Background: Over the past 4-5 months, the Coronavirus has rapidly spread to all parts of the world. Research is continuing to find a cure for this disease while there is no exact reason for this outbreak. As the number of cases to test for Coronavirus is increasing rapidly day by day, it is impossible to test due to the time and cost factors. Over recent years, machine learning has turned very reliable in the medical field. Using machine learning to predict COVID-19 in patients will reduce the time delay for the results of the medical tests and modulate health workers to give proper medical treatment to them. Objectives: The main goal of this thesis is to develop a machine learning model that could predict whether a patient is suffering from COVID-19. To develop such a model, a literature study alongside an experiment is set to identify a suitable algorithm. To assess the features that impact the prediction model. Methods: A Systematic Literature Review is performed to identify the most suitable algorithms for the prediction model. Then through the findings of the literature study, an experimental model is developed for prediction of COVID-19 and to identify the features that impact the model. Results: A set of algorithms were identified from the Literature study that includes SVM (Support Vector Machines), RF (Random Forests), ANN (Artificial Neural Network), which are suitable for prediction. Performance evaluation is conducted between the chosen algorithms to identify the technique with the highest accuracy. Feature importance values are generated to identify their impact on the prediction. Conclusions: Prediction of COVID-19 by using Machine Learning could help increase the speed of disease identification resulting in reduced mortality rate. Analyzing the results obtained from experiments, Random Forest (RF) was identified to perform better compared to other algorithms.

No description available.

<p>This&nbsp / thesis&nbsp / examines&nbsp / the&nbsp / changes&nbsp / that&nbsp / have&nbsp / occurred&nbsp / within&nbsp / West-Coast Renosterveld within&nbsp / the&nbsp / last 350 years, and assesses&nbsp / the viability of&nbsp / the&nbsp / remaining fragments.</p>

<p>This&nbsp / thesis&nbsp / examines&nbsp / the&nbsp / changes&nbsp / that&nbsp / have&nbsp / occurred&nbsp / within&nbsp / West-Coast Renosterveld within&nbsp / the&nbsp / last 350 years, and assesses&nbsp / the viability of&nbsp / the&nbsp / remaining fragments.</p>

Remote sensing techniques are vital for early detection of several problems such as natural disasters, ecological problems and collecting information necessary for finding optimum solutions to those problems. Remotely sensed information has also important uses in predicting the future risks, urban planning, communication.Recent developments in remote sensing instrumentation offered a challenge to the mathematical and statistical methods to process the acquired information. Classification of satellite images in the context of land cover classification is the main concern of this study. Land cover classification can be performed by statistical learning methods like additive models, decision trees, neural networks, k-means methods which are already popular in unsupervised classification and clustering of image scene inverse problems. Due to the degradation and corruption of satellite images, the classification performance is limited both by the accuracy of clustering and by the extent of the classification. In this study, we are concerned with understanding the performance of the available unsupervised methods with k-means, supervised methods with Gaussian maximum likelihood which are very popular methods in land cover classification. A broader approach to the classification problem based on finding the optimal discriminants from a larger range of functions is considered also in this work. A novel method based on threshold decomposition and Boolean discriminant functions is developed as an implementable application of this approach. All methods are applied to BILSAT and Landsat satellite images using MATLAB software.

