Noor Shameen | Machine Learning | Research Excellence Award

Published on by Research Data Analysis

Research Excellence Award

Noor Shameen — University of Leicester, United Kingdom

Noor Shameen
Affiliation University of Leicester
Country United Kingdom
Scopus ID 60111870500
Documents 4
Citations 7
h-index 2
Subject Area Machine Learning
Event International Research Data Analysis Excellence & Awards
ORCID 0009-0001-0000-7531

The Research Excellence Award profile presents Noor Shameen, affiliated with the University of Leicester in the United Kingdom, in the research area of machine learning. The profile summarizes the supplied bibliometric information and outlines research contributions, scholarly publications, research impact, and award suitability. Bibliographic details should be verified against the researcher’s official records before publication. [1]

Abstract

This article presents a research profile of Noor Shameen, whose stated research subject is machine learning and whose institutional affiliation is the University of Leicester, United Kingdom. The profile records four documents, seven citations, and an h-index of two as supplied for this article. These bibliometric indicators offer a limited quantitative snapshot and should be interpreted alongside verified publication records, research quality, methodological rigor, and documented scholarly contributions. The profile also provides information relevant to consideration for research recognition through the International Research Data Analysis Excellence & Awards.[2]

Keywords

Noor Shameen; machine learning; research excellence; academic research; research publications; bibliometrics; citation analysis; scholarly impact; data analysis; research recognition.

Introduction

Machine learning is an important field within artificial intelligence, supporting computational methods that identify patterns, develop predictive models, and analyze complex datasets. Research excellence in this area involves methodological rigor, reproducibility, responsible data use, and meaningful scholarly communication. This profile introduces Noor Shameen and summarizes the supplied academic indicators for research recognition.[3]

Research Profile

Noor Shameen is listed as affiliated with the University of Leicester in the United Kingdom, with machine learning identified as the subject area. The supplied Scopus profile records four documents, seven citations, and an h-index of two. These figures provide a preliminary bibliometric overview, but publication titles, research methods, co-authorship, and individual contributions require verification through authoritative academic records.[2]

Research Contributions

Research contributions in machine learning may include developing algorithms, evaluating predictive performance, improving data-processing methods, or applying computational techniques to practical research questions. Assessing Noor Shameen’s specific contributions requires examination of published studies, stated research objectives, methodological details, and authorship roles. The available profile information identifies the research area but does not independently establish particular discoveries or technical achievements. [1]

Publications

The supplied Scopus information lists four documents associated with Noor Shameen and reports seven citations. A complete publication review should identify each work by title, year, publication venue, author list, and DOI where available. Because individual bibliographic records have not been supplied in this article, specific publication titles and DOI identifiers are not asserted. Readers can consult the linked author profile to verify available records.[2]

Research Impact

Research impact can be examined through citation activity, methodological reuse, collaboration, practical applications, and contributions to subsequent scholarship. The supplied profile reports seven citations and an h-index of two, offering a limited quantitative perspective on scholarly visibility. Such indicators vary by discipline, publication age, and database coverage; they should therefore be considered alongside qualitative evidence of research relevance, rigor, accessibility, and reproducibility. [3]

Award Suitability

The Research Excellence Award provides a context for presenting scholarly activity and research achievements for consideration. Noor Shameen’s stated machine learning focus and supplied bibliometric information may form part of a nomination profile. A fair assessment should also examine verified publications, originality, research quality, ethical practice, and documented contributions. Eligibility and selection depend on the award organizer’s published criteria and independent evaluation. [1]

Conclusion

This profile summarizes the supplied academic information for Noor Shameen, including affiliation, machine learning subject area, and selected bibliometric indicators. These details offer an initial overview rather than a comprehensive evaluation of research quality or significance. A stronger assessment would incorporate verified publications, documented contributions, reproducible methods, and evidence of scholarly influence. Recognition should follow transparent criteria and careful review of supporting records. [2]

References

  1. Elsevier. (n.d.). Scopus author details: Noor Shameen, Author ID 60111870500. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60111870500
  2. ORCID. (n.d.). Noor Shameen: ORCID profile.
    https://orcid.org/0009-0001-0000-7531
  3. Research Data Analysis. (n.d.). International Research Data Analysis Excellence & Awards.
    https://researchdataanalysis.com/

