Gozde Doven | Robotics and Automation | Innovative Research Award

Published on by Environmental Scientists

Innovative Research Award

Gozde Doven
Affiliation Gebze Technical University
Country United Kingdom
Scopus ID 57210827251
Documents 3
Citations 691
h-index 3
Subject Area Robotics and Automation
Event Environmental Scientists
ORCID 0000-0003-3937-183X

Gozde Doven
Gebze Technical University

Gozde Doven is affiliated with and has established a scholarly profile in robotics and automation through published research and measurable citation impact. The available academic indicators demonstrate continued engagement in technology-oriented investigations, supporting recognition within the Innovative Research Award category.Her scholarly work explores the integration of intelligent robotic systems into organizational and industrial environments, with particular emphasis on the interaction between humans and autonomous technologies. Through publications in internationally recognized peer-reviewed journals and conference proceedings. [1]

Abstract

This article summarizes the academic profile of Gozde Doven, emphasizing research activity in robotics and automation. Publication metrics, citation performance, and research visibility collectively indicate a developing contribution to engineering scholarship.[1]

Keywords

Robotics, Automation, Intelligent Systems, Engineering Research, Scientific Innovation, Academic Publications, Citation Analysis, Innovative Research Award.

Introduction

Robotics and automation continue to transform industrial and environmental applications through intelligent technologies. Researchers contributing to this field support advancements in efficiency, precision, and sustainable engineering practices.[2]

Research Profile

Gozde Doven has authored indexed scientific publications and maintains a Scopus profile reflecting recognized research output. Citation statistics indicate that the published work has attracted scholarly attention within relevant academic communities.[1]

Research Contributions

The available publications contribute to knowledge in robotics and automation through technical investigations and engineering methodologies. These studies support ongoing developments in intelligent systems and applied technological research.[3]

Publications

The research portfolio currently includes three indexed documents with measurable citation performance. These publications provide evidence of participation in peer-reviewed scientific communication and continued academic engagement.[1]

Research Impact

With 691 citations and an h-index of 3, the research has demonstrated visibility among scholars. Citation-based indicators suggest that the published work has contributed to ongoing scientific discussions within the discipline.[1]

Award Suitability

Considering publication output, citation metrics, and research specialization, Gozde Doven demonstrates qualifications consistent with evaluation for the Innovative Research Award. The profile reflects scholarly productivity and measurable academic influence.[4]

Conclusion

The available academic evidence presents a concise profile of research activity supported by peer-reviewed publications and citation records. Such indicators contribute to an objective assessment of research excellence and professional recognition.[5]

References

  1. Elsevier. Scopus Author Details: Gozde Doven, Author ID 57210827251.
    https://www.scopus.com/authid/detail.uri?authorId=57210827251
  2. Doven, G., Sezen, B., Demir, K., & Balcioglu, Y. (2025). Industry 5.0: Are we going to accept robots as co-workers in office environments? An empirical analysis. Applied Sciences.
    https://www.mdpi.com/2076-3417/15/3/1591
  3. ORCID. Researcher Profile: Gozde Doven.
    https://orcid.org/0000-0003-3937-183X
  4. Doven, G. (2019). Industry 5.0 and human-robot co-working. Procedia Computer Science.
  5. Çelikkol, M., Kitapçi, H., & Döven, G. (2019). Culture’s impact on entrepreneurship and interaction effect of economic development level: An 81 country study. Journal of Business Economics and Management.
    https://doi.org/10.3846/jbem.2019.10180

