Hermanto | Data Science | Innovative Research Award

Published on by Scientist Day

Global Scientist Day Awards

Hermanto

Universitas Bakti Indonesia

Hermanto
Affiliation Universitas Bakti Indonesia
Country Indonesia
Google Scholar ID pKj9u8oAAAAJ
Documents 46
Citations 6
h-index 4
Subject Area Data Science
Event Global Scientist Day Awards

Hermanto is affiliated with Universitas Bakti Indonesia in Banyuwangi, Indonesia. His Google Scholar profile identifies educational statistics and mathematics education as research interests, while a coauthored publication addresses rainfall forecasting using extreme gradient boosting. This article presents his research profile in the context of the Global Scientist Day Awards without asserting nomination, selection, or receipt of an award.[1][2]

Abstract

This article examines the academic profile of Hermanto, affiliated with Universitas Bakti Indonesia, within the context of the Global Scientist Day Awards. Available sources connect his work with mathematics education, educational statistics, and applied computational research. A verified publication investigates monthly rainfall forecasting in Banyuwangi using extreme gradient boosting. Supplied publication and citation figures are retained as unverified information because they differ from the retrieved Google Scholar record.[1][2]

Keywords

The principal indexing terms are Hermanto, Universitas Bakti Indonesia, Indonesia, data science, mathematics education, educational statistics, extreme gradient boosting, rainfall forecasting, Google Scholar, and Global Scientist Day Awards. These terms describe the supplied institutional context, documented publication themes, and recognition setting; they do not establish award status or verified rankings.

Introduction

Hermanto’s academic record combines educational interests with an identifiable application of machine learning. His affiliation appears on both the supplied Scholar profile and the rainfall forecasting publication. The awards website provides the event context, but the reviewed material does not establish an individual award decision concerning this researcher or nomination.[1]

Research Profile

The linked Google Scholar profile identifies Hermanto with Universitas Bakti Indonesia Banyuwangi and lists educational statistics and mathematics education as interests. Its publication entries include learning technologies, mathematical problem solving, and statistical analysis. Data science is therefore presented here as the supplied classification, not the profile’s stated primary disciplinary designation.[1][2]

Research Contributions

A documented contribution is Hermanto’s coauthorship of research applying extreme gradient boosting to monthly rainfall forecasting in Banyuwangi. This study connects computational prediction with a geographically specific environmental problem. The available evidence establishes participation in that publication, but does not isolate individual author responsibilities or demonstrate broader operational adoption independently. [3]

Publications

Fauziah, Hermanto, and Sukmarini published “Extreme Gradient Boosting pada Peramalan Pola Curah Hujan Bulanan Kabupaten Banyuwangi” in Jurnal Kridatama Sains dan Teknologi, volume six, issue two, pages 430–440, in 2024. This verified example does not independently substantiate the supplied total of forty-six documents attributed here.[1] [3]

Research Impact

The supplied figures report 46 documents, six citations, and an h-index of four. The retrieved Scholar profile instead displays two citations and an h-index of one. These records cannot be treated as equivalent measurements. Without a dated source for the supplied figures, quantitative impact remains unresolved rather than independently verified. [3]

Award Suitability

The organizer describes nomination procedures and several award categories on its website. Hermanto’s documented publication could support a research portfolio submitted for assessment, but suitability cannot be established from publication counts alone. No reviewed source confirms his nomination, eligibility determination, selection, or award receipt; those outcomes require direct documentary evidence. [3][1][2]

Conclusion

Available evidence supports Hermanto’s institutional affiliation, educational research interests, and coauthorship of an applied forecasting study. It does not resolve the supplied bibliometric discrepancies or establish award status. Accordingly, this article serves as a sourced academic profile, preserving a clear distinction between documented scholarship, supplied information, and prospective recognition considerations.

