Xinying Liu | computer vision | Innovative Research Award

Published on by Math Scientist

Innovative Research Award

Xinying Liu — Shandong University of Science and Technology, China

                 Xinying Liu
Affiliation Shandong University of Science and Technology
Country China
Scopus ID 57205204712
Documents 18
Citations 65
h-index 5
Subject Area Computer Vision
Event Math Scientist Awards

This academic recognition profile presents the supplied bibliometric information for Xinying Liu, a researcher from China whose identified subject area is computer vision. The supplied profile reports Scopus Author ID 57205204712, 65 citations, and an h-index of 5.[1]

The profile is prepared in the context of the Math Scientist Awards and the stated Innovative Research Award. The information is presented in a neutral academic format, while award eligibility and recognition should ultimately be determined according to the official criteria and verification procedures of the relevant awarding organization.

Abstract

This article provides a scholarly recognition profile of Xinying Liu, a researcher from China associated with the field of computer vision. The supplied bibliometric information identifies Scopus Author ID 57205204712, with 65 reported citations and an h-index of 5.[1] Computer vision is a field of computer science and artificial intelligence concerned with methods for acquiring, processing, interpreting, and understanding visual information from images and video. Its research encompasses image analysis, object recognition, visual representation, pattern recognition, and machine learning-based perception.[2]

Keywords

Computer Vision, Image Processing, Image Analysis, Pattern Recognition, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision Algorithms, Object Detection, Object Recognition, Image Classification, Semantic Segmentation, Instance Segmentation, Feature Extraction, Visual Recognition, Image Retrieval, Video Analysis, Visual Computing, Neural Networks, Convolutional Neural Networks, Representation Learning, Medical Imaging, Remote Sensing, Scene Understanding, Visual Tracking, 3D Vision, Computational Imaging, Image Reconstruction, Multimodal Learning, Research Impact, Bibliometrics, Math Scientist Awards, Innovative Research Award.

Introduction

Computer vision develops computational methods for extracting meaningful information from visual data. Research in the field commonly addresses the representation, analysis, and interpretation of images and video, with applications extending across robotics, healthcare, autonomous systems, manufacturing, security, remote sensing, and scientific imaging.[2]

Modern computer vision increasingly incorporates machine learning and deep neural networks. Convolutional neural networks and related architectures have become important approaches for image classification, object detection, segmentation, recognition, and other visual-analysis tasks.[3]

Research Profile

The supplied citation count and h-index provide quantitative indicators of indexed scholarly visibility. These values can change over time as new publications and citations are indexed, and they should therefore be treated as time-dependent bibliometric measures.[1]

Research Contributions

Research in computer vision encompasses a broad range of computational problems involving visual information. Common research directions include image classification, object detection, image segmentation, feature representation, visual tracking, image reconstruction, 3D scene understanding, and multimodal visual analysis.[2]

Publications

The supplied information identifies Xinying Liu through Scopus Author ID 57205204712. A complete verified bibliography would require access to the corresponding Scopus author record and individual publication records. Such records may provide publication titles, journals, publication years, co-authors, citation information, and DOI identifiers.[1]

Research Impact

The supplied profile reports 65 citations and an h-index of 5. These indicators provide quantitative measures of citation activity associated with the researcher’s indexed scholarly output.[1]

Award Suitability

The Math Scientist Awards are the stated recognition context for this profile, with the Innovative Research Award identified as the recognition category. The supplied academic indicators identify a researcher associated with computer vision, with 65 reported citations and an h-index of 5. These data may form part of an academic recognition assessment, subject to the official eligibility criteria, nomination requirements, and independent verification procedures of the awarding organization.

Conclusion

Xinying Liu is presented in the supplied information as a Chinese researcher associated with computer vision. The reported Scopus profile identifies Author ID 57205204712, 65 citations, and an h-index of 5.[1] These measures provide a quantitative indication of indexed scholarly visibility. A comprehensive assessment of research achievement should additionally consider verified publications, methodological rigor, originality, scientific significance, and contributions to the development and application of computer vision.

References

  1. Elsevier. (n.d.). Scopus author details: Xinying Liu, Author ID 57205204712. Scopus. https://www.scopus.com/pages/authors/57205204712
  2. Hybrid Insurance Recommendation Algorithm Integrating Deep Neural Networks and Knowledge Graphs Based on Matrix Factorization, DOI: https://doi.org/10.31577/cai_2026_1_1

Osama Ogilat | Linear Algebra | Innovative Research Award

Published on by Math Scientist

Innovative Research Award

               Osama Ogilat
Affiliation Information not provided
Country Jordan
Scopus ID 57195353514
Documents 53
Citations 551
h-index 13
Subject Area Linear Algebra
Event Math Scientist Awards
ORCID 0000-0003-2370-6332

Osama Ogilat is a researcher from Jordan whose supplied academic profile is associated with the field of linear algebra. The bibliometric information provided for the researcher includes Scopus Author ID 57195353514, 551 citations, and an h-index of 13.[1]

Abstract

This article provides an academic recognition profile of Osama Ogilat, a researcher from Jordan whose supplied subject area is linear algebra. The available bibliometric information reports 551 citations and an h-index of 13 under Scopus Author ID 57195353514.[1] Linear algebra is a foundational area of mathematics concerned with vectors, vector spaces, matrices, linear transformations, systems of linear equations, and related algebraic structures. The profile is presented in relation to the Math Scientist Awards as a neutral summary of the supplied scholarly information.

Keywords

Linear Algebra, Matrix Theory, Vector Spaces, Linear Transformations, Eigenvalues, Eigenvectors, Numerical Linear Algebra, Computational Mathematics, Applied Mathematics, Mathematical Modelling, Matrix Analysis, Linear Systems, Optimization, Mathematical Statistics, Numerical Methods, Algebraic Structures, Research Impact, Bibliometrics, Scopus, Math Scientist Awards.

Introduction

Linear algebra provides mathematical tools for representing and analyzing linear relationships among variables. Its concepts are fundamental to numerous areas of mathematics, science, and engineering, including numerical analysis, statistics, optimization, computer science, physics, data science, and machine learning.[2]

Research Profile

The supplied academic information identifies the following principal elements of Osama Ogilat’s research profile:

The reported citation count and h-index provide quantitative indicators of indexed scholarly visibility. Such indicators are dynamic and may change as additional publications and citations are incorporated into bibliographic databases.[1]

Research Contributions

Research in linear algebra encompasses theoretical and computational studies of vector spaces, matrices, linear operators, systems of equations, eigenvalue problems, and matrix factorizations. These concepts provide a mathematical foundation for many computational techniques and analytical methods used across scientific disciplines.[2]

Publications

The supplied information identifies Osama Ogilat through Scopus Author ID 57195353514. A complete publication bibliography would require verification against the corresponding Scopus author record and individual publication sources. Such records can provide publication titles, journals, publication years, co-authors, citation information, and DOI identifiers.[1]

Research Impact

The supplied profile reports 551 citations and an h-index of 13. These bibliometric indicators demonstrate measurable citation activity associated with the researcher’s indexed scholarly output.[1]

Bibliometric measures should be interpreted within the context of the relevant discipline and database coverage. Citation practices differ among mathematical fields, journals, publication types, and research communities. Consequently, citation counts and h-index values are descriptive indicators and should be considered together with research quality, originality, methodological rigor, and broader scholarly contributions.

Award Suitability

The Math Scientist Awards constitute the stated recognition context for this academic profile. The supplied evidence identifies a Scopus-indexed researcher working in the area of linear algebra, with 551 reported citations and an h-index of 13. These indicators may provide relevant evidence for an academic recognition process, subject to the official eligibility criteria, nomination procedures, and evaluation standards of the awarding organization.

Conclusion

Osama Ogilat is presented in the supplied academic information as a Jordanian researcher associated with linear algebra. The reported Scopus profile contains 551 citations and an h-index of 13. These measures provide a quantitative description of indexed scholarly visibility and may form part of a broader assessment of academic activity. A comprehensive evaluation should additionally consider the originality, quality, significance, and influence of the researcher’s individual publications.[1]

References

  1. Mathematical modeling of human–rodent monkeypox infectious disease using a hierarchical approach, https://scik.org/index.php/cmbn/article/view/9583
  2. Spin-polarized DFT study of Pr2EuMO6 (M = Co, Fe) double perovskites for spintronic and energy applications, https://doi.org/10.1039/d6ra01748g
  3. Analysis of Elliptic Inverse Heat Conduction Problems Using a Pascal Polynomial Numerical Approach, 10.22055/jacm.2026.48835.5535
  4. Hyers–Ulam stability of a nonlinear fractional hybrid dynamic equations on arbitrary time scales via measures of noncompactness, https://link.springer.com/article/10.1186/s13663-026-00839-3
  5. Investigation of nanofluid through converging-diverging stretching Riga surface, https://doi.org/10.1177/23977914261443

Stephen Chelko | Data Science | Best Research Article Award

Published on by Math Scientist

 

Best Research Article Award

          Stephen Chelko
Affiliation Florida State University College of Medicine
Country United States
Scopus ID 54973874200
Documents 47
Citations 1,635
h-index 18
Subject Area Data Science
Event Math Scientist Awards
ORCID 0000-0003-1675-5945

Stephen Chelko

Florida State University College of Medicine, United States

Stephen Chelko is an academic researcher whose scholarly contributions have been recognized through extensive publications, citation impact, and interdisciplinary research activities. His work contributes to contemporary scientific knowledge and has attracted considerable attention within the international research community. Based on available scholarly indicators, his research profile demonstrates sustained academic productivity and measurable scientific influence.[1]

Abstract

This article summarizes the scholarly profile of Stephen Chelko in relation to the Best Research Article Award. The overview highlights publication activity, citation performance, research influence, and scholarly recognition using publicly available bibliometric indicators. Such metrics provide an objective framework for evaluating academic excellence while acknowledging that research quality is reflected through peer-reviewed publications, collaboration, and scientific impact.[1]

Keywords

Data Science, Scientific Research, Academic Excellence, Bibliometrics, Research Articles, Citation Analysis, Scopus, Innovation, Knowledge Discovery, Scientific Impact.

Introduction

Academic awards recognize sustained contributions to scientific advancement through high-quality research outputs. Evaluation criteria frequently include originality, citation influence, publication quality, interdisciplinary collaboration, and measurable research outcomes. Bibliometric indicators complement peer review by providing transparent evidence of scholarly productivity.[2]

Research Profile

  • Researcher: Stephen Chelko
  • Country: United States
  • Scopus Author ID: 54973874200
  • Citation Count: 1,635
  • h-index: 18
  • Subject Area: Data Science

Research Contributions

Stephen Chelko’s research portfolio reflects active engagement in scholarly publishing and contributes to the development of scientific knowledge within his research domain. Citation metrics indicate that published studies have received substantial attention from the international research community, supporting continued academic influence.[1]

Publications

  • Peer-reviewed journal articles indexed by Scopus.
  • Research contributions in Data Science and related interdisciplinary fields.
  • Publications demonstrating measurable citation impact.

Research Impact

The available bibliometric indicators show a citation count exceeding one thousand together with an h-index of eighteen. These indicators suggest that multiple publications have been consistently cited across the scientific literature, reflecting sustained scholarly visibility and influence.[1]

Award Suitability

Based on publicly available bibliometric evidence, Stephen Chelko demonstrates characteristics commonly considered during evaluation for research recognition, including publication productivity, citation impact, peer-reviewed scholarship, and measurable scientific influence. Final award decisions remain subject to the official assessment procedures established by the organizing committee.[2]

Conclusion

Stephen Chelko’s academic profile reflects an established record of scientific contribution supported by recognized bibliometric indicators. His publication activity, citation performance, and scholarly engagement collectively provide evidence of sustained research impact within the academic community.

References

  1. Acute binge alcohol increases risk of arrhythmias and myocardial fibrosis in a mouse model of arrhythmogenic cardiomyopathy, https://doi.org/10.1152/ajpheart.00416.202
  2. Desmoglein-2 deficiency drives mitochondrial morphological remodeling in cardiomyocytes, https://doi.org/10.1152/ajpheart.00368.2026
  3. PPARγ Antagonism: Expanding the Therapeutic Armamentarium in Arrhythmogenic Cardiomyopathy, https://www.ahajournals.org/doi/10.1161/CIRCGEN.126.005800
  4. A Paradigm Shift: Arrhythmogenic Cardiomyopathy Is an Inflammatory Disease, https://www.mdpi.com/2073-4409/15/10/868
  5. NETosis and Myeloperoxidase Promotes Inflammation and Cardiac Remodeling in Arrhythmogenic Cardiomyopathy, doi: https://doi.org/10.64898/2026.04.14.718596

 

 

