Abstract
The task of estimating the body mass from human skeletal remains based on bone measurements is an important one in bioarchaeology and forensic anthropology. Most of the current literature deals with this problem through mathematical linear regression formulas applied to various bones. In order to improve the existing results, two supervised learning-based regression models are proposed, using artificial neural networks and support vector machines, which are useful for expressing good (usually nonlinear) mappings between skeletal measurements and body mass. Several experiments performed on an open source data set show that the proposed applications of machine learning-based algorithms lead to better results than the current state of the art. Thus, the proposed methods are useful for producing good body mass estimations from skeletal measurements.
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This work was supported by a grant of the Romanian National Authority for Scientific Research, CNCS–UEFISCDI, project number PN-II-RU-TE-2014-4-0082.
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Ionescu, VS., Czibula, G., Teletin, M. (2018). Supervised Learning Techniques for Body Mass Estimation in Bioarchaeology. In: Balas, V., Jain, L., Balas, M. (eds) Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-319-62524-9_7
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DOI: https://doi.org/10.1007/978-3-319-62524-9_7
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