The DOI matches its Crossref record; 222 of the 222 references it cites were found in a registry or an index.
Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Open accessAbstract
Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.
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Xie, J., Sun, L., & Zhao, Y. F. (2025) · Engineering · doi:10.1016/j.eng.2024.04.024 · Cited by 112
Artificial intelligence techniques for implementation of intelligent machining
Hashmi, A. W., Mali, H. S., Meena, A., Khilji, I. A., Hashmi, M. F., & Saffe, S. N. B. M. (2022) · Materials Today: Proceedings · doi:10.1016/j.matpr.2021.11.277 · Cited by 24
Kotsiopoulos, T., Papakostas, G., Vafeiadis, T., Dimitriadis, V., Nizamis, A., Bolzoni, A., Bellinati, D., Ioannidis, D., Votis, K., Tzovaras, D., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124839 · Cited by 19
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Metadata
- Authors
- Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D., & Tzovaras, D.
- Venue
- Computer Science Review
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 40
- Pages
- 100341
- ISSN
- 1574-0137 (ISSN-L)
- OpenAlex
- W3132166193
- Cited by
- 341
- References
- 222
- FWCI
- 28.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
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