Prof. Gabriel Kabanda the Most Notable and Top Distinguished Full Professor of Computer Science and Information Systems who is an exceptional strategist, seasoned academician, expert evaluator, shrewd business consultant and leader talented with competences for dealing with business and with people. He is a Visiting Professor of Machine Learning at Woxsen University, Hyderabad, India; an Adjunct Professor of Cybersecurity at California State University, Chico (USA); and Professor of Applied Business Informatics (BIS/MIS) at the University of Zimbabwe Business School since January 2000. Gabriel is a Board Member of Board of Trustees of Crown University International Chartered Inc.; Vice Chairman of the Leadership Advisory Board of the IT Governance & Cybersecurity Institute (New York); Fellow of the African Scientific Institute (USA); Fellow and Secretary General of Zimbabwe Academy of Sciences; and Secretary General of the Africa-Asia Dialogue Network. Prof. Kabanda is the Former Pro Vice Chancellor (Research, Innovation & Enterprise Development) at Zimbabwe Open University for 13 years; and is currently an External Examiner of Computer Science programmes at the National University of Science and Technology, Zimbabwe; and the Information Systems programmes at the Midlands State University, ZIMBABWE. He is the Editor of the Journal of Signal and Information Processing, Scientific Research Publishing Inc. (USA); an Editorial Board member of the LC International Journal on STEM; and an Editorial Board Member of Africa Journal of Leadership & Governance.

Professor Kabanda was appointed jointly by the International Science Council (ISC), the World Federation of Engineering Organizations (WFEO) and the InterAcademy Partnership (IAP) in March 2019 to review the 2019 Global Sustainable Development Report. Gabriel received an international award on Outstanding Contributions to Education, the Golden Academic Excellence and Professional Achievement award in 2013 in Malaysia at the World Marketing Summit. He was also profiled by the South African Department of Science and Technology as one of Africa's accomplished scientists and outstanding researchers in the Africa Day commemoration newsletter Science News of 25th May, 2017, titled "ICT guru loves to educate". He was awarded the world-wide honour of Who's Who of Professionals in 1997. Gabriel is technically-savvy with outstanding relationship building, training and presentation skills.

He holds a Post-Doctorate of Science, Doctor of Science (D.Sc.) in Computer Science from Atlantic International University (USA), a Ph.D. degree in Computer Science (California, PWU), Master of Science in Computer Science (Swansea University, United Kingdom), B.Sc. in Mathematics and Physics (University of Zimbabwe), Gold Diploma in Computer Programming (London), a Certificate in Applied Meteorology (Reading, UK), a Certificate in E-Moderation (University of Cape Town) and a Certificate in Management of Higher Education Institutions (Galilee International Management Institute, Israel). Professor Kabanda has published 107 publications (11 books, 8 book chapters, 8 edited volumes and 80 research papers published in international refereed journals) and supervised 11 PhD candidates in Information Technology/ Computer Science/ Information Systems and over 100 Masters dissertations.

Title of Speech: "An Evaluation of Dimensionality Reduction Algorithms for Face Recognition in Machine Learning Paradigms"

Facial recognition is a way of identifying or confirming an individual’s identity using their face. A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against an authentic repository of faces or Eigenfaces. Algorithmic performance and detection accuracy affects the recognition stage in Machine Learning (ML) and Big Data Analytics (BDA) systems. Dimensionality reduction is a type of unsupervised learning for which input is images of higher-dimensional data and these images are represented with a lower-dimensional space such as the Principal Component Analysis (PCA) space. The purpose of the research paper is to evaluate the performance of Dimensionality Reduction Algorithms for face recognition using different approaches of ML. The key objective is to choose an optimum set of features of lower dimensionality to improve classification accuracy using both feature extraction and feature selection methods. The research uses the Interpretivist Paradigm characterised by a subjectivist epistemology, relativist ontology, naturalist methodology, and a balanced axiology. The quantitative methodology with an experimental research design was used. The results of the experiment show that only selecting the top M eigenfaces reduces the dimensionality of the data, and that too few eigenfaces results in too much information loss, and hence less discrimination between faces. Increasing the number of features will not always improve classification accuracy. In practice, the inclusion of more features might actually lead to worse performance. The number of training examples required increases exponentially with dimensionality d (i.e., kd). The performance of the Dimensioanlity Reduction Algorithm is compared against the Clustering Algorithm, Bayesian Algorithm, Genetic Algorithm, Reinforcement Q-Learning Algorithm and Reinforcement - A3C Algorithm. The outcome of the research makes significant value-adding contributions to the future of advances in Big Data Analytics and Machine Learning.

Keywords: Dimensionality Reduction Algorithms, Cybersecurity, Artificial Intelligence, Machine Learning, Deep Learning, Big Data Analytics, Facial Recognition.