Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions.
- DOI
- 10.1016/j.eswa.2022.118604
- Published
- 2023 Jan
- Container
- Expert systems with applications
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.eswa.2022.118604,
title = {Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions.},
author = {Bassiouni MM and Chakrabortty RK and Hussain OK and Rahman HF},
year = {2023},
journal = {Expert systems with applications},
doi = {10.1016/j.eswa.2022.118604},
url = {https://doi.org/10.1016/j.eswa.2022.118604}
}RIS
TY - JOUR TI - Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions. AU - Bassiouni MM AU - Chakrabortty RK AU - Hussain OK AU - Rahman HF PY - 2023 JO - Expert systems with applications DO - 10.1016/j.eswa.2022.118604 UR - https://doi.org/10.1016/j.eswa.2022.118604 ER -
APA
MM, B., RK, C., OK, H., & HF, R. (2023). Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions.. Expert systems with applications. https://doi.org/10.1016/j.eswa.2022.118604
Source records
- pubmed · retrieved 2026-09-27T09:22:38.305Z