Department of Computer Sciencehttp://hdl.handle.net/11394/412024-03-29T07:31:11Z2024-03-29T07:31:11ZA framework for the analysis of discrete irregular patterned sequential datasetsDandajena, Kudakwashehttp://hdl.handle.net/11394/105892023-11-30T00:02:05Z2023-01-01T00:00:00ZA framework for the analysis of discrete irregular patterned sequential datasets
Dandajena, Kudakwashe
Trial-and-error is often used for developing new deep learning models. There is no systematic procedure for improving existing models for predicting irregular sequential time-series. This research developed a systematic framework to improve state-of-the-art deep learning models for financial time-series prediction. The framework was used to create an enhanced model. A design science research methodology was used to design this framework with customised multidimensional evaluation criteria and metrics. The model was applied to predict currency exchange rates more accurately than existing state-of-the-art models found in the literature. The main contribution of this work is a framework that provides a procedure for improving deep learning models. As a proof of its usefulness, the framework was applied to develop an improved model.
Philosophiae Doctor - PhD
2023-01-01T00:00:00ZOn the efficacy of enhanced feature selection methods for supervised crime predictionMay, Sphamandla Innocenthttp://hdl.handle.net/11394/105652023-11-16T00:04:45Z2023-01-01T00:00:00ZOn the efficacy of enhanced feature selection methods for supervised crime prediction
May, Sphamandla Innocent
The challenge of crime across the globe has necessitated several considerations for crime preventive measures. There exist a variety of crime prevention strategies, such as the use of necessary weapons or tools to respond to crime. However, for resource-constrained nations such as South Africa, where the current police to civilian ratio is overwhelming, this may not suffice. Consequently, crime continues to be on the rise, necessitating alternative prevention strategies. Among alternative prevention approaches, the use of historical crime data can be explored through machine learning. Crime prediction using machine learning has been explored and has shown promising results. However, the choice of algorithm and feature selection methods play a critical role in creating an effective predictive model. This study, therefore, explores the efficacy of enhanced feature selection methods in supervised machine learning algorithms for crime prediction. Four (4) baseline algorithms are adopted, which are Random Forest (RF), Extremely Randomized Trees (ERT), Na¨ıve Bayes (NB), and Support Vector Machine (SVM). This research further proposes three algorithms, with the first derived from hybridizing RF and ERT (RF-Plus), while the other two (2) were obtained from enhancing NB and SVM using recursive feature elimination (RFE), obtaining (RFE-NB) and (RFE-SVM) respectively, totaling seven algorithms. Finally, a comparative evaluation of these algorithms with their respective baselines is conducted to report on their efficacy and contrasted against additional two (2) algorithms from the literature, which amounts to a total of nine (9) algorithms. The study conducted performance evaluation on the models using two distinct publicly available datasets, which are the Chicago and Los Angeles crime datasets. Results confirm that feature selection positively impacts prediction accuracy. The enhancement on the pure NB improved its accuracy from 72.5% to 96.6% and 80.45% to 95.78% for Chicago and Los Angeles datasets, respectively. The enhancement improved the accuracy of pure SVM from 74.73% to 89.91% and 75.73% to 88.70% for the Chicago and Los Angeles datasets, respectively, while achieving 97.04% and 95.5% on RF-Plus for both Chicago and Los Angeles datasets, respectively.
Masters of Science
2023-01-01T00:00:00ZExploring low-cost solution for 3D crime scene data gathering with immersive technologyMfundo Andrew, Manelihttp://hdl.handle.net/11394/103992023-07-18T00:01:39Z2023-01-01T00:00:00ZExploring low-cost solution for 3D crime scene data gathering with immersive technology
Mfundo Andrew, Maneli
3D crime scene data gathering is critical for law enforcement and investigators during crime scene
investigations. Crime scene investigations have seen the effective usage of Light Detection and Ranging
(LiDAR) scanners for 3D reconstruction alongside immersive technologies, such as Augmented Reality
(AR) and Virtual Reality (VR). However, the inability to afford the existing high-end devices that can
offer the desired accuracy of 3D scene data collection in low-resource settings cannot be overlooked, as
this may impede crime investigations or render some crime cases insoluble.
>Magister Scientiae - MSc
2023-01-01T00:00:00ZApplication of Several Time Series Methods to Three Important Financial Time SeriesO'Connell, Bryanhttp://hdl.handle.net/11394/103442023-06-27T00:02:16Z2007-01-01T00:00:00ZApplication of Several Time Series Methods to Three Important Financial Time Series
O'Connell, Bryan
This study is concerned with three different financial time series over an eight year period, namely: the government repurchase rate, the Rand-Dollar exchange rate and the Allshare Index. The aim is to better understand the statistical nature of the time series. The theory employed will be discussed briefly and then the results will be reported. Different methods are employed to model the different time series. The following topics are discussed: unit root tests, autoregressive integrated moving average models, outlier tests, transformations, generalised autoregressive conditional heteroscedasticity models, cointegration, transfer function models and vector autoregressive models.
>Magister Scientiae - MSc
2007-01-01T00:00:00Z