Bachelor thesis | Cultural Anthropology and Development Sociology (BSc)
closed access
This thesis explores the continuum of East African knowledge production with a particular focus on the dialogue between endogenous East African and Western colonial knowledge production through...Show moreThis thesis explores the continuum of East African knowledge production with a particular focus on the dialogue between endogenous East African and Western colonial knowledge production through colonialism. This dialogue is approached through an investigation of the colonial classifications of tribe and ethnicity, depicting how these classifications were created and enforced, and their interaction with and effect on endogenous East African knowledge production.Show less
Achtergrond: Anatomische MRI blijkt een goede voorspeller te zijn van Alzheimer, het is echter nog niet heel duidelijk wat de rol van functionele MRI (fMRI) in Alzheimer classificatie is en of dit...Show moreAchtergrond: Anatomische MRI blijkt een goede voorspeller te zijn van Alzheimer, het is echter nog niet heel duidelijk wat de rol van functionele MRI (fMRI) in Alzheimer classificatie is en of dit een toevoeging op anatomische MRI kan zijn. Doel: In dit onderzoek wordt onderzocht of fMRI van toegevoegde waarde is op anatomische MRI in het classificeren van Alzheimer. Methode: Om dit te kunnen onderzoeken worden er 3 classificatiemodellen met elkaar vergeleken, namelijk een model met alleen anatomische MRI-predictoren bestaande uit de grijze stofdichtheid en subcorticale volumes en een model met alleen fMRI-predictoren bestaande uit de functionele connectiviteit tussen 20 hersengebieden; en ten slotte een multimodaal model met zowel de anatomische als fMRI-predictoren. Om de meest relevante predictoren in de modellen te selecteren wordt er een LASSO algoritme toegepast. Daarnaast wordt er kruisvalidatie toegepast om de modellen te kunnen generaliseren. Om de prestatie van de modellen te evalueren worden de AUC-waardes van de modellen vergeleken. Resultaten: Het model dat alleen gebruikmaakt van anatomische MRI-predictoren kan Alzheimer accuraat classificeren met een AUC-waarde van 0.92. Het model dat alleen fMRIpredictoren gebruikt kan Alzheimer redelijk goed classificeren, maar niet beter dan model 1, met een AUC-waarde van 0.74. Het multimodale model classificeert Alzheimer het beste met een AUC-waarde van 0.93. Conclusie: Een multimodaal classificatiemodel bestaande uit anatomische MRI en fMRI presteert beter dan een model met alleen één van deze modaliteiten. Dit is echter een kleine verbetering ten opzichte van de anatomische MRI alleen, en het is niet duidelijk of dit klinisch relevant kan zijn.Show less
Due to recent advances within the field of machine learning and computing power becoming more readily available, the use of machine learning within the field of psychology has increased. However,...Show moreDue to recent advances within the field of machine learning and computing power becoming more readily available, the use of machine learning within the field of psychology has increased. However, potential remains for greater use of machine learning within the field of psychology. In this study the usability and performance of 3 machine learning models namely K-nearest neighbors, the Random forest, and the Support vector machine algorithms were assessed when predicting gender, marital status, and family size from Big 5 personality measures and the Holland Code Career Test. Repeated cross-validation was combined with grid search to ensure performance measure accuracy and to optimize model accuracy and F1-score. The performances of the 3 models were compared to the performance of logistic regression to assess whether these models could outperform a model regularly used within psychology. The 3 models consistently outperformed the logistic regression under almost all conditions and proved far superior for groups sizes over 500 even outperforming logistic regression by 10 percentage points under some conditions. However, caution was advised due to wide confidence intervals for small group sizes (n ≤ 200). Therefore, a study was proposed with the aim to enhance predictions for small group sizes, focusing on feedforward neural networks, known to be able to capture complex relationships even with limited data. Addressing these aspects could improve the usability of machine learning in psychology settings involving small group sizes.Show less
Artefact classification is one of the main themes and an important practice since the beginnings of archaeology, while machine learning (ML) became one of the most efficient approaches to increase...Show moreArtefact classification is one of the main themes and an important practice since the beginnings of archaeology, while machine learning (ML) became one of the most efficient approaches to increase our knowledge in a number of disciplines. This thesis describes a ML model developed for the classification of pottery assemblages, identifying its benefits and limitations, focusing on the importance of artefacts features for the identification of vessel shape classes, and to what extent this kind of knowledge can be used to replicate classifications made by experts. The research also analyses different classes structures based on the ML model. The research dataset was based on an assemblage of pottery vessels representing nine shape classes and four archaeological sites from the Bronze Age Northeastern Syria, made available by the Arcane project. The classification methodology was based on principles of quantitative archaeology, using vessel measurements and categorical features, implemented by supervised and unsupervised learning ML algorithms and supporting methods from the scikit-learn and SciPy libraries. The Anaconda platform, the Jupyter notebook environment and ImageJ for image processing complete the main software used through the research. The research results indicate benefits and limitations in the application of ML models in the classification of pottery assemblages. The limitations are especially related to number of samples versus target classes, the homogeneity of the vessels context in the dataset, and the quality of data available for the samples. The results suggest that a ML model can be useful to experts, assisting in the identification of the most relevant artefact features and similarities among classes of artefacts, as well possible misclassifications, ultimately providing new insights into the classification of pottery assemblages in archaeology.Show less
Master thesis | Classics and Ancient Civilizations (MA)
open access
2023-08-30T00:00:00Z
The Ancient South Arabian languages (ASA = Sabaic, Minaic, Qatabanic and Ḥaḍramitic) have traditionally been classified as South Semitic and since Nebes (1994) as Central Semitic, based on the...Show moreThe Ancient South Arabian languages (ASA = Sabaic, Minaic, Qatabanic and Ḥaḍramitic) have traditionally been classified as South Semitic and since Nebes (1994) as Central Semitic, based on the imperfect pattern of Sabaic in weak verbs, such as 2-W/Y. By analyzing inscriptions of all ASA languages from the CSAI-database, applying roughly the same method as Nebes (1994), this study does not only verify Nebes' conclusions for Sabaic, but also shows that not all ASA languages share the same imperfect pattern. Minaic is non-Central Semitic using the imperfect pattern *yVqattVl; the synchronic Qatabanic pattern b-yqtl could have originated from either *yVqtVl(u) or *yVqattVl(u); for Ḥaḍramitic the evidence is inconclusive. Therefore, ASA is no homogeneous group and the individual ASA languages should be reclassified within the Semitic language family.Show less
In this thesis I investigate the unusual features found in the Modern Irish colour classification system, relative to features of colour naming found in other languages. Within the Indo-European...Show moreIn this thesis I investigate the unusual features found in the Modern Irish colour classification system, relative to features of colour naming found in other languages. Within the Indo-European language family, the Celtic languages are the only ones that have a grue category, which means there is one colour term denoting both green and blue, rather than distinct terms that express both these categories. In Irish, however, there is a term for grue, plus two additional terms for green and blue. This is not just a feature unattested in IE languages, it is an anomaly worldwide as well. Other dissimilarities with IE languages include the basic referents of colour terms when describing humans: in many languages, colour terms refer to complexion, but in Irish this is haircolour. Lastly, the total number of basic colour terms of Irish is unusual as well: despite the colour lexicon being very extensive, the colour terms denoting orange, pink, purple, and brown, are considered non-basic or secondary terms.Show less
