Yooho Lee | Artificial Intelligence | Innovative Research Award

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/

Manickam S | Artificial Intelligence and Machine Learning | Research Excellence Award

Mr. Manickam S | Artificial Intelligence and Machine Learning | Research Excellence Award

Assistant Professor | Saveetha Engineering College | India

Mr. Manickam S is an emerging researcher in Artificial Intelligence and Machine Learning, with focused contributions spanning data analytics, secure systems, intelligent networks, and applied AI for real-world optimization. His scholarly output includes peer-reviewed journal and international conference publications addressing graph-based road network optimization, learning-assisted pathfinding, and cryptographic multi-server authentication using elliptic curve digital signatures. His research demonstrates strong integration of machine learning algorithms with networking, security, and intelligent transportation systems. Mr. Manickam S has an active innovation portfolio, with multiple Indian patents published and granted in domains such as IoT-enabled robotics, smart agriculture, edge-AI energy monitoring, cloud-integrated IoT resource allocation, solar panel automation, and AI-driven healthcare analytics. His work reflects a translational R&D orientation, emphasizing scalable, deployable intelligent systems. According to Google Scholar, he has 8 citations across 3 research documents with an h-index of 1. He has received recognition for academic innovation and contributes to the research ecosystem through conference participation, interdisciplinary AI research, and technology-driven problem solving.

Citation Metrics (Google Scholar)

10

8

6

4

2

0

Citations
8

Documents
3

h-index
1

Citations

Documents

h-index

View Scopus Profile   View ORCID Profile   View Google Scholar   View ResearchGate

Featured Publications

Secure multi server authentication system using elliptic curve digital signature
– IEEE ICCPCT Conference Proceedings, 2016 | Citations: 8

Optimizing Road Networks: A Graph-Based Analysis with Path-finding and Learning Algorithms
– International Journal of Intelligent Transportation Systems Research, 2024

Ibrahim Aromoye | Artificial Intelligence and Machine Learning | Editorial Board Member

Mr. Ibrahim Aromoye | Artificial Intelligence and Machine Learning | Editorial Board Member

Graduate Research Assistant | Universiti Teknologi PETRONAS | Malaysia

Mr. Ibrahim Aromoy is a promising researcher in Electrical and Electronic Engineering with a growing scholarly footprint in hybrid UAV systems, artificial intelligence, and intelligent surveillance technologies. His research is centered on the development of a Pipeline Inspection Air Buoyancy Hybrid Drone, a novel UAV concept that combines lighter-than-air and heavier-than-air technologies to improve flight endurance, stability, and inspection efficiency. By integrating deep learning–based object detection architectures into UAV platforms, his work advances real-time pipeline monitoring, anomaly identification, and autonomous decision-making for industrial applications. His contributions span AI-driven automation, robotics, swarm intelligence, energy-efficient IoT systems, and 5G-enabled surveillance technologies. He has authored several research papers in reputable international journals and conferences, including publications in IEEE Access, Neurocomputing, and Elsevier venues. These works address UAV reconnaissance, transformer-based detection models for pipeline integrity assessment, and optimization frameworks inspired by swarm behavior. His research output reflects measurable scholarly influence, with 15 Scopus citations, 7 indexed documents, and an h-index of 2. Mr. Ibrahim Aromoy has participated in multiple research and development projects, contributing to UAV design, embedded hardware integration, machine learning pipelines, and cyber-secure control systems. His work in hybrid drone architecture and automated surveillance has been supported by competitive institutional funding, reinforcing the technological relevance and innovation potential of his research. His scientific contributions extend beyond publications to academic service. He serves as a peer reviewer for high-impact journals such as IEEE Access and Results in Engineering, supporting the advancement of rigorous research dissemination in engineering and applied sciences. His expertise also includes AI vision systems, OpenCV-based automation, and embedded cybersecurity applications for unmanned systems, further strengthening his interdisciplinary research profile. Mr. Ibrahim Aromoy has been recognized with research-focused scholarships and academic distinctions that support his ongoing work in UAV innovation and intelligent automation. Through his integrated expertise in UAV engineering, deep learning, and intelligent inspection systems, he continues to contribute meaningfully to the evolution of smart surveillance, autonomous robotics, and AI-augmented engineering technologies.

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

Featured Publications

1. Aromoye, I., Lo, H., Sebastian, P., Ghulam, E., & Ayinla, S. (2025). Significant advancements in UAV technology for reliable oil and gas pipeline monitoring. Computer Modeling in Engineering & Sciences, 142(2), 1155.

2. Aromoye, I. A., Hiung, L. H., & Sebastian, P. (2025). P-DETR: A transformer-based algorithm for pipeline structure detection. Results in Engineering, 26, 104652.

3. Zahid, F., Ali, S. S. A., & Aromoye, I. A. (2025). Exploring the potential benefits and overcoming the constraints of virtual and augmented reality in operator training. Transportation Research Procedia, 84, 625–632.

4. Mansoor, Y., Zahid, F., Azhar, S. S., Rajput, S., & Aromoye, I. A. (2025). Energy-efficient solar water pumping: The role of PLCs and DC-DC boost converters in addressing water scarcity. Transportation Research Procedia, 84, 681–688.

5. Zahid, F., Rajput, S., Ali, S. S. A., & Aromoye, I. A. (2025). Challenges and innovations in 3D object recognition: The integration of LiDAR and camera sensors for autonomous applications. Transportation Research Procedia, 84, 618–624.