Deepa Gupta
- Artificial Intelligence top 5%
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Electrical and Electronic Engineering
- Co-authors
- Tripty SinghS. LalithaTina BabuSusmitha VekkotShikha TripathiMohammed ZakariahYousef Ajami AlotaibiManik Rakhra
- Topics
- Emotion and Mood Recognition (10 papers)Sentiment Analysis and Opinion Mining (7 papers)AI in cancer detection (7 papers)
- Journals
- SHILAP Revista de lepidopterologíaSensorsSensors and Actuators B Chemical
- Partner nations
- IndiaUnited Arab EmiratesSaudi Arabia
In The Last Decade
Deepa Gupta
81 papers receiving 879 citations
Peers
Comparison fields: 5 of 140
- Artificial Intelligence 329
- Computer Networks and Communications 122
- Computer Vision and Pattern Recognition 118
- Radiology, Nuclear Medicine and Imaging 101
- Electrical and Electronic Engineering 100
Countries citing papers authored by Deepa Gupta
This map shows the geographic impact of Deepa Gupta'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 Deepa Gupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepa Gupta more than expected).
Fields of papers citing papers by Deepa Gupta
This network shows the impact of papers produced by Deepa Gupta. 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 Deepa Gupta. The network helps show where Deepa Gupta may publish in the future.
Co-authorship network of co-authors of Deepa Gupta
This figure shows the co-authorship network connecting the top 25 collaborators of Deepa Gupta. A scholar is included among the top collaborators of Deepa Gupta 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 Deepa Gupta. Deepa Gupta 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 | 0 | |
| 3 | 30 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 4 | |
| 12 | 43 | |
| 13 | 2 | |
| 14 | 6 | |
| 15 | An Effective Performance Analysis of Machine Learning Techniques for Cardiovascular Disease | 20 |
| 16 | Acquisition and analysis of robotic data using machine learning techniques | 1 |
| 17 | 10 | |
| 18 | 5 | |
| 19 | The value of concentrate supplementation of berseem forage for milk production in buffaloes. | 0 |
| 20 | Effect of feeding lucerne on milk yield and composition as compared to berseem feeding. | 1 |
About Deepa Gupta
Deepa Gupta is a scholar working on Health Informatics, Signal Processing and Computer Vision and Pattern Recognition, having authored 102 papers that have together received 927 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (10 papers), Sentiment Analysis and Opinion Mining (7 papers) and AI in cancer detection (7 papers). The work is most often cited by research in Health Information Management (68 citations), Health Informatics (17 citations) and Artificial Intelligence (329 citations). Deepa Gupta has collaborated with scholars based in India, United Arab Emirates and Saudi Arabia. Frequent co-authors include Tripty Singh, S. Lalitha, Tina Babu, Susmitha Vekkot, Shikha Tripathi, Mohammed Zakariah, Yousef Ajami Alotaibi, Manik Rakhra, Dalwinder Singh and Arun Singh. Their work appears in journals such as SHILAP Revista de lepidopterología, Sensors and Sensors and Actuators B Chemical.
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.