Imanthi Subasinghe | Electronics | Women Researcher Award

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Mrs. Imanthi Subasinghe | Electronics | Women Researcher Award 

Mrs. Imanthi Subasinghe | Electronics | Higher Degree Researcher at The Queensland University of Technology | Australia

Mrs. Imanthi Subasinghe is a dedicated researcher and engineer specializing in Electrical and Power Systems Engineering, with expertise in smart grids, renewable energy systems, artificial intelligence applications, and computational modeling for power distribution networks. She holds advanced degrees in Electrical Engineering, including a research-intensive qualification from Clemson University, where she developed methodologies for estimating electric vehicle grid interfaces, power dispatch estimation, and voltage stability enhancement using digital twin and cellular computational networks. Mrs. Subasinghe has contributed significantly to collaborative research at the University of Moratuwa and the University of Jaffna, focusing on renewable energy integration, low-voltage congestion management, SCADA-based control systems, and digital signal processing applications in power systems. Her professional experience extends to industrial collaborations with Resource Management Associates Pvt. Ltd, where she participated in national projects on electrical transmission and distribution loss targets and solar system feasibility for Sri Lanka, contributing to energy efficiency and sustainability initiatives. Her research interests encompass power system optimization, electric vehicle integration, data-driven modeling, and AI-assisted decision-making in energy networks. Mrs. Subasinghe’s research skills include proficiency in simulation tools such as MATLAB, AutoCAD, and advanced AI algorithms, alongside a solid foundation in computational intelligence, control systems, and data analytics. She is an active member of IEEE, Power Globe Community, Institution of Engineers Sri Lanka (IESL), and ResearchGate, reflecting her engagement with global professional communities. Her academic excellence and technical innovation have earned her recognition within both academic and industrial research circles. In conclusion, Mrs. Imanthi Subasinghe continues to exemplify excellence in sustainable power research, emerging as a future leader in advancing digital transformation, renewable energy resilience, and AI-based engineering solutions for global energy systems.

Profile: Google scholar

Featured Publications

  1. Subasinghe, I. K., Venayagamoorthy, G. K., & Naidoo, R. (2024). Power dispatch estimation from electric vehicles in a distribution system. 7th International Conference on Electric Power and Energy Conversion, 1 citation.

  2. Subasinghe, I. K., & Venayagamoorthy, G. K. (2024). A methodology for estimating allocation of electric vehicle grid interfaces in a distribution system. IEEE PES/IAS PowerAfrica Conference, 1 citation.

  3. Hansika, T., Subasinghe, S. M. I. K., Ahmed, M. S. I., & Weesinghe, P. (2021). Electrical transmission and distribution loss targets for Sri Lanka 2021–2025. SLEMA Journal, 1 citation.

  4. Keerthinathan, P., Subasinghe, I. K., Krishnakumar, T., & Ariyanayagam, A. (2025). Prediction of thermal response of burning outdoor vegetation using UAS-based remote sensing and artificial intelligence. Remote Sensing, 1 citation.

  5. Subasinghe, I. K., & Venayagamoorthy, G. K. (2023). Voltage stability analysis using a computational cellular network with PMU data. IEEE Transactions on Smart Grid, 2 citations.

  6. Subasinghe, I. K., & Venayagamoorthy, G. K. (2023). Enhancing power system stability through digital twin-based voltage monitoring and prediction. International Journal of Electrical Power & Energy Systems, 3 citations.

  7. Subasinghe, I. K., & Krishnakumar, T. (2022). Transmission network topology processing graph generation and visualization using particle swarm optimization. Energy Informatics Journal, 2 citations.

Chisom Ogbuanya | Electronic | Best Researcher Award

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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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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).

  6. 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.

  7. 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.