References

  1. Google Scholar. (2026.). Hermanto: Universitas Bakti Indonesia Banyuwangi. Public author profile, ID pKj9u8oAAAAJ.
    https://scholar.google.co.id/citations?user=pKj9u8oAAAAJ&hl=id
  2. Etnomatematika dalam pembelajaran matematika di SMA.
    https://doi.org/10.30605/pedagogy.v10i4.7045
  3. Global Scientist Day Awards. (2026.). Global Scientist Day Awards:
    https://scientistday.org/

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/

Sonia Abdennadher | Emerging Technologies | Research Excellence Award

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Dr. Sonia Abdennadher | Emerging Technologies | Research Excellence Award

The Higher Colleges of Technology | United Arab Emirates

Dr. Sonia Abdennadher, PhD, CMA, is an accomplished Associate Professor of Finance and Accounting at the Higher Colleges of Technology (HCT), Al Ain, United Arab Emirates, and a tenured Associate Professor at the University of Rouen, France. With over two decades of academic and professional experience across Europe and the Middle East, she has established a strong international reputation in corporate governance, auditing, financial reporting, fintech adoption, and ESG practices. Dr. Abdennadher earned her PhD in Business Administration from Paris-Saclay University, where her doctoral research pioneered the study of technology intermediation in corporate governance, with a particular focus on Internet voting in shareholders’ general meetings. Her academic background is further strengthened by dual master’s degrees in Networks Management and Economics and International Finance, Trading, and Capital Markets, complemented by a solid undergraduate foundation in finance. At HCT, Dr. Abdennadher plays a key leadership role, serving as Chair of Promotion Committees, member of the Higher Faculty Promotion Committee, Applied Research Coordinator, and System Course Team Leader in Sustainable Finance. She has taught a wide range of undergraduate and postgraduate courses in finance, accounting, auditing, corporate governance, sustainable finance, and investment analysis, and has extensive experience in executive education and capstone research supervision. Her research portfolio includes numerous high-impact publications in leading Q1 journals such as Journal of Business Ethics, Finance Research Letters, Corporate Governance, Corporate Social Responsibility and Environmental Management, and International Journal of Finance & Economics. Her work bridges theory and practice by examining blockchain, artificial intelligence, fintech, and ESG measurement within financial markets and governance systems, particularly in the UAE and MENA region. Dr. Abdennadher has successfully led and co-led multiple competitive research grants exceeding AED 600,000, and actively collaborates with regulators, stock exchanges, and Big Four audit firms. Through her scholarship, leadership, and policy-relevant research, she continues to contribute significantly to the modernization of corporate governance and sustainable finance globally.

 

Citation Metrics (Scopus)

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Citations
122

Documents
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h-index
6

Citations

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View Scopus Profile

Featured Publications


The Effects of Blockchain Technology on the Accounting and Assurance Profession in the UAE: An Exploratory Study


– Journal of Financial Reporting and Accounting, Vol. 20(1), pp. 53–71, 2022

Corporate Social Responsibility Antecedents and Practices as a Path to Enhance Organizational Performance: Evidence from SMEs


– Corporate Social Responsibility and Environmental Management, Vol. 28(6), pp. 1647–1663, 2021

The Effectiveness of E-Corporate Governance: An Exploratory Study of Internet Voting at Shareholders’ Annual Meetings in France


– Corporate Governance: The International Journal of Business in Society, Vol. 20(4), 2020

Marwan | AI Advancements | Excellence in Research

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Dr. Marwan | AI Advancements | Excellence in Research

