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/

Qing Wang | Neuroscience Discoveries | Best Innovation Award

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Prof. Qing Wang | Neuroscience Discoveries | Best Innovation Award

Prof. Qing Wang | Southern Medical University | China

Professor Dennis Qing Wang is a distinguished neurologist and neuroscientist recognized for his pioneering research in neurodegenerative diseases, brain imaging, and translational neuroscience. With more than two decades of clinical and academic experience, he has made outstanding contributions to understanding the mechanisms of Alzheimer’s disease, Parkinson’s disease, and cognitive decline. Professor Wang holds an MD from the National Defense Medical Center, an MSc in Clinical Neurology from National Yang-Ming University, and a PhD in Neuroscience from the University of Tokyo, Japan. His multidisciplinary work bridges advanced neuroimaging and molecular neuroscience, fostering early diagnostics and therapeutic innovations that bridge laboratory research and patient care. He has authored 66 peer-reviewed scientific papers, which have collectively received 3,423 citations across 3,040 documents, demonstrating the global impact of his research. With an h-index of 30, his scholarship reflects both depth and sustained influence in the neuroscience community. As Professor of Neurology at Changhua Christian Hospital, he leads research initiatives in neurogenetics, cognitive neuroscience, and neurodegeneration, supervising emerging scientists and clinicians in the field. He has been invited to deliver lectures at leading international conferences and actively contributes to global collaborations in clinical neuroscience.

Profile: Scopus

Featured Publications

Wang, D. Q., et al. (2025). Development and validation of a polysomnography-based nomogram for predicting amnestic mild cognitive impairment in middle-aged and elderly people. Sleep Medicine.

Wang, D. Q., et al. (2025). Vascular depression: A comprehensive exploration of the definition, mechanisms, and clinical challenges.

Wang, D. Q., et al. (2025). Cellular fibronectin exacerbates α-synuclein aggregation via integrin α4β1-mediated PARP1 and SCD elevation. Neurotherapeutics.

Wang, D. Q., et al. (2025). Fibrinogen exacerbates α-synuclein aggregation and mitochondrial dysfunction via α5β3 integrin in Parkinson’s disease.

Wang, D. Q., et al. (2024). Lee Silverman Voice Treatment for dysarthria in patients with Parkinson’s disease: A systematic review and meta-analysis.

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