ICT4DA 2019 is the 2nd international conference that aims
to bring together researchers, engineers, developers,
and practitioners from the academia and the industry to present
and discuss their research work in the area of ICT for Development.
This conference seeks to facilitate the transfer of experience, adaptation
of methods, and where possible, foster collaboration among different groups.
The topics of interest cover all aspects of ICT towards the socioeconomic development.
The Conference is categorized under five tracks.
Machine learning has been successfully used for prediction analytics. Most research on machine learning assumes that the attributes of training and tests instances are not only completely specified but are also free from noise. Real world Big data, however, often suffer from corruptions or noise but not always known. This is the heart of information-based models. However, blindly applying such machine learning techniques to noisy evaluation data may fail to make very good or perfect predictions. Unfortunately, despite extensive research over the last decades, the impact of poor quality of data especially noise on the accuracy of prediction models has attracted less attention, even though it remains a significant problem for many. This talk investigates the robustness of five machine learning supervised algorithms to noisy environment. In particular, we show that when noise is added to a Big real-world domain, a significant and disproportionate number of total errors are contributed by class noise compared to attribute noise; thus, in the presence of noise, it is noise on the class variable that are responsible for the poor predictive accuracy of the learning concept.
Bhekisipho Twala is the Director School of Engineering and Professor in Artificial Intelligence and Data Science with the Department of Electrical and Mining Engineering at the University of South Africa. Before then he was the Director of the Institute for Intelligent Systems at the University of Johannesburg (UJ) and was also Head of the Electrical and Electronic Engineering Science Department at UJ. Before then, Prof Twala was a Principal Research Scientist at the Council for Science and Industrial Research (CSIR) within the Modelling and Digital Science Unit (where he is currently an Advisor). His research work at the CSIR involved an expanded swath of data, analytics, and optimization approaches that bring a complete understanding of digital customer experiences. Prof. Twala was also a post-doctoral researcher at Brunel University in the UK, mainly focussing on empirical software engineering research and looking at data quality issues in software engineering. Currently, his work involves promoting and conducting research in artificial intelligence within the electrical and mining engineering science fields and developing novel and innovative solutions to key research problems in these areas. Prof Bhekisipho Twala earned his Bachelor's degree in Economics and Statistics from the University of Swaziland in 1993; followed by a Post Graduate Certificate in Statistics, and an MSc in Statistics from Southampton University (UK) in 1995. He further graduated with a PhD in Machine Learning and Statistical Science from the Open University (UK) in 2005. His broad research interests include image and signal processing, multivariate statistics, applied and theoretical machine learning, knowledge discovery and reasoning with uncertainty, and the interface between statistics and computing. Also, he has particular interests in applications in finance, medicine, psychology, software engineering and most recently in robotics and has published over 150 scientific papers. Prof. Bhekisipho Twala has a wide-ranging work experience in organizations ranging from banks, through universities, to governments. He is the recipient of the 2016 NSTF/SOUTH32 TW Kambule (Research & Outputs) award. Prof Twala is also Editor-in-Chief of the International Journal of Semantic and Infrastructure Services; Regional Editor of the International Journal of Big Data Intelligence; an Associate Editor of the Intelligent Data Analysis Journal, Journal of Computers, International Journal of Advanced Information Science and Technology, International Journal of Big Data Intelligence, Journal of Image and Data Fusion, Journal of Information Processing Systems, International Journal of Internet of Things & Its Applications. Prof Twala is also a registered professional scientist and a fellow of the Royal Statistical Society. Other professional memberships include the Association for Computing Machinery (ACM); the Chartered Institute of Logistics and Transport in South Africa (CITSA), Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), member of the International Association of Engineers (IAENG); South African Council for Automation; and International Federation of Automatic Control (IFAC).