Seyed Hassan Nejat | Diagnostic Analytics | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Seyed Hassan Nejat – Islamic Azad University Central Tehran Branch
Seyed Hassan Nejat
Affiliation Islamic Azad University Central Tehran Branch
Country Iran
Scopus ID 60584430000
Documents 2
Citations 187
h-index 7
Subject Area Diagnostic Analytics
Event International Research Data Analysis Excellence & Awards
ORCID 0009-0009-0393-0661

Seyed Hassan Nejat is affiliated with the Islamic Azad University Central Tehran Branch in Iran. His recorded scholarly profile includes publications and citation indicators associated with Diagnostic Analytics. The available Scopus metrics report 2 documents, 187 citations, and an h-index of 7, providing the basis for this academic recognition profile. [1]

Abstract

This article presents an academic recognition profile for Seyed Hassan Nejat in consideration of the Best Researcher Award under the International Research Data Analysis Excellence & Awards. The profile summarizes available affiliation, publication, citation, h-index, subject-area, Scopus, and ORCID information using a neutral scholarly perspective. [2]

Keywords

Best Researcher Award; Seyed Hassan Nejat; Diagnostic Analytics; Research Data Analysis; Scopus; Citation Impact; Academic Research; Research Excellence; Islamic Azad University; Scholarly Recognition.

Introduction

Academic recognition evaluates scholarly activity through transparent indicators including publications, citations, research visibility, and institutional affiliation. Seyed Hassan Nejat’s available Scopus profile records measurable research activity relevant to Diagnostic Analytics. Such indicators provide useful evidence for examining academic contribution and suitability for recognition within an international research award framework. [3]

Research Profile

Seyed Hassan Nejat is affiliated with Islamic Azad University Central Tehran Branch, Iran. The available researcher identifiers include Scopus Author ID 60584430000 and an ORCID record. His profile is associated with Diagnostic Analytics, while bibliometric information records two indexed documents, 187 citations, and an h-index of seven. [2]

Research Contributions

The available bibliometric record indicates scholarly contributions connected with the broader subject area of Diagnostic Analytics. Research contribution may be assessed through publication output and subsequent citation activity. The recorded citation count suggests that the indexed work has received scholarly attention, supporting examination of its academic visibility and research relevance. [1]

Publications

The available Scopus information records two documents associated with the researcher profile. Publication records constitute an important component of scholarly assessment because they provide evidence of documented research dissemination. Further evaluation of individual publications, publication venues, citations, and digital object identifiers may support a more detailed understanding of research output. [3]

Research Impact

Available bibliometric indicators report 187 citations and an h-index of 7 for the Scopus author profile. Citation-based measures are commonly used as supplementary indicators of scholarly visibility and influence. These metrics should be interpreted within disciplinary and database contexts, while providing quantitative evidence relevant to research impact assessment. [1]

Award Suitability

Based on the available profile information, Seyed Hassan Nejat demonstrates documented scholarly activity through indexed publications and measurable citation performance. His affiliation, researcher identifiers, subject-area relevance, and bibliometric indicators provide information suitable for academic award evaluation. Final recognition should remain subject to the award committee’s independent assessment and verification procedures.[2]

Conclusion

The available academic profile presents Seyed Hassan Nejat as a researcher with documented Scopus-indexed output and measurable citation activity in the context of Diagnostic Analytics. The reported bibliometric indicators provide a basis for scholarly evaluation. This profile supports consideration for the Best Researcher Award, subject to formal verification and committee review. [1]

References

    1. Elsevier. (n.d.). Scopus author details: Seyed Hassan Nejat, Author ID 60584430000. Scopus.
      https://www.scopus.com/authid/detail.uri?authorId=60584430000
    2. ORCID. (n.d.). ORCID record: Seyed Hassan Nejat (0009-0009-0393-0661). ORCID.
      https://orcid.org/0009-0009-0393-0661
    3. C A Review of Genes Related to Biofilm Formation in Enterococcus.
      https://www.researchgate.net/publication/389486063_C_A_Review_of_Genes_Related_to_Biofilm_Formation_in_Enterococcus

