Vimala Roselin | Computer Science | Research Excellence Award

Published on by Academic Excellence

Research Excellence Award

Vimala roselin
Kristu Jayanti University, India

Vimala roselin
Affiliation Kristu Jayanti University
Country India
Google Scholar ID 6UYBA_UAAAAJ&hl
Documents 5
Citations 1
h-index 1
Subject Area Computer Science
Event International Academic Excellence Awards

Vimala roselin is a researcher associated with Kristu Jayanti University, India, whose documented publication record is focused on computer science and applied artificial intelligence. The supplied scholarly record includes research on generative adversarial networks, machine learning, deep learning, neural-network equalisation, clustering, human freedom indices, and big-data security. The available profile information records five documents, one citation, and an h-index of 1. [1]

Abstract

The research profile presents an interdisciplinary application of computational intelligence to practical problems. Recent work includes synthetic image generation for crop disease classification, automated waste classification, neural-network-based system equalisation, and improved clustering methods. The record also includes earlier work concerning secure sensitive-data sharing on big-data platforms. Collectively, these publications indicate engagement with machine learning, deep learning, computer vision, data security, and computational modelling. [2] [3]

Keywords

Computer science; machine learning; deep learning; generative adversarial networks; image classification; waste classification; neural networks; clustering; big data; data security.

Introduction

The supplied publication record spans 2015 to 2026 and demonstrates an evolving interest in computational methods. Recent publications emphasize artificial intelligence and machine learning applications, while the earlier record addresses information security and big-data environments. The combination illustrates the broad application of computer science techniques to classification, optimisation, infrastructure, and data-management problems. [4]

Research Profile

The stated subject area is Computer Science. The supplied profile metrics identify five documents, one citation, and an h-index of 1. Because bibliometric indicators vary according to database coverage and indexing dates, these figures represent the supplied snapshot rather than a permanent measure of research activity. The Google Scholar record provides the associated publication list used for this article. [1]

Research Contributions

  • Application of generative adversarial networks to synthetic image generation for crop disease classification. [2]
  • Development of automated waste classification approaches using machine learning and deep learning. [3]
  • Investigation of neural-network-based equalisation within narrowband electric-grid systems. [4]
  • Use of clustering and computational methods for comparative analysis and data-oriented applications. [5]

Publications

Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks (2025) examines synthetic image generation for crop disease classification using generative adversarial networks. [2]

Extrapolation against categorisation for neural network-based system equalisation in a narrowband electric grid (2026) addresses computational approaches to system equalisation and appears in the International Journal of Critical Infrastructures. [4]

A Framework for Automated Waste Classification System using Machine Learning and Deep Learning Techniques (2025) focuses on automated classification using machine learning and deep learning techniques. [3]

Comparative Analysis of Improved K-Means Clustering for Human Freedom Index (2024) applies an improved clustering approach to comparative analysis of the Human Freedom Index. [5]

Secure sensitive data sharing on a big data platform (2015) concerns secure information sharing within big-data environments. [6]

Research Impact

The supplied profile reports one citation and an h-index of 1 across five documents. The publication topics nevertheless demonstrate a range of applied computer science problems, from agricultural image analysis and waste classification to infrastructure systems and data security. Citation counts can change as databases are updated and should therefore be interpreted with the relevant publication dates and indexing coverage. [1]

Award Suitability

The documented publication portfolio provides evidence of research activity relevant to a Research Excellence Award in Computer Science. Its themes include artificial intelligence, machine learning, deep learning, clustering, computer vision, networked infrastructure, and data security. The record may therefore be considered alongside the complete academic profile, publication evidence, and applicable criteria of the International Academic Excellence Awards.

Conclusion

vimala roselin’s supplied research record reflects activity across several areas of applied Computer Science. The publications demonstrate the use of contemporary computational techniques for image classification, waste management, infrastructure systems, clustering, and secure data sharing. The available bibliometric indicators and publication record provide a concise academic profile for consideration within the International Academic Excellence Awards framework.

References

  1. Google Scholar. (n.d.). Vimala Roselin — publication profile.
    https://scholar.google.com/citations?hl=en&user=6UYBA_UAAAAJ&view_op=list_works&sortby=title
  2. Roselin, J. V., et al. (2025). Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks. International Workshop on AI & ML-Frontiers in Cross Disciplinary Applications.
    Google Scholar publication record
  3. Roselin, J. V., et al. (2025). A Framework for Automated Waste Classification System using Machine Learning and Deep Learning Techniques. 5th International Conference on Expert Clouds and Applications (ICOECA).
    Google Scholar publication record
  4. Renjith, E. J., Roselin, J. V., Ramyadevi, R., Prema, R., & Priscila, S. S. (2026). Extrapolation against categorisation for neural network-based system equalisation in a narrowband electric grid. International Journal of Critical Infrastructures, 22(4), 351–376.
    Google Scholar publication record
  5. Ilyas, F. M., Priscila, S. S., Sheela, K., Vimala Roselin, J., Sona, K. V., & Prema, R. (2024). Comparative Analysis of Improved K-Means Clustering for Human Freedom Index. International Conference on Advancements in Smart Computing and Information.
    Google Scholar publication record
  6. Roselin, V. (2015). Secure sensitive data sharing on a big data platform. Tsinghua Science and Technology.
    Google Scholar publication record

SATEESH GORIKAPUDI | Computer Science | Research Excellence Award

Published on by Academic Excellence

Research Excellence Award

SATEESH GORIKAPUDI
Koneru Lakshmaiah Education Foundation, India

SATEESH GORIKAPUDI
Affiliation Koneru Lakshmaiah Education Foundation
Country India
Scopus ID 58249169300
Documents 13
Citations 68
h-index 4
Subject Area Computer Science
Event International Academic Excellence Awards
ORCID 0000-0002-9280-9581

SATEESH GORIKAPUDI is a computer science researcher affiliated with Koneru Lakshmaiah Education Foundation, India. The supplied research record includes publications addressing anomaly detection, machine learning, image processing, fuzzy logic, clustering, energy-efficient Internet of Things (IoT) communication, and optimization. The available bibliographic information records 13 documents, 68 citations, and an h-index of 4 in the stated Scopus profile data. [1]

Abstract

The research profile of SATEESH GORIKAPUDI reflects work across applied computer science and intelligent computational methods. The documented publications examine practical problems using machine learning, fuzzy logic, clustering, optimization, image processing, and IoT networking. Recent work includes anomaly detection in road traffic analysis and counterfeit currency detection using ensemble machine learning and image processing methods. Earlier studies address disease diagnosis, energy-efficient IoT routing, and optimized clustering. [2] [3]

Keywords

Machine learning; anomaly detection; image processing; fuzzy logic; clustering; optimization; Internet of Things; computer science; disease diagnosis; traffic analysis.

Introduction

The research record spans several computational applications in which data-driven techniques are used to identify patterns, classify information, optimize systems, or improve decision-support processes. The publication portfolio from 2023 to 2025 indicates continuing engagement with contemporary computational problems, including IoT communication, medical diagnosis, image-based recognition, and road-traffic analysis. [4]

Research Profile

The stated subject area is Computer Science. The supplied Scopus information lists 13 documents, 68 citations, and an h-index of 4. These indicators provide a bibliometric snapshot of the indexed research record and should be interpreted in relation to publication year, field, document type, and database coverage. [1]

Research Contributions

  • Road-traffic research addressing anomaly detection and computational analysis. [2]
  • Machine-learning and image-processing approaches for counterfeit currency detection. [3]
  • Fuzzy logic and machine learning applied to early disease diagnosis. [4]
  • Optimization and clustering methods for energy-efficient IoT routing and network applications. [5]

Publications

A Comprehensive Review of Anomaly Detection in Road Traffic Analysis (2025), International Journal of Computing and Digital Systems. [2]

Detection of counterfeit currency using ensemble machine learning models and image processing methods (2025), AIP Conference Proceedings. [3]

Fuzzy Logic-Driven Machine Learning Algorithms for Improved Early Disease Diagnosis (2024), International Journal of Advanced Computer Science and Applications. [4]

An Optimized Clustering Model for Energy-Efficient Routing in IoT Networks (2023), 2023 IEEE International Conference on Contemporary Computing and Communications. [5]

A novel clustering model via optimized fuzzy C-means algorithm and sandpiper optimization with cycle crossover process in IoT (2023), Concurrency and Computation: Practice and Experience. [6]

Research Impact

The supplied bibliometric record reports 68 citations and an h-index of 4 across 13 documents. The publication portfolio also demonstrates application-oriented research across multiple computational contexts. Citation indicators are database-dependent and can change as new publications and citations are indexed. [1]

Award Suitability

The documented research themes provide a substantive basis for consideration under a Research Excellence Award in the Computer Science category. The portfolio contains peer-reviewed journal and conference publications covering machine learning, intelligent systems, optimization, IoT, image processing, and anomaly detection. This assessment is based on the supplied publication and bibliometric information rather than an independent evaluation of the complete academic record.

Conclusion

SATEESH GORIKAPUDI’s documented research profile represents an application-focused body of work in Computer Science. Publications from 2023–2025 demonstrate engagement with machine learning, optimization, clustering, IoT networks, image processing, medical diagnosis, and anomaly detection. The supplied Scopus indicators and publication record provide a concise basis for academic recognition within the International Academic Excellence Awards framework.

References

  1. Elsevier. (n.d.). Scopus author details: SATEESH GORIKAPUDI, Author ID 58249169300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58249169300
  2. Gorikapudi, S. (2025). A Comprehensive Review of Anomaly Detection in Road Traffic Analysis. International Journal of Computing and Digital Systems. DOI: 10.12785/ijcds/1571111482
  3. Gorikapudi, S. (2025). Detection of counterfeit currency using ensemble machine learning models and image processing methods. AIP Conference Proceedings. DOI: 10.1063/5.0296454
  4. Gorikapudi, S. (2024). Fuzzy Logic-Driven Machine Learning Algorithms for Improved Early Disease Diagnosis. International Journal of Advanced Computer Science and Applications. DOI: 10.14569/IJACSA.2024.0151111
  5. Gorikapudi, S. (2023). An Optimized Clustering Model for Energy-Efficient Routing in IoT Networks. 2023 IEEE International Conference on Contemporary Computing and Communications. DOI: 10.1109/inc457730.2023.10263199
  6. Gorikapudi, S. (2023). A novel clustering model via optimized fuzzy C-means algorithm and sandpiper optimization with cycle crossover process in IoT. Concurrency and Computation: Practice and Experience. DOI: 10.1002/cpe.7776