Philosophiae Doctor - PhD / South Africa

<div>Maize (Zea mays L.) is one of the most important crops worldwide for its critical importance in agriculture, economic stability, and food security. Many agricultural research and commercial breeding programs target the efficiency of this crop, seeking to increase productivity with fewer inputs and becoming more environmentally sustainable and resistant to impacts of climate and other external factors. For the purpose of analyzing the performance of the new varieties and management strategies, accurate and constant monitoring is crucial and yet, still performed mostly manually, becoming labor-intensive, time-consuming, and costly.<br></div><div>Flowering is one of the most important stages for maize, and many other grain crops, requiring close attention during this period. Any physical or biological negative impact in the tassel, as a reproductive organ, can have significant consequences to the overall grain development, resulting in production losses. Remote sensing observation technologies are currently seeking to close the gap in phenotyping in monitoring the development of the plants’ geometric structure and chemistry-related responses over the growth and reproductive cycle.</div><div>For this thesis, remotely sensed hyperspectral imagery were collected, processed and, explored to detect tassels in maize crops. The data were acquired in both a controlled facility using an imaging conveyor, and from the fields using a PhenoRover (wheel-based platform) and a low altitude UAV. Two pixel-based classification experiments were performed on the original hyperspectral imagery (HSI) using Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) supervised classifiers. Feature reduction methods, including Principal Component Analysis (PCA), Locally Linear Embedding (LLE), and Isometric Feature Mapping (Isomap) were also investigated, both to identify features for annotating the reference data and in conjunction with classification.</div><div>Collecting the data from different systems allowed the identification of strengths and weaknesses for each system and the associated tradeoffs. The controlled facility allowed stable lighting and very high spatial and spectral resolution, although it lacks on supplying information about the plants’ interactions in field conditions. Contrarily, the in-field data from the PhenoRover </div><div>and the UAV exposed the complications related to the plant’s density within the plots and the variability in the lighting conditions due to long times of data collection required. The experiments implemented in this study successfully classified pixels as tassels for all images, performing better with higher spatial resolution and in the controlled environment. For the SAM experiment, nonlinear feature extraction via Isomap was necessary to achieve good results, although at a significant computational expense. Dimension reduction did not improve results for the SVM classifier.</div>

The aim is to develop a new decision-making model for credit-loans. The model will be specific for credit applicants of the OKQ8 bank, becauseit is based on data of earlier applicants of credit from the client (the bank). The final model is, in effect, functional enough to use informationabout a new applicant as input, and predict the outcome to either the good risk group or the bad risk group based on the applicant’s properties.The prediction may then lay the foundation for the decision to grant or deny credit loan. Because of the skewed distribution in the response variable, different sampling techniques are evaluated. These include oversampling with SMOTE, random undersampling and pure oversampling in the form of scalar weighting of the minority class. It is shown that the predictivequality of a classifier is affected by the distribution of the response, and that the oversampled information is not too redundant. Three classification techniques are evaluated. Our results suggest that a multi-layer neural network with 18 neurons in a hidden layer, equippedwith an ensemble technique called boosting, gives the best predictive power. The most successful model is based on a feed forward structure andtrained with a variant of back-propagation using conjugate-gradient optimization. Two other models with a good prediction quality are developed using logistic regression and a decision tree classifier, but they do not reach thelevel of the network. However, the results of these models are used to answer the question regarding which customer properties are importantwhen determining credit risk. Two examples of important customer properties are income and the number of earlier credit reports of the applicant. Finally, we use the best classification model to predict the outcome of a set of applicants declined by the existent filter. The results show that thenetwork model accepts over 60 % of the applicants who had previously been denied credit. This may indicate that the client’s suspicionsregarding that the existing model is too restrictive, in fact are true.

