Theses and dissertations (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/4

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Development of a frugal crop planning decision support system for subsistence farmers
(2016-12) Friedland, Adam; Olugbara, Oludayo O.; Duffy, Kevin Jan
This dissertation reports on the original study that undertakes the development of a frugal information system to support subsistence farmers through the use of the Agricultural Production Systems Simulator (APSIM) as a support tool to assist them in optimal strategic decisions making. The field of agriculture is vast and in-depth and a number of critical factors like soil type, rainfall and temperature are involved that farmers have to take into account. Farmers persistently face the challenges of increasing and sustaining yields to meet with the populaces demand with often limited resources, which makes strategic decisions on what to plant, when to plant, where to plant and how to plant in a particular season imperative. The way in which this study attempts to solve this agricultural decision making problem is with the use of the APSIM. This technology platform provides an advanced simulation of agricultural systems that can enable subsistence farmers to simulate a number of variables ranging from plant types, soil, climate and even management interactions. This research presents a frugal web-based crop planning decision support system that subsistence farmers can take advantage with the use of the APSIM. The APSIM platform was used to run simulations for various regions with the results containing the expected level of success along with other useful information for a specified crop in the vicinity, using state of the art software platforms and tools ranging from Google Maps application programming interfaces, Microsoft’s model view controller framework, JavaScript and others. The validity of this system was tested through a number of design science methods including structural testing and illustrative scenarios, show capability of the information system. The results obtained from this evaluation show a small but powerful tool that has the capability of servicing a multitude of farmers with crop management decisions.
Machine learning : a data-point approach to solving misclassifications in the imbalanced Credit Card Datasets
(2021-10-30) Mqadi, Nhlakanipho Michael; Naicker, N.; Adeliyi, Timothy Temitope
Machine learning (ML) uses algorithms with the complexity to iterate over massive datasets to analyse the data for past behaviour with the aim to predict future outcomes. Financial institutions are using ML to detect Credit Card Fraud (CCF) by learning the patterns that distinguish between legitimate and fraudulent actions from historic data of credit card transactions to combat CCF. The market economic order has been negatively affected by CCF, which has contributed to low consumer confidence in financial institutions, and loss of interest from investors. The CCF loses continue increasing every year despite existing efforts to prevent fraud, which amount to billions of dollars lost annually. ML techniques consume large volumes of historical credit card transaction data as examples for learning. In ordinary credit card datasets, there are far fewer fraudulent transactions than legitimate transactions. In dealing with the credit card data imbalance problem, the ideal solution must have low bias, low variance, and high accuracy. The aim of this study was to provide an in-depth experimental investigation of the effect of using the data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The study focused on finding a novel way to handle imbalanced data, to improve the performance of ML algorithms in identifying fraud or anomaly patterns in massive amounts of financial transaction records, where the class distribution was imbalanced. The experiment led to the introduction of two unique multi-level hybrid data-point approach solutions, namely, Feature Selection with Near Miss Undersampling; and Feature Selection with SMOTe based Oversampling. The results were obtained using four widely used ML algorithms, namely, Random Forest, Support Vector Machine, Decision Tree, and Logistic Regression to build the classifiers. These algorithms were implemented for classification of credit card datasets and the performance was assessed using selected performance metrics. The findings show that using the data-point approach improved the predictive accuracy of the ML fraud detection solution.
Software reliability prediction of mobile applications using machine learning techniques
(2021-04-30) Hoosen, Sumaya; Singh, Alveen
Software reliability is an important aspect for evaluating the quality of a software product. In a growing global software industry of increasingly complex systems, reliability becomes crucial urging software engineers to strive toward the development of failure free software and to ensure high reliability before delivery. This positions software reliability as one of the key attributes required to achieve high quality software products. In response to this stature, software companies invest considerable resources boosting apps development into a multi-billion Rand global industry. In recent times smart devices are established as one of the most used electronic device with apps being the more popular medium for bringing a multitude of functionalities to a wide user base. However, current literature portrays a far from ideal reliability rate for apps. Despite the availability of a wide range of approaches focused on improved reliability these mostly remain cumbersome and costly to implement from a software management perspective. Hence, there is a need to investigate approaches beyond current dominant thinking that underpins reliability measurements in the mobile app development space. At the same time, Machine Learning (ML) is a recent recipient of much attention from researchers and practitioners that offers a bouquet of tools and techniques that when applied correctly could potentially improve reliability prediction. In line with the above, the overall aim of this study is to provide a ML modelling approach to assist with the reliability prediction of mobile apps. It is hoped that the findings of this study may provide a useful ML modelling approach to help developers increase the reliability rates of apps. For this study ML techniques were applied to 3 feature sets of data extracted from the Eclipse JDT core dataset. These feature sets based on software systems and their histories, include the source code metrics set, process metric sets, and a combination of both metric sets. All metric sets went through stages of data cleaning and pre-processing before they were modelled using five machine learning algorithms, namely, Random Forest, Support Vector Machine, Naïve Bayes, Decision Trees and Neural Networks. During the modelling process, all the results were evaluated using ML evaluation scores to determine which ML modelling approach is most useful for reliability prediction. The results indicate that Random Forest generated better results in all cases and can be used for predicting app reliability since it predicted reliability more accurately and precisely compared to the other ML algorithms. Random Forest also achieved the highest evaluation score when it was applied to the combined metric set of data. This means that the modelling approach of applying Random Forest to a combination of source code and process metrics generated the highest prediction performance. This further implies that developers should consider these selected features within the combined metric set, as they could serve as useful indicators for predicting reliability of apps.