Dr. Bhekisipho  Twala
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Dr. Bhekisipho Twala

Associate Professor
University of Johannesburg, South Africa


Highest Degree
Ph.D. in Artificial Intelligence from Milton Keynes Univeristy, UK

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Biography

Dr. Bhekisipho Twala is currently working as Director at Institute for Intelligent Systems, University of Johannesburg, Johannesburg, South Africa. He has completed his Ph.D. in Artificial Intelligence from Milton Keynes University, UK. Previously he was appointed as Advisor and Principal Research Scientist at Council of Scientific and Industrial Research, Manager at Statistics South Africa, Research Fellow at Brunel University, and Bournemouth University, Consultant at London Imperial College, and Research Assistant at Open University. He is having 20 years’ experience in putting Mathematics to Scientific use in the form of Data Comparison, Inference, Analysis, and presentation to Design, Collect, and interpret Data Experiments surrounding the fields of Transport, Medical, Artificial Intelligence, Software Engineering, and Robotics and most recently in Electrical and Electronic Science Engineering. He has track record of publications in workshops, conferences and journals and the ability to communicate scientific ideas to an informed lay audience. His intention is to continue exploring problems in various fields by applying artificial intelligence (machine learning) and statistical sciences and be committed to the highest levels of professional and personal excellence. He has published 25 research articles in journals, 78 refereed conference proceedings, and 5 book chapters contributed as author/co-author. He also supervised number of PhD, and Masters level students. He is professional member of Royal Statistical Society, Association of Computing Machinery (ACM), Chartered Institute of Transport South Africa (CITSA), Institute of Electrical and Electronics Engineers (IEEE), International Association of Engineers (IAENG), and South African Council for Automation and Control. His main area of research interest focuses on Image and Signal Processing, Intelligent Systems, Knowledge Discovery and Reasoning under Uncertainty, Sensor Data Fusion and Inference, and Theoretical and Applied Research in Machine Learning.

Area of Interest:

Computer Sciences
100%
Artificial Intelligence
62%
Systems Engineering
90%
Data Analysis
75%
Pattern Recognition
55%

Research Publications in Numbers

Books
0
Chapters
0
Articles
0
Abstracts
0

Selected Publications

  1. Twala, B., 2015. Predicting software faults in large space systems using incomplete data. Frontiers Aerospace Eng. (In Press). .
  2. Twala, B., 2015. Ensemble classifiers for target tracking using noisy data. J. Comput. Sci. (In Press). .
  3. Mia, M. and B. Twala, 2015. Target tracking using state-of-the-art supervised classification methods. Int. J. Adv. Res. Artif. Intelli., Vol. 1 (In Press). .
  4. Twala, B., 2014. Reasoning with noisy software engineering data. Applied Artif. Intelli., 28: 533-554.
  5. Twala, B., 2014. Extracting grey relational systems from incomplete road traffic accidents data: The case of Gauteng province in South Africa. Expert Syst., 31: 220-231.
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  6. Hasan, A., B. Twala, K. Ouahada and T. Marwala, 2014. Energy usage optimisation in South African mines. Arch. Mining Sci., 59: 53-69.
  7. Twala, B., 2013. Impact of noise on credit risk prediction: Does data quality really matter? Intelli. Data Anal., 17: 1115-1134.
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  8. Molale, P., B. Twala and S.M. Seeletse, 2013. Fingerprint prediction using statistical and machine learning methods. ICIC Express Lett., 7: 311-316.
  9. Duma, M., T. Marwala, B. Twala and F. Nelwamondo, 2013. Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm. Applied Soft Comput., 13: 4461-4480.
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  10. Twala, B., 2012. Handling Out-of-Sequence Measurements Using Copulas. In: Mobile Intelligent Autonomous Systems, Raol, J. and A. Gopal (Eds.). Taylor & Francis, USA.
  11. Twala, B., 2012. Dancing with dirty road traffic accidents data: The case of Gauteng Province in South Africa. J. Transport. Safety Security, 4: 323-335.
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  12. Paul, S., B. Twala and T. Marwala, 2012. Organizational adaptation to complexity: A study of the South African insurance market as a complex adaptive system through statistical risk analysis. Syst. Eng. Procedia, 4: 1-8.
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  13. Duma, M., B. Twala, F. Nelwamondo and T. Marwala, 2012. Predictive modelling with missing data using automated relevance discrimination ensemble a comparative study. Applied Artif. Intelli., 26: 967-984.
  14. Duma, M., B. Twala, F. Nelwamondo and T. Marwala, 2012. Predictive modeling with missing data using an automatic relevance determination ensemble: A comparative study. Applied Artif. Intelli., 26: 967-984.
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  15. Twala, B., 2011. Predicting software faults in large space systems using machine learning techniques. Defence Sci. J., 61: 306-316.
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  16. Twala, B., 2010. Multiple classifier application to credit risk assessment. Expert Syst. Appl. Int. J., 37: 3326-3336.
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  17. Twala, B., 2010. Handling out-of-sequence data: Kalman filter methods or statistical imputation?. Defence Sci. J., 60: 87-99.
  18. Twala, B., 2010. Handling Out-of-sequence data using model-based statistical imputation. Electronic Lett., 46: 302-304.
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  19. Twala, B. and M. Phorah, 2010. Predicting incomplete gene microarray data with the use of supervised learning algorithms. Pattern Recognition Lett., 31: 2061-2069.
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  20. Twala, B. and M. Cartwright, 2010. Ensemble imputation methods for missing software engineering data. Intell. Data Anal., 14: 1-33.
  21. Twala, B., 2009. Comparison of techniques for handling incomplete data using decision trees. Applied Artif. Intell., 23: 373-405.
  22. Twala, B., 2009. Combining classifiers for credit risk prediction. J. Syst. Sci. Syst. Eng., 18: 292-311.
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  23. Twala, B.E.T.H., M.C. Jones and D.J. Hand, 2008. Good methods for coping with missing data in decision trees. Pattern Recognition Lett., 29: 950-956.
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  24. Twala, B., M. Cartwright and M. Shepperd, 2007. Applying Rule Induction in Software Prediction. In: Advances in Machine Learning Applications in Software Engineering, Zhang, D. and J.P. Tsai (Eds.). Idea Group Inc., Chicago, USA., pp: 265-286.
  25. Song, Q. and M. Shepperd, 2007. A new imputation method for small software project data sets. J. Syst. Software, 80: 51-62.
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