Yirga Abebe Belay | Machine learning | Innovative Research Award

Published on by Math Scientist

Innovative Research Award

           Yirga Abebe Belay
Researcher Yirga Abebe Belay
Affiliation Academic Research Institution
Country Thailand
Scopus ID 57896228800
Documents 16
Citations 23
h-index 3
Subject Area Machine Learning
Event Math Scientist Awards
ORCID 0000-0002-6112-2154

Yirga Abebe Belay is a researcher whose scholarly activities are associated with the field of machine learning and related computational methodologies. The recognition of the Innovative Research Award within the framework of the Math Scientist Awards acknowledges research contributions that demonstrate originality, methodological rigor, and relevance to contemporary scientific and technological challenges. The award highlights the role of innovative inquiry in advancing knowledge and fostering interdisciplinary collaboration across emerging research domains.[1]

Abstract

This article presents an academic overview of Yirga Abebe Belay in relation to the Innovative Research Award presented through the Math Scientist Awards. The discussion focuses on research activity, scholarly output, machine learning applications, publication performance indicators, and the broader significance of innovation-oriented scientific inquiry. The article adopts a neutral and encyclopedic perspective intended for academic documentation and recognition purposes.[1]

Keywords

Machine Learning; Artificial Intelligence; Data Analytics; Scientific Innovation; Computational Research; Predictive Modeling; Research Recognition; Scholarly Publications; Academic Impact; Math Scientist Awards.

Introduction

Innovation plays a central role in contemporary scientific progress, particularly within computational disciplines where algorithmic development and data-driven methodologies continue to transform research practices. Recognition programs such as the Math Scientist Awards seek to identify researchers whose work demonstrates originality, practical significance, and scholarly integrity. Within this context, Yirga Abebe Belay’s documented research activity contributes to the growing body of literature associated with machine learning and intelligent systems.[2]

Research Profile

The research profile of Yirga Abebe Belay reflects engagement with machine learning methodologies and computational research practices. According to available publication metrics, the researcher has produced sixteen indexed documents, received twenty-three citations, and attained an h-index of three. These indicators provide a quantitative perspective on scholarly visibility and academic contribution while complementing qualitative assessments of research innovation and relevance.[1]

Research Contributions

Research contributions associated with machine learning frequently involve the design of predictive models, optimization techniques, classification frameworks, and data-driven decision systems. Such work supports applications across engineering, healthcare, education, environmental studies, and business analytics. Contributions within this domain are evaluated not only by publication output but also by methodological innovation, reproducibility, and practical applicability.[2]

Publications

Publication records serve as a primary indicator of academic engagement and knowledge dissemination. Indexed documents contribute to scholarly communication by making research findings accessible to the broader scientific community. The publication portfolio associated with Yirga Abebe Belay demonstrates ongoing participation in peer-reviewed academic research and contributes to measurable scholarly impact.[1]

Research Impact

Research impact may be assessed through multiple indicators, including citation performance, publication quality, collaborative engagement, and practical implementation. Citation activity demonstrates that published work has been referenced by subsequent research efforts, while indexed visibility supports international accessibility. The combination of publication output and citation metrics provides evidence of participation within the global research ecosystem.[1]

Award Suitability

The Innovative Research Award recognizes scholarly efforts characterized by originality, measurable contribution, and intellectual advancement. Based on available publication indicators, machine learning specialization, and participation in peer-reviewed research dissemination, Yirga Abebe Belay’s academic profile aligns with the objectives commonly associated with innovation-focused recognition programs. Evaluation of suitability considers both quantitative metrics and broader contributions to scientific development.[1]

Conclusion

Yirga Abebe Belay’s documented scholarly activity reflects engagement with machine learning research and participation in the dissemination of scientific knowledge through indexed publications. The Innovative Research Award presented through the Math Scientist Awards serves as a formal acknowledgment of research-oriented achievement and the broader value of innovation within contemporary academic practice. Continued research activity, publication development, and interdisciplinary collaboration remain important factors in advancing scientific impact and scholarly recognition.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Yirga Abebe Belay, Author ID [INSERT]. Scopus.[INSERT]
  2. Math Scientist Awards. (n.d.). Award criteria and academic recognition framework.[INSERT]
  3. ORCID. (n.d.). Researcher identification and scholarly communication standards.[INSERT]
  4. Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85–117.DOI:
    https://doi.org/10.1016/j.neunet.2014.09.003