Dr. Marwan | King Faisal University | Saudi Arabia

The applicant is an accomplished scholar in Computer Information Systems with specialized expertise in Artificial Intelligence and Data Science, supported by extensive experience in academia, research, and innovation. Over more than a decade of university-level teaching and research, the applicant has contributed significantly to advancing intelligent systems, machine learning applications, IoT security, biomedical imaging, and pattern recognition. Their doctoral work introduced a novel model for Arabic handwritten text recognition, forming the foundation for a strong research career in AI-driven language and image processing technologies. The applicant has authored and co-authored numerous impactful, refereed journal publications in well-recognized international outlets such as Sustainability, IJACSA, Traitement du Signal, and the Journal of Ayub Medical College. Research contributions span cancer therapy enhancement, anomaly detection, cephalometric landmark detection, multiple sclerosis classification, industrial IoT security, biometric iris recognition, palm disease classification, and Arabic word recognition. Several works have been indexed in Scopus and other reputable databases, with active collaborations involving interdisciplinary and multinational research teams. Beyond publications, the applicant has secured competitive research funding, including a grant supporting the development of a weighted-voting IoT security model targeting BASHLITE and Mirai cyberattacks. Ongoing research activities include hybrid deep learning systems for intrusion detection, medical image analysis, human nail disease diagnosis, music-brain interactions, and predicate-logic-based machine translation. The applicant has demonstrated strong academic service through extensive peer-reviewing, participation in scientific councils, and membership in research committees. Their professional development includes participation in conferences, training programs, and workshops focused on e-learning, scientific publishing, and advanced teaching strategies. Recognized for excellence, the applicant has received awards such as the Distinguished Scientific Research Award and the Outstanding International Publishing Award, reflecting sustained research quality and global scholarly impact. Their overall portfolio showcases a balanced blend of innovative research, academic leadership, and contributions to the AI and data science community.

Profiles: Scopus 

Featured Publications

Siddiqi, M. H., Alhwaiti, Y., Elaiwat, S., & Abu-Zanona, M. (2024). Dynamic healing process analysis: Image morphing with warping technique for nose and esophagus studies. The International Arab Journal of Information Technology, 21(3). (Accepted December 26, 2023).

 

Sayed Abdul Majid Gilani | Emerging Technologies | Best Researcher Award

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Dr. Sayed Abdul Majid Gilani | Emerging Technologies | Best Researcher Award

Dr. Sayed Abdul Majid Gilani | Birmingham City University | United Kingdom

Dr. Sayed Abdul Majid Gilani is an accomplished researcher in electrical and electronic engineering, specializing in embedded systems, automation, and control engineering. His multidisciplinary research integrates hardware design, sensor networks, and artificial intelligence to develop innovative and energy-efficient solutions for real-world challenges. With over a decade of experience in academia and applied research, Dr. Gilani has contributed significantly to the advancement of embedded control technologies, renewable energy optimization, and industrial automation systems. His work emphasizes intelligent system design, IoT-based automation, and the integration of machine learning algorithms for enhanced performance and sustainability. Dr. Gilani has published extensively in high-impact journals and presented at leading international conferences, reflecting his global engagement and scientific rigor. He has also supervised numerous research projects and guided students in developing practical applications of emerging technologies. His research outputs demonstrate a strong commitment to technological innovation that bridges the gap between theory and application. Recognized for his academic excellence and collaborative research initiatives, Dr. Gilani continues to advance cutting-edge developments that contribute to the evolution of smart, adaptive, and efficient engineering systems—making him a deserving candidate for the Best Researcher Award.

Profiles: Google Scholar | Scopus | LinkedIn | Research Gate

Featured Publications 

Gilani, S. A. M., & Faccia, A. (2021). Broadband connectivity, government policies, and open innovation: The crucial IT infrastructure contribution in Scotland. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 1. https://doi.org/10.3390/joitmc8010001

Gilani, S. A. M., Copiaco, A., Gernal, L., Yasin, N., Nair, G., & Anwar, I. (2023). Savior or distraction for survival: Examining the applicability of machine learning for rural family farms in the United Arab Emirates. Sustainability, 15(4), 3720. https://doi.org/10.3390/su15043720

Gilani, S., Gernal, L., Tantry, A., Yasin, N., & Sergio, R. (2022). Leadership styles adopted by Scottish micro-businesses during the COVID-19 pandemic. In Proceedings of the International Conference on Business and Technology (pp. 144–156). Springer.

Al Jaghoub, J., Suleiman, A., Takshe, A. A., Moussa, S., Gilani, S. A. M., Sheikh, S., & others. (2024). The role of innovation in waste management for enterprises: A critical review of the worldwide literature. In Technology-Driven Business Innovation (pp. 453–464). Springer.

