Academic Work
Publications
Research spanning AI for Social Good, Natural Language Processing, Signal Processing, and Wireless Communications Engineering. Work spans journal articles, international conference papers, and ongoing PhD research.
- Conference Paper● Published2025
Leveraging Electronic Syndromic Surveillance Synthetic Data to Predict Diarrhoea in Zimbabwean Children Under-Five: An Explainable AI Framework
Chikotie, T., Watson, B., Kimanuka, U., Banda, T.
2025 IST-Africa Conference (IST-Africa)
📚 1 citation
This study investigates the use of synthetic data in developing an electronic syndromic surveillance system to predict diarrhoeal outcomes among Zimbabwean children under five. Given the high morbidity and mortality rates linked to diarrhoeal diseases in Zimbabwe, implementing a real-time surveillance system can significantly enhance early outbreak detection and response. Machine learning models, including Random Forest, XGBoost, and Long Short-Term Memory (LSTM), were trained on synthetic data created through Generative Adversarial Networks (GANs), simulating real-world conditions and enhancing dataset diversity. Explainable AI technique like SHAP was employed for model interpretability, revealing crucial predictors, such as healthcare-seeking behaviours and socio-demographic factors, which drive diarrheal outcomes. The findings emphasise the potential of AI-driven surveillance in low-resource settings, offering actionable insights for public health interventions. This research provides a foundational framework for implementing electronic syndromic surveillance to improve public health resilience in Zimbabwe.
syndromic surveillancediarrhoeaexplainable AIsynthetic dataGANSHAPpublic healthZimbabwe - Conference Paper● Published2023
MasakhaneNews: News Topic Classification for African Languages
Adelani, D. I., Masiak, M., Azime, I. A., Alabi, J., Tonja, A. L., Mwase, C., Ogundepo, O., Kimanuka, U., et al.
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
📚 55 citations
Despite representing roughly a fifth of the world population, African languages are underrepresented in NLP research, in part due to a lack of datasets. While there are individual language-specific datasets for several tasks, only a handful of tasks (e.g. named entity recognition and machine translation) have datasets covering geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS — the largest dataset for news topic classification covering 16 languages widely spoken in Africa. We provide and evaluate a set of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives for transfer learning to improve classification in low-resource settings.
news classificationAfrican languagesNLPMasakhanemultilingualtext classification
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