Speaker
Description
Makhamisa Senekane1, Naleli Jubert Matjelo2, Thabo Koetje2, Lerato Lerato3
1Institute for Intelligent Systems, University of Johannesburg, Johannesburg, South Africa
2Department of Physics & Electronics, National University of Lesotho, Roma, Lesotho
3Department of Mathematics & Computer Science, National University of Lesotho, Roma, Lesotho
Water-borne diseases such as typhoid fever do pose a threat to communities, especially those communities in the Global South. This threat can be addressed by assessing the quality of water that is being consumed by the said communities. One approach that can be adopted in this assessment of water quality involves the use of Machine Learning (ML) techniques. ML is a branch of Artificial Intelligence (AI) that enables computers to learn from data without being explicitly programmed. In this paper, we present water quality assessment using convolutional Graph Neural Networks (GNNs). The performance results obtained from the study reported in this paper underline the importance of the use of convolutional GNNs to assess water quality.
Level for award;(Hons, MSc, PhD, N/A)?
N/A
| Apply to be considered for a student ; award (Yes / No)? | No |
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