Prof. Ebenezer Esenogho is a Professor and Head of the Centre for Artificial Intelligence and Multidisciplinary Innovation Studies at the University of South Africa, specializing in artificial intelligence, telecommunications, and emerging network technologies. He holds academic qualifications in computer engineering and advanced degrees in electronics and telecommunications engineering. His professional career spans teaching, research, and leadership roles across multiple universities, where he has contributed to curriculum development, postgraduate supervision, and multidisciplinary research initiatives. His research interests include machine learning, wireless communication systems, medical AI, and sustainable computing, with numerous peer-reviewed publications and conference contributions. He has received several competitive research grants and fellowships and holds a National Research Foundation rating, reflecting international recognition. He also serves in editorial and academic leadership capacities, supporting research development and collaboration.
Professional Profile
Education
Prof. Ebenezer Esenogho has a strong academic background in computer engineering, electronics, and telecommunications. He began with foundational training in computer engineering, developing core technical competencies in computing systems and digital technologies. He advanced to a bachelor’s degree that strengthened his analytical and engineering design skills, followed by a master’s degree focused on electronics and telecommunications engineering, where he gained deeper expertise in communication systems and signal processing. He later earned a doctoral degree in electronic engineering, with a research focus on advanced wireless networks and intelligent communication systems. His academic journey reflects a consistent progression toward specialization in cutting-edge technologies such as artificial intelligence and next-generation communication systems. This solid educational foundation has enabled him to integrate theoretical knowledge with practical innovation, preparing him for impactful research, teaching, and leadership in multidisciplinary and technology-driven environments.
Professional Experience
Prof. Esenogho has built a distinguished academic and research career across several reputable institutions, progressing from lecturer to senior academic and research leadership roles. He has contributed extensively to teaching undergraduate and postgraduate students, curriculum development, and supervision of advanced research projects. His leadership experience includes coordinating research programs, mentoring emerging scholars, and contributing to institutional strategic initiatives in technology and innovation. He has also been involved in international academic engagements and collaborative projects, supporting knowledge exchange and capacity building. In addition to teaching, he has played key roles in developing academic programs and strengthening research frameworks in engineering and artificial intelligence. His experience extends to multidisciplinary environments, where he has contributed to advancing digital transformation and innovation. Through these roles, he has demonstrated strong leadership, administrative capability, and commitment to academic excellence and research-driven impact.
Research Interest
Prof. Esenogho’s research interests lie at the intersection of artificial intelligence, machine learning, telecommunications, and interdisciplinary technological applications. His work focuses on intelligent wireless communication systems, advanced network optimization, and the integration of AI in solving real-world challenges. He has contributed to emerging areas such as deepfake detection, medical and healthcare AI, sustainable computing, and data-driven decision-making systems. His research also explores reinforcement learning, edge computing, and innovative approaches to improving network efficiency and reliability. He has authored and co-authored numerous peer-reviewed journal articles and conference papers, reflecting both depth and breadth in his research contributions. His interdisciplinary approach allows him to address complex societal and technological challenges, bridging gaps between theory and application. His work continues to contribute to advancements in next-generation communication technologies and AI-driven solutions across diverse sectors.
Award and Honor
Prof. Esenogho has received multiple recognitions that reflect his excellence in research, teaching, and academic leadership. He is a recipient of competitive research grants, fellowships, and scholarships awarded by academic institutions and industry partners, highlighting the impact and relevance of his work. He holds a National Research Foundation rating, signifying international recognition for his research contributions and scholarly influence. His achievements also include awards for outstanding research presentations and contributions to academic conferences. In addition, he has participated in international research collaborations and academic exchange initiatives that strengthen global partnerships. He serves in editorial and review capacities for scholarly publications, contributing to the advancement of knowledge in his field. His involvement in academic committees and research strategy groups further demonstrates his leadership and commitment to advancing innovation and research excellence within the global academic community.
Conclusion
Prof. Ebenezer Esenogho stands out as a dedicated scholar, researcher, and academic leader with a strong commitment to advancing knowledge in artificial intelligence and telecommunications. His solid educational background, extensive professional experience, and impactful research contributions position him as a key figure in multidisciplinary innovation. Through his work, he has consistently demonstrated the ability to address complex technological challenges and contribute meaningful solutions that benefit both academia and society. His recognition through awards, research funding, and international collaborations highlights his influence and growing global reputation. Beyond his achievements, he remains committed to mentoring the next generation of researchers and fostering collaborative research environments. His career reflects a balance of academic excellence, leadership, and innovation, making him a highly deserving candidate for recognition and continued contribution to the advancement of science, technology, and education on a global scale.
Publications Top Noted
- A neural network ensemble with feature engineering for improved credit card fraud detection
Author: E. Esenogho, I. D. Mienye, T. G. Swart, K. Aruleba, G. Obaido
Year: 2022
Citations: 421
- Integrating artificial intelligence Internet of Things and 5G for next-generation smartgrid: A survey of trends challenges and prospect
Author: E. Esenogho, K. Djouani, A. M. Kurien
Year: 2022
Citations: 307
- Telemedicine: a survey of telecommunication technologies, developments, and challenges
Author: C. O. Alenoghena, H. O. Ohize, A. O. Adejo, A. J. Onumanyi, E. E. Ohihoin, et al.
Year: 2023
Citations: 187
- A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease
Author: I. D. M. Mienye, S. A. Ebiaredoh-Mienye, E. Esenogho, T. G. Swart
Year: 2022
Citations: 139
- Crypto hash algorithm-based blockchain technology for managing decentralized ledger database in oil and gas industry
Author: L. A. Ajao, J. Agajo, E. A. Adedokun, L. Karngong, et al.
Year: 2019
Citations: 129
- Blockchain-based security mechanism for the medical data at fog computing architecture of internet of things
Author: D. Ngabo, D. Wang, C. Iwendi, J. H. Anajemba, L. A. Ajao, C. Biamba, et al.
Year: 2021
Citations: 114
- A proposed model for card fraud detection based on Catboost and deep neural network
Author: N. Nguyen, T. Duong, T. Chau, V. H. Nguyen, T. Trinh, D. Tran, T. Ho, et al.
Year: 2022
Citations: 111
- Toward integrating intelligence and programmability in open radio access networks: A comprehensive survey
Author: A. Arnaz, J. Lipman, M. Abolhasan, M. Hiltunen, et al.
Year: 2022
Citations: 97
- An interpretable machine learning approach for hepatitis B diagnosis
Author: G. Obaido, B. Ogbuokiri, T. G. Swart, N. Ayawei, S. M. Kasongo, K. Aruleba, et al.
Year: 2022
Citations: 89