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Research Publications (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/212

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Subject: search.filters.subject.Machine learning

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    Integration of an autoencoder model with an actor-oriented system
    (Advances in Artificial Intelligence and Machine Learning, 2024) Dyubele, Sithembiso; Cele, Noxolo Pretty; Mbangata, Lubabalo
    Traditional 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.
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    A meta-analysis of educational data mining for predicting students performance in programming
    (The Science and Information Organization, 2021-02) Moonsamy, Devraj; Naicker, Nalindren; Adeliyi, Timothy T.; Ogunsakin, Ropo E.
    An essential skill amid the 4th industrial revolution is the ability to write good computer programs. Therefore, higher education institutions are offering computer programming as a module not only in computer related programmes but other programmes as well. However, the number of students that underperform in programming is significantly higher than the non-programming modules. It is, therefore, crucial to be able to accurately predict the performance of students pursuing programming since this will help in identifying students that may underperform and the necessary support interventions can be timeously put in place to assist these students. The objective of this study is therefore to obtain the most effective Educational Data Mining approaches used to identify those students that may underperform in computer programming. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analysis) approach was used in conducting the meta-analysis. The databases searched were, namely, ACM, Google Scholar, IEEE, Pro-Quest, Science Direct and Scopus. A total of 11 scientific research publications were included in the meta-analysis for this study from 220 articles identified through database searching. The residual amount of heterogeneity was high (τ2 = 0.03; heterogeneity I2 = 99.46% with heterogeneity chi-square = 1210.91, a degree of freedom = 10 and P = >0.001). The estimated pooled performance of the algorithms was 24% (95% CI (13%, 35%). Meta-regression analysis indicated that none of the moderators included have influenced the heterogeneity of studies. The result of effect estimates against its standard error indicated publication bias with a P-value of 0.013. These meta-analysis findings indicated that the pooled estimate of algorithms is high.