Mohamed Abdelkader | AI-Based Robot Perception | Research Excellence Award

Research Excellence Award

Mohamed Abdelkader
Prince Sultan University

Mohamed Abdelkader
Affiliation Prince Sultan University
Country Saudi Arabia
Scopus ID 57197062034
Documents 35
Citations 575
h-index 11
Subject Area AI-Based Robot Perception
Event International Robotics and Automation Awards
ORCID 0000-0002-0518-852X

Mohamed Abdelkader, affiliated with Prince Sultan University, is a researcher whose work focuses on AI-based robot perception, autonomous systems, computer vision, and intelligent robotic navigation. His scholarly contributions demonstrate the integration of artificial intelligence with robotic sensing technologies to improve perception, localization, and autonomous decision-making. His research output has attracted significant academic attention through publications indexed in Scopus and citations across robotics and artificial intelligence literature.[1]

Abstract

Mohamed Abdelkader has established an active research profile in AI-based robot perception through investigations involving autonomous navigation, computer vision, robotic sensing, and intelligent decision-making. His publications contribute to improving robotic awareness of dynamic environments by integrating perception algorithms with artificial intelligence techniques. These studies support advancements in autonomous systems capable of operating safely and efficiently across diverse applications. His scholarly productivity, citation record, and interdisciplinary collaborations demonstrate sustained contributions to robotics research while promoting practical innovation, scientific dissemination, and continued academic development within intelligent robotic technologies.[1][2]

Keywords

AI-Based Robot Perception, Computer Vision, Autonomous Robotics, Robotic Navigation, Machine Learning, Artificial Intelligence, Intelligent Systems, Mobile Robots, Sensor Fusion, Autonomous Vehicles.

Introduction

Artificial intelligence has significantly transformed robotic perception by enabling machines to interpret complex environments through visual and sensor-based information. Mohamed Abdelkader’s research reflects these developments by investigating perception frameworks that improve robotic autonomy, environmental understanding, and adaptive navigation while supporting reliable performance across intelligent robotic applications.[2]

Research Profile

Working at Prince Sultan University, Mohamed Abdelkader has developed a research portfolio centered on autonomous robotics, perception systems, and artificial intelligence. His publications emphasize practical solutions that combine computer vision, localization, mapping, and machine learning techniques to enhance robotic awareness and decision-making in real-world environments while encouraging interdisciplinary collaboration.[1]

Research Contributions

His research contributions include improving robotic perception algorithms, integrating intelligent sensing technologies, and advancing autonomous navigation capabilities. These studies support greater operational efficiency for robotic platforms while expanding the application of artificial intelligence in perception-driven systems used across industrial, academic, and emerging autonomous environments.[3]

Publications

The researcher’s publication record includes peer-reviewed articles addressing robot perception, intelligent navigation, machine learning, and computer vision. These publications have contributed to the growing body of literature in robotics by presenting methodologies that improve environmental understanding and autonomous performance while maintaining scientific rigor and reproducibility.[2]

Research Impact

With 35 indexed publications, 575 citations, and an h-index of 11, Mohamed Abdelkader has achieved measurable scholarly visibility within robotics and artificial intelligence research. These metrics indicate continued academic engagement and demonstrate that his research findings have influenced related investigations in autonomous robotic perception and intelligent systems.[1]

Award Suitability

The Research Excellence Award recognizes researchers who demonstrate sustained scholarly productivity, impactful publications, and meaningful scientific contributions. Mohamed Abdelkader’s achievements in AI-based robot perception, supported by recognized research metrics and interdisciplinary innovation, align with the objectives of honoring excellence in robotics and automation research.[4]

Conclusion

Mohamed Abdelkader’s research reflects consistent advancement in AI-driven robotic perception through scientifically validated investigations and collaborative innovation. His academic record demonstrates meaningful contributions to autonomous robotics while supporting future developments in intelligent perception technologies and practical robotic applications across multiple research domains.[1][4]

