N. V. Subba Reddy
- Organic Chemistry top 5%
- Molecular Biology
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Media Technology top 5%
- Co-authors
- U. Dinesh AcharyaÄhmed KamalJayant R. HaritsaVeena NayakK. Rajender ReddyChandrakant BagulAlka RaoG. Sathish Kumar
- Topics
- Handwritten Text Recognition Techniques (14 papers)Vehicle License Plate Recognition (8 papers)Catalytic C–H Functionalization Methods (7 papers)
- Partner nations
- IndiaChinaUnited States
In The Last Decade
N. V. Subba Reddy
69 papers receiving 925 citations
Peers
Comparison fields: 5 of 113
- Organic Chemistry 394
- Molecular Biology 211
- Computer Vision and Pattern Recognition 188
- Artificial Intelligence 148
- Media Technology 74
Countries citing papers authored by N. V. Subba Reddy
This map shows the geographic impact of N. V. Subba Reddy'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 N. V. Subba Reddy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites N. V. Subba Reddy more than expected).
Fields of papers citing papers by N. V. Subba Reddy
This network shows the impact of papers produced by N. V. Subba Reddy. 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 N. V. Subba Reddy. The network helps show where N. V. Subba Reddy may publish in the future.
Co-authorship network of co-authors of N. V. Subba Reddy
This figure shows the co-authorship network connecting the top 25 collaborators of N. V. Subba Reddy. A scholar is included among the top collaborators of N. V. Subba Reddy 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 N. V. Subba Reddy. N. V. Subba Reddy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 22 | |
| 2 | 20 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 31 | |
| 6 | 39 | |
| 7 | 11 | |
| 8 | 81 | |
| 9 | 42 | |
| 10 | 44 | |
| 11 | 34 | |
| 12 | 3 | |
| 13 | 1 | |
| 14 | 3 | |
| 15 | 12 | |
| 16 | 1 | |
| 17 | 2 | |
| 18 | 15 | |
| 19 | Analyzing plan diagrams of database query optimizers | 56 |
| 20 | 2 |
About N. V. Subba Reddy
N. V. Subba Reddy is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Health Information Management, having authored 76 papers that have together received 993 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (14 papers), Vehicle License Plate Recognition (8 papers) and Catalytic C–H Functionalization Methods (7 papers). The work is most often cited by research in Organic Chemistry (394 citations), Health Information Management (63 citations) and Process Chemistry and Technology (29 citations). N. V. Subba Reddy has collaborated with scholars based in India, China and United States. Frequent co-authors include U. Dinesh Acharya, Ähmed Kamal, Jayant R. Haritsa, Veena Nayak, K. Rajender Reddy, Chandrakant Bagul, Alka Rao, G. Sathish Kumar, R. Arun Kumar and Vunnam Srinivasulu. Their work appears in journals such as Chemical Communications, The Journal of Urology and BMC Bioinformatics.
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.