Browsing by Author "Bradshaw, Karen"

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Computer Science Education in Selected Countries from Sub-Saharan Africa
(Special Issue, 2024-03) Bradshaw, Karen; Ujakpa, Martin; Nabende, Joyce; Nderu, Lawrence; Neema, Mduma,; Kihoza, Patrick; Irungu, Annette
Computer Science education in sub-Saharan Africa has evolved over the past decades. The number of institutions offering distinct undergraduate programs has grown, thus increasing the number of students enrolling in the computer science discipline. Several computer science degree programs have emerged with one of the objectives being to satisfy the growing demand for local talent and skills. In this paper, we provide a snapshot of the evolution of undergraduate computer science education in selected countries in Sub-Saharan Africa over the past 20+ years and an overview of the developments in computer science education and observed trends. The setup of educational institutions in Africa and the operational context requires unique modalities for the design and delivery of computer science education that meets the demands of the industry, amongst others. This paper provides insights into the best practices in the computer science curricula in the selected countries, as well as an overview of the pedagogical and delivery approaches to computer science education. The paper highlights case studies from institutions in the selected countries, namely Uganda, South Africa, Ghana, Tanzania, and Kenya with a consolidated summary of the current and emerging challenges and opportunities in all these countries. The paper concludes by providing persectives on the future landscape of computer science in Sub-Saharan Africa.
Computer Science Education in Selected Countries from Sub-Saharan Africa
(ACM Inroads, 2024-02-20) Bainomugisha, Engineer; Bradshaw, Karen; Ujakpa, Martin; Nakatumba-Nabende, Joyce; Nderu, Lawrence; Mduma, Neema; Kihoza, Patrick; Irungu, Annette
Computer Science education in sub-Saharan Africa has evolved over the past decades. The number of institutions offering distinct undergraduate programs has grown, thus increasing the number of students enrolling in the computer science discipline. Several computer science degree programs have emerged with one of the objectives being to satisfy the growing demand for local talent and skills. In this paper, we provide a snapshot of the evolution of undergraduate computer science education in selected countries in Sub-Saharan Africa over the past 20+ years and an overview of the developments in computer science education and observed trends. The setup of educational institutions in Africa and the operational context requires unique modalities for the design and delivery of computer science education that meets the demands of the industry, amongst others. This paper provides insights into the best practices in the computer science curricula in the selected countries, as well as an overview of the pedagogical and delivery approaches to computer science education. The paper highlights case studies from institutions in the selected countries, namely Uganda, South Africa, Ghana, Tanzania, and Kenya with a consolidated summary of the current and emerging challenges and opportunities in all these countries. The paper concludes by providing persectives on the future landscape of computer science in Sub-Saharan Africa.
Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach
(Taylor & Francis Group, 2021-09-06) Loyani, Loyani; Bradshaw, Karen; Machuve, Dina
Tuta absoluta is a major threat to tomato production, causing losses ranging from 80% to 100% when not properly managed. Early detection of T. absoluta’s effects on tomato plants is important in controlling and preventing severe pest damage on tomatoes. In this study, we propose semantic and instance segmentation models based on U-Net and Mask RCNN, deep Convolutional Neural Networks (CNN) to segment the effects of T. absoluta on tomato leaf images at pixel level using field data. The results show that Mask RCNN achieved a mean Average Precision of 85.67%, while the U-Net model achieved an Intersection over Union of 78.60% and Dice coefficient of 82.86%. Both models can precisely generate segmentations indicating the exact spots/areas infested by T. absoluta in tomato leaves. The model will help farmers and extension officers make informed decisions to improve tomato productivity and rescue farmers from annual losses.