Autor segons l'article: Del Bove, A; Veneziano, A
Departament: Història i Història de l'Art
Autor/s de la URV: Del Bove, Antonietta
Paraules clau: Skulls Sexual dimorphism Sample Protocol Populations Physical anthropology Neural network analysis Human cranium Dimorphism Age Accuracy
Resum: Featured Application The method presented can be used to estimate sex attributes from a small set of cranial metric traits. The morphology of the human cranium allows for reconstructing important information about the identity of an individual, such as age, ancestry, sex, and health status. The estimation of sex from morphology is a key component of the work of physical anthropologists, and in the last decade, the field has witnessed an increase in the use of novel algorithm-based methodologies to tackle the aforementioned task. Nevertheless, several limitations (e.g., small training/testing sample size, training-test data relatedness, limited population inclusiveness, overfitting) have hampered the application of such methods as a standardised procedure in the field. Here, we propose a population-inclusive protocol for estimating sex from a small set of cranial metric traits (10 measurements) based on a neural network architecture trained to maximise the probability of sex attribution and prevent overfitting. The cross-validation returned an accuracy of 86.7% +/- 0.02% and log loss of 0.34 +/- 0.03. The protocol developed was tested on data unrelated to that of the training and validation phase and returned an estimated accuracy of 84.3% and log loss of 0.348. The model and the related code to use it are made publicly available.
Àrees temàtiques: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: antonietta.delbove@urv.cat antonietta.delbove@urv.cat
Data d'alta del registre: 2024-05-23
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.mdpi.com/2076-3417/12/18/9285
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Applied Sciences-Basel. 12 (18):
Referència de l'ítem segons les normes APA: Del Bove, A; Veneziano, A (2022). A Generalised Neural Network Model to Estimate Sex from Cranial Metric Traits: A Robust Training and Testing Approach. Applied Sciences-Basel, 12(18), -. DOI: 10.3390/app12189285
DOI de l'article: 10.3390/app12189285
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications
Repositori URV