Bassant Elkalzah | Data Science | Best Researcher Award

Published on by Scientist Day

Global Scientist Day Awards

Bassant Elkalzah
Imam Mohammad Ibn Saud Islamic University

Bassant Elkalzah
Affiliation Imam Mohammad Ibn Saud Islamic University
Country Saudi Arabia
Google Scholar ID n8GWOIAAAAAJ&hl
Documents 33
Citations 84
h-index 6
Subject Area Data Science
Event Global Scientist Day Awards
ORCID 0000-0003-3218-6228

Global Scientist Day Awards is an international recognition initiative intended to highlight scientific contributions and encourage continued engagement in research. This profile presents the researcher information supplied for Bassant Elkalzah, affiliated with Imam Mohammad Ibn Saud Islamic University in Saudi Arabia, with a stated subject area of Data Science. The profile summarizes reported bibliometric indicators and links to external researcher records for further verification. The inclusion of these details provides a structured overview of the researcher’s academic profile; it does not independently verify publication records, establish award selection, or imply endorsement by the listed institution. [1] [2]

Abstract

This article presents an academic profile of Bassant Elkalzah, whose stated institutional affiliation is Imam Mohammad Ibn Saud Islamic University in Saudi Arabia and whose subject area is Data Science. The supplied profile records 33 documents, 84 citations, and an h-index of 6. These indicators provide a quantitative snapshot of reported scholarly activity, although their interpretation depends on database coverage, publication types, citation dates, and author identification. Links to Google Scholar, Scopus, and ORCID enable readers to consult external records. [1][2]

Keywords

Bassant Elkalzah; Data Science; academic research; research metrics; scholarly publications; citation analysis; Imam Mohammad Ibn Saud Islamic University; Saudi Arabia; Global Scientist Day Awards; ORCID; Google Scholar; Scopus.

Introduction

Data Science combines statistical reasoning, computational methods, and data-driven analysis to investigate questions across scientific and professional disciplines. Research in this field can support pattern identification, predictive modelling, decision-making, and the interpretation of complex datasets. Academic profiles help readers locate publications and examine selected indicators of scholarly activity. However, metrics should be interpreted alongside research quality, methodological transparency, reproducibility, and disciplinary context rather than as standalone measures of scientific merit. [1]

Research Profile

The information supplied for Bassant Elkalzah identifies Imam Mohammad Ibn Saud Islamic University in Saudi Arabia as the institutional affiliation and Data Science as the subject area. The associated Google Scholar and Scopus links provide starting points for examining indexed scholarly output, while the ORCID record supplies a persistent researcher identifier. The stated profile metrics are 33 documents, 84 citations, and an h-index of 6. [2] [3]

Research Contributions

An assessment of contributions in Data Science ordinarily considers the research questions addressed, the appropriateness of analytical methods, the quality and accessibility of data, and the reproducibility of results. Potential contributions may include developing analytical techniques, applying computational approaches to domain-specific problems, or producing evidence that supports further investigation. The supplied profile does not identify individual publications or specific findings, so particular discoveries and methodological achievements cannot be attributed to the researcher without reviewing the underlying scholarly records. [1]

Publications

The supplied information reports 33 documents associated with the research profile. To establish a reliable publication list, readers should consult the linked Google Scholar and Scopus records and compare titles, author names, publication venues, years, and digital object identifiers where available. Database records can differ because of indexing policies, duplicate entries, author-name variations, and differences in document types. Individual publications and their DOIs have not been enumerated here because a verified bibliography was not included in the supplied information. [2] [3]

Research Impact

The profile reports 84 citations and an h-index of 6. The h-index is a bibliometric measure that reflects a combination of publication productivity and citation frequency: an h-index of 6 generally means that at least six indexed publications have each received six or more citations within the database and time of measurement. Citation counts can indicate scholarly attention, but they do not directly measure accuracy, originality, societal benefit, or research quality. [4]

Award Suitability

Global Scientist Day Awards provides the context for this academic recognition profile. The reported institutional affiliation, subject area, and bibliometric indicators may serve as initial information for a research-related nomination. A fair award assessment should additionally examine the candidate’s verified publications, originality, methodological quality, contribution to the field, research integrity, and relevance to the award category. [2] [3][4]

Conclusion

This profile summarizes the supplied academic information for Bassant Elkalzah, including the stated affiliation with Imam Mohammad Ibn Saud Islamic University, Saudi Arabia, the subject area of Data Science, and the reported metrics of 33 documents, 84 citations, and an h-index of 6. External researcher identifiers offer routes for further examination of the underlying records.

