Deep Learning Algorithms in Industry 5.0: A Comprehensive Experimental Study
| Autor: | Shchepkina Natalia, Chandramauli Awadhesh, Ahuja Suniana, Prathibha Swaraj P., Ranjan Rajiv |
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| Jazyk: | English<br />French |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | BIO Web of Conferences, Vol 86, p 01067 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2117-4458 20248601 |
| DOI: | 10.1051/bioconf/20248601067 |
| Popis: | This extensive experimental research provides strong empirical proof of the revolutionary power of deep learning algorithms when integrated into Industry 5.0. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and Transformers are a few examples of deep learning algorithms that have shown remarkable accuracy rates of 92.3%, 88.7%, and 95.1%, respectively. Furthermore, the processing durations, which vary between 15 and 25 milliseconds, confirm their ability to make decisions in real time. The abundance of various data accessible in Industry 5.0 is highlighted by data collection sources such as picture databases (300 GB), text corpora (150 GB), equipment records (250 GB), and IoT sensor data (500 GB). The significant energy savings, shown by 20% reductions across a range of machine types, highlight the financial and ecological advantages of deep learning integration. Moreover, the noteworthy improvements in production quality, exhibiting up to 50% reductions in defect rates, highlight the potential of deep learning in quality assurance. These results provide tangible proof of the critical roles deep learning algorithms play in streamlining production lines, increasing energy economy, and boosting product quality in the ever-changing Industry 5.0 environment. |
| Databáze: | Directory of Open Access Journals |
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