Vikas Deep
Impact in
-
- Artificial Intelligence in Healthcare
- Artificial Intelligence top 10%
- AI in cancer detection
Papers in
- Software 2
- Co-authors
- Purushottam SharmaManoj KumarMohammed AlshehriRayed AlGhamdiS. K. GuptaVinod Kumar ShuklaOsama AlfarrajDeepti Mehrotra
- Journals
- Plant Foods for Human Nutrition (1 paper)Mobile Networks and Applications (1 paper)Concurrency and Computation Practice and Experience (1 paper)Recent Advances in Computer Science and Communications (1 paper)International Journal of Engineering & Technology (1 paper)
- Partner nations
- IndiaUnited Arab EmiratesSaudi Arabia
In The Last Decade
Vikas Deep
31 papers receiving 340 citations
Peers
Comparison fields: 5 of 96
- Health Information Management 42
- Artificial Intelligence 108
- Oncology 79
- Computer Vision and Pattern Recognition 58
- Information Systems 60
Countries citing papers authored by Vikas Deep
This map shows the geographic impact of Vikas Deep'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 Vikas Deep with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vikas Deep more than expected).
Fields of papers citing papers by Vikas Deep
This network shows the impact of papers produced by Vikas Deep. 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 Vikas Deep. The network helps show where Vikas Deep may publish in the future.
Co-authorship network
The 18 scholars most cited alongside Vikas Deep, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2021 | 5 | |
| 3 | 2021 | 10 | |
| 4 | 2021 | 1 | |
| 5 | 2020 | 30 | |
| 6 | 2020 | 108 | |
| 7 | 2019 | 9 | |
| 8 | 2019 | 1 | |
| 9 | 2019 | 5 | |
| 10 | 2019 | 1 | |
| 11 | 2018 | 5 | |
| 12 | 2018 | 1 | |
| 13 | 2018 | 1 | |
| 14 | 2018 | 39 | |
| 15 | 2016 | 6 | |
| 16 | 2016 | 18 | |
| 17 | Expert system for the management of insect-pests in pulse crops | 2015 | 2 |
| 18 | 2014 | 3 | |
| 19 | TRANSIENT ANALYSIS OF GRID CONNECTED DOUBLY FED INDUCTION GENERATOR COUPLED WITH WIND TURBINE | 2012 | 1 |
| 20 | 2003 | 19 |
About Vikas Deep
Vikas Deep is a scholar working on Software, Signal Processing, Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 34 papers that have together received 368 indexed citations. Recurring topics across this work include Software Engineering Research (3 papers), IoT and GPS-based Vehicle Safety Systems (3 papers), IoT and Edge/Fog Computing (3 papers), Network Security and Intrusion Detection (3 papers), IoT-based Smart Home Systems (3 papers), Smart Parking Systems Research (2 papers), Vehicle License Plate Recognition (2 papers) and Face and Expression Recognition (2 papers). The work is most often cited by research in Health Information Management (42 citations), Artificial Intelligence (108 citations), Oncology (79 citations), Computer Vision and Pattern Recognition (58 citations) and Information Systems (60 citations). Vikas Deep has collaborated with scholars based in India, United Arab Emirates and Saudi Arabia. Frequent co-authors include Purushottam Sharma, Manoj Kumar, Mohammed Alshehri, Rayed AlGhamdi, S. K. Gupta, Vinod Kumar Shukla, Osama Alfarraj, Deepti Mehrotra, Naveen Garg and Renu Jain. Their work appears in journals such as Plant Foods for Human Nutrition, Mobile Networks and Applications, Concurrency and Computation Practice and Experience, Recent Advances in Computer Science and Communications and International Journal of Engineering & Technology.
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.