Tuniyazi Abudoureheman | Intelligent Systems | Research Excellence Award

Published on by Environmental Scientists

Dr. Tuniyazi Abudoureheman | Intelligent Systems | Research Excellence Award

Dr. Tuniyazi Abudoureheman | Intelligent Systems | Hiroshima University | Japan

Dr. Tuniyazi Abudoureheman is a dedicated researcher and Ph.D. student at Hiroshima University whose work focuses on advanced sensing, machine vision, and robotic system diagnostics, contributing meaningfully to the fields of high-frame-rate (HFR) imaging, vibration analysis, and automated detection systems. Dr. Tuniyazi Abudoureheman began his academic journey with foundational studies that eventually led him to pursue graduate-level research, culminating in his current doctoral studies where he continues to expand his expertise in robotics and intelligent sensing technology. Throughout his professional experience, Dr. Tuniyazi Abudoureheman has actively engaged in collaborative research projects, working alongside multidisciplinary teams to design, implement, and validate methods involving HFR video, wing-beat frequency detection, and robot health monitoring across multiple postures. His early work also includes contributions to multi-target tracking using Kalman Filtering in complex environments, demonstrating both versatility and technical depth even before entering advanced doctoral research. The core research interests of Dr. Tuniyazi Abudoureheman include high-speed imaging, robotic vibration analysis, automated industrial inspection, bio-inspired detection systems, and machine vision algorithms, all of which align with the evolving demands of next-generation intelligent robotics. His research skills span HFR camera-based data acquisition, signal processing, vibration modeling, robotic motion evaluation, and applied machine learning, supported by strong analytical ability and experience with experimental system design. Dr. Tuniyazi Abudoureheman has also developed valuable competencies in publishing scientific results, presenting at conferences, and contributing to collaborative engineering investigations, which collectively strengthen his academic and professional profile. Although early in his academic career, Dr. Tuniyazi Abudoureheman has already earned recognition through peer-reviewed publications, citations, and participation in reputable conferences such as IEEE SENSORS, positioning him as an emerging scholar in robotics and sensing technology. His work has received growing scholarly attention, reflected in increasing citation counts and inclusion in respected journals covering robotics and mechatronics. In conclusion, Dr. Tuniyazi Abdurrahman continues to advance as a promising researcher whose technical contributions, methodological rigor, and commitment to innovation place him on a strong path toward future academic excellence and impactful scientific discovery.

Academic Profile: ORCID | Google Scholar

Featured Publications:

  1. Li, J., Shimasaki, K., Tuniyazi, A., Ishii, I., & Ogihara, M. (2023). HFR video-based hornet detection approach using wing-beat frequency analysis. 3 citations.

  2. Abudoureheman, T., Wang, F., Shimasaki, K., & Ishii, I. (2025). HFR-video-based vibration analysis of a multi-jointed robot manipulator. 1 citation.

  3. Abudoureheman, T., Otsubo, H., Wang, F., Shimasaki, K., & Ishii, I. (2025). High-frame-rate camera-based vibration analysis for health monitoring of industrial robots across multiple postures.

 

Goncalo Galvao | Electronics and Computer Engineering | Best Researcher Award

Published on by Environmental Scientists

Dr. Goncalo Galvao | Electronics and Computer Engineering | Best Researcher Award

International Society For Executive Learning | Portugal

Dr. Goncalo Galvao is a dedicated PhD student at ISEL whose research spans Electronics and Computer Engineering, Optoelectronics, and Machine Learning, with a strong focus on intelligent mobility systems. His academic trajectory reflects a commitment to innovation, particularly in the integration of Visible Light Communication and deep reinforcement learning to develop adaptive traffic control solutions that enhance vehicular communication, improve traffic flow efficiency, and reduce congestion in emerging smart-city environments. His earlier work explored advanced optical wireless systems for connected vehicles, leading to high-quality scientific contributions and recognition through a Best Paper Award at an international conference. Dr. Goncalo Galvao has authored a substantial body of research indexed in Scopus, comprising 21 documents with 64 citations across 42 citing sources and a 4 h-index, demonstrating the growing impact and visibility of his contributions to optical communication and AI-driven traffic management. His research involvement includes participation in a funded project centered on intelligent transportation and urban mobility challenges, where he applies data-driven engineering approaches to develop sustainable and efficient solutions. His ongoing doctoral research further advances this trajectory, positioning him at the forefront of innovative developments in smart mobility and next-generation transportation systems. Through his scholarly output, project engagements, and academic involvement, Dr. Goncalo Galvao continues to contribute meaningfully to advancements in optical wireless communication, machine learning applications in engineering, and the development of intelligent systems that support safer, smarter, and more efficient urban mobility infrastructures.

Profiles: Scopus | Orcid | Google Scholar | Researchgate

Featured Publications

Vieira, M. A., Galvão, G., Vieira, M., Louro, P., Vestias, M., & Vieira, P. (2024). Enhancing urban intersection efficiency: Visible light communication and learning-based control for traffic signal optimization and vehicle management. Symmetry, 16(2), 240.

Vieira, M., Vieira, M. A., Galvão, G., Louro, P., Véstias, M., & Vieira, P. (2024). Enhancing urban intersection efficiency: Utilizing visible light communication and learning-driven control for improved traffic signal performance. Vehicles, 6(2), 666–692.

Galvão, G., Vieira, M., Louro, P., Vieira, M. A., Véstias, M., & Vieira, P. (2023). Visible light communication at urban intersections to improve traffic signaling and cooperative trajectories. In 2023 7th International Young Engineers Forum (YEF-ECE) (pp. 60–65).

Vieira, M., Galvão, G., Vieira, M. A., Vestias, M., Louro, P., & Vieira, P. (2024). Integrating visible light communication and AI for adaptive traffic management: A focus on reward functions and rerouting coordination. Applied Sciences, 15(1), 116.

Galvão, G., Vieira, M., Louro, P., Vieira, M. A., Véstias, M., & Vieira, P. (2024). Multi agent reinforcement learning system for vehicular and pedestrian traffic control with visible light communication. In 2024 8th International Young Engineers Forum on Electrical and Computer Engineering.