Anand Panangadan
- Electrical and Electronic Engineering
- Artificial Intelligence top 10%
- Computer Networks and Communications top 10%
- Biomedical Engineering
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Archana J. McEligotRishabh SharmaValerie PoynorAshit TalukderViktor K. PrasannaGaurav S. SukhatmeCharalampos ChelmisKen B. Cooper
- Topics
- Energy Efficient Wireless Sensor Networks (9 papers)Time Series Analysis and Forecasting (8 papers)Sentiment Analysis and Opinion Mining (4 papers)
- Partner nations
- United StatesBrazilChina
In The Last Decade
Anand Panangadan
56 papers receiving 598 citations
Peers
Comparison fields: 5 of 134
- Electrical and Electronic Engineering 122
- Artificial Intelligence 99
- Computer Networks and Communications 73
- Biomedical Engineering 73
- Computer Vision and Pattern Recognition 66
Countries citing papers authored by Anand Panangadan
This map shows the geographic impact of Anand Panangadan'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 Anand Panangadan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anand Panangadan more than expected).
Fields of papers citing papers by Anand Panangadan
This network shows the impact of papers produced by Anand Panangadan. 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 Anand Panangadan. The network helps show where Anand Panangadan may publish in the future.
Co-authorship network of co-authors of Anand Panangadan
This figure shows the co-authorship network connecting the top 25 collaborators of Anand Panangadan. A scholar is included among the top collaborators of Anand Panangadan 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 Anand Panangadan. Anand Panangadan 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 | 1 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 201 | |
| 6 | 1 | |
| 7 | GPU-Accelerated Parameter Optimization for Classification Rule Learning. | 1 |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 9 | |
| 11 | 26 | |
| 12 | 5 | |
| 13 | 5 | |
| 14 | 5 | |
| 15 | 3 | |
| 16 | 3 | |
| 17 | Markov decision processes for control of a sensor network-based health monitoring system | 18 |
| 18 | 8 | |
| 19 | 0 | |
| 20 | Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World | 1 |
About Anand Panangadan
Anand Panangadan is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications, having authored 60 papers that have together received 623 indexed citations. Recurring topics across this work include Energy Efficient Wireless Sensor Networks (9 papers), Time Series Analysis and Forecasting (8 papers) and Sentiment Analysis and Opinion Mining (4 papers). The work is most often cited by research in Signal Processing (50 citations), Transportation (27 citations) and Ocean Engineering (57 citations). Anand Panangadan has collaborated with scholars based in United States, Brazil and China. Frequent co-authors include Archana J. McEligot, Rishabh Sharma, Valerie Poynor, Ashit Talukder, Viktor K. Prasanna, Gaurav S. Sukhatme, Charalampos Chelmis, Ken B. Cooper, Robert J. Dengler and Peter H. Siegel. Their work appears in journals such as Nutrients, IEEE Sensors Journal and Engineering Applications of Artificial Intelligence.
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.