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.
- MeSH
- Data Mining MeSH
- Dentition, Permanent * MeSH
- Child MeSH
- Humans MeSH
- Linear Models MeSH
- Adolescent MeSH
- Young Adult MeSH
- Neural Networks, Computer MeSH
- Child, Preschool MeSH
- Radiography, Panoramic MeSH
- Decision Trees MeSH
- Software MeSH
- Support Vector Machine MeSH
- Age Determination by Teeth methods MeSH
- Tooth growth & development MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Child, Preschool MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈 3 , 8 〉 and 〈 8 , 15 〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.
- MeSH
- Accelerometry methods MeSH
- Algorithms MeSH
- Bayes Theorem MeSH
- Exercise MeSH
- Bicycling * MeSH
- Fitness Trackers * MeSH
- Humans MeSH
- Cell Phone instrumentation MeSH
- Neural Networks, Computer MeSH
- Signal Processing, Computer-Assisted MeSH
- Motion MeSH
- Reproducibility of Results MeSH
- Pattern Recognition, Automated MeSH
- Software MeSH
- Heart Rate * MeSH
- Models, Statistical MeSH
- Support Vector Machine MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Determination of sex is one of the most important and challenging disciplines in biological anthropology. Creating a robust tool for sexing crania is crucial for forensic anthropology, especially in this period of migration, travel, and globalization, when different populations are mixed together in one region. Many different approaches to sex estimation using the skull have been published; however, population specificity and oscillation of variable sexual dimorphism typically reduces their effectiveness. The aim of this study was to create a robust classifier using virtual anthropology without the use of a CT scanner. The entire cranial surface was analyzed using coherent point drift-dense correspondence analysis and classification was performed using a support vector machine with a radial kernel, minimizing subjective error. The study sample consisted of 103 CT scans of a recent southern French population. Virtual scans of 52 males and 51 females (age from 18 to 92) were analyzed using 3D software systems (Rapidform, Avizo, Morphome3cs) and innovative approaches in geometric morphometrics. Leave-one-out crossvalidation was also applied. Sex differences in shape and form were displayed by colour scale maps. The whole cranial surface was significantly different between males and females in size (form). Sexual dimorphism was significantly lower in senile skulls. The most exclusive areas were the supraorbital region, orbits, cheek bones, nasal apertures, mastoids, and external occipital protuberances. The method provided a high level of classification accuracy (90.3%) in sexing male and female skulls and is a valuable tool for sex determination.
- MeSH
- Adult MeSH
- Skull anatomy & histology MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Forensic Anthropology MeSH
- Support Vector Machine MeSH
- Sex Determination by Skeleton methods MeSH
- Imaging, Three-Dimensional MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- France MeSH
From a wide range of techniques appropriate to relate spectra measurements with soil properties, partial least squares (PLS) regression and support vector machines (SVM) are most commonly used. This is due to their predictive power and the availability of software tools. Both represent exclusively statistically based approaches and, as such, benefit from multiple responses of soil material in the spectrum. However, physical-based approaches that focus only on a single spectral feature, such as simple linear regression using selected continuum-removed spectra values as a predictor variable, often provide accurate estimates. Furthermore, if this approach extends to multiple cases by taking into account three basic absorption feature parameters (area, width, and depth) of all occurring features as predictors and subjecting them to best subset selection, one can achieve even higher prediction accuracy compared with PLS regression. Here, we attempt to further extend this approach by adding two additional absorption feature parameters (left and right side area), as they can be important diagnostic markers, too. As a result, we achieved higher prediction accuracy compared with PLS regression and SVM for exchangeable soil pH, slightly higher or comparable for dithionite-citrate and ammonium oxalate extractable Fe and Mn forms, but slightly worse for oxidizable carbon content. Therefore, we suggest incorporating the multiple linear regression approach based on absorption feature parameters into existing working practices.
