Investigating the role of UAVs and convolutional neural networks in the identification of invasive plant species in the Albany Thicket
- Authors: Wesson, Frank Cameron
- Date: 2023-04
- Subjects: Drone aircraft -- Control systems , Drone -- South Africa , Albany Thicket -- South Africa
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/61097 , vital:69755
- Description: The study aimed to determine whether plant species could be classified by using high resolution aerial imagery and a convolutional neural network (CNN). The full capabilities of a CNN were examined including testing whether the platform could be used for land cover and the evaluation of land change over time. A drone or unmanned aerial vehicle (UAV) was used to collect the aerial data of the study area, and 45 subplots were used for the image analysis. The CNN was coded and operated in RStudio, and digitised data from the input imagery were used as training and validation data by the programme to learn features. Four classifications were performed using various quantities of input data to access the performance of the neural network. In addition, tests were performed to understand whether the CNN could be used as a land cover and land change detection tool. Accuracy assessments were done on the results to test reliability and accuracy. The best-performing classification achieved an average user and producer accuracy of above 90%, while the overall accuracy was 93%, and the kappa coefficient score was 0.86. The CNN was also able to predict the land coverage area of Opuntia to be within 4% of the ground truthing data area. A change in land cover over time was detected by the programme after the manual clearing of the invasive plant had been undertaken. This research has determined that the use of a CNN in remote sensing is a very powerful tool for supervised image classifications and that it can be used for monitoring land cover by accurately estimating the spatial distribution of plant species and by monitoring the species' growth or decline over time. A CNN could also be used as a tool for landowners to prove that they are making efforts to clear invasive species from their land. , Thesis (MSc) -- Faculty of Science, School of Environmental Sciences, 2023
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- Date Issued: 2023-04
Validation and adaptation of statistical models based on the SAPS III score to predict in-hospital mortality in a South African ICU
- Authors: Pazi, Sisa
- Date: 2023-04
- Subjects: Police -- South Africa -- Eastern Cape , Statistics – South Africa , Mortality – South Africa
- Language: English
- Type: Doctoral's theses , text
- Identifier: http://hdl.handle.net/10948/61360 , vital:70602
- Description: In-hospital mortality prediction remains an important task in Intensive Care Units (ICUs). In particular, the estimated in-hospital mortality risk is essential to describe case-mix, for research and clinical auditing purposes. Furthermore, in settings with limited hospital resources (e.g beds) such as the South African public health care system, the estimated in-hospital mortality risk is essential for resource allocation and to inform local patient triage guidelines. Commonly used models for prediction of in-hospital mortality in ICU patients includes, but not limited to, the Simplified Acute Physiology Score III (SAPS III). The SAPS III model was developed in 2005. Notably, the SAPS III model was developed without data collected from African based hospitals. Given the general application of the SAPS III model, including benchmarking and quality control, the development of such a model based on local data is of paramount importance. To this end, this study developed a model for prediction of in-hospital mortality based on data collected in a hospital in South Africa. Logistic regression modelling was used to develop the proposed mortality risk assessment model. The results indicated that the proposed model exhibited superior discrimination and classification abilities compared to the SAPS III model. Future research includes the external validation of the proposed model in different hospitals in South Africa. , Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2023
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- Date Issued: 2023-04