@@ -0,0 +1,13 @@
+@article{fernandez2025recognizing,
+ abstract = {This study explores the application of advanced deep learning techniques in analyzing Upper Palaeolithic hand stencil representations, focusing on sex classification of individuals involved in prehistoric rock art activity. The research highlights the effectiveness of deep learning models, particularly EfficientNetV2-S, which achieved an accuracy rate of 81.03% for experimental blown hand stencils and 95.08% in delineated contemporary hand image samples for sex identification, surpassing traditional morphometric methods. The study demonstrates that deep learning can differentiate male and female hand stencils with high precision, suggesting a mixed-sexual participation in creating prehistoric art, with a slight prevalence of male hand representations in the studied caves. The integration of user-friendly platforms, such as Google Colab, facilitates the reproducibility and validation of these findings, promoting methodological transparency. However, the accuracy of deep learning models is contingent on the quality and preservation of the images, presenting challenges when working with deteriorated or incomplete samples. This work highlights the potential of advanced technologies in archaeological research, opening new avenues for investigating the creation of prehistoric graphic expressions and their social implications.},
+ author = {Verónica Fernández-Navarro and Aitor González-Marfil and Ignacio Arganda-Carreras and Diego Garate},
+ doi = {https://doi.org/10.1016/j.daach.2025.e00453},
+ issn = {2212-0548},
+ journal = {Digital Applications in Archaeology and Cultural Heritage},
+ keywords = {Deep learning, Rock art, Hand stencil, Paleolithic, Ichnology, Machine learning},
+ pages = {e00453},
+ title = {Recognizing past shapes: sex differentiation through deep learning on European Upper Palaeolithic hand stencils},
+ url = {https://www.sciencedirect.com/science/article/pii/S2212054825000554},
+ volume = {38},
+ year = {2025}
+}
@@ -0,0 +1,41 @@
+---
+title: 'Recognizing past shapes: sex differentiation through deep learning on European
+  Upper Palaeolithic hand stencils'
+authors:
+- Verónica Fernández-Navarro
+- Aitor González-Marfil
+- Ignacio Arganda-Carreras
+- Diego Garate
+date: '2025-01-01'
+publishDate: '2025-08-28T08:46:19.432446Z'
+publication_types:
+- article-journal
+publication: '*Digital Applications in Archaeology and Cultural Heritage*'
+doi: https://doi.org/10.1016/j.daach.2025.e00453
+abstract: This study explores the application of advanced deep learning techniques
+  in analyzing Upper Palaeolithic hand stencil representations, focusing on sex classification
+  of individuals involved in prehistoric rock art activity. The research highlights
+  the effectiveness of deep learning models, particularly EfficientNetV2-S, which
+  achieved an accuracy rate of 81.03% for experimental blown hand stencils and 95.08%
+  in delineated contemporary hand image samples for sex identification, surpassing
+  traditional morphometric methods. The study demonstrates that deep learning can
+  differentiate male and female hand stencils with high precision, suggesting a mixed-sexual
+  participation in creating prehistoric art, with a slight prevalence of male hand
+  representations in the studied caves. The integration of user-friendly platforms,
+  such as Google Colab, facilitates the reproducibility and validation of these findings,
+  promoting methodological transparency. However, the accuracy of deep learning models
+  is contingent on the quality and preservation of the images, presenting challenges
+  when working with deteriorated or incomplete samples. This work highlights the potential
+  of advanced technologies in archaeological research, opening new avenues for investigating
+  the creation of prehistoric graphic expressions and their social implications.
+tags:
+- Deep learning
+- Rock art
+- Hand stencil
+- Paleolithic
+- Ichnology
+- Machine learning
+links:
+- name: URL
+  url: https://www.sciencedirect.com/science/article/pii/S2212054825000554
+---