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/

Martin Sagayam Kulandairaj | Machine learning | Editorial Board Member

Published on by Scientist Day

Global Scientist Day Awards

Martin Sagayam Kulandairaj
SRM TRP Engineering College

Martin Sagayam Kulandairaj
Affiliation SRM TRP Engineering College,Trichy
Country India
Scopus ID 56938711400
Documents 171
Citations 3,382
h-index 32
Subject area Machine learning
Event Global Scientist Day Awards
ORCID 0000-0003-2080-0497

Global Scientist Day Awards is an academic recognition initiative associated with acknowledging researchers, scientific contributions, and innovation across disciplines. This profile presents the supplied bibliometric information for Martin Sagayam Kulandairaj, affiliated with SRM TRP Engineering College in Trichy, India, and identifies machine learning as the stated subject area. It summarizes research-related indicators, outlines the broader context of scholarly impact, and discusses potential award suitability without asserting an independently verified award outcome. Author identification, publication records, and citation indicators should be checked against the relevant scholarly databases before formal publication or use in an award evaluation.[1][2]

Abstract

This article presents an academic recognition profile for Martin Sagayam Kulandairaj of SRM TRP Engineering College, Trichy, India, with machine learning identified as the principal subject area. The supplied bibliometric indicators report 171 documents, 3,382 citations, and an h-index of 32 under Scopus author identification number 56938711400.

Keywords

Global Scientist Day Awards; Martin Sagayam Kulandairaj; machine learning; computational research; bibliometrics; scientific publications; citation analysis; h-index; scholarly impact; research recognition; Scopus; ORCID; engineering research; academic evaluation; India.

Introduction

Academic recognition initiatives provide a framework for highlighting research achievements, encouraging knowledge exchange, and increasing the visibility of scientific work. The Global Scientist Day Awards website presents the event’s institutional platform and related information for prospective participants and researchers.[4][1][2]

Research Profile

The supplied profile associates Martin Sagayam Kulandairaj with SRM TRP Engineering College in Trichy, India, and identifies machine learning as the relevant subject area. Machine learning is a branch of artificial intelligence concerned with computational methods that identify patterns in data and support prediction, classification, optimization, and related analytical tasks.[1][2]

Research Contributions

The specific research contributions of Martin Sagayam Kulandairaj should be established through examination of verified publications and their documented findings. Within machine learning, scholarly contributions may include methodological development, algorithm evaluation, model interpretability, data analysis, or applications in engineering and other scientific fields. [3]

Publications

The supplied profile reports 171 documents associated with Scopus author ID 56938711400. This figure should be treated as a reported database metric rather than an independently confirmed publication count. A publication review should establish which records are journal articles, conference papers, reviews, book chapters, or other document types and determine whether duplicate records or author-name ambiguities are present. [1]

Research Impact

The supplied bibliometric profile reports 3,382 citations and an h-index of 32. Citation counts indicate how frequently indexed documents have been cited within a database and its coverage period, while the h-index combines publication productivity and citation frequency according to a defined threshold. These indicators can help describe patterns of scholarly attention, but they are influenced by career duration, field-specific citation practices, database coverage, co-authorship, and document type. [1][3]

Award Suitability

The supplied research profile may be considered as a starting point for evaluating suitability for the Global Scientist Day Awards. The reported publication and citation indicators provide measurable information that an assessment panel could verify, while the stated machine learning subject area may help guide disciplinary review. [4]

Conclusion

This academic profile summarizes the supplied information for Martin Sagayam Kulandairaj, including the stated affiliation with SRM TRP Engineering College, Trichy, the machine learning subject area, and the reported Scopus indicators of 171 documents, 3,382 citations, and an h-index of 32. Scopus and ORCID provide useful starting points for checking the researcher’s identity and scholarly record.

References

    1. Elsevier. (2026.). Scopus author details: Martin Sagayam Kulandairaj, Author ID 56938711400. Scopus.
      https://www.scopus.com/authid/detail.uri?authorId=56938711400
    2. ORCID. (2026.). ORCID record: 0000-0003-2080-0497. ORCID.
      https://orcid.org/0000-0003-2080-0497
    3. A Multilevel Deep Learning Model for Automated Brain Tumor Segmentation Using Magnetic Resonance Images.
      https://www.mdpi.com/2075-4418/16/14/2259
    4. Global Scientist Day Awards. (2026.). Official event website.
      https://scientistday.org/

Samia ZAOUI | AI Advancements | Women Researcher Award

Published on by Scientist Day

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

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.