Este trabalho tem por objetivo apresentar e discutir alguns modelos para cálculo de perdas técnicas e não técnicas em sistemas de distribuição, considerando diversas alternativas em função da disponibilidade de dados. A discussão é de fundamental importância, na medida que o setor elétrico passa a enfrentar novos desafios, tais como o cálculo de redes de baixa tensão com cadastro incompleto e o cálculo de perdas não técnicas com sua respectiva parcela de perdas técnicas. Para o cálculo das perdas em redes de baixa tensão com cadastro incompleto, duas situações são consideradas. Na primeira, a rede é conhecida, porém não se conhece a localização de seus consumidores. Na segunda situação, também a rede é desconhecida. Neste último caso, para superar o problema de ausência de informações, são utilizadas técnicas de classificação para definir um conjunto de padrões de redes típicas e posterior associação de cada rede a um padrão previamente estabelecido. Também são utilizados alguns modelos de distribuição da carga e a consideração de incertezas é contemplada a partir de números difusos. Para calcular as perdas não técnicas com sua respectiva parcela de perdas técnicas, propõe-se um método para corrigir a energia faturada dos consumidores a partir do conhecimento da energia medida e das perdas técnicas calculadas. Uma extensão desse método ainda permite calcular as perdas de forma rápida e sem grandes esforços computacionais (método expedito), partindo do resultados de um cálculo realizado com um método convencional. Todos os modelos propostos são aplicados a redes de distribuição reais. Os resultados obtidos são analisados e comparados a valores de referência e é discutida a aplicabilidade dos modelos, bem como suas respectivas faixas de validade. / This work aims at presenting and discussing some models for calculating technical and non-technical losses in distribution systems. The proposed methods comprise several possibilities depending on the available data. This discussion is very important since the electric sector faces new challenges, such as technical loss estimation in low voltage networks with incomplete data. The evaluation of non-technical losses is also herein discussed. Regarding loss estimation with incomplete data, two conditions are considered. In the first one, the network data is known, but the customers location are unavailable. In the second one, the network data is not completely known as well. In this latter condition, in order to overcome the lack of sufficient data, classification techniques are used to establish a set of typical network patterns and to associate each network to a previously established pattern. Some load distribution models are also used and the uncertainties are considered by the use of fuzzy sets. In order to estimate the non-technical losses and their related technical losses, a method to adjust the billed energy in all customers is proposed. This is accomplished by using the computed technical losses and the measured energy at the substation site. This method also makes possible the assessment of technical losses in a quick way with a low computational effort (fast method). Such method is accomplished by using the results of a calculation previously performed using the conventional method. All the proposed methods are applied to real distribution networks. The obtained results are discussed and compared to the results obtained with the conventional method. The applicability of each model is also discussed.

Este trabalho tem por objetivo apresentar e discutir alguns modelos para cálculo de perdas técnicas e não técnicas em sistemas de distribuição, considerando diversas alternativas em função da disponibilidade de dados. A discussão é de fundamental importância, na medida que o setor elétrico passa a enfrentar novos desafios, tais como o cálculo de redes de baixa tensão com cadastro incompleto e o cálculo de perdas não técnicas com sua respectiva parcela de perdas técnicas. Para o cálculo das perdas em redes de baixa tensão com cadastro incompleto, duas situações são consideradas. Na primeira, a rede é conhecida, porém não se conhece a localização de seus consumidores. Na segunda situação, também a rede é desconhecida. Neste último caso, para superar o problema de ausência de informações, são utilizadas técnicas de classificação para definir um conjunto de padrões de redes típicas e posterior associação de cada rede a um padrão previamente estabelecido. Também são utilizados alguns modelos de distribuição da carga e a consideração de incertezas é contemplada a partir de números difusos. Para calcular as perdas não técnicas com sua respectiva parcela de perdas técnicas, propõe-se um método para corrigir a energia faturada dos consumidores a partir do conhecimento da energia medida e das perdas técnicas calculadas. Uma extensão desse método ainda permite calcular as perdas de forma rápida e sem grandes esforços computacionais (método expedito), partindo do resultados de um cálculo realizado com um método convencional. Todos os modelos propostos são aplicados a redes de distribuição reais. Os resultados obtidos são analisados e comparados a valores de referência e é discutida a aplicabilidade dos modelos, bem como suas respectivas faixas de validade. / This work aims at presenting and discussing some models for calculating technical and non-technical losses in distribution systems. The proposed methods comprise several possibilities depending on the available data. This discussion is very important since the electric sector faces new challenges, such as technical loss estimation in low voltage networks with incomplete data. The evaluation of non-technical losses is also herein discussed. Regarding loss estimation with incomplete data, two conditions are considered. In the first one, the network data is known, but the customers location are unavailable. In the second one, the network data is not completely known as well. In this latter condition, in order to overcome the lack of sufficient data, classification techniques are used to establish a set of typical network patterns and to associate each network to a previously established pattern. Some load distribution models are also used and the uncertainties are considered by the use of fuzzy sets. In order to estimate the non-technical losses and their related technical losses, a method to adjust the billed energy in all customers is proposed. This is accomplished by using the computed technical losses and the measured energy at the substation site. This method also makes possible the assessment of technical losses in a quick way with a low computational effort (fast method). Such method is accomplished by using the results of a calculation previously performed using the conventional method. All the proposed methods are applied to real distribution networks. The obtained results are discussed and compared to the results obtained with the conventional method. The applicability of each model is also discussed.