Zulqurnain Sabir | Artificial Neural Networks | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Zulqurnain Sabir
Lebanese American University, Lebanon

Zulqurnain Sabir
Affiliation Lebanese American University
Country Lebanon
Scopus ID 56184182600
Documents 301
Citations 8494
h-index 54
Subject Area Artificial Neural Networks
Event Environmental Scientists
ORCID 0000-0001-7466-6233

Zulqurnain Sabir is a researcher affiliated with Lebanese American University whose scholarly work has contributed to the advancement of artificial neural networks and computational mathematics. His publication record, citation impact, and sustained research productivity demonstrate continuous academic engagement and international visibility within interdisciplinary scientific research.[1]

Abstract

This article summarizes the academic profile of Zulqurnain Sabir, highlighting his sustained research activity, publication performance, and scholarly influence in artificial neural networks. His scientific contributions reflect consistent engagement with computational methodologies and interdisciplinary applications.[1]

Keywords

Artificial neural networks, computational mathematics, numerical analysis, scientific computing, differential equations, optimization, machine learning, research excellence.

Introduction

Modern computational research increasingly integrates artificial intelligence with mathematical modelling to address complex scientific problems. Zulqurnain Sabir has contributed to this evolving field through peer-reviewed publications and collaborative academic research.[2]

Research Profile

The research profile demonstrates extensive publication activity supported by strong citation metrics and a high h-index. These indicators suggest sustained scholarly productivity and recognition within the international research community.[1]

Research Contributions

His work emphasizes artificial neural networks, numerical techniques, and mathematical optimization for solving engineering and scientific challenges. These studies support methodological improvements across computational science and applied mathematics.[3]

Publications

With more than 300 indexed publications, the research portfolio reflects consistent authorship in reputable international journals. The publication record illustrates long-term commitment to scientific dissemination and collaborative investigation.[1]

Research Impact

Citation performance exceeding eight thousand references demonstrates broad academic visibility and continued influence among researchers. Such impact indicates that the published work has contributed to ongoing developments in computational research.[4]

Award Suitability

The combination of research productivity, measurable citation impact, and international publication activity supports recognition for research excellence. These achievements align with the objectives of the Environmental Scientists recognition program in acknowledging sustained academic contributions.[5]

Conclusion

Zulqurnain Sabir has established a well-documented scholarly profile through continuous publication, citation growth, and interdisciplinary research. His academic achievements reflect meaningful contributions to computational science and artificial neural network research.[1]

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Zulqurnain Sabir, Author ID 56184182600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=56184182600
  2. ORCID. (n.d.). Zulqurnain Sabir ORCID record.
    https://orcid.org/0000-0001-7466-6233
  3. Sabir, Z., Muhammad, N., Zhang, S., & Khan, I. (2026). Hybrid radial basis and log-sigmoid neural network using Rprop for dengue–COVID-19 co-infection dynamics. SSRN Electronic Journal (Preprint).
    https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6910764
  4. Sabir, Z., Kobba, T., & Fadel, H. (2026). A computational design-based scale conjugate neural network to solve the nonlinear Rabinovich–Fabrikant model. Journal of Circuits, Systems and Computers. Advance online publication
    https://www.worldscientific.com/doi/10.1142/S0218126626501203
  5. Sabir, Z., Bichbich, I., Umar, M., Salahshour, S., & Bayram, M. (2026). An artificial neural network based on radial basis methodology using delay effects in the Parkinson’s disease model. Computational Biology and Chemistry, 115, Article 109033.

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.

 

Asmaa Seyam | Data Science | Best Researcher Award

Published on by Environmental Scientists

Mrs. Asmaa Seyam | Data Science | Best Researcher Award

Ph.D student, University of Wollongong, Australia

Asmaa Seyam is a seasoned computer engineering professional and educator with over a decade of academic and research experience. Her career spans institutions such as Zayed University and the Islamic University of Gaza, where she has significantly contributed to the fields of programming, networking, and system development. Asmaa is known for her dedication to excellence in teaching and her active involvement in curriculum development and academic leadership.

Profile

Scopus

🎓 Education

Asmaa earned her Master’s degree in Computer Engineering from Jordan University of Science and Technology (2009–2011), graduating with an excellent GPA of 89.4%. Her thesis focused on optimizing node placement for energy-efficient clustering in wireless sensor networks. She completed her Bachelor’s in Computer Engineering at the Islamic University of Gaza (2003–2008) with an outstanding GPA of 90.67%, showcasing her strong foundation with a SCADA project for power distribution.

💼 Professional Experience

Asmaa Seyam served as an Instructor at Zayed University in Abu Dhabi from 2012 to 2022, where she taught a range of IT and engineering courses such as Web Development, Programming, HCI, and Networking. She also worked as a Teaching Assistant at the Islamic University of Gaza and was a Network Trainer at the Ministry of Interiors, demonstrating hands-on expertise in server management, routing protocols, and system maintenance. Her roles were marked by leadership in academic planning, assessment design, student mentorship, and institutional service.

🔬 Research Interest

Her research interests lie in Internet of Things (IoT), Machine Learning, Artificial Intelligence, and Wireless Sensor Networks. She has published and presented in esteemed journals and international conferences, contributing to the evolution of smart and efficient network systems.

🏆 Awards and Honors

Asmaa’s achievements include the Zuhair Hijjawi Award for Scientific Research (2008), a DAAD Scholarship for her Master’s studies, and several institutional service recognitions including the IBM Artificial Intelligence Analyst Mastery Award (2019) and the Advance HE Fellowship (2022). She also earned two separate 5-Year Service Awards from Zayed University and CISCO Networking Academy.

📚 Publications

  1. Energy-Efficient Clustering Algorithm for Wireless Sensor Networks Using the Virtual Field Force
    Published in: 5th International Conference on New Technologies, Mobility and Security (NTMS), Istanbul, 2012.
    Cited by: 60+ articles
    📌 IEEE Xplore

  2. Energy-Efficient and Coverage-Aware Clustering in Wireless Sensor Networks
    Published in: Wireless Engineering and Technology, Vol. 3, No. 3, 2012, pp. 142–151.
    Cited by: 90+ articles
    📌 Scientific Research Publishing

  3. Characterizing Realistic Signature-based Intrusion Detection Benchmarks
    Published in: Proceedings of the 6th International Conference on Info Technology: IoT and Smart City, ACM, Hong Kong, 2018.
    Cited by: 20+ articles
    📌 ACM Digital Library

🏁 Conclusion

Asmaa Seyam is a highly qualified and accomplished educator and researcher whose background reflects a strong commitment to both teaching and scholarly work. Her technical breadth, early recognition in research, and academic contributions position her as a strong candidate for the Best Researcher Award. Strengthening her recent research portfolio and expanding her research leadership roles would further elevate her profile.