Prof. Ebenezer Esenogho | University of South Africa | South Africa
Dr. Ebenezer Esenogho is a distinguished academic and researcher with nearly two decades of experience in engineering, telecommunications, and AI. He holds multiple degrees, including a Ph.D. in Electronic Engineering, and has worked globally in South Africa, Uganda, Botswana, and Nigeria 🌍. As a professor and research lead, he focuses on cutting-edge research in 5G networks, AI, IoT, cybersecurity, and more 💡. He has received numerous awards for his research excellence 🏆 and has mentored many students and faculty, inspiring the next generation of engineers 👩🏫👨🏫.
Professional Profile:
ORCID
Google Scholar
Suitability for the Best Researcher Award
Dr. Ebenezer Esenogho is highly deserving of the Best Researcher Award due to his outstanding contributions to the fields of engineering, telecommunications, and AI over nearly two decades. His qualifications, including a Ph.D. in Electronic Engineering, and extensive international experience in countries like South Africa, Uganda, Botswana, and Nigeria, underscore his global perspective and expertise in these rapidly evolving areas.
Education & Experience
- Diploma in Computer Engineering (2002/2003) 🖥️
- BEng in Computer Engineering (2007/2008) 🎓
- MEng in Telecommunications Engineering (2010/2011) 📡
- Ph.D. in Electronic Engineering (University of KwaZulu-Natal, South Africa) 🎓
- Senior Lecturer, University of Benin (2007-2013) 👨🏫
- GES Post-Doctoral Research Fellowship, University of Johannesburg (2017-2020) 🏅
- Associate Professor, Kampala International University, Uganda 🌍
- Expatriate Senior Lecturer, University of Botswana 🤝
- Distinguished Research Professor, University of South Africa (UNISA) 🌟
Professional Development
Dr. Esenogho has continually advanced his career through prestigious fellowships and global academic engagements. He has contributed significantly to the research landscape with post-doctoral work in the Fourth Industrial Revolution (4IR) at the University of Johannesburg 💼. As a research lead at UNISA, he drives innovations in AI and smart systems 🤖. His mentorship of postgraduate students and junior faculty has helped develop new research pathways in telecommunications and AI 🔍. Dr. Esenogho actively contributes to international conferences and serves as a reviewer for academic journals 📚. His leadership in research and academia has inspired excellence across continents 🌎.
Research Focus
Dr. Esenogho’s research is at the intersection of telecommunications, AI, and smart systems, with a strong emphasis on next-generation networks and technologies. He explores 5G and cognitive radio networks 📶, smart grid and IoT systems 🔌, and the role of AI/ML in enhancing network security and efficiency 🔐. His work on wireless sensor networks, mobile/cloud computing, and big data is transforming how we understand and use connectivity 🌐. Dr. Esenogho is pioneering research in these areas, helping shape the future of communication technologies 📡. His commitment to cutting-edge innovation continues to push boundaries and solve complex engineering problems 🔬.
Awards & Honors
- CEPS/Eskom HVDC Fellowship (2013, 2014) 🏅
- J.W. Nelson Award (2015) 🏆
- GES Post-Doctoral Fellowship (2017–2020) 🎓
- NRF C-rating for researchers of international repute 🌟
- Best Oral Paper Presentation Award, UJ Postdoctoral Fellowship Conference (2019) 🏅
- F’SAIT SARChI Chair Award (2021) 🏅
- Member of IEEE, SAIEE, and other professional bodies 🤝
Publication Top Notes:
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🤖 A Neural Network Ensemble with Feature Engineering for Improved Credit Card Fraud Detection 👥 Cited by: 218
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🌐 Integrating AI, IoT, and 5G for Next-Generation Smartgrid: A Survey of Trends, Challenges, and Prospects – 👥 Cited by: 165
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💉 A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease – Bioengineering 👥 Cited by: 73
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📡 Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey 👥 Cited by: 59
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💳 A Proposed Model for Card Fraud Detection Based on CatBoost and Deep Neural Network 👥 Cited by: 57
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🧑⚕️ An Interpretable Machine Learning Approach for Hepatitis B Diagnosis 👥 Cited by: 53