Fulufhelo Nelwamondo, PhD, PrEng

Fulufhelo Nelwamondo, PhD, PrEng

South Africa
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About

Fulufhelo Nelwamondo is the Chief Executive Officer (CEO) of the National Research…

Activity

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Experience

  • The National Research Foundation of South Africa (NRF) Graphic

    The National Research Foundation of South Africa (NRF)

    Pretoria, Gauteng, South Africa

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    South Africa

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    South African Astronomical Observatory

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    Johannesburg

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    Gauteng, South Africa

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    South Africa

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    South Africa

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    Pretoria

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    Pretoria Area, South Africa

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    Pretoria

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    South Africa

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Education

Volunteer Experience

  • Funding Education

    Personal

    - Present 14 years

    Education

    I have been paying for University registration fees for top performing learners in Maths & Science (two learners, chosen amongst all schools in Lwamondo)

  • Donor

    Educational Support

    Poverty Alleviation

    I sponsor needy school learners with school uniforms/shoes, jerseys, etc.

Publications

  • A dynamic programming approach to missing data estimation using neural networks

    Journal of Information Sciences

    Other authors
    See publication

  • Partial Imputation of Unseen Records to Improve Classification Using Hybrid Multi-Layered Artificial Immune System and Genetic Algorithm.

    http://www.sciencedirect.com/science/journal/15684946

    Missing data in large insurance datasets affects the learning and classification accuracies in predictive modelling. Insurance datasets will continue to increase in size as more variables are added to aid in managing client risk and will therefore be even more vulnerable to missing data. This paper proposes a hybrid multi-layered artificial immune system and genetic algorithm for partial imputation of missing data in datasets with numerous variables. The multi-layered artificial immune system…

    Missing data in large insurance datasets affects the learning and classification accuracies in predictive modelling. Insurance datasets will continue to increase in size as more variables are added to aid in managing client risk and will therefore be even more vulnerable to missing data. This paper proposes a hybrid multi-layered artificial immune system and genetic algorithm for partial imputation of missing data in datasets with numerous variables. The multi-layered artificial immune system creates and stores antibodies that bind to and annihilate an antigen. The genetic algorithm optimises the learning process of a stimulated antibody. The evaluation of the imputation is performed using the RIPPER, k-nearest neighbour, naïve Bayes and logistic discriminant classifiers. The effect of the imputation on the classifiers is compared with that of the mean/mode and hot deck imputation methods. The results demonstrate that when missing data imputation is performed using the proposed hybrid method, the classification improves and the robustness to the amount of missing data is increased relative to the mean/mode method for data missing completely at random (MCAR) missing at random (MAR), and not missing at random (NMAR).The imputation performance is similar to or marginally better than that of the hot deck imputation.

    Other authors

Honors & Awards

  • Leadership Excellence Award

    CSIR Modelling and Digital Science

  • Team Dynamics and Effectiveness Award

    CSIR Modelling and Digital Science

Organizations

  • Engineering Council of South Africa (ECSA)

    Professional Enginer

    - Present

  • Institute of Electical and Electronics Engineers (IEEE)

    Senior Member

    - Present

  • Association for Computing Machinery (ACM)

    Member

    - Present

  • South African Institute of Electrical Engineers

    Member

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