Dr. Mlungisi Duma’s diverse contributions to the fields of artificial intelligence and evolutionary computation, along with his leadership in both academia and industry, make him highly suitable for the ‘Research for Best Researcher Award’. His ability to bridge the gap between theoretical research and real-world applications, coupled with his strong publication record and professional recognition, demonstrate his excellence as a researcher. He is well-positioned to receive this award, as his work contributes significantly to advancing the field of artificial intelligence.
Education
Dr. Mlungisi Duma holds a Master’s degree in Computer Science and a PhD in Electronic and Electrical Engineering, both from the University of Johannesburg. His PhD specialization in Artificial Intelligence equipped him with expertise in machine learning, evolutionary computation, and optimization algorithms. Throughout his academic journey, Dr. Duma has actively engaged in cutting-edge research projects, collaborating with renowned academics and professionals. His commitment to education has been reflected in his role as a judge and reviewer at various academic institutions and scientific journals. As a Senior Member of IEEE and ACM, he continues to contribute to the advancement of AI technologies. Dr. Duma’s academic background is a testament to his passion for innovation, making significant contributions to the field of Artificial Intelligence.
Experience
With 19 years of experience in the IT industry, Dr. Mlungisi Duma has held various technical and leadership roles. For nine years, he worked as a software developer, honing his skills in coding, system architecture, and application design. Following this, he spent a decade as a Development Manager and Software Architect at First National Bank, where he led teams responsible for developing and maintaining ATM systems. Currently, he is a Senior Researcher and Development Manager at the University of Johannesburg, where he leads AI research initiatives. His extensive experience spans both academic and industry settings, bridging the gap between theoretical research and practical applications. Dr. Duma’s ability to manage multidisciplinary teams and deliver innovative solutions reflects his leadership and technical acumen.
Awards and Honors
Dr. Mlungisi Duma has received several prestigious awards and honors for his contributions to Artificial Intelligence and software development. He is a Senior Member of IEEE and ACM, recognizing his influence in the academic and professional communities. Dr. Duma’s work has also been recognized through his membership in the Golden Key International Honour Society, a distinction for top-performing academics globally. He has served as a judge at the University of Johannesburg’s Academy of Computer Science project day and has been a frequent reviewer for renowned journals, including IEEE Access, Applied Soft Computing, and the Journal of Mathematical Problems in Engineering. These honors highlight Dr. Duma’s commitment to academic excellence and his significant contributions to advancing AI technologies.
Research Focus
Dr. Mlungisi Duma’s research focuses on Artificial Intelligence, particularly in the fields of machine learning, evolutionary computation, and optimization algorithms. He has contributed extensively to developing novel AI models for predictive modeling, control parameter optimization, and feature selection. His recent work includes optimizing ant colony algorithms and using artificial immune systems for collaborative filtering in recommender systems. Dr. Duma’s research is not only theoretical but also has practical applications, as demonstrated by his consultancy projects for First National Bank, where AI-driven solutions are implemented in ATM systems. He has published widely in top-tier journals, reflecting his thought leadership in AI and its applications in diverse sectors. His current projects aim to enhance AI’s role in automation and decision-making across industries.
Publication Top Notes
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Sparseness reduction in collaborative filtering using a nearest neighbour artificial immune system with genetic algorithms
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Optimising latent features using artificial immune system in collaborative filtering for recommender systems
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Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm
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Classification with missing data using multi-layered artificial immune systems
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Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection