Alvin Modales | Computer Science | Innovative Research Award

Innovative Research Award

Alvin Modales
National Taiwan Ocean University

Alvin Modales
Affiliation National Taiwan Ocean University
Country Romania
Scopus ID 57853468800
Documents 3
Citations 10
h-index 2
Subject area Computer Science
Event International PopularScientist Awards
ORCID 0000-0002-0605-331X

The Innovative Research Award profile presents the supplied academic affiliation, bibliometric indicators, research subject area, and researcher-identification links for Alvin Modales. The profile is intended to provide a structured overview for academic recognition associated with the International PopularScientist Awards. The information supplied for this article includes a Scopus author identifier, reported publication and citation metrics, and a list of five research records attributed in the source material to a different named researcher.  [1] [2]

Abstract

This article documents the supplied academic profile of Alvin Modales in connection with the Innovative Research Award associated with the International PopularScientist Awards. The reported profile information identifies National Taiwan Ocean University as the institutional affiliation, Romania as the country, Computer Science as the subject area, and a Scopus author identifier of 57853468800. The supplied bibliometric indicators are three documents, ten citations, and an h-index of two. These figures are presented as reported values and have not been independently verified against a current author record. [1]

Keywords

Innovative Research Award; Alvin Modales; academic recognition; Computer Science; research evaluation; bibliometrics; Scopus author profile; citation analysis; ORCID; research impact; scholarly communication; International PopularScientist Awards.

Introduction

Academic recognition programmes commonly consider research outputs, publication quality, citation activity, originality, and the broader relevance of a researcher’s work. These dimensions provide complementary perspectives: publication counts describe output volume, citation indicators offer a partial view of scholarly attention, and qualitative review examines research methods, originality, reproducibility, and significance. No single bibliometric indicator can adequately represent the full value of an academic contribution. [3]

Research Profile

The supplied profile assigns Computer Science as the research subject area. No verified list of publications authored by Alvin Modales, detailed research statement, or independently confirmed institutional biography was included in the information provided. It is therefore not possible to establish specific research specialisations, methodological expertise, or a sustained research programme solely from the supplied profile fields. [2]

Research Contributions

The supplied information does not provide sufficient verified evidence to describe specific scientific discoveries, software systems, theoretical advances, datasets, or other original contributions by Alvin Modales. A scholarly account of contributions should be based on confirmed publications and supporting documentation rather than inferred from an award title or a general subject classification.

Publications

Five bibliographic records were included in the supplied source material. The source notes attribute these records to contributors including J. Hrib, J. Šmucler, and other named researchers, with some entries described as originating from a Scopus export associated with Roman Šmucler. The supplied information does not establish a bibliographic connection between those records and Alvin Modales or Scopus author ID 57853468800. They are consequently listed as unverified records supplied for review, not as confirmed publications by the nominee. [1]

Research Impact

Research impact can be assessed through a combination of quantitative indicators and qualitative evidence. Citation totals and h-index values provide limited information about the attention received by indexed publications, while field, publication age, collaboration patterns, research quality, and the intended application of findings influence how these indicators should be interpreted. Bibliometric indicators are most informative when used alongside contextual assessment rather than as standalone measures of scientific quality. [3]

Award Suitability

The Innovative Research Award is presented in this article in the context of the International PopularScientist Awards. Assessing a candidate’s suitability for research recognition ordinarily requires evidence of identifiable research contributions, verified authorship, appropriate methodological standards, and a demonstrable contribution to the relevant discipline. The assessment should also consider the criteria officially published by the awarding organisation.

Conclusion

This article organises the supplied academic information for Alvin Modales in a Wikipedia-inspired research recognition format. It records National Taiwan Ocean University as the supplied institutional affiliation, Romania as the supplied country, Computer Science as the subject area, and the Scopus and ORCID identifiers provided in the source material. The reported metrics are three documents, ten citations, and an h-index of two; they remain unverified in this article.

References

    1. Elsevier. (n.d.). Scopus author details: Alvin Modales, Author ID 57853468800. Scopus. The supplied identifier and reported metrics should be checked against the live author record; this article does not independently confirm their association with the named researcher.
      https://www.scopus.com/authid/detail.uri?authorId=57853468800
    2. ORCID. (n.d.). ORCID record identified by 0000-0002-0605-331X. The supplied identifier is included for record discovery; the identity match and associated works have not been independently confirmed in this article.
      https://orcid.org/0000-0002-0605-331X
    3. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572. This article introduces the h-index as a bibliometric measure.
      https://doi.org/10.1073/pnas.0507655102
    4. Hrib, J., Sirc, J., Lesny, P., Hobzova, R., Duskova-Smrckova, M., Michalek, J., and colleagues. (2017). Hydrogel tissue expanders for stomatology. Part I. Methacrylate-based polymers. Journal of Materials Science: Materials in Medicine. Bibliographic details and contributor list are reproduced from the supplied material; attribution to Alvin Modales has not been established.
      https://doi.org/10.1007/s10856-016-5818-y

