Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment
- DOI
- 10.1016/j.jksuci.2022.05.007
- Published
- 2023-01
- Container
- Journal of King Saud University - Computer and Information Sciences
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jksuci.2022.05.007,
title = {Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment},
author = {Salah Zidi and Alaeddine Mihoub and Saeed Mian Qaisar and Moez Krichen and Qasem Abu Al-Haija},
year = {2023},
journal = {Journal of King Saud University - Computer and Information Sciences},
doi = {10.1016/j.jksuci.2022.05.007},
url = {https://doi.org/10.1016/j.jksuci.2022.05.007}
}RIS
TY - JOUR TI - Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment AU - Salah Zidi AU - Alaeddine Mihoub AU - Saeed Mian Qaisar AU - Moez Krichen AU - Qasem Abu Al-Haija PY - 2023 JO - Journal of King Saud University - Computer and Information Sciences DO - 10.1016/j.jksuci.2022.05.007 UR - https://doi.org/10.1016/j.jksuci.2022.05.007 ER -
APA
Zidi, S., Mihoub, A., Qaisar, S. M., Krichen, M., & Al-Haija, Q. A. (2023). Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2022.05.007
Source records
- crossref · retrieved 2026-11-10T11:29:39.870Z