Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model
- DOI
- 10.1038/s41598-023-50176-3
- Published
- 2023-12-28
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-023-50176-3,
title = {Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model},
author = {Micheal T. Pillay and Noboru Minakawa and Yoonhee Kim and Nyakallo Kgalane and Jayanthi V. Ratnam and Swadhin K. Behera and Masahiro Hashizume and Neville Sweijd},
year = {2023},
journal = {Scientific Reports},
doi = {10.1038/s41598-023-50176-3},
url = {https://doi.org/10.1038/s41598-023-50176-3}
}RIS
TY - JOUR TI - Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model AU - Micheal T. Pillay AU - Noboru Minakawa AU - Yoonhee Kim AU - Nyakallo Kgalane AU - Jayanthi V. Ratnam AU - Swadhin K. Behera AU - Masahiro Hashizume AU - Neville Sweijd PY - 2023 JO - Scientific Reports DO - 10.1038/s41598-023-50176-3 UR - https://doi.org/10.1038/s41598-023-50176-3 ER -
APA
Pillay, M. T., Minakawa, N., Kim, Y., Kgalane, N., Ratnam, J. V., Behera, S. K., Hashizume, M., & Sweijd, N. (2023). Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model. Scientific Reports. https://doi.org/10.1038/s41598-023-50176-3
Source records
- crossref · retrieved 2026-09-26T03:51:54.813Z