A multi-objective hybrid algorithm for optimizing neural network architectures in wildlife conservation: a theoretical framework with practical validation
- DOI
- 10.1038/s41598-025-21539-9
- Published
- 2025-12-11
- 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/s41598-025-21539-9,
title = {A multi-objective hybrid algorithm for optimizing neural network architectures in wildlife conservation: a theoretical framework with practical validation},
author = {Freeson Kaniwa and Otlhapile Dinakenyane and Mpho Phuthego},
year = {2025},
journal = {Scientific Reports},
doi = {10.1038/s41598-025-21539-9},
url = {https://doi.org/10.1038/s41598-025-21539-9}
}RIS
TY - JOUR TI - A multi-objective hybrid algorithm for optimizing neural network architectures in wildlife conservation: a theoretical framework with practical validation AU - Freeson Kaniwa AU - Otlhapile Dinakenyane AU - Mpho Phuthego PY - 2025 JO - Scientific Reports DO - 10.1038/s41598-025-21539-9 UR - https://doi.org/10.1038/s41598-025-21539-9 ER -
APA
Kaniwa, F., Dinakenyane, O., & Phuthego, M. (2025). A multi-objective hybrid algorithm for optimizing neural network architectures in wildlife conservation: a theoretical framework with practical validation. Scientific Reports. https://doi.org/10.1038/s41598-025-21539-9
Source records
- crossref · retrieved 2026-11-08T15:07:28.809Z