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/

Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

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Dr. Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

Doctor | Beijing University of Posts and Telecommunications | China

Dr. Boyuan Bai is an emerging researcher in advanced visual computing, with a focused contribution to 3D reconstruction, Gaussian Splatting, multi-view scene modeling, and uncertainty-aware machine learning. His work integrates computer graphics, deep learning, and computational geometry to develop intelligent systems capable of producing highly accurate and stable indoor scene reconstructions. With 83 citations, 4 Scopus-indexed publications, and an h-index of 3, he is rapidly establishing a strong research footprint. Dr. Boyuan Bai’s notable scientific contribution centers on UncertainGS, an uncertainty-aware indoor reconstruction framework published in Neurocomputing (SCI/Scopus). This research introduces a novel pipeline that integrates cross-modal uncertainty prediction to guide the optimization of Gaussian Splatting. His methodological innovation improves the fidelity of reconstructed surfaces, especially in textureless or geometrically ambiguous indoor regions. His incorporation of Manhattan-world constraints into the Gaussian Splatting process represents a significant leap forward in aligning 3D surface geometry with real-world structural patterns. His research areas broadly span multi-view 3D reconstruction, Gaussian Splatting, uncertainty modeling, scene understanding, and deep reinforcement learning for geometric perception. He actively contributes to the development of next-generation 3D vision technologies, with applications in robotics, digital twins, AR/VR environments, and autonomous spatial intelligence. His work shows strong potential for large-scale deployment in real-time virtual reconstruction and simulation systems. Dr. Boyuan Bai’s scholarly output includes peer-reviewed journal publications, research project leadership, and scientific contributions that address fundamental challenges in computational imaging. His research achievements demonstrate clear innovation, technical depth, and growing influence in the fields of computer vision and graphics. Through ongoing academic collaborations and continued focus on high-impact research problems, he is emerging as a promising researcher in intelligent 3D scene modeling and uncertainty-aware visual computing.

Profiles: Scopus | IEEE Xplore | ACM Digital Library 

Featured Publications

1. Bai, B., Qiao, X., Lu, P., Zhao, H., Shi, W., & others. (2025). Two grids are better than one: Hybrid indoor scene reconstruction framework with adaptive priors. Neurocomputing, 618(C). https://doi.org/10.1016/j.neucom.2024.129118

2. Huang, Y., Bai, B., Zhu, Y., Qiao, X., Su, X., Yang, L., & others. (2024). ISCom: Interest-aware semantic communication scheme for point cloud video streaming on Metaverse XR devices. IEEE Journal on Selected Areas in Communications, 42(4). https://doi.org/10.1109/JSAC.2023.3345430

3. Zhu, Y., Huang, Y., Qiao, X., Tan, Z., Bai, B., & others. (2023). A semantic-aware transmission with adaptive control scheme for volumetric video service. IEEE Transactions on Multimedia, 25. https://doi.org/10.1109/TMM.2022.3217928

4. Huang, Y., Zhu, Y., Qiao, X., Tan, Z., & Bai, B. (2021). AITransfer: Progressive AI-powered transmission for real-time point cloud video streaming. In Proceedings of the 29th ACM International Conference on Multimedia (MM ’21). https://doi.org/10.1145/3474085.3475624

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

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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.

Soufiane Bacha | Artificial Intelligence | Best Researcher Award

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Mr. Soufiane Bacha | Artificial Intelligence | Best Researcher Award

PhD Student, University of Science and Technology Beijing, Algeria

Mr. Soufiane Bacha is a promising young researcher in Artificial Intelligence and Data Quality with a strong academic background and growing international exposure. He is currently pursuing a Ph.D. in Data Quality at the University of Science and Technology Beijing (2023–ongoing) and a Ph.D. in Cancer Epidemiology at the Department of Computer Science, Ibn Khaldoun University of Tiaret, Algeria (2021–2025). He also holds a Master’s degree in Software Engineering (2019–2021), where he ranked first in his class and completed a thesis on imbalanced datasets and boosting methods, and a B.Sc. in Computer Science (2016–2019) with strong foundations in algorithms, cryptography and programming. Professionally, Mr. Soufiane Bacha gained valuable international research experience through an internship at the Faculty of Polytechnic Mons, UMONS University in Belgium, where he worked on Internet of Things (IoT) applications involving Raspberry Pi, Arduino and sensor technologies. He has served as a part-time lecturer in Graph Theory and as an ICT trainer in web development, demonstrating strong teaching, leadership, and communication skills. His research interests span artificial intelligence, data quality, machine learning for imbalanced datasets, cancer epidemiology, distributed applications and business analytics. He is proficient in Python, C/C++, Java, SQL and data analysis tools, with expertise in OLAP, data mining, and deep learning frameworks. His achievements include an NVIDIA Deep Learning Institute Certificate, participation in AI workshops, and a Scopus-indexed publication. With a dual doctoral training and interdisciplinary focus, Mr. Soufiane Bacha is well-positioned to make impactful contributions to AI-driven data quality research and healthcare analytics on a global scale.

Profile: ORCID | Google Scholar | ResearchGate

Featured Publications

1. Bacha, S., Ning, H., Mostefa, B., Sarwatt, D. S., & Dhelim, S. (2025). A novel double pruning method for imbalanced data using information entropy and roulette wheel selection for breast cancer diagnosis (arXiv:2503.12239).