Yooho Lee | Artificial Intelligence | Innovative Research Award

Published on by New Scientists

Innovative Research Award

             Research Profile
Researcher Yooho Lee
Affiliation Dong-A University
Country South Korea
Scopus ID 57223295187
Documents 4
Citations 29
h-index 3
Subject Area Artificial Intelligence
Event New Scientists Awards
ORCID 0000-0002-9714-6799
Google Scholar Scholar Profile

Yooho Lee

 Dong-A University, South Korea

Yooho Lee is a researcher affiliated with Dong-A University whose scholarly work is centered on Artificial Intelligence. His academic profile demonstrates active participation in AI research through peer-reviewed publications indexed by Scopus, supported by internationally recognized researcher identifiers including ORCID and Google Scholar. His publications contribute to the growing body of knowledge in intelligent systems, machine learning, and computational technologies while reflecting measurable scholarly impact through citation metrics.[1]

Abstract

This article provides a concise academic overview of Yooho Lee and his research profile in Artificial Intelligence. The overview summarizes institutional affiliation, publication metrics, citation performance, and research visibility using recognized scholarly databases. It is intended to present a neutral, encyclopedic profile suitable for academic recognition and professional reference.[1]

Keywords

Artificial Intelligence, Machine Learning, Intelligent Systems, Computational Intelligence, Deep Learning, Dong-A University, South Korea, Scientific Research, Scopus Author, Innovative Research Award.

Introduction

Artificial Intelligence has become one of the most rapidly evolving scientific disciplines, influencing fields such as healthcare, robotics, automation, cybersecurity, and data analytics. Researchers in this domain contribute to the development of algorithms, intelligent decision-making systems, and computational models that address complex real-world challenges. Yooho Lee’s academic activities reflect participation in this evolving field through peer-reviewed publications and internationally indexed scholarly work.[2]

Research Profile

  • Researcher: Yooho Lee
  • Affiliation: Dong-A University
  • Country: South Korea
  • Subject Area: Artificial Intelligence
  • Scopus Author ID: 57223295187
  • Indexed Documents: 4
  • Citations: 29
  • h-index: 3

Research Contributions

Yooho Lee has contributed to Artificial Intelligence research through scholarly publications that enhance understanding of intelligent computational methods and modern AI applications. His research reflects scientific engagement in algorithm development, data-driven analysis, and emerging computational technologies. Although at an early stage of scholarly productivity, the available publication record demonstrates an active commitment to advancing AI research within the international scientific community.[3]

Publications

  • Four peer-reviewed publications indexed within the Scopus database.
  • Research outputs available through Scopus Author Profile and Google Scholar.
  • Academic publications contribute to Artificial Intelligence and related computational research.

Research Impact

Research impact is commonly evaluated through publication quality, citation frequency, researcher identifiers, and international database visibility. Yooho Lee’s Scopus metrics, combined with ORCID registration and Google Scholar indexing, demonstrate transparent scholarly documentation and measurable research dissemination within the Artificial Intelligence community.[1]

Award Suitability

The Innovative Research Award recognizes researchers who demonstrate scientific originality, research integrity, and measurable academic contributions. Yooho Lee’s publication record, institutional affiliation, citation performance, and internationally verifiable academic identifiers collectively support a scholarly profile that aligns with commonly recognized evaluation criteria for emerging research excellence in Artificial Intelligence.[5]

Conclusion

Yooho Lee has established a developing academic profile through contributions to Artificial Intelligence research supported by peer-reviewed publications, international indexing, and recognized researcher identifiers. His scholarly record reflects participation in contemporary AI research while contributing to the broader advancement of intelligent computing and scientific innovation.[4]

References

  1. Elsevier. (n.d.). Scopus author details: Yooho Lee, Author ID 57223295187. Scopus.
    https://www.scopus.com/pages/authors/57223295187
  2. ORCID. (n.d.). ORCID Record for Yooho Lee.
    https://orcid.org/0000-0002-9714-6799
  3. Google Scholar. (n.d.). Scholar Profile of Yooho Lee.
    https://scholar.google.com/citations?user=c6z3a-gAAAAJ&hl=en&oi=ao
  4. Nature. (2021). Artificial Intelligence and Scientific Computing. DOI:
    https://doi.org/10.1038/s41586-021-03819-2
  5. New Scientists Awards. (n.d.). Innovative Research Award Program.
    https://newscientists.net/

Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

Published on by New Scientists

Assist. Prof. Dr. Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

Assistant Professor | Ferdowsi University of Mashhad | Iran

Assist. Prof. Dr. Amir R. Masoodi is a highly accomplished structural engineering researcher whose work spans nonlinear mechanics, composite structures, vibration analysis, finite element modeling, and advanced material systems. With 1,789 Scopus citations, 77 publications, and an h-index of 29, he has established a strong international research footprint in computational mechanics, structural stability, soil–structure interaction, wave propagation, and multiscale modeling of advanced composites. His research contributions include developing novel finite element formulations for beams, plates, and shells, particularly for functionally graded materials (FGMs), carbon nanotube (CNT)-reinforced composites, graphene nanocomposites, and porous structural systems. Assist. Prof. Dr. Amir R. Masoodi’s work on nonlinear dynamic analysis, thermal–mechanical coupling, shell instability, and multiscale behavior of nano-engineered materials has been widely cited and influential in advancing modern structural design methodologies. He has published extensively in leading journals such as Composite Structures, Engineering Structures, Mechanics of Advanced Materials and Structures, Aerospace Science and Technology, Scientific Reports, and Applied Sciences. His publications address cutting-edge topics including vibration of hybrid nano-reinforced shells, multiscale characterization of nanocomposites, nonlinear buckling behavior of tapered beams, thermomechanical modeling of composite cables, and smart materials incorporating shape-memory alloys. Assist. Prof. Dr. Amir R. Masoodi has presented his findings at numerous international conferences and contributed several book chapters, including work on nanofillers and thermal properties in advanced materials. His research output extends to R&D projects, predictive modeling, and computational innovations in structural and nano-engineered systems. He has been recognized with multiple distinguished researcher awards, national elite recognitions, and research excellence honors. His expertise is further reflected in his editorial board memberships and contributions as a reviewer for reputable journals. Overall, Assist. Prof. Dr. Amir R. Masoodi’s research stands at the intersection of computational mechanics, smart materials, and multiscale structural engineering, offering impactful advances for next-generation civil, mechanical, and aerospace systems.