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Mohamed Abdelkader, Author ID 57197062034. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57197062034
  2. Google Scholar. (n.d.). Scholar profile of Mohamed Abdelkader.
    https://scholar.google.com/citations?user=hk5GW30AAAAJ&hl=en&oi=sra
  3. MDPI. (2022). OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results.
    https://doi.org/10.3390/s22239240
  4. International Robotics and Automation Awards. (2026). Research Excellence Award.
    https://roboticsandautomation.org/

Mr. Zhaohui Chen | AI-Based Robot Perception | Research Excellence Award

Mr. Zhaohui Chen | AI-Based Robot Perception | Research Excellence Award

The University of Sydney | Australia 

Mr. Zhaohui Chen is a PhD candidate in Civil Engineering at the University of Sydney, focusing on AI-driven infrastructure assessment, disaster response, and digital-twin systems. His research combines computer vision, multimodal learning, and agentic AI to develop scalable, interpretable frameworks for large-scale damage assessment, AI-Based Robot Perception and decision support. He has published multiple first-author papers in leading journals, including Nature Communications and Automation in Construction. His work aims to enable real-world engineering decision-making under uncertainty by integrating robotics-enabled inspection, intelligent automation, and digital-twin technologies.

Citation Metrics (Scopus)

24
18
12
6
0

Citations
23

Documents
2

h-index
2

Citations

Documents

h-index


View Scopus Profile

Featured Publications


An average pooling designed Transformer for robust crack segmentation


– Automation in Construction, 2024 (Open Access)

Mr. Emmanuel Ebikabowei Enemugha | AI-Based Robot Perception | Best Researcher Award

Mr. Emmanuel Ebikabowei Enemugha | AI-Based Robot Perception | Best Researcher Award

University Malaya Department of Mechanical Engineering | Nigeria

Mr. Enemugha Emmanuel Ebikabowei is a dedicated mechanical engineer and researcher, currently a Ph.d. Candidate in mechanical engineering at the University of Malaya, Malaysia, with specialization in computational fluid dynamics, gas-turbine performance, pump-impeller blade design, and energy systems optimization. He also serves as a lecturer in the department of mechanical engineering at Nigeria maritime university. His publication record on researchgate lists 5 documents with 69 reads, though his citation and h-index metrics are not publicly indicated. His scholarly work includes notable contributions such as a hybrid optimization of mixed-axial flow pump impellers using taguchi method, genetic algorithms, AI-Based Robot Perception and neural networks. additionally, He has conducted experimental analyses on firewood combustion efficiency for sustainable cooking in bayelsa state, Nigeria. His research is shaping the future of efficient pump systems and clean energy solutions for both industrial and community-scale applications.

Profile: Orcid

Featured Publications

Enemugha, E. E., Ab Karim, M. S. B., & Nik Ghazali, N. N. B. (2025). Hybrid optimisation of mixed-axial flow pump impellers parameter using Taguchi, genetic algorithms, and artificial neural networks. Next Research.

Enemugha, E. E., & Munuakuro, A. E. (2025). Experimental analysis of firewood combustion efficiency and fuel consumption patterns for sustainable cooking in Bayelsa State, Nigeria. International Journal for Research in Applied Science and Engineering Technology, 13(4).

Enemugha, E. E. (2025). The effects of impeller blade count on centrifugal pump performance and efficiency under different operating conditions: A comparison of numerical prediction. International Journal for Research in Applied Science and Engineering Technology, 13(4).

Bratua, I., Burubai, W., & Enemugha, E. E. (2025). Comparative analysis of fuelwood weight loss and energy efficiency in Bayelsa State, Nigeria. World Journal of Advanced Engineering Technology and Sciences, 14(3).

Enemugha, E. E., Ab Karim, M. S., & Nik Ghazali, N. N. (2025). Comprehensive optimization of centrifugal pump performance through the integration of the Taguchi method and polynomial regression models. Global Journal of Engineering and Technology Advances, 22(2).