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Ing. Zdeněk Buk, Ph.D.

Publikace

Age estimation of adult human remains from hip bones using advanced methods

Autoři
Štepanovský, M.; Buk, Z.; Koterova, A.; Navega, D.; Brůžek, J.; Cunha, E.
Rok
2018
Publikováno
Forensic Science International. 2018, 287 163-175. ISSN 0379-0738.
Typ
Článek
Anotace
The assessment of age-at-death is an important and challenging part of investigations of human skeletal remains. The main objective of the present study was to apply different mathematical approaches in order to reach more accurate and reliable results in age estimation. A multi-ethnic dataset (n = 941) of evaluated age-related changes on the pubic symphysis and the auricular surface of the hip bone was used. Two research groups examined nine different mathematical approaches. The best results were reached by Multi-linear regression, followed by the Collapsed regression model, with MAE values of 9.7 and 9.9 years, respectively, and with RMSE values of 12.1 and 12.2, respectively. The mean accuracy of decision tree models ranged between 30.7% and 72.3%, with the model using only the PUSx indicator performing the best. Moreover, our results indicate that the limiting factor of age estimation can be the visual evaluation of age-related changes. Further research is required to objectify the proposed methods for estimating age. (c) 2018 Elsevier B.V. All rights reserved.

Estimation of Chronological Age from Permanent Teeth Development

Autoři
Štepanovský, M.; Buk, Z.; Ibrová, A.; Veleminská, J.
Rok
2017
Publikováno
ITAT 2017: Information Technologies – Applications and Theory. Aachen: CEUR Workshop Proceedings, 2017. p. 153-158. vol. 1885. ISSN 1613-0073.
Typ
Stať ve sborníku
Anotace
This paper compares traditional averages-based model with other various age estimation models in the range from the simplest to the advanced ones, and introduces novel Tabular Constrained Multiple-linear Regression (TCMLR) model. This TCMLR model has similar complexity as traditional averages-based model (it can by evaluated manually), but improves the mean absolute error in average about 0.30 years (approx. 3.6 months) for males, and 0.18 years (approx. 2.2 months) for females, respectively. For all models, the chronological age of an individual is estimated from mineralization stages of dentition. This study was based on a sample of 976 orthopantomographs taken of 662 boys and 314 girls of Czech nationality aged between 2.7 and 20.5 years.

Novel age estimation model based on development of permanent teeth compared with classical approach and other modern data mining methods

Autoři
Štepanovský, M.; Buk, Z.; Ibrová, A.; Velemínská, J.
Rok
2017
Publikováno
Forensic Science International. 2017, 279 72-82. ISSN 0379-0738.
Typ
Článek
Anotace
In order to analyze and improve the dental age estimation in children and adolescents for forensic purposes, 22 age estimation methods were compared to a sample of 976 orthopantomographs (662 males, 314 females) of healthy Czech children and adolescents aged between 2.7 and 20.5 years. All methods are compared in terms of the accuracy and complexity and are based on various data mining methods or on simple mathematical operations. The winning method is presented in detail. The comparison showed that only three methods provide the best accuracy while remaining user-friendly. These methods were used to build a tabular multiple linear regression model, an M5P tree model and support vector machine model with first-order polynomial kernel. All of them have mean absolute error (MAE) under 0.7 years for both males and females. The other well-performing data mining methods (RBF neural network, K-nearest neighbors, Kstar, etc.) have similar or slightly better accuracy, but they are not user-friendly as they require computing equipment and the implementation as computer program. The lowest estimation accuracy provides the traditional model based on age averages (MAE under 0.96 years). Different relevancy of various teeth for the age estimation was found. This finding also explains the lowest accuracy of the traditional averages-based model. In this paper, a technique for missing data replacement for the cases with missing teeth is presented in detail as well as the constrained tabular multiple regression model. Also, we provide free age prediction software based on this wining model.

GPU-Accelerated Recurrent Neural Networks

Autoři
Rok
2015
Publikováno
Proceedings of the 12th International Mathematica Symposium. Praha: České vysoké učení technické v Praze, Fakulta elektrotechnická, 2015. ISBN 978-80-01-05623-3.
Typ
Stať ve sborníku
Anotace
The paper presents application of OpenCLLink in Wolfram Mathematica to accelerate fully recurrent neural networks using GPU. We also show the idea of automatically generated parts of source code using SymbolicC.