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/

Dr. Nadeem Tariq Beigh | Bio-Inspired Robot Design | Best Researcher Award

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Dr. Nadeem Tariq Beigh | Bio-Inspired Robot Design | Best Researcher Award

Khalifa University | United Arab Emirates

Dr. Nadeem Tariq Beigh is an Indian Researcher and postdoctoral fellow at the smart and intelligent sensors lab, department of mechanical and nuclear engineering, Khalifa University, Abu Dhabi. With a Ph.d. in electrical engineering from the Indian Institute of Technology Delhi, he has established himself as a rising scholar in sustainable sensing, self-powered systems, mems/nems, and energy harvesting. His research emphasizes the design and development of dual piezoelectric/triboelectric nanocomposites, micro/nano energy harvesters, and intelligent sensor systems integrated with machine learning for smart health care and industry 5.0 applications. Dr. Beigh has previously served as a research fellow at nanyang technological university, singapore, and a research assistant at iit delhi, contributing to several high-impact projects funded by dst, Bio-Inspired Robot Design fit, and international collaborations. He has authored 33 research documents, accumulated 239 citations from 152 scholarly works, and holds an h-index of 8, reflecting his growing influence in advanced materials and micro-energy systems research. His interdisciplinary expertise bridges electrical, mechanical, and materials engineering, driving innovations in sustainable, flexible, and intelligent sensor technologies that advance the frontiers of smart electronics and self-powered devices.

Profiles: Scopus | Orcid

Featured Publications

Beigh, N. T., & Alcheikh, N. (2025, October 8). Vapor-induced porosity in graphene/PDMS: A scalable route to high-performance pressure sensors. Microsystems & Nanoengineering.

Amara, H., Beigh, N. T., & Alcheikh, N. (2025, September 17). Smart resonant micro-sensor and micro-actuator: High-performance, wide-range bi-axial magnetic sensitive/insensitive micro-device for multifunctional sensing applications. Microsystems & Nanoengineering.

Alcheikh, N., Amara, H., & Beigh, N. T. (2025, May 5). Smart resonant micro-sensor and micro-actuator: High-performance, wide-range bi-axial magnetic sensitive/insensitive micro-device for multifunctional sensing applications.

Singh, S., Beigh, N. T., Gupta, P., Mallick, D., & Goswami, A. (2025, April). Polarity-dependent surface charge retention on CYTOP fluoropolymer for durable TENG applications. Applied Materials Today.

Beigh, N. T. (2025, March 19). High-performance MEMS magnetic sensor based on a smart tunable resonator. In Proceedings of the 2025 IEEE 38th International Conference on Micro Electro Mechanical Systems (MEMS). IEEE.

Ms. Tamanna | Neural Networks for Robot Control | Best Researcher Award

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Ms. Tamanna | Neural Networks for Robot Control | Best Researcher Award

Goethe University, Frankfurt | Germany

Tamanna is a Ph.d. researcher at Goethe University, Frankfurt, Germany, specializing in geochemistry with a focus on integrating data science and machine learning into geo-scientific research. Her work aims to bridge the gap between traditional geoscience and modern computational methodologies by leveraging data-driven approaches to analyze complex geochemical systems. She holds a bs-ms degree in earth and environmental sciences from the Indian institute of science education and research, Bhopal. her expertise spans elemental and isotopic geochemistry, geospatial analysis, statistical modeling, and predictive analytics for geochemical processes. She is proficient in programming and data visualization using python, Neural Networks for Robot Control, matlab, qgis, and arcgis. Tamanna has authored two research documents with approximately one citation and an h-index of 1 for those publications; overall, her author-level record includes 99 citations and an h-index of 6, according to google scholar. Beyond her technical skills, she is actively involved in interdisciplinary research that combines quantitative methods, laboratory work, and field investigations to enhance the understanding of earth’s chemical evolution. fluent in english and hindi, with working knowledge of german, she represents the next generation of data-driven geoscientists.

Profile: Orcid

Featured Publications

Tamanna, Hezel, D. C., & Marschall, H. R. (2025, October). MRMinerals and MineralTD: Machine‐Readable Mineral Formula and Compositions Data Set for Data‐Driven Research. Geoscience Data Journal.

Tamanna, Hezel, D. C., Srivastava, N., & Faber, J. (2025, August 13). Using machine learning for automatic rock classification. American Mineralogist.