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/

Marco Zanotti | Data Science | Best Researcher Award

Published on by Scientist Day

Mr. Marco Zanotti | Data Science | Best Researcher Award

Mr. Marco Zanotti | University of Milan-Bicocca | Italy

Marco Zanotti is an accomplished Machine Learning Engineer and Data Scientist specializing in time series analysis, forecasting, anomaly detection, and econometrics. With extensive experience across sectors such as e-commerce fashion, tourism, aviation, and digital services, he has played a key role in designing and improving advanced forecasting systems that drive data-informed business decisions. He currently serves as a Data Scientist and Forecasting Specialist at Wanan Luxury (Rome, Italy), following previous positions at Blogmeter, T-Voice, and Uvet Amex GBT, where he contributed to predictive modeling, machine learning optimization, and process automation. In academia, Marco is an Adjunct Professor at the University of Milan and other leading Italian universities, where he teaches modern time series forecasting, machine learning, programming, and statistics at both Bachelor’s and Master’s levels. He holds a Ph.D. in Statistics from the University of Milano-Bicocca, a Post-graduate Diploma in Data Science for Economics, Business and Finance, and an M.Sc. in Economics and Finance from the University of Milan. Fluent in Italian, English, and French, Marco is proficient in R, Python, SQL, Git, Shiny, and Google Cloud Platform. A member of the International Institute of Forecasters, he is passionate about bridging the gap between academic research and industrial applications, mentoring young data scientists, and advancing the science of predictive analytics.

Profile: Orcid

Featured Publications

Zanotti, M. (2025, December). On the retraining frequency of global models in retail demand forecasting. Machine Learning with Applications. https://doi.org/10.1016/j.mlwa.2025.100769

Zanotti, M., & Mazzucchelli, L. (2021). dispositionEffect [Research protocol or software].

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.