Integrating Traditional Machine Learning and Convolutional Neural Networks Into the Geometric Morphometric Pipeline for Taxonomic Classification (GM-ML-CNN)
- DOI
- 10.1093/iob/obag048
- Published
- 2026
- Container
- Integrative Organismal Biology
- Publisher
- Oxford University Press (OUP)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1093/iob/obag048,
title = {Integrating Traditional Machine Learning and Convolutional Neural Networks Into the Geometric Morphometric Pipeline for Taxonomic Classification (GM-ML-CNN)},
author = {G Deku and R Combey},
year = {2026},
journal = {Integrative Organismal Biology},
doi = {10.1093/iob/obag048},
url = {https://doi.org/10.1093/iob/obag048}
}RIS
TY - JOUR TI - Integrating Traditional Machine Learning and Convolutional Neural Networks Into the Geometric Morphometric Pipeline for Taxonomic Classification (GM-ML-CNN) AU - G Deku AU - R Combey PY - 2026 JO - Integrative Organismal Biology DO - 10.1093/iob/obag048 UR - https://doi.org/10.1093/iob/obag048 ER -
APA
Deku, G., & Combey, R. (2026). Integrating Traditional Machine Learning and Convolutional Neural Networks Into the Geometric Morphometric Pipeline for Taxonomic Classification (GM-ML-CNN). Integrative Organismal Biology. https://doi.org/10.1093/iob/obag048
Source records
- crossref · retrieved 2026-09-24T23:05:04.295Z