P. Radha Krishna
- Artificial Intelligence top 10%
- Information Systems top 5%
- Computational Theory and Mathematics top 5%
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 10%
- Co-authors
- Supriya Kumar DeRaju S. BapiPradeep KumarD. V. L. N. SomayajuluKamalakar KarlapalemJ. V. R. MurthyVadlamani RaviA. Damodaram
- Topics
- Data Management and Algorithms (13 papers)Data Mining Algorithms and Applications (12 papers)Metaheuristic Optimization Algorithms Research (7 papers)
- Partner nations
- IndiaAustraliaUnited States
In The Last Decade
P. Radha Krishna
48 papers receiving 380 citations
Peers
Comparison fields: 5 of 89
- Artificial Intelligence 201
- Information Systems 170
- Computational Theory and Mathematics 94
- Computer Vision and Pattern Recognition 73
- Signal Processing 73
Countries citing papers authored by P. Radha Krishna
This map shows the geographic impact of P. Radha Krishna's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by P. Radha Krishna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites P. Radha Krishna more than expected).
Fields of papers citing papers by P. Radha Krishna
This network shows the impact of papers produced by P. Radha Krishna. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by P. Radha Krishna. The network helps show where P. Radha Krishna may publish in the future.
Co-authorship network of co-authors of P. Radha Krishna
This figure shows the co-authorship network connecting the top 25 collaborators of P. Radha Krishna. A scholar is included among the top collaborators of P. Radha Krishna based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with P. Radha Krishna. P. Radha Krishna is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 0 | |
| 10 | 1 | |
| 11 | 8 | |
| 12 | 4 | |
| 13 | Leveraging Latent Representations of Speech for Indian Language Identification. | 0 |
| 14 | 1 | |
| 15 | 20 | |
| 16 | A Simple and Efficient Lane Detection using Clustering and Weighted Regression | 10 |
| 17 | A novel approach for statistical and fuzzy association rule mining on quantitative data | 7 |
| 18 | 14 | |
| 19 | Functional Dependency Driven Auxiliary Relation Selection for Materialized Views Maintenance. | 2 |
| 20 | 5 |
About P. Radha Krishna
P. Radha Krishna is a scholar working on Signal Processing, Artificial Intelligence and Information Systems, having authored 56 papers that have together received 410 indexed citations. Recurring topics across this work include Data Management and Algorithms (13 papers), Data Mining Algorithms and Applications (12 papers) and Metaheuristic Optimization Algorithms Research (7 papers). The work is most often cited by research in Information Systems (170 citations), Signal Processing (73 citations) and Artificial Intelligence (201 citations). P. Radha Krishna has collaborated with scholars based in India, Australia and United States. Frequent co-authors include Supriya Kumar De, Raju S. Bapi, Pradeep Kumar, D. V. L. N. Somayajulu, Kamalakar Karlapalem, J. V. R. Murthy, Vadlamani Ravi, A. Damodaram, Mukesh Mohania and Kotagiri Ramamohanarao. Their work appears in journals such as Expert Systems with Applications, Fuzzy Sets and Systems and Applied Soft Computing.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.