Manasi Patwardhan
- Artificial Intelligence top 10%
- Epidemiology
- Computer Vision and Pattern Recognition top 10%
- Pediatrics, Perinatology and Child Health
- Experimental and Cognitive Psychology
- Co-authors
- Anjali MahajanShirish KarandeEdgar Hernández‐AndradeSuchaya LuewanM. Cruz‐LeminiBeena RaiBernard GonikSonia S. Hassan
- Topics
- Sentiment Analysis and Opinion Mining (8 papers)Topic Modeling (8 papers)Multimodal Machine Learning Applications (6 papers)
- Partner nations
- IndiaUnited StatesUnited Kingdom
In The Last Decade
Manasi Patwardhan
41 papers receiving 420 citations
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 109
- Epidemiology 76
- Computer Vision and Pattern Recognition 62
- Pediatrics, Perinatology and Child Health 50
- Experimental and Cognitive Psychology 50
Countries citing papers authored by Manasi Patwardhan
This map shows the geographic impact of Manasi Patwardhan'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 Manasi Patwardhan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manasi Patwardhan more than expected).
Fields of papers citing papers by Manasi Patwardhan
This network shows the impact of papers produced by Manasi Patwardhan. 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 Manasi Patwardhan. The network helps show where Manasi Patwardhan may publish in the future.
Co-authorship network of co-authors of Manasi Patwardhan
This figure shows the co-authorship network connecting the top 25 collaborators of Manasi Patwardhan. A scholar is included among the top collaborators of Manasi Patwardhan 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 Manasi Patwardhan. Manasi Patwardhan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 36 | |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 5 | |
| 10 | 22 | |
| 11 | 10 | |
| 12 | 18 | |
| 13 | 5 | |
| 14 | 33 | |
| 15 | 20 | |
| 16 | 13 | |
| 17 | 3 | |
| 18 | 37 | |
| 19 | 11 | |
| 20 | Development and functional test of electronic metering mechanism for bullock drawn Jyoti Multicrop planter. | 2 |
About Manasi Patwardhan
Manasi Patwardhan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Obstetrics and Gynecology, having authored 46 papers that have together received 440 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (8 papers), Topic Modeling (8 papers) and Multimodal Machine Learning Applications (6 papers). The work is most often cited by research in Obstetrics and Gynecology (42 citations), Experimental and Cognitive Psychology (50 citations) and Applied Psychology (19 citations). Manasi Patwardhan has collaborated with scholars based in India, United States and United Kingdom. Frequent co-authors include Anjali Mahajan, Shirish Karande, Edgar Hernández‐Andrade, Suchaya Luewan, M. Cruz‐Lemini, Beena Rai, Bernard Gonik, Sonia S. Hassan, Roberto Romero and Linda O. Eckert. Their work appears in journals such as Scientific Reports, ACM Computing Surveys and Obstetrics and Gynecology.
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.