Racism against Black soccer players in the English Premier League
- Authors: Zinyemba, Douglas Takudzwa
- Date: 2023-10-13
- Subjects: Racism in sports , Soccer players England , Athletes, Black England , FA Premier League , Racism in mass media , Soccer fans , Race discrimination , Online hate speech
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/425300 , vital:72227
- Description: This study analyses racism against black players in the English Premier League. To that end, this thesis studies online articles published between 2018 and 2021 by two British tabloids, namely, The Daily Mail and The Sun, to make sense of the various ways in which black soccer players experience racism. This thesis uses the theoretical concept of “racial xenophobia” to analyse and understand expressions of antipathy towards black players playing in the Premier League. A key finding in the thesis suggests that fans and players racially abuse black players in the stadiums by liking them to animals and treating them as sub-human. Another finding in the study is that fans use the bad performances of black players as an excuse to racially abuse them via social media platforms. Social media in the 21st century has now accelerated the rate at which racism is perpetrated as fans now have more access to players through their accounts. The study also found that tabloids do not only report about racist abuse but are also guilty of portraying black players in racially stereotypical ways. This research concludes that racism against black players remains a constant feature of football in the English Premier League from the time black players started to feature in the sport in the 1970s. , Thesis (MA) -- Faculty of Humanities, Political and International Studies, 2023
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- Authors: Zinyemba, Douglas Takudzwa
- Date: 2023-10-13
- Subjects: Racism in sports , Soccer players England , Athletes, Black England , FA Premier League , Racism in mass media , Soccer fans , Race discrimination , Online hate speech
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/425300 , vital:72227
- Description: This study analyses racism against black players in the English Premier League. To that end, this thesis studies online articles published between 2018 and 2021 by two British tabloids, namely, The Daily Mail and The Sun, to make sense of the various ways in which black soccer players experience racism. This thesis uses the theoretical concept of “racial xenophobia” to analyse and understand expressions of antipathy towards black players playing in the Premier League. A key finding in the thesis suggests that fans and players racially abuse black players in the stadiums by liking them to animals and treating them as sub-human. Another finding in the study is that fans use the bad performances of black players as an excuse to racially abuse them via social media platforms. Social media in the 21st century has now accelerated the rate at which racism is perpetrated as fans now have more access to players through their accounts. The study also found that tabloids do not only report about racist abuse but are also guilty of portraying black players in racially stereotypical ways. This research concludes that racism against black players remains a constant feature of football in the English Premier League from the time black players started to feature in the sport in the 1970s. , Thesis (MA) -- Faculty of Humanities, Political and International Studies, 2023
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Selected medicinal plants leaves identification: a computer vision approach
- Authors: Deyi, Avuya
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424552 , vital:72163
- Description: Identifying and classifying medicinal plants are valuable and essential skills during drug manufacturing because several active pharmaceutical ingredients (API) are sourced from medicinal plants. For many years, identifying and classifying medicinal plants have been exclusively done by experts in the domain, such as botanists, and herbarium curators. Recently, powerful computer vision technologies, using machine learning and deep convolutional neural networks, have been developed for classifying or identifying objects on images. A convolutional neural network is a deep learning architecture that outperforms previous advanced approaches in image classification and object detection based on its efficient features extraction on images. In this thesis, we investigate different convolutional neural networks and machine learning algorithms for identifying and classifying leaves of three species of the genus Brachylaena. The three species considered are Brachylaena discolor, Brachylaena ilicifolia and Brachylaena elliptica. All three species are used medicinally by people in South Africa to treat diseases like diabetes. From 1259 labelled images of those plants species (at least 400 for each species) split into training, evaluation and test sets, we trained and evaluated different deep convolutional neural networks and machine learning models. The VGG model achieved the best results with 98.26% accuracy from cross-validation. , Thesis (MSc) -- Faculty of Science, Mathematics, 2023
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- Authors: Deyi, Avuya
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424552 , vital:72163
- Description: Identifying and classifying medicinal plants are valuable and essential skills during drug manufacturing because several active pharmaceutical ingredients (API) are sourced from medicinal plants. For many years, identifying and classifying medicinal plants have been exclusively done by experts in the domain, such as botanists, and herbarium curators. Recently, powerful computer vision technologies, using machine learning and deep convolutional neural networks, have been developed for classifying or identifying objects on images. A convolutional neural network is a deep learning architecture that outperforms previous advanced approaches in image classification and object detection based on its efficient features extraction on images. In this thesis, we investigate different convolutional neural networks and machine learning algorithms for identifying and classifying leaves of three species of the genus Brachylaena. The three species considered are Brachylaena discolor, Brachylaena ilicifolia and Brachylaena elliptica. All three species are used medicinally by people in South Africa to treat diseases like diabetes. From 1259 labelled images of those plants species (at least 400 for each species) split into training, evaluation and test sets, we trained and evaluated different deep convolutional neural networks and machine learning models. The VGG model achieved the best results with 98.26% accuracy from cross-validation. , Thesis (MSc) -- Faculty of Science, Mathematics, 2023
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The application of statistical classification to predict sovereign default
- Authors: Vele, Rendani
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424563 , vital:72164
- Description: When considering sovereign loans, it is imperative for a financial institution to have a good understanding of the sovereign they are transacting with. Defaults can occur if proper evaluation steps are not considered. To aid in the prediction of potential sovereign defaults, financial institutions, together with grading companies, quantify the risk associated with issuing a loan to a sovereign by developing sovereign default early warning systems (EWS). Various classification models are considered in this study to develop sovereign default EWS. These models are the binary logit, probit, Bayesian additive regression trees, and artificial neural networks. This study investigates the predictive performance of the various classification techniques. Sovereign information is not readily available, so missing data techniques are considered in order to counter the data availability issue. Sovereign defaults are rare, which results in an imbalance in the distribution of the binary dependent variable. To assess data sets with such characteristics, metrics for imbalanced data are considered for model performance comparison. From the findings, the Bayesian additive regression technique generated better results than the other techniques when considering a basic data analysis. Moreover when cross-validation was considered, the neural network technique performed best. In addition, regional models had better results than the global model when considering model predictive capability. The significance of this study is to develop sovereign default prediction models using various classification techniques focused on enhancing previous literature and analysis through the application of Bayesian additive regression trees. , Thesis (MSc) -- Faculty of Science, Statistics, 2023
- Full Text:
- Authors: Vele, Rendani
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424563 , vital:72164
- Description: When considering sovereign loans, it is imperative for a financial institution to have a good understanding of the sovereign they are transacting with. Defaults can occur if proper evaluation steps are not considered. To aid in the prediction of potential sovereign defaults, financial institutions, together with grading companies, quantify the risk associated with issuing a loan to a sovereign by developing sovereign default early warning systems (EWS). Various classification models are considered in this study to develop sovereign default EWS. These models are the binary logit, probit, Bayesian additive regression trees, and artificial neural networks. This study investigates the predictive performance of the various classification techniques. Sovereign information is not readily available, so missing data techniques are considered in order to counter the data availability issue. Sovereign defaults are rare, which results in an imbalance in the distribution of the binary dependent variable. To assess data sets with such characteristics, metrics for imbalanced data are considered for model performance comparison. From the findings, the Bayesian additive regression technique generated better results than the other techniques when considering a basic data analysis. Moreover when cross-validation was considered, the neural network technique performed best. In addition, regional models had better results than the global model when considering model predictive capability. The significance of this study is to develop sovereign default prediction models using various classification techniques focused on enhancing previous literature and analysis through the application of Bayesian additive regression trees. , Thesis (MSc) -- Faculty of Science, Statistics, 2023
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