Researcher: Kgaugelo Mphahlele, University of The Witwatersrand, Johannesburg
Supervisor: Dr Farai Mlambo, University of the Witwatersrand, Johannesburg

Wavelet Neural Networks were inspired by neural networks as well as wavelet decomposition, and as such Zhang and Beneveniste sort to combine the two concepts. This particular network is often used in the financial industry for making forecasts related to the stock market and as such this Investigation sort to compare the results of stock market forecasts made several implementations of the Wavelet Neural Networks, with different activation functions. The main findings from this investigation found that the ShannonWavelet Neural Network and the GaussianNeural Network were the best performing followed by the Mexican Hat Neural Network.

Researcher: Johannah Moepi, University of the Witwatersrand, Johannesburg
Supervisor: Professor Nukri Komin, University of the Witwatersrand, Johannesburg

With two types of EAS produced, the IACT’s detect both of them including the background events.
As such the IACT’s requires sophisticated image analysis that can be trained to distinguish gamma rays from the hadrons.

Researcher: Tshinanne Mbedzi, University Venda
Supervisor: Professor Turgay Çelik and Dr Michael Klipin, University of the Witwatersrand, Johannesburg

Disease classification may be defined as a category system to which morbid entities are allocated according to the criteria established. The purpose of the ICD is to allow the systematic analysis, interpretation and comparison of data collected at different times on mortality and morbidity[1].
ICD, supported by the WHO(World Health Organization), is translated into a multitude of languages and is the main source of diagnostic definition. [2]. As such the IACT’s requires sophisticated image analysis that can be trained to distinguish gamma rays from the hadrons.

Researcher: Joshua Nel, University of the Witwatersrand, Johannesburg
Supervisor: Professor Rod Alence, University of the Witwatersrand, Johannesburg

The unprecedented growth in size and volume of data requires the examination of novel technologies. The research project applies text mining applications to a section of Truth and Reconciliation Transcripts to determine their viability within investigative journalism.

Researcher: Promise Magoma, University of Venda
Supervisors: Dr Farai Mlambo, University of the Witwatersrand, Johannesburg

The main aim of this research project is to develop an image compression model based on discrete cosine transform (DCT) to reduce image redundancy (noise).

Researcher: Thomas Lancaster, University of the Witwatersrand, Johannesburg
Supervisor: Professor Rod Alence, University of the Witwatersrand, Johannesburg

The current study aimed to examine the utility of text mining methods within the broader process of qualitative content analysis. The study aimed to examine whether text mining; in the form of word frequency analysis and topic modelling, could be utilized in the role of an exploratory text analysis method. In order to attempt to answer this question, the study aimed to examine the utility of text mining in the examination of the concept of spirituality within extracts of narrative sections of both the AA and NA primary texts. The paper worked under the assumption that words associated with the concept of spirituality would be heavily represented within the text corpus.

Researcher: Thanyani Gumani, University of Venda
Supervisor: Professor Bruce Mellado, University of the Witwatersrand, Johannesburg

The standard model of particle physics outlines our understanding of the fundamental particles of existence and their interactions. To enhance our understanding of this area, experiments with ever greater energies and intensities have been needed, generating extremely large and detailed data samples. The use of machine learning methods revolutionizes the analysis of these data samples and greatly increases current and future research in their capacity for exploration. There is an overview into the ATLAS experiment and the LHC and Decision Trees and the debate about possible insights and issues. The connections between the machine learning and energy physics analysis are discussed. We consider the supervised machine learning classification in this paper. In this study we apply the MVA methods proposed to analyse their performance using the di-lepton data from the ATLAS experiment at the LHC. Results demonstrate the good performance of the chosen MVA methods, where TMVA is used for computation.

Researcher: Pranish Desai, University of the Witwatersrand, Johannesburg
Supervisor: Professor Rod Alence, University of the Witwatersrand, Johannesburg

Previous studies into the effect of population density on governance in Africa have been mainly qualitative due to problems with reliable population data. In recent years population data has become more reliable but no quantitative study into the density -governance relationship hasoccurred, a void this study aimed to fill.

Researcher: Nkateko Baloyi, University of the Witwatersrand, Johannesburg
Supervisors: Professor Bruce Mellado, University of the Witwatersrand, Johannesburg

The search for new Bosons implies that protons in the LHC collide at very high energies and high Luminosity. High luminosity increases the probability of discovering new particles be-yond the standard model (BMS) and also increases the back-ground. The aim is to define a Machine learning algorithm that can suppress the background for signal enhancement and be applied on the search for new bosons.

Researchers: Tshauambea Murendeni, University of Venda
Supervisor: Ms Nothabo Ndebele, University of the Witwatersrand, Johannesburg

Clustering is a method where information items are grouped to attain the objective of maximizing within cluster resemblance and dissimilarity of different clusters [1]. The kmeans algorithm is commonly used, simple and ease to implement, unsupervised partitioning clustering algorithm. The kmeans convergence to the optimal solution is dependent on the initialization strategy. This study utilizes 3 initialization strategies namely: the random, k-means++ and farthest transversal to experiment on the k-means algorithm. The experiments were conducted on various consumer segmentation data sets of different sizes and data structures. The comparison made on these initialization strategies were the quantity of steps the k-means algorithm took to reach its optimal solution. The experiments show that all the initialization strategies lead to the same optimal solution of the kmeans algorithm. However the k-means++ reachs the optimal solution with less iterations compared to other initialization strategies used in this study. For the data sets utilized in this research the k-means++ initialized k-means is more efficient or faster than the k-medoids algorithms to reach their optimal solution.