Innovative Research Award

Asma Mahgoub
Qatar University,Qatar

Asma Mahgoub is affiliated with Qatar University and has developed an emerging research profile in engineering with particular emphasis on semantic communication, artificial intelligence, machine learning, image understanding, and next-generation wireless systems. Her scholarly publications investigate methods that improve communication efficiency while maintaining semantic fidelity across modern digital networks.[1]

Abstract

This article summarizes the academic profile of Asma Mahgoub in recognition of her nomination for the Innovative Research Award. Her research combines semantic communication, explainable artificial intelligence, image captioning, transformer models, and intelligent networking to improve data transmission efficiency and communication quality. The body of work demonstrates interdisciplinary engineering research addressing future communication infrastructures, including 6G and edge intelligence, while contributing practical methodologies for semantic-aware information exchange.[2]

Keywords

Semantic Communication, Engineering, Artificial Intelligence, Transformer Models, Image Captioning, Edge Learning, 6G Networks, Explainable AI, Deep Learning, Machine Learning.

Introduction

Modern communication systems increasingly focus on semantic information rather than conventional bit-level transmission. This paradigm supports efficient utilization of bandwidth while preserving contextual meaning. Asma Mahgoub’s publications contribute to this evolving discipline by integrating natural language processing, vision-language models, and engineering optimization into semantic communication frameworks suitable for intelligent wireless environments.[3]

Research Profile

According to the provided scholarly indicators, the researcher has authored 11 indexed publications with 106 citations and an h-index of 4. The publication record reflects consistent engagement in emerging engineering topics including semantic text communication, image semantic transmission, explainable metrics, BERT embeddings, and transformer-based communication architectures. These contributions indicate sustained participation in internationally recognized engineering research.[4]

Research Contributions

• Advanced transformer-based semantic communication systems.
• Developed explainable metrics for semantic image communication.
• Integrated image captioning with intelligent communication models.
• Investigated semantic communication for future 6G and edge learning platforms.
• Applied BERT embeddings to improve text semantic transmission.

Publications

• Document-Level Transformer-Based Text Semantic Communication System (2026).
• Efficient Explainable Metric for Semantic Communication of Images Using Image Captioning (2026).
• Metaverse Unbound: Semantic Communication, 6G and Edge Learning (2025).
• Semantic Communication of Images Using Image Generation and Image Captioning Models (2025).
• On Using BERT Embeddings for Text Semantic Communication (2024).

Research Impact

The published work contributes to efficient information exchange in intelligent communication systems through semantic-aware methodologies. Research on explainability, multimodal learning, and transformer architectures supports future developments in engineering applications including smart networks, autonomous systems, and next-generation wireless communication technologies.[5]

Award Suitability

The Innovative Research Award recognizes researchers demonstrating originality, scholarly quality, and measurable academic contribution. Based on the documented publication record, citation performance, and interdisciplinary engineering research, Asma Mahgoub’s work aligns with these objectives through meaningful contributions to semantic communication research and advanced intelligent networking technologies.

Conclusion

Asma Mahgoub has established an active research portfolio focused on semantic communication and intelligent engineering systems. Her publications address practical and theoretical challenges relevant to future communication technologies while demonstrating continued scholarly development. The combination of research productivity, citation influence, and innovation supports recognition within international academic award programs.

External Links

• ORCID Profile
• Scopus Author Profile
• Google Scholar Profile
• International Academic Excellence Awards

References

1. Mahgoub A. Document-Level Transformer-Based Text Semantic Communication System. Machine Learning and Knowledge Extraction. 2026. DOI: 10.3390/make8080225
2. Mahgoub A. Efficient Explainable Metric for Semantic Communication of Images Using Image Captioning. IEEE Access. 2026. DOI: 10.1109/ACCESS.2026.3654009
3. Mahgoub A. Metaverse Unbound: A Survey on Synergistic Integration Between Semantic Communication, 6G, and Edge Learning. IEEE Access. 2025. DOI: 10.1109/ACCESS.2025.3555753
4. Mahgoub A. Semantic Communication of Images Using Image Generation and Image Captioning Models. 2025. DOI: 10.1007/978-981-96-1483-7_11
5. Mahgoub A. On Using BERT Embeddings for Text Semantic Communication. HONET 2024. DOI: 10.1109/HONET63146.2024.10822884

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

• ORCID Profile
• Scopus Author Profile
• Google Scholar Profile
• International Academic Excellence Awards

References

1. Scopus Author Profile. Research metrics and indexed publications. https://www.scopus.com/pages/authors/57193136350
2. 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
3. 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
4. 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).
5. 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
6. 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