Personal profile
Research Interests
Data Science; Machine Learning; Ensembles; Big Data; Artificial Intelligence.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 4 Quality Education -
SDG 9 Industry, Innovation, and Infrastructure -
SDG 10 Reduced Inequalities -
SDG 12 Responsible Consumption and Production -
SDG 17 Partnerships for the Goals
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Dive into the research topics where Tinofirei Museba is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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A Machine Learning Approach for Customer Churn Prediction
Museba, T., 2024, Proceedings of 2024 4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024. Zuva, T., Brown, A. & Rikhotso, M. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 400-406 7 p. (Proceedings of 2024 4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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A Machine Learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge
Mokheleli, T., Bokaba, T., Museba, T. & Ntshingila, N., 2024, Emerging Technologies for Developing Countries - 6th EAI International Conference, AFRICATEK 2023, Proceedings. Masinde, M., Möbs, S. & Bagula, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 93-106 14 p. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; vol. 520 LNICST).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
3 Citations (Scopus) -
Incremental Machine Learning-Based Approach for Credit Scoring in the Age of Big Data
Museba, T., 2024, Towards Digitally Transforming Accounting and Business Processes - Proceedings of the International Conference of Accounting and Business iCAB, Johannesburg 2023. Moloi, T. & George, B. (eds.). Springer Nature, p. 547-565 19 p. (Springer Proceedings in Business and Economics).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Machine Learning Approach for Detecting Money Laundering Transactions
Museba, T., 2024, Proceedings of 2024 4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024. Zuva, T., Brown, A. & Rikhotso, M. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 390-399 10 p. (Proceedings of 2024 4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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An Adaptive and Dynamic Heterogeneous Ensemble Model for Credit Scoring
Museba, T., 2023, Digital-for-Development: Enabling Transformation, Inclusion and Sustainability Through ICTs - 12th International Development Informatics Association Conference, IDIA 2022, Revised Selected Papers. Ndayizigamiye, P., Twinomurinzi, H., Bwalya, K., Kalema, B. & Bembe, M. (eds.). Springer Science and Business Media Deutschland GmbH, p. 304-319 16 p. (Communications in Computer and Information Science; vol. 1774 CCIS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review