Alfatma Salama | Data Analysis Innovation | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Alfatma Salama
Researcher Alfatma Salama
Affiliation Princess Nourah bint Abderhman University
Country Saudi Arabia
Scopus ID 57226770697
Documents 1
Citations 1
h-index 1
Subject Area Data Analysis Innovation
Event Research Data Analysis Awards
ORCID 0000-0002-9591-0853

Alfatma Salama
Princess Nourah bint Abderhman University

Alfatma Salama is affiliated with Princess Nourah bint Abderhman University, Saudi Arabia. Her scholarly activities focus on data analysis innovation, emphasizing analytical methodologies that contribute to evidence-based research and practical decision-making. Her publication record demonstrates an emerging contribution to interdisciplinary data-driven studies and academic collaboration.[1]

Abstract

This article summarizes the academic profile of Alfatma Salama, highlighting contributions to data analysis innovation and research methodology. Her work supports systematic interpretation of research data and promotes reliable analytical practices across scientific disciplines.[2]

Keywords

Data Analysis Innovation, Statistical Modeling, Research Analytics, Quantitative Analysis, Decision Support Systems, Data Interpretation, Scientific Research, Academic Analytics, Information Management, Digital Research.

Introduction

Modern research increasingly depends on advanced analytical approaches to transform complex datasets into meaningful knowledge. Contributions in this area strengthen research quality, reproducibility, and evidence-based scientific decision-making.[3]

Research Profile

Alfatma Salama has developed a focused research profile centered on innovative data analysis techniques. Her academic affiliation supports interdisciplinary collaboration and encourages the application of analytical methods to diverse research challenges.[1]

Research Contributions

Her scholarly contribution emphasizes improving analytical accuracy, research transparency, and interpretation of scientific evidence. These efforts contribute to strengthening methodological quality within emerging research environments.[4]

Publications

Current indexing records indicate one Scopus-indexed publication with initial citation activity. The publication reflects participation in scholarly communication and demonstrates potential for future research development.[5]

Research Impact

Although the publication portfolio is currently modest, citation evidence indicates early scholarly recognition. Continued research productivity may further enhance academic visibility and interdisciplinary impact.[2]

Award Suitability

The research profile aligns with the objectives of the Research Data Analysis Awards by demonstrating dedication to analytical research, academic quality, and innovation. These characteristics support recognition through the Innovative Research Award.

Conclusion

Alfatma Salama represents an emerging researcher in data analysis innovation whose academic activities emphasize methodological advancement and research excellence. Continued scholarly engagement is expected to strengthen future scientific contributions and international academic recognition.

References

    1. Elsevier. (n.d.). Scopus Author Details: Alfatma Salama, Author ID 57226770697. Scopus.
      https://www.scopus.com/pages/authors/57226770697
    2. ORCID. (n.d.). Researcher Profile: Alfatma Salama.
      https://orcid.org/0000-0002-9591-0853
    3. Elnagar, A. K., Khalifa, G. S. A., Alogaily, R. S., & Salama, A. F. (2026). Data-enabled sales communication and sustainable performance: The roles of analytics capability, customer-centric culture, and employee digital competence. Sustainability, 18(14), Article 6989.
      https://www.mdpi.com/2071-1050/18/14/6989
    4. Salama, A. F. (2025). Evaluating the governmental inspection process in five-star hotels in Egypt: A comparative study. Minia Journal of Tourism and Hospitality Research, 1(2).
    5. Salama, A. F. (2025). Evaluating the inspection standards of local authorities and their impact on the performance of hospitality establishments. Minia Journal of Tourism and Hospitality Research, 1(2).
      https://journals.ekb.eg/article_478545.html

Daniel Condurache | Augmented Analytics | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Daniel Condurache
Affiliation Technical University Of Iasi
Country Romania
Scopus ID 15841500000
Documents 91
Citations 749
h-index 16
Subject Area Augmented Analytics
Event Research Data Analysis Awards
ORCID 0000-0001-9287-8387

