Mohammed AlAmeri | Artificial Intelligence | Innovative Research Award

Published on by New Scientists

Innovative Research Award

Mohammed AlAmeri 
Khalifa University , United Arab Emirates

Mohammed AlAmeri
Affiliation Khalifa University
Country United Arab Emirates
Scopus ID 57203369001
Documents 14
Citations 40
h-index 5
Subject Area Artificial Intelligence
Event New Scientists Awards

Mohammed AlAmeri is a researcher affiliated with Khalifa University in the United Arab Emirates, with scholarly contributions associated with the field of Artificial Intelligence and computational technologies. His research profile reflects participation in scientific investigations involving intelligent systems, machine learning methodologies, and data-driven analytical approaches relevant to contemporary digital innovation. Indexed academic records demonstrate measurable scholarly engagement through publication activity, citation performance, and interdisciplinary research dissemination.[1]

Abstract

This article presents a structured academic overview of the research profile and scholarly contributions of Mohammed AlAmeri in the field of Artificial Intelligence. The profile highlights publication visibility, citation performance, and interdisciplinary scientific engagement associated with intelligent systems and computational technologies. Indexed academic records demonstrate participation in peer-reviewed research dissemination and measurable scholarly activity relevant to contemporary Artificial Intelligence research domains. The article further evaluates the suitability of the researcher for recognition within the New Scientists Awards program.[2]

Keywords

Artificial Intelligence; Machine Learning; Intelligent Systems; Computational Intelligence; Data Analytics; AI Research; Scientific Publications; Interdisciplinary Computing; Research Impact; Academic Recognition

Introduction

Artificial Intelligence has become one of the most transformative scientific and technological fields of the twenty-first century, influencing areas such as automation, predictive analytics, robotics, healthcare systems, cybersecurity, and smart infrastructure. Contemporary AI research integrates computer science, mathematics, engineering, and data science to develop intelligent computational systems capable of advanced learning and decision-making processes.[3]

 His scholarly profile reflects measurable academic participation through indexed publications, citation activity, and interdisciplinary research dissemination relevant to Artificial Intelligence and digital innovation.[1]

Research Profile

Mohammed AlAmeri is affiliated with Khalifa University and maintains an academic profile indexed within internationally recognized scholarly databases. His research metrics include citation-based indicators and publication records associated with Artificial Intelligence and related computational research areas. The profile reflects participation in peer-reviewed scientific dissemination and interdisciplinary collaboration.[1]

Research Contributions

The research contributions associated with Mohammed AlAmeri involve scientific activities related to Artificial Intelligence methodologies, intelligent computational systems, and data-driven analytical approaches. Such contributions are relevant to the advancement of machine learning applications, predictive modeling, and automated decision-support technologies used across multiple scientific and industrial sectors.[4]

Publication records and indexed citation activity indicate participation in peer-reviewed scientific communication and interdisciplinary collaboration. The integration of Artificial Intelligence into engineering, information systems, and technological innovation frameworks further emphasizes the contemporary relevance of this research domain.[2]

Publications

The publication profile of Mohammed AlAmeri demonstrates scholarly engagement with Artificial Intelligence and computational research themes through peer-reviewed academic dissemination. Indexed scientific outputs contribute to interdisciplinary discussions associated with intelligent systems and emerging digital technologies.[5]

  1. Peer-reviewed publications related to Artificial Intelligence methodologies and intelligent systems.
  2. Research outputs indexed through Scopus and other scholarly databases.
  3. Scientific contributions involving machine learning and computational analytics.
  4. Interdisciplinary publications associated with technological innovation and data science applications.

Research Impact

Research impact within Artificial Intelligence is frequently evaluated through publication dissemination, citation accumulation, and interdisciplinary scientific relevance. Mohammed AlAmeri’s academic profile includes indexed publications and citation activity demonstrating measurable engagement within the scientific research community. Citation-based indicators further support the visibility of his scholarly contributions within computational and AI-related research domains.[2]

The availability of indexed academic records through Scopus provides additional evidence of research accessibility and scholarly dissemination. Such indicators are commonly utilized within academic evaluation frameworks and scientific recognition programs to assess publication visibility and research influence.[1]

Award Suitability

The academic profile of Mohammed AlAmeri demonstrates characteristics commonly associated with eligibility for scientific recognition programs, including publication activity, interdisciplinary collaboration, and participation in emerging technological research domains. His work in Artificial Intelligence aligns with contemporary scientific priorities focused on intelligent systems, digital transformation, and computational innovation.[4]

The documented publication metrics, citation indicators, and interdisciplinary research relevance associated with Mohammed AlAmeri support consideration for recognition within the Innovative Research Award category.[5]

Conclusion

Mohammed AlAmeri has established a developing academic profile within the field of Artificial Intelligence through publication activity, indexed research dissemination, and scholarly engagement in computational research domains.  The documented academic contributions collectively support recognition within international scientific award initiatives focused on research excellence and emerging innovation.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Mohammed AlAmeri, Author ID 57203369001. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57203369001
  2. Elsevier. (n.d.). Research metrics and scholarly indexing for Artificial Intelligence publications. Scopus Database.
    https://www.scopus.com/
  3. Stanford Encyclopedia of Philosophy. (n.d.). Artificial Intelligence overview and scientific foundations.
    https://plato.stanford.edu/entries/artificial-intelligence/
  4. DOI Foundation. (2021). Artificial Intelligence and intelligent systems research publication.
    https://doi.org/10.1016/j.artint.2021.103558
  5. New Scientists Awards. (n.d.). International scientific recognition and academic excellence initiative.
    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

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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.