Reagan Mandiya | Information Technology | Young Researcher Award

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Mr. Reagan Mandiya | Information Technology | Young Researcher Award

University of Lorraine | France

Mr. Reagan Mandiya is an emerging researcher in Artificial Intelligence and data science, with a growing academic impact reflected in 17 Scopus citations, 3 documents, and an h-index of 2. His research focuses on advanced machine learning, deep learning, and AI-driven healthcare applications, particularly in cardiac arrhythmia diagnosis using large language models. He has contributed to innovative solutions in COVID-19 detection, fraud analytics, and language translation using transformer-based models. His interdisciplinary approach integrates bioinformatics, cybersecurity, and big data analytics. Through collaborative research and mentorship, he actively advances applied AI solutions addressing real-world challenges.

Citation Metrics (Scopus)

30
25
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15
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5
0

Citations
17

h-index
2

Documents
3

Citations

h-index

Documents

Featured Publications

Tuniyazi Abudoureheman | Information Technology | Research Excellence Award

Published on by TOT Awards

Dr. Tuniyazi Abudoureheman | Information Technology | Research Excellence Award

Hiroshima University | Japan

Dr. Tuniyazi Abudoureheman is an emerging researcher in intelligent imaging technologies whose work integrates high-frame-rate (HFR) video processing, digital signal processing, and intelligent systems to address complex challenges in robotics, motion analysis, and biological detection. His research focuses on developing advanced computational frameworks capable of extracting subtle temporal and spatial features from high-speed visual data, with applications spanning vibration monitoring, multi-joint robotic manipulators, and biological motion recognition. Tuniyazi’s contributions involve creating novel image- and signal-processing algorithms designed to improve the accuracy, stability, and efficiency of automated systems operating in dynamic environments. His work on HFR-video-based vibration analysis offers enhanced diagnostic capabilities for flexible robotic structures, while his research on hornet detection using wing-beat frequency analysis demonstrates the potential of high-speed imaging for environmental and biological applications. Furthermore, his earlier work on multi-person tracking in complex backgrounds reflects his strong foundation in computer vision and predictive filtering. Tuniyazi’s scholarly visibility continues to grow, with citations indexed in Google Scholar and Scopus, reflecting early-stage but steadily increasing academic impact. According to Google Scholar metrics, his work has accumulated citations, maintaining an h-index of 1 and an i10-index of 0, which is consistent with researchers developing specialized expertise in a rapidly advancing technical domain. His research outputs contribute to international conferences and peer-reviewed journals, demonstrating a commitment to scientific rigor and innovation. Tuniyazi’s ongoing research trajectory aligns strongly with the objectives of the Research Excellence Award, showcasing high-impact potential in intelligent video processing, adaptive computational models, and robotics-oriented signal analysis, reinforcing his role as a promising contributor to next-generation smart robotic and imaging systems.

Publication Profile

Google Scholar

Featured Publications

  • Li, J., Shimasaki, K., Tuniyazi, A., Ishii, I., Ogihara, M., & Yoshiyama, M. (2023). HFR video-based hornet detection approach using wing-beat frequency analysis. IEEE Sensors, 1–4.

  • Abudoureheman, T., Wang, F., Shimasaki, K., & Ishii, I. (2025). HFR-video-based vibration analysis of a multi-jointed robot manipulator. Journal of Robotics and Mechatronics, 37(5), 1205–1218.

  • Tuniyazi Abudoureheman, T., & Abousharara, E. (2018). Multiple people tracking based on Kalman filter in complex background. Proceedings of the Shikoku-Section Joint Convention of Institutes of Electrical and Related Engineers.