Faculty of Accounting and Informatics
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Item Integration of an autoencoder model with an actor-oriented system(Advances in Artificial Intelligence and Machine Learning, 2024) Dyubele, Sithembiso; Cele, Noxolo Pretty; Mbangata, LubabaloTraditional machine learning frameworks often struggle with scalability, modularity, and efficient resource management, especially when dealing with vast data. Actor-Oriented Systems offer a robust framework for building such scalable systems, allowing concurrent processing and efficient handling of large datasets. This study investigated the integration of Autoencoders (AE), which are pivotal in unsupervised learning, with Actor-Oriented Systems to enhance the modularity, scalability, and maintainability of the model training process. The study seeks to leverage the capabilities of AE and Actor-Oriented Systems to achieve high-quality image reconstruction and efficient processing. The study also attempted to understand the underlying patterns in the data, assess the performance of the model, and demonstrate the benefits of modular and scalable systems. Key findings from the results showed significant improvements in training efficiency and performance of the model, especially when using Actor-Oriented Systems. The training time was reduced from 16.96 seconds to 14.21 seconds, and the validation loss improved from 0.2768 to 0.2100, indicating better generalisation and learning. Data augmentation techniques further enhanced the robustness of the model, leading to more accurate reconstructions of the test images. Actor-Oriented Systems facilitated concurrent processing, improved modularity, and enabled the system to scale efficiently with increasing data volume. This study also highlighted the practical benefits of integrating AE with Actor-Oriented Systems, providing valuable insights into building more robust, maintainable, and scalable machine learning workflows.Item Examining perceptions of academic staff on the factors affecting the use of smartphones as a Constructivist Learning Tool : a proposed model(2021-07-05) Dyubele, Sithembiso; Heukelman, Delene; Soobramoney, SubashnieThe rapid growth of mobile technology has brought enormous benefits in terms of communication and how some tasks may be accomplished using this technology. However, although many benefits have been identified, many disadvantages have also been identified. The focus of this study was to determine the perceptions of academic staff members on the factors that affect the use of smartphones as a constructivist learning tool by students rather than as a mere communication and distraction gadget. The factors that could affect the use of smartphones as a constructivist learning tool were identified through a comprehensive literature review. Based on the factors found, a model depicting the relevant factors was constructed, and the model was validated. Six independent constructs for the model; Demographics, Attitudes towards smartphones, Facilitating Conditions, Perceived Ease of Use, Perceived Usefulness, and Performance Expectations, were identified by grouping variables to measure each construct together. A questionnaire, based on the constructs and variables, was administered. The resulting data were analysed to validate the model. The conceptual model, tested by the survey, showing the significance of each factor, indicated that all the independent constructs impact the use of smartphones as a constructivist learning tool, either for communication and/or sharing academic-related activities. The results of this study found that Demographics, such as academic departments, Attitudes towards smartphones, Facilitating Conditions, Perceived Ease of Use, Perceived Usefulness, and Performance Expectations all impact the use of smartphones as a constructivist learning tool.