Innovative Research Award
Oluwole Famoriji
Federal University of Technology and Environmental Sciences, Iyin-Ekiti, Nigeria
Oluwole Famoriji is an engineering researcher whose scholarly work emphasizes power systems, renewable energy integration, antenna technologies, electromagnetic analysis, and machine learning applications for intelligent electrical engineering. His publication portfolio demonstrates sustained contributions to advanced engineering research while addressing practical challenges involving energy optimization, communication systems, and computational prediction methods. The combination of peer-reviewed publications, measurable citation impact, and interdisciplinary collaborations reflects continued academic engagement within internationally recognized journals and conferences.[1]
Abstract
The academic profile of Oluwole Famoriji illustrates continued research activity in electrical and electronic engineering with a strong emphasis on intelligent systems, renewable energy, machine learning, and electromagnetic modelling. His work integrates theoretical development with engineering applications to improve prediction accuracy, voltage regulation, wireless communication performance, and sustainable energy management. The available publication record and citation indicators demonstrate consistent scientific productivity and international collaboration across multiple engineering disciplines.[2]
Keywords
- Engineering
- Machine Learning
- Power Systems
- Renewable Energy
- Electromagnetic Radiation
- Antenna Arrays
- Artificial Intelligence
Introduction
Modern engineering increasingly depends on intelligent computational approaches to address energy efficiency, communication reliability, and infrastructure resilience. Oluwole Famoriji’s research aligns with these priorities by combining advanced analytical techniques with practical engineering solutions. His investigations contribute to renewable energy coordination, machine learning prediction, electromagnetic modelling, and wireless system optimization, supporting technological development within rapidly evolving engineering environments.[3]
Research Profile
The researcher has produced 64 indexed scholarly documents with 472 citations and an h-index of 15, reflecting measurable scientific influence. His publications span internationally recognized journals including IEEE Access, Applied Sciences, Energies, and other peer-reviewed engineering outlets. Research themes consistently focus on machine learning, intelligent power systems, antenna technologies, and computational engineering methods.[4]
Research Contributions
Major contributions include intelligent photovoltaic energy coordination under uncertainty, machine learning estimation of electromagnetic radiation near 5G infrastructure, multiclass support vector machine methods for direction-of-arrival estimation, systematic reviews of artificial intelligence in power system prediction, and structural electromagnetic modelling for millimeter-wave antenna performance. Collectively these studies advance engineering knowledge through computational innovation and practical system analysis.[5]
Publications
- An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties (2025).
- Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations (2025).
- A Multiclass Support Vector Machine Based Direction-of-Arrival Estimation Technique using Spherical Antenna Array (2024).
- Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review (2024).
- Modeling of Coupled Structural Electromagnetic Statistical Concept for Examining Performance Sensitivity of Antenna Array to Distortion at Millimeter-Wave (2024).
Research Impact
The research demonstrates relevance to sustainable energy, smart electrical networks, telecommunications, and intelligent engineering systems. Citation performance and publication consistency indicate recognition by the academic community. The interdisciplinary nature of the work supports technological advancement through data-driven engineering methodologies and computational optimization.[6]
Award Suitability
Based on the documented scholarly record, Oluwole Famoriji demonstrates qualities associated with the Innovative Research Award, including sustained publication activity, interdisciplinary engineering research, international collaboration, and measurable scientific impact. His contributions to intelligent energy systems and computational engineering align with the objectives of recognizing innovation that advances engineering knowledge and practical applications.
Conclusion
The academic achievements of Oluwole Famoriji reflect a balanced combination of research productivity, engineering innovation, and scholarly influence. His investigations in renewable energy, machine learning, electromagnetic systems, and intelligent power networks contribute to contemporary engineering research while supporting future developments in sustainable and computational technologies.
External Links
References
- Scopus Author Profile. Research metrics and indexed publications. https://www.scopus.com/pages/authors/57193136350
- Famoriji O.J. et al. An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties. Energies (2025). DOI: 10.3390/en18133481
- Famoriji O.J. Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations. Applied Sciences (2025). DOI: 10.3390/app15137302
- Famoriji O.J. A Multiclass Support Vector Machine Based Direction-of-Arrival Estimation Technique using Spherical Antenna Array. Indonesian Journal of Electrical Engineering and Informatics (2024).
- Makanju T.D., Shongwe T., Famoriji O.J. Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review. IEEE Access (2024). DOI: 10.1109/ACCESS.2024.3397676
- Famoriji O.J. Modeling of Coupled Structural Electromagnetic Statistical Concept for Examining Performance Sensitivity of Antenna Array to Distortion at Millimeter-Wave. Applied Sciences (2024). DOI: 10.3390/app14167111