Hussein Ali Ahmed Ghanim | Artificial Intelligence | Innovative Research Award

Published on by New Scientists

Innovative Research Award

        Hussein Ali Ahmed                        Ghanim
Author Hussein Ali Ahmed Ghanim
Affiliation University of Kassala
Country Sudan
ORCID ID 0009-0008-0639-967X
Subject Area Artificial Intelligence
Event New Scientists Awards

Hussein Ali Ahmed Ghanim

University of Kassala, Sudan

Hussein Ali Ahmed Ghanim is an academic researcher affiliated with the University of Kassala in Sudan, with a stated research area in Artificial Intelligence. His academic profile is associated with contemporary developments in computational methods, intelligent systems, machine learning, and data-driven technologies. The researcher is also identified through an ORCID record, providing a persistent identifier for distinguishing scholarly work and research activities.[1]

Abstract

This academic recognition profile presents Hussein Ali Ahmed Ghanim, a researcher affiliated with the University of Kassala, Sudan, whose stated subject area is Artificial Intelligence. The profile summarizes the available academic information concerning his institutional affiliation, research specialization, researcher identification, and potential relevance to an Innovative Research Award. ORCID provides a persistent digital identifier intended to distinguish researchers and connect them with their scholarly contributions.[1]

Keywords

Artificial Intelligence, Machine Learning, Intelligent Systems, Computational Research, University of Kassala, Sudan, Research Innovation, Scholarly Communication, Innovative Research Award, New Scientists Awards.

Introduction

Artificial Intelligence is a multidisciplinary field concerned with computational systems capable of performing tasks that ordinarily require aspects of human intelligence, including learning, reasoning, perception, classification, and decision-making. Current research encompasses machine learning, deep learning, natural language processing, computer vision, intelligent agents, optimization, and data-driven modelling. Scholarly research in these areas contributes to the development and evaluation of computational techniques across scientific and applied domains.[2]

Within this broader research environment, Hussein Ali Ahmed Ghanim is associated with the University of Kassala and identifies Artificial Intelligence as his subject area. His ORCID identifier provides a persistent mechanism for linking his scholarly identity with research outputs and professional activities.[1]

Research Profile

The available profile information identifies Hussein Ali Ahmed Ghanim as a researcher at the University of Kassala in Sudan. His stated academic specialization is Artificial Intelligence, placing his research within a rapidly developing area of computer science and interdisciplinary computational research.

  • Researcher: Hussein Ali Ahmed Ghanim
  • Institution: University of Kassala
  • Country: Sudan
  • Subject Area: Artificial Intelligence
  • ORCID: 0009-0008-0639-967X

ORCID is widely used within scholarly communication to provide persistent identifiers for researchers and improve the attribution and discoverability of research contributions. The ORCID record associated with Hussein Ali Ahmed Ghanim can therefore serve as a reference point for distinguishing the researcher from individuals with similar names.[1]

Research Contributions

Research in Artificial Intelligence can involve the design, implementation, testing, and evaluation of computational systems that learn from data or perform tasks associated with intelligent behavior. Depending on the specific research problem, contributions may involve algorithms, predictive models, optimization procedures, knowledge representation, automated decision-making, or applications of intelligent technologies.

Because detailed publication-level information was not supplied for this profile, specific research findings or individual technical contributions are not attributed here without supporting bibliographic evidence. This approach distinguishes the documented subject specialization from claims about particular research outcomes.

Publications

Publication records are an important component of academic evaluation because they provide evidence of research dissemination through scholarly communication channels. For Hussein Ali Ahmed Ghanim, publication information should be verified through authoritative academic indexing services or the researcher’s maintained scholarly profiles before individual papers, citation counts, journal details, or DOI metadata are attributed to the researcher.

  • ORCID provides a persistent researcher identifier for scholarly attribution.[1]
  • Scopus can be used to verify indexed author and publication records where an author profile is available.[3]
  • Google Scholar can provide an additional discovery route for scholarly publications and citations when a verified author profile is available.[4]

Research Impact

Academic research impact may be considered through several complementary indicators, including the quality and relevance of publications, citation activity, research collaborations, adoption of findings, and contribution to the advancement of a field. Bibliometric databases can assist with measuring aspects of scholarly visibility, although citation indicators should be interpreted in relation to disciplinary context and the underlying publication record.[3]

For a researcher working in Artificial Intelligence, research impact may also arise through the development or evaluation of computational methods, datasets, software, models, or applications. A complete assessment should therefore consider qualitative research contributions alongside quantitative bibliometric indicators.

Award Suitability

The Innovative Research Award is presented within the context of the New Scientists Awards. Hussein Ali Ahmed Ghanim’s stated specialization in Artificial Intelligence provides a relevant disciplinary basis for consideration within an innovation-focused research recognition program. The assessment of award suitability should ultimately be based on the applicable nomination criteria, documented research outputs, originality, scholarly contribution, and evidence supplied during the formal evaluation process.[5]

Artificial Intelligence is an active research field in which methodological innovation and interdisciplinary applications are important areas of scholarly development. Recognition within such a field may appropriately consider the originality and significance of a candidate’s documented research rather than relying solely on bibliometric measures.

Conclusion

Hussein Ali Ahmed Ghanim is affiliated with the University of Kassala in Sudan and has identified Artificial Intelligence as his research subject area. His academic identity is supported by an ORCID identifier, which provides a persistent mechanism for scholarly identification and attribution.[1]

The profile provides an academic basis for consideration for the Innovative Research Award while avoiding unsupported claims concerning specific publications or research outcomes. Further evaluation can incorporate verified publication records, research findings, scholarly impact, and the formal criteria established by the New Scientists Awards program.

References

  1. ORCID. (n.d.). ORCID record: Hussein Ali Ahmed Ghanim. ORCID.
    https://orcid.org/0009-0008-0639-967X
  2. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
    https://aima.cs.berkeley.edu/
  3. Elsevier. (n.d.). Scopus Author Search and Abstract & Citation Database. Scopus.
    https://www.scopus.com/
  4. Google. (n.d.). Google Scholar. Google Scholar.
    https://scholar.google.com/
  5. New Scientists Awards. (n.d.). New Scientists Awards — Official Award Website.
    https://newscientists.net/

Hui Lyu | Artificial Intelligence and Machine Learning | Best Researcher Award

Published on by New Scientists

Dr. Hui Lyu | Artificial Intelligence and Machine Learning | Best Researcher Award

Student, Zibo Normal College | China

Dr. Hui Lyu is an interdisciplinary researcher specializing in complex-valued neural networks, infrared image enhancement, intelligent signal processing, data fusion, and optimization-based machine learning. Her peer-reviewed output includes 4 SCI-indexed journal articles and international conference papers, with publications in IEEE Sensors Journal, IEEE Access, Neural Processing Letters, and Signal, Image and Video Processing. Her research integrates adaptive convolutional filtering, quaternion extreme learning machines, and multisensor fault diagnosis frameworks. She is a contributor to national and municipal funded research on infrared vision systems, sensor testing, and complex neural learning algorithms, and holds 5 granted invention patents in infrared imaging and intelligent detection systems. Her work has received regional scientific achievement recognition.

View Scopus Profile  View ORCID Profile

Featured Publications

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

Published on by New Scientists

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

Soufiane Bacha | Artificial Intelligence | Best Researcher Award

Published on by New Scientists

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).