For scientific or socio-economic reasons it is often necessary or desirable that biological material be identified. Given that there are an estimated 10 million living organisms on Earth, the identification of biological material can be problematic. Consequently the services of taxonomist specialists are often required. However, if such expertise is not readily available it is necessary to attempt an identification using an alternative method. Some of these alternative methods are unsatisfactory or can lead to a wrong identification. One of the most common problems encountered when identifying specimens is that important diagnostic features are often not easily observed, or may even be completely absent. A number of techniques can be used to try to overcome this problem, one of which, the Self Organizing Map (or SOM), is a particularly appealing technique because of its ability to handle missing data. This thesis explores the use of SOMs as a technique for the identification of indigenous trees of the Acacia species in KwaZulu-Natal, South Africa. The ability of the SOM technique to perform exploratory data analysis through data clustering is utilized and assessed, as is its usefulness for visualizing the results of the analysis of numerical, multivariate botanical data sets. The SOM’s ability to investigate, discover and interpret relationships within these data sets is examined, and the technique’s ability to identify tree species successfully is tested. These data sets are also tested using the C5 and CN2 classification techniques. Results from both these techniques are compared with the results obtained by using a SOM commercial package. These results indicate that the application of the SOM to the problem of biological identification could provide the start of the long-awaited breakthrough in computerized identification that biologists have eagerly been seeking. / Dissertation (MSc)--University of Pretoria, 2011. / Computer Science / unrestricted

Sex estimation is a challenging problem in both forensic anthropology and bioarchaeology. Sexual dimorphism is most noticeably displayed by the pelvis; however in instances when it is not preserved, sex is estimated by skull. There is a multitude of approaches that use the skull, however, their population specificity and variable sexual dimorphism oscillation reduces their effectiveness (Bruzek and Murail, 2006). We base our contribution on the study by Abdel Fatah et al. (2014) that estimates sex based on exocranial and endocranial surfaces with a high success rate of 97%. Our approach uses anonymized CT scans of skulls from recent french population, from which the exocranial surface was segmented. On these surfaces, CPD-DCA (Dupej et al., 2014) was performed. We analyzed both form and shape (form after size normalization) of these surfaces in 104 skulls (53 males, 51 females) aged 18 to 92 years. The mean age was 58 years in females and 52.46 years in males. Classification was performed using support vector machines (SVM) with a radial kernel. Leave-one-out crossvalidation was also applied. The highest success rate (87.5 %) was achieved with the first 27 principal components of form. Classification of shape was less accurate by only 2 %. Even though our success rate was lower than that of Abdel...