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...

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Autores principales:	Yasin Atilkan, Berk Kirik, Eren Tuna Acikbas, Fatih Ekinci, Koray Acici, Tunc Asuroglu, Recep Benzer, Mehmet Serdar Guzel, Semra Benzer
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Nature Portfolio 2025-12-01
Colección:	Scientific Reports
Materias:	Crayfish Sex identification Deep learning Machine learning Kolmogorov-Arnold networks Stacked autoencoders
Acceso en línea:	https://doi.org/10.1038/s41598-025-34095-z
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