Browsing by Subject "AI"

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    Open Access
    Evaluating deep learning for enhanced breast cancer diagnosis: a comparative analysis of CNN architectures
    (2025) Frankle, Solyle; Sinkala, Musalula
    Artificial Intelligence (AI), particularly its machine learning (ML) subfield, has revolutionised various sectors, including healthcare. In breast cancer care, AI's ability to analyse vast datasets and extract complex patterns from medical images has the potential to transform diagnostics and treatment strategies. Breast cancer remains one of the most prevalent cancers affecting women globally, with early and accurate diagnosis being crucial for effective treatment. AI, through its advanced image analysis capabilities, significantly improves the accuracy and efficiency of breast cancer diagnosis, specifically in distinguishing between cancer subtypes. Here, we aim to explore the application of deep learning, particularly convolutional neural networks (CNNs), in breast cancer subtype classification using histology images. A custom CNN model, alongside well-established models like ResNet50 and EfficientNetB0, was developed and evaluated for its accuracy in predicting benign and malignant breast cancer subtypes. The results demonstrated that while the custom CNN achieved an accuracy of 65% for malignant and 67% for benign subtypes with ROC-AUC scores of 0.86 and 0.90, respectively, ResNet50 significantly outperformed both the custom model and EfficientNetB0. ResNet50 attained an accuracy of 77% in classifying malignant subtypes and 77% for benign subtypes, accompanied by ROC-AUC scores of 0.92 and 0.96, respectively. Additionally, ResNet50 exhibited higher precision (0.68 for malignant, 0.67 for benign), recall (0.65 for malignant, 0.67 for benign), and F1 scores (0.65 for malignant, 0.67 for benign) across most subtypes, underscoring its robust performance and reliability in clinical settings. In conclusion, AI, specifically through advanced CNN architectures, can greatly enhance breast cancer diagnosis by providing more accurate subtype classifications. Future work should focus on integrating these models into clinical workflows, enabling faster and more personalised treatment planning. Moreover, continued refinement of these models, including addressing the complexities of tumour heterogeneity and incorporating multimodal data, will be crucial for their widespread adoption in oncology.
  • No Thumbnail Available
    Item
    Open Access
    Examining personality assessment in asynchronous video interviews (AVI): convergence between human personality judgements and AI/ML scoring
    (2025) Cronje, Jacobus Fouche; de Kock, Francois
    The assessment of personality is an essential component of personnel selection due to its validity in predicting job performance. To assess personality, asynchronous video interviews (AVIs) scored using artificial intelligence (AI) algorithms are increasingly used, allowing candidates to record responses to interview prompts that are subsequently evaluated automatically by AI algorithms and/or human raters. As questions remain about the validity of AI-based AVI scoring approaches, this study examines the convergence between human-and AI-scored personality assessments. To measure personality, the study focuses on the HEXACO model, which measures Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience. Verbal responses were transcribed from videotaped AVIs of 161 mock interview candidates who answered five AVI questions. Responses were scored by 15 trained human raters and a closed-dictionary text-analysis keyword-counting AI algorithm developed for this study, respectively. The correlation between trait-level scores produced by human judges and AI scoring was tested both across traits and within traits (trait-level) to assess scoring convergence. Moreover, in addition to comparing score levels produced by the two scoring methods (AI vs. human raters), score spread (i.e., variability), rank-order stability, and rating reliability were evaluated. The findings revealed a moderately positive and significant overall convergence (r = .29, p < .001) across traits between human and AI evaluations, which suggests that AI scoring may potentially be useful as a replacement of human evaluations when general screening is desired. Trait-level convergence varied between scoring methods, with the scoring consensus between human raters and AI being higher for some traits than for others, suggesting that these methods rely on different information and/or may interpret interview responses differently. The research highlights the potential of AI to complement human- based scoring of AVIs used in recruitment, selection, and assessment while also identifying the limitations of algorithm-based scoring in capturing complex human behaviour in interviews. The findings may further contribute to understanding the role of AI in personality assessment and implications for organisational practices.
  • No Thumbnail Available
    Item
    Open Access
    The critical success factors and competitive advantage of a South African AI Hub
    (2026) Althoff-Thomson, Savannah; Van Belle, Jean-Paul
    Background: Despite South Africa's ambition to become a global 4IR leader through AI advancements, little research has been done to examine national AI capacity, capability, and competitiveness. AI offers even the least developed countries valuable opportunities to harness potential and gain a competitive edge, simultaneously restructuring their economies and driving digital transformation. However, foreign AI applications adopted in Africa may lack contextual relevance and fail to harness the unique resources and skills in the local landscape. The development of an AI hub in Cape Town could address these issues – alongside the contextualisation of knowledge and technology creation, an AI hub offers the distinct opportunity to harness AI competitive advantage through aligning interventions with strategically valuable national resources and capabilities. Objective: The objective of this study is to explore the critical success factors and competitive advantage of an AI hub in Cape Town. Methodology: This study was interpretivist, abductive, and followed a qualitative approach, interviewing AI-knowledgeable South African stakeholders from diverse sectors using purposive sampling and semi-structured interviews. This study was guided by Porter's Diamond of National Advantage and Cluster Theory as the theoretical frameworks, informing the interview guide and the thematic analysis. Findings: Vision, bottom-up hub functions, collaboration, proximity, funding, leadership and location emerged as major critical success factors for an AI hub in Cape Town. These critical success factors are moderated by government involvement and rapid AI change. In discussing the potential competitive advantage of an AI hub, and our unique resources and capabilities, participants overwhelmingly pointed out the ability to solve African use cases using artisanal capabilities, the need to create and safeguard South African data, and the harnessing of our diversity and culture. Using deductively derived themes, the study confirmed the relevance and interdependence of the constructs in Porter's Diamond of National Advantage in a developing context. The framework is enriched by the emergent sub-themes. The physical proximity inherent in Cluster Theory was extended to include digital proximities. Contribution: Considering theoretical contributions, the research foregrounded the capacity, capability and competitiveness of South Africa in the 4IR, adding to the literature on hubs in developing contexts and Porter's frameworks. Moreover, it addresses the government proposal to establish an African AI Institute. Practically, the study provides a starting point for the conceptualisation and competitive advantage of an AI hub in Cape Town. Actionable recommendations to specific stakeholders groups on their role in nurturing South African AI are outlined, catalysing the advancement of AI in Africa.
  • No Thumbnail Available
    Item
    Open Access
    Use of ChatGPT for student co-creation of open textbooks
    (Digital Open Textbooks for Development, 2024-02) Cox, Glenda; Willmers, Michelle; Held, Michael; Brown, Robyn
    This is a presentation by members of the Digital Open Textbooks for Development (DOT4D) initiave, Asso. Prof Glenda Cox and Michelle Willmers, and collaborators Dr Michael Held and Robyn Brown, as part of the Centre for Innovation in Learning and Teaching's (CILT) Brown Bag seminar series in February 2024.