Ganapati Panda
- Computational Mechanics top 2%
- Signal Processing top 2%
- Artificial Intelligence top 5%
- Electrical and Electronic Engineering
- Control and Systems Engineering top 5%
- Co-authors
- Pyari Mohan PradhanNithin V. GeorgeBabita MajhiNirmal Kumar RoutDebi Prasad DasNiladri B. Puhanno-firstname VasundharaSatyasai Jagannath Nanda
- Topics
- Advanced Adaptive Filtering Techniques (26 papers)Blind Source Separation Techniques (19 papers)Speech and Audio Processing (17 papers)
- Journals
- SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsMechanical Systems and Signal Processing
- Partner nations
- IndiaUnited KingdomSpain
In The Last Decade
Ganapati Panda
44 papers receiving 811 citations
Peers
Comparison fields: 5 of 55
- Computational Mechanics 417
- Signal Processing 322
- Artificial Intelligence 219
- Electrical and Electronic Engineering 217
- Control and Systems Engineering 191
Countries citing papers authored by Ganapati Panda
This map shows the geographic impact of Ganapati Panda'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 Ganapati Panda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ganapati Panda more than expected).
Fields of papers citing papers by Ganapati Panda
This network shows the impact of papers produced by Ganapati Panda. 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 Ganapati Panda. The network helps show where Ganapati Panda may publish in the future.
Co-authorship network of co-authors of Ganapati Panda
This figure shows the co-authorship network connecting the top 25 collaborators of Ganapati Panda. A scholar is included among the top collaborators of Ganapati Panda 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 Ganapati Panda. Ganapati Panda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 24 | |
| 3 | 8 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 25 | |
| 8 | Development and Performance Evaluation of Three Novel Prediction Models for Mutual Fund NAV Prediction | 6 |
| 9 | 4 | |
| 10 | 3 | |
| 11 | 4 | |
| 12 | 1 | |
| 13 | A reduced complexity adaptive legendre neural network for nonlinear active noise control | 14 |
| 14 | 16 | |
| 15 | 3 | |
| 16 | 45 | |
| 17 | 2 | |
| 18 | 6 | |
| 19 | 5 | |
| 20 | 7 |
About Ganapati Panda
Ganapati Panda is a scholar working on Signal Processing, Computational Mechanics and Control and Systems Engineering, having authored 46 papers that have together received 834 indexed citations. Recurring topics across this work include Advanced Adaptive Filtering Techniques (26 papers), Blind Source Separation Techniques (19 papers) and Speech and Audio Processing (17 papers). The work is most often cited by research in Signal Processing (322 citations), Computational Mechanics (417 citations) and Control and Systems Engineering (191 citations). Ganapati Panda has collaborated with scholars based in India, United Kingdom and Spain. Frequent co-authors include Pyari Mohan Pradhan, Nithin V. George, Babita Majhi, Nirmal Kumar Rout, Debi Prasad Das, Niladri B. Puhan, no-firstname Vasundhara, Satyasai Jagannath Nanda, Debiprasad Priyabrata Acharya and Babita Majhi. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Mechanical Systems and Signal Processing.
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.