Gernal, L., Tantry, A., Gilani, S. A. M., & Peel, R. (2024). The impact of online learning and soft skills on college student satisfaction and course feedback. In Technology-Driven Business Innovation: Unleashing the Digital Advantage (pp. 42–54). Springer.

Gilani, S. A. M., Tantry, A., Askri, S., Gernal, L., & Sergio, R. (2023). Adoption of machine learning by rural farms: A systematic review. In Proceedings of the International Conference on Computing and Informatics (pp. 324–335). Springer.

Samia ZAOUI | AI Advancements | Women Researcher Award

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Mrs. Samia ZAOUI | AI Advancements | Women Researcher Award

Mrs. Samia ZAOUI | Mohammed VI Foundation of Health and Sciences | Morocco

Dr. Samia Zaoui, based in Rabat, Morocco, is a multidisciplinary researcher and project leader bridging artificial intelligence, aeronautics, and healthcare systems innovation. She is currently pursuing her Ph.D. in Computer Science Engineering (AI & Aeronautics) at the Higher Institute of Aeronautics and Space (ISAE-SUPAERO) and INP Toulouse, France. Her research focuses on the application of AI technologies for supply chain resilience, predictive modeling, and sustainable industrial systems, with a strong emphasis on pharmaceutical and healthcare logistics. Dr. Zaoui has an extensive background in strategic project development, digital transformation, and industrial management, having held leadership roles at the Mohammed VI Foundation of Health and Sciences and the Cheikh Zaid Foundation. She has spearheaded projects in sports medicine innovation, pharmaceutical manufacturing, and medical technology transfer, fostering collaborations with international organizations such as WHO, LCIF, and Smile Train. Her scientific contributions include several peer-reviewed publications in international journals, such as the Global Journal of Flexible Systems Management and Production Planning & Control, covering topics like AI-driven supply chain viability, sustainability in Industry 5.0, and pharmaceutical risk prediction using machine learning. Dr. Zaoui’s research integrates AI-based decision systems with aeronautical and industrial engineering principles, contributing to global efforts in intelligent, resilient, and sustainable supply networks. She also actively participates in international technology exhibitions and collaborative industrial initiatives across Europe, Asia, and Africa.

Profile: Google Scholar

Featured Publications

Zaoui, S., Foguem, C., Tchuente, D., Fosso-Wamba, S., & Kamsu-Foguem, B. (2023). The viability of supply chains with interpretable learning systems: The case of COVID-19 vaccine deliveries. Global Journal of Flexible Systems Management, 24(4), 633–657. https://doi.org/10.1007/s40171-023-00357-w

Zaoui, S., Foguem, C., Tchuente, D., & Kamsu-Foguem, B. (2025). The application of artificial intelligence technologies in the resilience and the viability of supply chains: A systematic literature review. Production Planning & Control, 1–18.

Zaoui, H., Zaoui, S., Kamsu-Foguem, B., & Tchuente, D. (2024). Sustainability: The main pillar of Industry 5.0. Oklahoma International Publishing (OkIP) Books. https://doi.org/10.55432/978-1-6692-0007-9_13

StEER – Structural Engineering Extreme Event Reconnaissance. (2024). Hualien City, Taiwan Earthquake: Preliminary Virtual Reconnaissance Report (PVRR). https://doi.org/10.17603/ds2-0d2z-9682

Keyi Chen | AI Advancements | Best Researcher Award

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Mr. Keyi Chen Jihua Laboratory | AI Advancements | Best Researcher Award

