Enhancing crayfish sex identification with Kolmogorov-Arnold networks and stacked autoencoders

Abstract Crayfish play an important role in freshwater ecosystems, and sex classification is crucial for analyzing their demographic structures. This study performed binary classification using traditional machine learning and deep learning models on tabular and image datasets with an imbalanced cla...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Yasin Atilkan, Berk Kirik, Eren Tuna Acikbas, Fatih Ekinci, Koray Acici, Tunc Asuroglu, Recep Benzer, Mehmet Serdar Guzel, Semra Benzer
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Nature Portfolio 2025-12-01
Colecção:	Scientific Reports
Assuntos:	Crayfish Sex identification Deep learning Machine learning Kolmogorov-Arnold networks Stacked autoencoders
Acesso em linha:	https://doi.org/10.1038/s41598-025-34095-z
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!