Research Excellence Award
SATEESH GORIKAPUDI
Koneru Lakshmaiah Education Foundation, India
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.
External Links
References
- Elsevier. (n.d.). Scopus author details: SATEESH GORIKAPUDI, Author ID 58249169300. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=58249169300
- 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
- Gorikapudi, S. (2025). Detection of counterfeit currency using ensemble machine learning models and image processing methods. AIP Conference Proceedings. DOI: 10.1063/5.0296454
- 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
- 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
- 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