Mr. Osheyor Gidiagba | Machine Learning | Best Researcher Award 

Mr. Osheyor Gidiagba | Machine Learning | University of Johannesburg | South Africa

Mr. Osheyor Joachim Gidiagba is an accomplished researcher and engineer whose expertise lies in Mechanical and Industrial Engineering, currently pursuing his Ph.D. at the University of Johannesburg, South Africa, where his research focuses on developing a hybrid model combining Machine Learning and Multi-Criteria Decision-Making (MCDM) to enhance sustainable supplier selection and performance optimization in industrial systems. His academic foundation includes a Master’s in Applied Science Mechanics (Cum Laude) and a Bachelor’s degree in Mechanical Engineering (First Class Honors), underscoring his consistent academic excellence and technical depth. Professionally, Mr. Gidiagba has worked as an Asset Management Engineer at the Ministry of Power and Domestic Water Development, Awka, Nigeria, where he successfully supervised and implemented multiple infrastructure projects, including the installation of electrical transformers and overhead water tanks across several communities. His work emphasized system reliability, supplier evaluation, and maintenance optimization, demonstrating his ability to translate research into impactful real-world engineering applications. His research interests encompass machine learning applications in decision-making, sustainable engineering systems, reliability-centered maintenance, industrial data analytics, and asset integrity management. His technical skills include data modeling, predictive maintenance, statistical analysis, multi-criteria decision-making, and system reliability evaluation, supported by proficiency in computational tools and industrial analytics. Mr. Gidiagba has published 7 Scopus-indexed research papers, accumulating 30 citations with an h-index of 3, reflecting his growing scholarly influence. His key contributions, such as applying fuzzy logic, TOPSIS, and hybrid decision models in sustainable industrial practices, highlight his innovative approach to bridging the gap between artificial intelligence and engineering sustainability. He has also engaged in international research collaborations that focus on improving decision-support systems and operational efficiency in industrial and mining sectors.