Nooshin Nemati | Computer Science | Best Researcher Award

Ms. Nooshin Nemati | Computer Science | Best Researcher Award

Ankara University, Turkey

Dr. Nooshin Nemati is a dedicated researcher in the fields of Artificial Intelligence, Deep Learning, and Medical Image Processing, currently pursuing her PhD in Computer Engineering at Ankara University, where she also contributes to multiple AI-based cancer detection projects. She holds a Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University and a Bachelor’s from Qazvin Azad University.

Profile:

Educational Background:

Nooshin Nemati is currently a PhD candidate in Computer Engineering at Ankara University. She earned her Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University with a completed her undergraduate studies at Qazvin Azad University in Iran.

Research Areas:

Her main research interests lie in Artificial Intelligence, Deep Learning, Medical Image Processing, and Computer Vision, particularly applied to cancer detection in histopathology images. She focuses on segmentation, classification, and detection tasks using advanced deep learning frameworks.

Projects and Contributions:

She has actively contributed to significant research initiatives such as the TUBITAK 1001 Project, focused on deep learning methodologies for breast cancer detection, and the BAP Project, which deals with cancer region detection in histopathology images. She has also been involved in the development of important datasets such as NuSeC and MiDeSeC, aimed at supporting machine learning in medical imaging. In addition, she has applied her technical skills in software development projects including system analysis and automation tools for banks.

Technical Skills:

Nooshin is proficient in AI, Machine Learning, Deep Learning, and programming frameworks such as ASP.NET and WordPress. She also holds certifications like Network+ and CCNA, showcasing her broad technical competence.

Citation Metrics:

  • Total Citations: 75

  • Citations Since 2020: 71

  • h-index: 6

  • h-index Since 2020: 5

  • i10-index: 3

  • i10-index Since 2020: 3

Publication Top Notes:

  • An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images
    2023
    Citations: 22

  • Detection of colorectal cancer with vision transformers
    2022
    Citations: 11

  • Effect of color normalization on nuclei segmentation problem in H&E stained histopathology images
    2022
    Citations: 10

  • A hybridized deep learning methodology for mitosis detection and classification from histopathology images
    2023
    Citations: 8

  • CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images
    2024
    Citations: 7

 

Belal Hamed | Computer Science | Best Researcher Award

Dr. Belal Hamed | Computer Science | Best Researcher Award

Assistant Lecturer at Department of Computer Science, Faculty of Science, Minia University, Egypt

Belal Ahmed Mohammed Hamed is an Assistant Lecturer at the Department of Computer Science, Faculty of Science, Minia University, and at the Department of Artificial Intelligence, Minia National University, Egypt. He holds a master’s degree in Computer Science, with research expertise in bioinformatics, machine learning, and graph-based disease prediction models. His work focuses on developing advanced algorithms for pattern recognition in DNA sequences and medical data analysis. He has published in Scopus and SCI-indexed journals, and contributed to six research projects, including two funded ones. He also serves as a reviewer for journals such as Scientific Reports and The Journal of Supercomputing. His notable contributions include a high-accuracy Graph Convolutional Network model for Alzheimer’s gene prediction.

Profile:

Academic Background:

Belal holds a Master’s degree in Computer Science. His academic training and research work are rooted in computer science, with a focus on interdisciplinary applications in healthcare and genomics.

Research Areas:

  • Bioinformatics

  • Machine Learning

  • SNP-based Disease Prediction

  • Graph Neural Networks

  • DNA Pattern Matching Algorithms

Research Contributions:

Belal developed a deep learning model that integrates SNP data and Graph Convolutional Networks (GCNs) to predict gene-disease associations, specifically in Alzheimer’s disease. The model achieved 98.04% accuracy and AUROC of 0.996, identifying both known and novel genes. His framework is adaptable for use in other diseases, supporting personalized medicine and clinical research.

Publications & Impact:

  • 4 research papers in SCI/Scopus-indexed journals (Springer Nature, Wiley)

  • Google Scholar Citations: 73

  • h-index: 3

Research & Projects:

  • Participated in 6 research projects, including 2 funded

  • Contributed to 1 industry-academic collaboration in medical data analysis

Editorial Roles:

  • Reviewer for The Journal of Supercomputing, Scientific Reports, and Medical Data Mining Journal

  • Young Scientist – Medical Data Mining Journal

Collaborations:

Active in interdisciplinary research teams, particularly in genomics and artificial intelligence.

Publication Top Notes: