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

Sheeja Rani S | Computer Science Award | Best Researcher Award

Published on by Academic Excellence

Dr. Sheeja Rani S | Computer Science Award | Best Researcher Award

👤 Dr. Sheeja Rani S, American University of Sharjah, United Arab Emirates

Dr. Sheeja Rani S is a visionary researcher and academician specializing in Computer Science and Engineering, with a strong focus on Wireless Sensor Networks, IoT, and Smart Grids. She earned her Ph.D. from Noorul Islam Centre for Higher Education in 2023, where her thesis emphasized energy-efficient clustering algorithms for wireless sensor networks. Her academic journey is complemented by over a decade of teaching and research experience, where she worked on innovative solutions in cybersecurity, cloud computing, and machine learning. Currently serving as a Postdoctoral Research Assistant at the American University of Sharjah, Dr. Sheeja collaborates with leading experts on cutting-edge projects. With over 20 journal papers, numerous conference contributions, and a passion for impactful research, she strives to advance technology and foster intellectual growth. Her mission is to combine her expertise and mentorship skills to inspire future innovators while contributing to meaningful explorations in academia and beyond.

Professional Profile

scopus

google scholar

🌟 Evaluation of Dr. Sheeja Rani S for the Research for Best Researcher Award

Summary of Suitability

Dr. Sheeja Rani S stands out as a highly qualified candidate for the “Research for Best Researcher Award,” showcasing an exceptional academic trajectory, prolific research output, and impactful contributions to multiple interdisciplinary domains. With a Ph.D. in Computer Science and Engineering focusing on improving energy efficiency in wireless sensor networks (WSNs), her research has addressed critical challenges in IoT, cloud computing, and smart grid technologies. These fields are not only contemporary but also pivotal for sustainable and secure technological advancements.

🎓 Education 

  • Ph.D. in Computer Science and Engineering (2023)
    Noorul Islam Centre for Higher Education
    Thesis: Improving Energy Efficiency Based on Clustering Algorithms for Wireless Sensor Networks.
  • M.E. in Computer Science and Engineering (2012)
    Noorul Islam Centre for Higher Education
  • M.Sc. Integrated Software Engineering (2009)
    Anna University, Chennai

Dr. Sheeja’s academic pursuits are rooted in innovation, particularly in optimizing computational techniques for energy efficiency and data security. Her Ph.D. research laid a foundation for creating advanced clustering mechanisms in wireless sensor networks, while her postgraduate and undergraduate studies focused on mastering computer science fundamentals and software engineering. She remains committed to lifelong learning and applying her knowledge to address emerging technological challenges.

💼  Professional Experience 

  • Postdoctoral Research Assistant (2023-Present)
    American University of Sharjah

    • Research on cybersecurity, smart grids, and cloud computing.
    • Published 12 journal papers in high-impact areas like IoT and machine learning.
  • Research Assistant (2022-2023)
    University of Sharjah

    • Focused on IoT, WSNs, and cloud computing.
    • Published 11 journal papers on financial distress prediction and IoT advancements.
  • Assistant Professor (2012-2021)
    John Cox Memorial CSI Institute of Technology

    • Taught advanced programming and database systems.
    • Managed academic coordination and examination processes.

Dr. Sheeja’s professional journey showcases a blend of teaching, research, and academic leadership, reflecting her dedication to advancing the field of computer science.

🏅 Awards and Recognitions 

  • Best Researcher Award (2023) – Recognized for impactful research in IoT and WSN.
  • Academic Excellence Award (2021) – Awarded for outstanding teaching and mentorship.
  • Research Grant Award (2022) – Funded for innovative studies on machine learning and cybersecurity.
  • Publication Excellence Award (2023) – Honored for prolific contributions to reputed journals.

Dr. Sheeja has consistently received accolades for her exceptional academic and research contributions. Her achievements reflect her dedication to excellence and her ability to produce innovative solutions that address global challenges.

🌍  Research Skills On Computer Science Award 

Dr. Sheeja’s research expertise spans:

  • Wireless Sensor Networks (WSN): Energy-efficient routing and clustering.
  • IoT: Developing secure and scalable architectures for smart environments.
  • Machine Learning: Applying predictive models for financial and cybersecurity domains.
  • Smart Grids: Integration of AI for optimal energy distribution.
  • Cloud Computing: Enhancing reliability and fault tolerance in virtualized environments.

📖 Publication Top Notes

Improved buffalo optimized deep feed forward neural learning based multipath routing for energy-efficient data aggregation in WSN
    • Authors: SS Rani, KS Sankar
    • Citation: Measurement: Sensors 27, 100662
    • Cited by: 8
    • Year: 2023
Optimized deep learning for Congestion-Aware continuous target tracking and boundary detection in IoT-Assisted WSN
    • Authors: AM Khedr, SS Rani, M Saad
    • Citation: IEEE Sensors Journal 23 (7), 7938-7948
    • Cited by: 8
    • Year: 2023
Enhancing Supply Chain Management with Deep Learning and Machine Learning Techniques: A Review
    • Authors: SSR Khedr, Ahmed M
    • Citation: Journal of Open Innovation: Technology, Market, and Complexity, 100379
    • Cited by: 5
    • Year: 2024
Hybridized Dragonfly and Jaya algorithm for optimal sensor node location identification in mobile wireless sensor networks
    • Authors: AM Khedr, SS Rani, M Saad
    • Citation: The Journal of Supercomputing 79 (15), 16940-16962
    • Cited by: 4
    • Year: 2023
Enhancing financial distress prediction through integrated Chinese Whisper clustering and federated learning
    • Authors: AI Al Ali, AM S S Rani Khedr
    • Citation: Journal of Open Innovation: Technology, Market, and Complexity 10 (3), 100344
    • Cited by: 2
    • Year: 2024