R. Manikandan
- Artificial Intelligence top 2%
- Computer Networks and Communications top 2%
- Information Systems top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- Jafar A. AlzubiAmir H. GandomiRizwan PatanOmar A. AlzubiRamesh SekaranDeepak GuptaAmbeshwar KumarAshish Singh
- Topics
- IoT and Edge/Fog Computing (17 papers)COVID-19 diagnosis using AI (15 papers)Brain Tumor Detection and Classification (11 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsExpert Systems with Applications
- Partner nations
- IndiaAustraliaUnited States
In The Last Decade
R. Manikandan
138 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 145
- Artificial Intelligence 687
- Computer Networks and Communications 585
- Information Systems 388
- Radiology, Nuclear Medicine and Imaging 328
- Computer Vision and Pattern Recognition 327
Countries citing papers authored by R. Manikandan
This map shows the geographic impact of R. Manikandan'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 R. Manikandan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. Manikandan more than expected).
Fields of papers citing papers by R. Manikandan
This network shows the impact of papers produced by R. Manikandan. 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 R. Manikandan. The network helps show where R. Manikandan may publish in the future.
Co-authorship network of co-authors of R. Manikandan
This figure shows the co-authorship network connecting the top 25 collaborators of R. Manikandan. A scholar is included among the top collaborators of R. Manikandan 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 R. Manikandan. R. Manikandan 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 | 4 | |
| 3 | 8 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 16 | |
| 7 | 14 | |
| 8 | 14 | |
| 9 | 2 | |
| 10 | 12 | |
| 11 | 9 | |
| 12 | 6 | |
| 13 | 29 | |
| 14 | 67 | |
| 15 | 58 | |
| 16 | 29 | |
| 17 | Automatic Accident Detection, Ambulance Rescue and Traffic Signal Controller | 2 |
| 18 | 0 | |
| 19 | Deterministic and probabilistic models on VLSI cell placement - A survey | 1 |
| 20 | Application of Hypergraph in VLSI Cell Partitioning | 0 |
About R. Manikandan
R. Manikandan is a scholar working on Health Information Management, Computer Networks and Communications and Artificial Intelligence, having authored 148 papers that have together received 2.2k indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (17 papers), COVID-19 diagnosis using AI (15 papers) and Brain Tumor Detection and Classification (11 papers). The work is most often cited by research in Health Informatics (55 citations), Computer Networks and Communications (585 citations) and Health Information Management (115 citations). R. Manikandan has collaborated with scholars based in India, Australia and United States. Frequent co-authors include Jafar A. Alzubi, Amir H. Gandomi, Rizwan Patan, Omar A. Alzubi, Ramesh Sekaran, Deepak Gupta, Ambeshwar Kumar, Ashish Singh, Ashish Khanna and Fadi Al‐Turjman. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.
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.