References

  1. Google Scholar. (2026). Bassant Elkalzah: Google Scholar author profile.
    https://scholar.google.com/citations?user=n8GWOIAAAAAJ&hl=en
  2. Elsevier. (2026.). Scopus author details: Bassant Elkalzah, Author ID 60258457200.
    https://www.scopus.com/authid/detail.uri?authorId=60258457200
  3. ORCID. (2026.). Bassant Elkalzah: ORCID researcher record.
    https://orcid.org/0000-0003-3218-6228
  4. Enhanced estimation for distribution function using auxiliary variable: Application with radiation data.
    https://www.sciencedirect.com/science/article/pii/S1687850725003462
  5. Global Scientist Day Awards. (2026.). Official award website.
    https://scientistday.org/

Shadi Shayan | Emerging Technologies | Best Researcher Award

Published on by Scientist Day

Dr. Shadi Shayan | Emerging Technologies | Best Researcher Award

Dr. Shadi Shayan | Adelaide University | Australia

Dr. Shadi Shayan’s research lies at the intersection of project management, smart technologies, and social sustainability, focusing on how large-scale urban innovation programs can effectively manage social risks and deliver equitable outcomes. His scholarly work explores the dynamic relationships between technological transformation, social response, and governance frameworks in smart city development. By integrating change management models with risk management processes, Dr. Shayan has developed innovative frameworks that address the socio-demographic dimensions of smart city transitions—bridging theory, practice, and policy. His doctoral research, “Integrating change models and risk management processes: A framework to manage social risks in smart city programs”, provides a comprehensive model for mitigating community resistance and fostering inclusive participation in technologically driven urban initiatives. Dr. Shayan’s publications in leading journals such as Smart Cities, Sustainable Cities and Society, and International Journal of Construction Management advance understanding of how social factors, demographic variables, and stakeholder perceptions influence the success of smart city and infrastructure projects. A consistent theme in his research is the application of systems thinking and socio-technical analysis to enhance decision-making in project and program management. He also examines the evolving roles of professionals, including engineers and quantity surveyors, in adapting to emerging technological and societal challenges. Dr. Shayan’s work has significant implications for both academia and industry. It informs policy frameworks for smart urban governance, supports organizational strategies for managing social risk, and contributes to sustainable and resilient infrastructure planning. Through interdisciplinary collaborations and active engagement with the Smart Cities Council Australia New Zealand, he connects research with real-world impact—helping shape cities that are not only technologically advanced but also socially responsible and inclusive.

Profiles: Google Scholar | LinkedIn

Featured Publications 

Shayan, S., & Kim, K. P. (2025). Social responses and change management strategies in smart city transitions: A socio-demographic perspective. Smart Cities, 8(6), 188. https://doi.org/10.3390/smartcities8060188

Shayan, S., & Kim, K. P. (2023). Understanding correlations between social risks and sociodemographic factors in smart city development. Sustainable Cities and Society, 89, 104320. https://doi.org/10.1016/j.scs.2022.104320

Shayan, S., & Kim, K. P. (2022). A conceptual framework to manage social risks for smart city development programs. In Resilient and responsible smart cities (pp. 77–86). Springer, Cham. https://doi.org/10.1007/978-3-030-98423-6_6

Shayan, S., Kim, K. P., Ma, T., & Nguyen, T. H. D. (2020). The first two decades of smart city research from a risk perspective. Sustainability, 12(21), 9280. https://doi.org/10.3390/su12219280

Shayan, S., & Kim, K. P., & Tam, V. (2019). Critical success factor analysis for effective risk management at the execution stage of a construction project. International Journal of Construction Management, 22(3), 379–386. https://doi.org/10.1080/15623599.2019.1624678

Saleem Ramadan | Data Science | Best Researcher Award

Published on by Scientist Day

Assoc. Prof. Dr. Saleem Ramadan | Data Science | Best Researcher Award

Assoc. Prof. Dr. Saleem Ramadan | Al Hussein Technical University | Jordan

Dr. Saleem Z. Ramadan is an accomplished Data Analyst and Business Analyst with a strong interdisciplinary background in industrial engineering, systems optimization, and data science. With academic and consulting experience across the U.S. and Jordan, he has applied data-driven decision-making, predictive analytics, and optimization modeling to complex problems in healthcare, manufacturing, and finance. Dr. Ramadan holds a Ph.D. in Systems Engineering from Ohio University and has served as Acting Chair and Associate Professor at Al Hussein Technical University, leading research and teaching initiatives integrating machine learning and operations analytics. He has developed impactful analytics solutions—ranging from hydroponic resource optimization and radiology workflow improvement to financial risk dashboards—using tools such as Python, SQL, Power BI, Tableau, and Minitab. His consulting work with Healthcare Operations & Performance Excellence (HOPE) led to measurable improvements in hospital performance through Six Sigma and process control techniques. A Certified Analytics Professional (CAP) and Microsoft Power BI Data Analyst Associate, Dr. Ramadan has authored 20 peer-reviewed publications, accumulating 236 citations from 230 documents with an h-index of 6. His recent works focus on machine learning–driven optimization, surgical scheduling prediction, and additive manufacturing parameter tuning. Dr. Ramadan’s combination of technical proficiency, academic leadership, and applied research impact uniquely positions him at the intersection of analytics innovation and business performance excellence.

Profiles:  Scopus | Google Scholar | LinkedIn

Featured Publications

Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2025). A data-driven approach for predicting remaining intra-surgical time and enhancing operating room efficiency. Journal of Industrial Engineering and Management.

Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2024). Optimizing tensile strength and energy consumption for FDM through mixed-integer nonlinear multi-objective optimization and design of experiments. Heliyon.