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

Published on by Robotics and Automation

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/

Assoc. Prof. Dr. Entesar Eliwa | Deep Learning for Robotic Vision | Excellence in Research Award

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Assoc. Prof. Dr. Entesar Eliwa | Deep Learning for Robotic Vision | Excellence in Research Award

King Faisal University | Saudi Arabia

Dr. Entesar Hamed I. Eliwa is an Associate Professor at King Faisal University, Faculty of Science, Department of Mathematics and Statistics. She holds a B.Sc. in Computer Science from Minia University, where she also served as a Teaching Assistant before completing her M.Sc. and Ph.D. in the Computer Science Department. After joining King Faisal University as an Assistant Lecturer, her strong research productivity and academic contributions led to her promotion to Associate Professor. Her work focuses on data mining, knowledge discovery, predictive modeling, supervised learning, classification, association rule mining, Deep Learning for Robotic Vision and artificial intelligence. She has successfully completed 28 research projects and is currently leading 6 ongoing studies. Her scholarly influence is reflected in her most recent citation metrics, with 443 total citations across 373 citing documents, demonstrating a solid and expanding research footprint. She has produced 22 research documents contributing to advancements in computational intelligence, and she maintains an h-index of 8, underscoring the depth and consistency of her academic impact. Through her research, publications, and academic service, Dr. Eliwa continues to strengthen the fields of computer science, data analytics, and artificial intelligence within both regional and global research communities.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

El Koshiry, A., Eliwa, E., Abd El-Hafeez, T., & Tony, M. A. A. (2026). The effectiveness of an e-learning platform in developing digital citizenship skills among blind students.  https://doi.org/10.1007/978-3-031-94770-4_21

Hamed, E., & Abd El-Hafeez, T. (2025). Deep learning for sustainable agriculture: Automating rice and paddy ripeness classification for enhanced food security. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2025.100785

Amr, A., Eliwa, E., Tony, A. A., Shalgham, A., & Contributors from King Faisal University; Minia University; Arish University. (2025). The effectiveness of using Box-to-Box technology to develop some of the composite physical and technical capabilities of footballers. Fusion: Practice and Applications. https://doi.org/10.54216/fpa.170224

Eliwa, E. H. I., & Abd El-Hafeez, T. (2025). A robust deep learning pipeline for multi-class cervical cancer cell identification. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2025.100787

Eliwa, E. H. I., & Abd El-Hafeez, T. (2025). A novel YOLOv11 framework for enhanced tomato disease detection. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.3200

Mr. Angelos Athanasiadis | Deep Learning for Robotic Vision | Research Excellence Award

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Mr. Angelos Athanasiadis | Deep Learning for Robotic Vision | Research Excellence Award

Aristotle University of Thessaloniki | Greece

Mr. Angelos Athanasiadis is a Ph.d. candidate in electrical and computer engineering at the Aristotle University of Thessaloniki, specializing in fpga-based acceleration of convolutional neural networks and heterogeneous computing systems. he holds an M.Eng. in electronics and computer systems and an mba with high distinction, combining strong technical expertise with strategic insight. his research focuses on full-precision CNN acceleration, FPGA architectures, cyber-physical systems, Deep Learning for Robotic Vision and distributed embedded system emulation. Angelos has contributed to major eu-funded research projects, including the adviser and redesign projects, and has completed industrial internships at cadence design systems in Munich. He has also worked in r&d and embedded development roles at exapsys and seems pc, strengthening his applied engineering experience. Academically, he has collaborated with Professor Ioannis papaefstathiou and assistant professor nikolaos tampouratzis, contributing to innovations in energy-efficient cnn inference and high-fidelity system emulation. his open-source framework, fusion, integrates qemu and omnet++ using hla/certi for deterministic, timing-accurate, multi-node execution. Although early in his publication journey, angelos has 1 citation, 1 scopus-listed document, and an h-index of 1, reflecting the initial impact of his contributions. Driven by interdisciplinary research, he aims to advance reconfigurable computing for next-generation autonomous and embedded intelligent systems.

Profiles: Orcid | Google Scholar

Featured Publications

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, 102625.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal – Works in Progress in Embedded Computing, Article 62.

Katselas, L., Athanasiadis, A., Jiao, H., Papameletis, C., Hatzopoulos, A., & Marinissen, E. J. (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs. In 2017 27th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS) (pp. 1–8). IEEE.

Prof. Dr. Abdelkrim Kamel Oudjida | Real-Time Robotic Control | Research Excellence Award

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Prof. Dr. Abdelkrim Kamel Oudjida | Real-Time Robotic Control | Research Excellence Award

Centre de Développement des Technologies Avancées | Algeria

Prof. Dr. Abdelkrim Kamel Oudjida is a distinguished director of research at the Centre De Développement des Technologies Avancées, recognized for his extensive contributions to computer arithmetic and its applications in Cryptography, DSP, robotics, Real-Time Robotic Control and VLSI digital design. With a strong academic and professional background, he has authored 22 publications, delivered 35 communications, and contributed to 2 patents, establishing himself as a leading figure in advanced digital circuit design. his research achievements include developing advanced binary recoding methods and pioneering new arithmetic strategies for efficient asic/fpga implementation of linear systems. His work has earned him 328 citations, based on 263 citing documents, supported by 34 documents, and an h-index of 8, reflecting his growing scholarly impact. in addition to his role as a full-time researcher, he serves as a lecturer in computer arithmetic and cryptography at the national higher school of mathematics. He has supervised more than 50 engineering, master’s, and phd projects, further strengthening the scientific community through mentorship. As a reviewer for prestigious journals such as ieee tcas, elsevier microelectronics, and jolpe, and collaborating with several european research labs, oudjida continues to advance the field through innovation, leadership, and international engagement.

Profiles: Scopus | Orcid

Featured Publications

Liacha, A., Oudjida, A. K., & Bellal, R. N. (2025). On the complexity of constant multiplication problem. International Journal of Circuit Theory and Applications. Advance online publication.

Nait-Abdesselam, F., Oudjida, A. K., Khouas, A., & Liacha, A. (2025). On computing the double point multiplication in elliptic curve cryptography. Cryptologia. Advance online publication.

Echikr, A., Yachir, A., Kerrache, C. A., Oudjida, A. K., & Sahraoui, Z. (2024). Interoperable IoRT for healthcare: Securing intelligent systems with decentralized blockchain. Acta Informatica Pragensia.

Oudjida, A. K., & Liacha, A. (2021). Radix-2ʷ arithmetic for scalar multiplication in elliptic curve cryptography. IEEE Transactions on Circuits and Systems I: Regular Papers, 68(5), 1984–1997.

Oudjida, A. K. (n.d.). Multiple constant multiplication algorithm for high-speed and low-power design.

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

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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).