Ms. Chisom Ogbuanya | Electronic | Best Researcher Award
Ms. Chisom Ogbuanya | Electronic | Lecturer at University of Nigeria at Nigeria
Ms. Chisom Ogbuanya is a highly accomplished academic and researcher in Electronic and Computer Engineering, with specialized expertise in pattern recognition, intelligence detection systems, machine learning, deep learning, and computer vision. She earned her Ph.D. in Control Science and Engineering from Jiangsu University, China, and her Master of Engineering in Digital and Computer Engineering from the University of Nigeria, Nsukka. Currently, she serves as a Lecturer II at the University of Nigeria, Nsukka, where she teaches undergraduate courses in physical electronics and artificial intelligence while actively conducting research in AI and medical imaging. Her research interests include multimodal medical image fusion, retinal biometric recognition, and optimization algorithms for neural network systems. Ms. Ogbuanya demonstrates advanced research skills, including data analysis, algorithm development, structural equation modeling, and computer vision implementation. She has contributed significantly to both national and international research projects and has published in high-impact journals and conference proceedings, receiving recognition for her contributions to AI and engineering education. Her work has been cited multiple times, reflecting her growing influence in the field. She has received awards and honors for research excellence, conference presentations, and contributions to student mentorship and academic development. Ms. Ogbuanya is actively engaged in leadership roles within academic communities, promotes collaborations, and participates in international conferences to advance research and knowledge exchange. Strengths include her robust publication record, interdisciplinary approach, technical expertise, and commitment to mentoring emerging engineers. Areas for further development include expanding global collaborations and translating research into industrial and clinical applications. Looking forward, she is poised to make substantial contributions to AI-driven medical imaging, intelligent systems, and optimization technologies. In conclusion, Ms. Chisom Ogbuanya exemplifies research excellence, innovation, and academic leadership, demonstrating outstanding potential for continued impact and advancement in the field of Electronic and Computer Engineering.
Profile: Google Scholar
Featured Publications
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Ogbuanya, C. E. (2021). Improved dimensionality reduction of various datasets using novel multiplicative factoring principal component analysis (MPCA). International Journal of Computer and Communication Engineering, 10(4), 85–95. 3 citations.
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Ogbuanya, C. E., Obayi, A., Larabi-Marie-Sainte, S., Saad, A. O., & Berriche, L. (2025). A hybrid optimization approach for accelerated multimodal medical image fusion. PLOS ONE, 20(7), e0324973. 1 citation.
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Ogbuanya, C. E., Ezika, I., & Iloanusi, O. (2018). Evaluating the impacts of pregnancy on retinal biometric recognition. Faculty of Engineering, University of Nigeria, Nsukka 1st International Conference Proceeding, 1.
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Asiegbu, N. C., Ahaneku, M. A., Chijindu, V. C., & Ogbuanya, C. E. (2024). Mutual coupling computation and measurement studies of a designed 2 x 2 MIMO microstrip patch antenna using element adjustment method. Conference Faculty of Engineering, Chukwuemeka Odumegwu Ojukwu University, Anambra State, Nigeria, Paper ID: ICEAPRID/2024/EEE/022.
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Ogbuefi, U., & Ogbuanya, C. E. (2025). The effects of sexist comments on the self-concept, diminished motivation and aggressive tendencies of girls in engineering: A narrative review. Journal for Family & Society Research, 4(1).
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Han, F., Mehta, S., Ling, Q. H., & Ogbuanya, C. E. (2024). Enhancement of multiobjective Darwinian particle swarm optimization for neural-network-based multimodal medical image fusion. Neural Approximated Variable-Order Fractional Calculus Multiobjective DPso Optimized Gradient Compass for Multimodal Medical Image Fusion.
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Ogbuanya, C. E. (2023). Advanced AI techniques for pattern recognition in intelligent detection systems. International Journal of Engineering and Computer Science Research, 15(3), 45–60.