Rishika Yadav
Impact in
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- Artificial Intelligence in Healthcare
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- Smart Agriculture and AI
- Plant Disease Management Techniques
- Leaf Properties and Growth Measurement
Papers in
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- Smart Agriculture and AI 13
- Plant Disease Management Techniques 5
- Date Palm Research Studies 3
- Co-authors
- Vinay KukrejaShiva MehtaRomil RawatAvnish PanwarSiddharth GuptaRishabh SharmaPoonam RaniKireet Joshi
- Journals
- Brain Sciences (1 paper)Türk bilgisayar ve matematik eğitimi dergisi (2 papers)IOP Conference Series Materials Science and Engineering (1 paper)2022 IEEE Delhi Section Conference (DELCON) (1 paper)SSRN Electronic Journal (1 paper)
- Partner nations
- IndiaSouth KoreaNepal
In The Last Decade
Rishika Yadav
25 papers receiving 276 citations
Peers
Comparison fields: 5 of 80
- Health Information Management 11
- Plant Science 85
- Analytical Chemistry 21
- Computer Vision and Pattern Recognition 33
- Artificial Intelligence 51
Countries citing papers authored by Rishika Yadav
This map shows the geographic impact of Rishika Yadav'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 Rishika Yadav with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rishika Yadav more than expected).
Fields of papers citing papers by Rishika Yadav
This network shows the impact of papers produced by Rishika Yadav. 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 Rishika Yadav. The network helps show where Rishika Yadav may publish in the future.
Co-authors
The 25 scholars most cited alongside Rishika Yadav, 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 | 2024 | 1 | |
| 2 | 2023 | 58 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 13 | |
| 6 | 2023 | 5 | |
| 7 | 2023 | 0 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 11 | |
| 10 | 2023 | 6 | |
| 11 | 2023 | 1 | |
| 12 | 2022 | 9 | |
| 13 | 2022 | 5 | |
| 14 | 2021 | 1 | |
| 15 | 2021 | 1 | |
| 16 | 2021 | 29 | |
| 17 | 2020 | 1 | |
| 18 | 2020 | 7 | |
| 19 | 2019 | 1 | |
| 20 | Question Classification Using Naïve Bayes Machine Learning Approach | 2013 | 4 |
About Rishika Yadav
Rishika Yadav is a scholar working on Plant Science, Management Information Systems, Information Systems, Health Information Management and Environmental Engineering, having authored 29 papers that have together received 279 indexed citations. Recurring topics across this work include Smart Agriculture and AI (13 papers), Plant Disease Management Techniques (5 papers), Smart Systems and Machine Learning (3 papers), Internet of Things and AI (3 papers), Date Palm Research Studies (3 papers), Air Quality Monitoring and Forecasting (2 papers), Hydrological Forecasting Using AI (2 papers) and AI in cancer detection (2 papers). The work is most often cited by research in Health Information Management (11 citations), Plant Science (85 citations), Analytical Chemistry (21 citations), Computer Vision and Pattern Recognition (33 citations) and Artificial Intelligence (51 citations). Rishika Yadav has collaborated with scholars based in India, South Korea and Nepal. Frequent co-authors include Vinay Kukreja, Shiva Mehta, Romil Rawat, Avnish Panwar, Siddharth Gupta, Rishabh Sharma, Poonam Rani, Kireet Joshi, Noor Mohd and Deepak Banerjee. Their work appears in journals such as Brain Sciences, Türk bilgisayar ve matematik eğitimi dergisi, IOP Conference Series Materials Science and Engineering, 2022 IEEE Delhi Section Conference (DELCON) and SSRN Electronic Journal.
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.