Profiles: Scopus | ORCID | Google Scholar | Sci Profiles | Web of Science

Featured Publications

1. Sobhani, E., Masoodi, A. R., & Ahmadi-Pari, A. (2021). Vibration of FG-CNT and FG-GNP sandwich composite coupled conical–cylindrical–conical shell. Composite Structures, 273, 114281.

2. Sobhani, E., Masoodi, A. R., Civalek, O., & Ahmadi-Pari, A. R. (2021). Agglomerated impact of CNT vs. GNP nanofillers on hybridization of polymer matrix for vibration of coupled hemispherical–conical–conical shells. Aerospace Science and Technology, 120, 107257.

3. Rezaiee-Pajand, M., Sobhani, E., & Masoodi, A. R. (2020). Free vibration analysis of functionally graded hybrid matrix/fiber nanocomposite conical shells using multiscale method. Aerospace Science and Technology, 105, 105998.

4. Rezaiee-Pajand, M., Arabi, E., & Masoodi, A. R. (2019). Nonlinear analysis of FG-sandwich plates and shells. Aerospace Science and Technology, 87, 178–189.

5. Rezaiee-Pajand, M., Masoodi, A. R., & Mokhtari, M. (2018). Static analysis of functionally graded non-prismatic sandwich beams. Advances in Computational Design, 3(2), 165–190.

Wei Pan | Artificial Intelligence | Best Researcher Award

Published on by New Scientists

Dr. Wei Pan | Artificial Intelligence | Best Researcher Award

Researcher | OPT Machine Vision | Japan

Dr. Wei Pan is an accomplished researcher specializing in machine vision, 3D imaging, computational geometry, and optical metrology, currently contributing to OPT Machine Vision Corporation in Japan. His research is positioned at the intersection of machine learning, geometric learning, and computer-aided design, with applications in precision manufacturing, intelligent inspection, and automation. With 26 Scopus-indexed publications, 146 citations, and an h-index of 7, Dr. Wei Pan’s research has advanced computational methodologies for 3D reconstruction, point cloud processing, mesh denoising, phase-shifting profilometry, and surface metrology. His works have appeared in leading journals including Advanced Photonics, Optics Express, Computer-Aided Design, Automation in Construction, and The Visual Computer. Notably, his 2024 publications explore deep-learning-embedded structured light imaging and topology-aware transformers for point cloud registration, reflecting his pioneering integration of AI and optical engineering. Dr. Wei Pan has demonstrated exceptional innovation through 39 patents across domains such as 3D data filtering, surface defect detection, structured light reconstruction, and intelligent robotic calibration. These inventions strengthen industrial imaging precision and automation efficiency. His patent WO-2022057250-A1 on mesh denoising and CN-118397020-A on image segmentation and contour extraction exemplify impactful R&D contributions to intelligent vision systems. Beyond publications and patents, Dr. Wei Pan actively engages in collaborative research and R&D leadership, driving algorithmic innovation in structured-light metrology and computer vision. His research excellence has been recognized with multiple distinctions, including the President’s Graduate Fellowship (Singapore) and the Kuang-Chi Young Talents Award (China). Through his interdisciplinary expertise bridging optical design, machine learning, and computational modeling, Dr. Wei Pan continues to advance the frontiers of intelligent manufacturing and vision-based automation technologies.

Profiles: Scopus | ORCID | Google Scholar | ResearchGate

Featured Publications

  • Liu, J., Hao, J., Lin, H., Pan, W., Yang, J., Feng, Y., Wang, G., Li, J., Jin, Z., Zhao, Z., & Liu, Z. (2023). Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction. Patterns, 4(9), Article 100953.

  • Lu, L., Bu, C., Su, Z., Guan, B., Yu, Q., Pan, W., & Zhang, Q. (2024). Generative deep-learning-embedded asynchronous structured light for three-dimensional imaging. Advanced Photonics, 6(4), 046004–046004.

  • Chen, S., Wang, J., Pan, W., Gao, S., Wang, M., & Lu, X. (2023). Towards uniform point distribution in feature-preserving point cloud filtering. Computational Visual Media, 9(2), 249–263.

  • Lu, L., Jia, Z., Pan, W., Zhang, Q., Zhang, M., & Xi, J. (2020). Automated reconstruction of multiple objects with individual movement based on PSP. Optics Express, 28(18), 28600–28611.

  • Si, G. Y., Leong, E. S. P., Pan, W., Chum, C. C., & Liu, Y. J. (2014). Plasmon-induced transparency in coupled triangle-rod arrays. Nanotechnology, 26(2), 025201.