Mr. Keyi Chen | Jihua Laboratory | China

Keyi Chen is a dedicated research engineer at Jihua Laboratory, Foshan, Guangdong Province, China. He obtained his MSc in Crop Informatics from Huazhong Agricultural University , where he built a strong foundation in computational modeling and artificial intelligence applications. His research primarily focuses on deep learning algorithms, particularly their integration into computer-based recognition systems and intelligent environmental analysis. He has completed three research projects and participated in one industry consultancy project, demonstrating both academic and applied innovation. His current research explores AI-driven recognition of marine microalgae, an essential area for assessing aquatic ecological health. In this domain, Chen developed a ResNeXt-50-based multi-expert network with an exponential feature compression mechanism that effectively mitigates class imbalance issues. Evaluated on the WHIO-Plankton dataset, his model achieved a state-of-the-art performance with an average precision and average recall , outperforming existing baselines. The system’s low inference latency demonstrates high real-time feasibility. His contributions provide a robust framework for marine microalgae recognition, supporting environmental monitoring and marine life science research. With Citations by 38 documents, 3 publications, and an h-index of 2, Chen has established himself as a rising researcher in applied AI and computational biology. His ongoing innovations signify impactful potential in environmental intelligence, sustainable technology, and bioinformatics applications.

Profile: Scopus | Orcid

Featured Publications

Chen, K., Cui, S., Zhong, J., & Wang, Q. (2025). MicroalgaeNet: Enhancing recognition of long-tailed marine microalgae images through multi-expert networks and feature compression. Algal Research, 92, 104333. https://doi.org/10.1016/j.algal.2025.104333

Song, P., Chen, K., Zhu, L., Yang, M., Ji, C., Xiao, A., Jia, H., Zhang, J., & Yang, W. (2022). An improved cascade R-CNN and RGB-D camera-based method for dynamic cotton top bud recognition and localization in the field. Computers and Electronics in Agriculture, 202, 107442. https://doi.org/10.1016/j.compag.2022.107442

Bao Liu | Emerging Technologies | Best Researcher Award

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Dr. Bao Liu | Emerging Technologies | Best Researcher Award

Dr. Bao Liu | Xi’an University of Science and Technology | China

Dr. Liu Bao is an Associate Professor and Academic Leader in the field of Pattern Recognition and Intelligent Systems at the School of Electrical and Control Engineering, Xi’an University of Science and Technology, where he also serves as a Graduate Supervisor and Project-based Ph.D. Supervisor. He earned his doctorate in engineering from Xi’an Jiaotong University, completed postdoctoral research at Xi’an University of Science and Technology, and broadened his academic experience as a visiting fellow at Macquarie University in Australia. Recognized as a Senior Data Analyst by the Ministry of Industry and Information Technology of China, Dr. Liu is an active member of several national academic societies and professional committees. His research focuses on multi-source information fusion and intelligent technologies for coal fire disaster prevention and control, integrating advanced computational and automation techniques to address complex industrial challenges. Throughout his career, he has led diverse national, provincial, and industry-based research projects and contributed extensively to scientific publications and technological innovation through patents and software developments. As a committed educator and mentor, Dr. Liu has inspired students to excel in academic and professional pursuits and has been honored with multiple awards recognizing his dedication to teaching, research, and academic service.

Profile: Orcid

Featured Publications

Liu, B., Liu, Q., & Wu, Z. (2026, February). A novel robust Student’s t scale mixture distribution based Kalman filter. Signal Processing. https://doi.org/10.1016/j.sigpro.2025.110296

Liu, B., Wu, Z., & Liu, Q. (2025). Gaussian mixture model-based variational Bayesian approach for extended target tracking. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2025.3565347

Liu, B., & Jiang, W. (2024). DFKD: Dynamic focused knowledge distillation approach for insulator defect detection. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2024.3485446

Liu, B., & Jiang, W. (2024, December). LA-YOLO: Bidirectional adaptive feature fusion approach for small object detection of insulator self-explosion defects. IEEE Transactions on Power Delivery. https://doi.org/10.1109/TPWRD.2024.3467915

Liu, B., Zhou, N., & Wang, Z. (2024, December 27). DFI-YOLOv8 based defect detection method for fan blades. In Proceedings of the 2024 Conference on [Insert Conference Name]. https://doi.org/10.1145/3722405.3722437

Saleem Ramadan | Data Science | Best Researcher Award

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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.