Raghavendran Prabakaran | Mathematics | Innovative Research Award

Published on by Innovation Technologist

Innovative Research Award

Raghavendran Prabakaran
Easwari Engineering College, India

Raghavendran Prabakaran
Affiliation Easwari Engineering College
Country India
Scopus ID 58670546100
Documents 56
Citations 325
h-index 11
Subject Area Mathematics
Event Global Innovation Technologist Awards
ORCID 0009-0001-7333-6555

Raghavendran Prabakaran is a mathematics researcher affiliated with Easwari Engineering College, India. The documented research profile comprises 56 Scopus-indexed documents, 325 citations and an h-index of 11. His recent publication activity connects mathematical analysis with fractional calculus, integral equations, machine learning, physics-informed neural networks and computational modeling. These themes illustrate an interdisciplinary research direction in which mathematical methods are applied to complex analytical and predictive problems.[1]

Abstract

The research profile represented by the available publication record centers on contemporary mathematical techniques for modeling, approximation and prediction. Recent work addresses random fractional functional Volterra–Fredholm integro-differential equations, physics-informed learning, mathematical transformations, complex systems and fractional drug-release models. The publications indicate an application-oriented research trajectory combining established mathematical frameworks with computational and machine-learning approaches.[2]

Keywords

Mathematics; fractional calculus; integro-differential equations; machine learning; physics-informed neural networks; computational modeling; mathematical transformations; predictive analysis.

Introduction

Mathematical research increasingly incorporates computational intelligence to address nonlinear, fractional and otherwise complex systems. In this context, hybrid methods can combine analytical formulations with data-driven approximation and prediction. The listed publications associated with Prabakaran’s research demonstrate this intersection through applications involving artificial neural networks, transformers, physics-informed methods and fractional differential models.[3]

Research Profile

The supplied bibliometric profile records 56 documents, 325 citations and an h-index of 11 in Scopus. These indicators provide quantitative measures of indexed publication output and citation activity, while the publication record provides additional context regarding subject breadth and methodological development.[1]

Research Contributions

  • Development and analysis of fractional and integro-differential mathematical models.
  • Application of machine-learning and neural-network methods to mathematical prediction and approximation.
  • Integration of physics-informed computational approaches with mathematical modeling.
  • Application of mathematical models to interdisciplinary problems, including nanomaterials and drug-release systems.

Publications

Recent publications include studies on nanomaterial selection and PINN-based prediction, random fractional functional Volterra–Fredholm integro-differential equations with ANN approximation, physics-informed transformer frameworks for EEG forecasting, Upadhyaya transforms with machine learning, and fractional integro-differential equations for paracetamol drug-release modeling.[4][5]

Research Impact

The reported citation count and h-index provide measurable evidence of scholarly visibility within the indexed record. The recent publications further show a research program extending mathematical techniques toward computational prediction and interdisciplinary applications. Such evidence should be interpreted alongside publication quality, authorship contribution, journal characteristics and independent citation context when evaluating research impact.

Award Suitability

For the Innovative Research Award category, the documented profile provides evidence relevant to consideration, particularly through its combination of mathematical research, computational methods and interdisciplinary applications. The available bibliometric indicators and recent publications can form part of an evidence-based recognition assessment within the Global Innovation Technologist Awards framework.[1]

Conclusion

Raghavendran Prabakaran’s documented research profile combines mathematical analysis with emerging computational approaches. The available Scopus indicators and recent publications demonstrate sustained scholarly activity across fractional mathematics, integro-differential equations, machine learning and interdisciplinary modeling. These records provide a structured basis for academic recognition and further evaluation of research contributions.

References

  1. Elsevier. (n.d.). Scopus author details: Raghavendran Prabakaran, Author ID 58670546100. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58670546100
  2. MDPI. (2026). Analysis of Random Fractional Functional Volterra–Fredholm Integro-Differential Equations with Infinite Delay and ANN-Based Approximation. Fractal and Fractional.
    DOI: 10.3390/fractalfract10090646
  3. International Journal of Neuroscience and Neuroinformatics. (2026). A Physics-Informed Transformer Framework With a Four-Compartment NRSF Brain-State Model for EEG Forecasting.
    DOI: 10.4018/IJNN.419364
  4. Next Materials. (2026). Application of nanomaterial selection and PINN-based prediction using neutrosophic over soft complex locally closed sets and locally continuous functions.
    DOI: 10.1016/j.nxmate.2026.103274
  5. Transactions on Computational Modeling and Intelligent Systems. (2026). Application of Upadhyaya transforms with machine learning for predictive and analytical solutions in complex systems.
    DOI: 10.65112/tcmis.10023
  6. Oriental Journal Of Chemistry. (2026). Application of Fractional Integro-Differential Equations in Paracetamol Drug Release Modeling.
    DOI: 10.13005/ojc/420208

Nataša Djurdjević | Mathematics | Best Researcher Award

Published on by Innovation Technologist

Best Researcher Award

Nataša Djurdjević
University of Belgrade – Faculty of Agriculture

Nataša Djurdjević
Affiliation University of Belgrade – Faculty of Agriculture
Country Serbia
Scopus ID 58717994600
Documents 4
Citations 15
h-index 2
Subject Area Mathematics
Event Global Innovation Technologist Awards
ORCID 0000-0002-0885-4735

Nataša Djurdjević is a Serbian mathematician and researcher affiliated with the University of Belgrade – Faculty of Agriculture. Her scholarly work primarily focuses on differential geometry, CR submanifolds, and nearly Kähler manifolds, particularly the geometric structures associated with the manifold S3×S3. Through peer-reviewed journal articles and conference contributions, she has contributed to advancing theoretical understanding within modern geometry and mathematical structures.[1] Her academic publications demonstrate sustained engagement with advanced geometric classifications and submanifold theory.[2]

Abstract

This article presents an overview of the academic contributions and scholarly recognition of Nataša Djurdjević in the field of mathematics. Her research activities are centered on differential geometry and the study of CR submanifolds in homogeneous nearly Kähler manifolds. Through analytical classification methods and theoretical investigations, her publications have contributed to the broader understanding of geometric structures and manifold theory in contemporary mathematics.[3]

Keywords

Differential Geometry, CR Submanifolds, Nearly Kähler Manifolds, Mathematical Structures, Geometry Research, S3×S3, Mathematical Classification, Topological Analysis

Introduction

Mathematics remains a foundational discipline in scientific advancement, particularly in the areas of geometry and theoretical analysis. Researchers working in manifold theory and differential geometry contribute significantly to the development of abstract mathematical frameworks used across scientific disciplines. Nataša Djurdjević has participated in this academic field through specialized studies on CR submanifolds and nearly Kähler geometry, producing research that addresses classification problems and geometric properties of higher-dimensional manifolds.[4]

Research Profile

Djurdjević’s research profile reflects active scholarly participation in peer-reviewed mathematical research. Her studies frequently examine geometric structures within homogeneous nearly Kähler manifolds and related CR submanifold classifications. Her work has been indexed through Scopus and ORCID platforms, providing international academic visibility and accessibility for researchers in geometry and topology.[1]

Research Contributions

  • Contributed to the classification of four-dimensional CR submanifolds in homogeneous nearly Kähler manifolds.
  • Published analytical studies related to almost complex distributions and geometric orthogonality.
  • Investigated umbilical sections and structural properties within S3×S3 manifolds.
  • Presented conference research focused on CR submanifold products and manifold geometry.

Publications

  • “Four-Dimensional CR Submanifolds of the Homogeneous Nearly Kähler Product Manifold S3×S3” — Mathematics (2026).
  • “Classification of Four-Dimensional CR Submanifolds of the Homogenous Nearly Kähler S3×S3” — Mathematics (2025).
  • “Some classes of CR submanifolds with an umbilical section of the nearly Kähler S3×S3” — Journal of Geometry and Physics (2021).
  • “Three-dimensional CR submanifolds of the nearly Kähler S3×S3” — Annali di Matematica Pura ed Applicata (2019).

Research Impact

The research contributions of Nataša Djurdjević support the advancement of theoretical mathematics, particularly within the specialized field of geometric manifold analysis. Her work contributes to the academic dialogue surrounding CR submanifolds and nearly Kähler geometry, offering classification frameworks and analytical methods relevant to future mathematical investigations.[5] Citation records and indexed publications further indicate scholarly engagement within the mathematical research community.[2]

Award Suitability

The Best Researcher Award recognition under the Global Innovation Technologist Awards acknowledges sustained scholarly contributions, publication quality, and subject-specific research engagement. Djurdjević’s work in advanced geometry, combined with peer-reviewed publications and international indexing visibility, aligns with the evaluation criteria commonly associated with academic distinction and emerging research excellence in mathematics.[6]

Conclusion

Nataša Djurdjević has established an academic profile centered on differential geometry and CR submanifold theory. Her research publications and conference contributions demonstrate focused engagement with geometric analysis and manifold classification. Through continued scholarly activity and indexed research dissemination, her work contributes to ongoing developments in mathematical sciences and theoretical geometry.

References

  1. Elsevier. (n.d.). Scopus author details: Nataša Djurdjević, Author ID 58717994600. Scopus.
    www.scopus.com/authid/detail.uri?authorId=58717994600
  2. ORCID. (n.d.). Nataša Djurdjević researcher profile and scholarly records.
    orcid.org/0000-0002-0885-4735
  3. Djurdjević, N. (2026). Four-Dimensional CR Submanifolds of the Homogeneous Nearly Kähler Product Manifold S3×S3. Mathematics.
    doi.org/10.3390/math14111790
  4. Djurdjević, N. (2025). Classification of Four-Dimensional CR Submanifolds of the Homogenous Nearly Kähler S3×S3. Mathematics.
    doi.org/10.3390/math13162638
  5. Djurdjević, N. (2021). Some classes of CR submanifolds with an umbilical section of the nearly Kähler S3×S3. Journal of Geometry and Physics.
    doi.org/10.1016/j.geomphys.2020.104002
  6. Djurdjević, N. (2019). Three-dimensional CR submanifolds of the nearly Kähler S3×S3. Annali di Matematica Pura ed Applicata.
    doi.org/10.1007/s10231-018-0770-8