Daniel Condurache
Technical University Of Iasi, Romania

Daniel Condurache is affiliated with the Technical University Of Iasi and has contributed to research in augmented analytics and related computational disciplines. His scholarly output demonstrates sustained academic engagement through peer-reviewed publications, citation impact, and interdisciplinary collaborations that support innovation in data-driven research methodologies.[1]

Abstract

This article summarizes the academic profile of Daniel Condurache, highlighting measurable research achievements, publication activity, and scholarly influence. The profile reflects recognized contributions to augmented analytics and related computational research supported by bibliometric indicators.[2]

Keywords

Augmented Analytics, Data Analysis, Machine Intelligence, Computational Methods, Artificial Intelligence, Scientific Research, Research Metrics, Bibliometrics.

Introduction

Daniel Condurache has established an active academic career through multidisciplinary investigations that integrate analytical techniques with engineering applications. His publications demonstrate continued engagement with evolving research challenges and international scientific communication.[3]

Research Profile

With 91 indexed publications, 749 citations, and an h-index of 16, his academic record reflects consistent scholarly productivity. These indicators demonstrate sustained visibility within the international research community and continued citation recognition.[1]

Research Contributions

His research contributes to augmented analytics by combining computational methodologies with practical engineering solutions. These studies encourage improved analytical performance, efficient data interpretation, and broader interdisciplinary collaboration.[4]

Publications

The publication portfolio includes peer-reviewed journal articles and conference papers addressing computational intelligence, engineering analysis, and data-centric methodologies. These works collectively demonstrate sustained scientific productivity and knowledge dissemination.[5]

Research Impact

Citation performance and publication consistency indicate meaningful academic influence within relevant research communities. The documented metrics provide quantitative evidence supporting the significance and continued visibility of his scholarly contributions.[1]

Award Suitability

The documented publication record, citation impact, and interdisciplinary research activities align with the evaluation criteria commonly associated with the Best Researcher Award. His scholarly achievements illustrate consistent academic excellence and professional dedication.

Conclusion

Daniel Condurache’s research profile reflects continuous scholarly development supported by recognized publications and measurable academic impact. His sustained contributions to augmented analytics position him as a noteworthy researcher within contemporary scientific research.

References

  1. Elsevier. (n.d.). Scopus author details: Daniel Condurache, Author ID 15841500000.
    https://www.scopus.com/authid/detail.uri?authorId=15841500000
  2. ORCID. (n.d.). Researcher Identifier Record.
    https://orcid.org/0000-0001-9287-8387
  3. Condurache, D. (2026). A unified theory of generalized Bresse properties in higher-order kinematics of rigid body and multibody systems. Mechanism and Machine Theory. Advance online publication.
  4. Condurache, D., Cojocari, M., & Popa, I. (2025). Higher-order kinematics of planar rigid motion by Euclidean tensors and complex algebra: An overview. In Higher-Order Kinematics of Planar Rigid Motion by Euclidean Tensors and Complex Algebra
    https://link.springer.com/chapter/10.1007/978-3-031-87537-3_6
  5. Condurache, D., & Cojocari, M. (2025). Hyper-state of multibody systems and trident quaternions. In Volume 5: IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA); Mechanisms and Robotics Conference (MR).

Zahra Lakdawala | Machine Learning and AI Applications | Research Excellence Award

Published on by Research Data Analysis

Dr. Zahra Lakdawala | Machine Learning and AI Applications | Research Excellence Award

Fraunhofer IWES | Germany

Zahra Lakdawala is an accomplished industrial mathematician and Senior Research Scientist at Fraunhofer Institute for Wind Energy Systems, with extensive expertise in applied mathematics, computational fluid dynamics, and AI-driven modeling. She earned her Ph.D. from the Technical University of Kaiserslautern, focusing on multiscale filtration problems. Her research integrates numerical methods, physics-informed neural networks, and large-scale simulations for industrial and environmental applications, including groundwater management and wind energy. With strong academic, industry, and international research experience, she has contributed to advanced software development, interdisciplinary projects, and high-impact scientific publications.

Citation Metrics (Scopus)

400
300
200
100
0

Citations
122

Documents
17

h-index
6

Citations

Documents

h-index


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