Vaibhav Rupapara
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- Artificial Intelligence in Healthcare 3
- Artificial Intelligence top 2%
- Sentiment Analysis and Opinion Mining 5
- Imbalanced Data Classification Techniques 4
- Machine Learning in Healthcare 2
- Information Systems top 5%
- Spam and Phishing Detection 6
- Web Data Mining and Analysis 3
- Medical Laboratory Technology top 10%
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- Advanced Malware Detection Techniques 3
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- Misinformation and Its Impacts 2
- Co-authors
- Furqan RustamSaleem UllahGyu Sang ChoiArif MehmoodMuhammad UmerMichele NappiSeyedali MirjaliliSaima Sadiq
- Partner nations
- United StatesPakistanSouth Korea
In The Last Decade
Vaibhav Rupapara
19 papers receiving 958 citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Health Information Management 175
- Artificial Intelligence 597
- Information Systems 232
- Medical Laboratory Technology 12
- Health Informatics 8
Countries citing papers authored by Vaibhav Rupapara
This map shows the geographic impact of Vaibhav Rupapara'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 Vaibhav Rupapara with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vaibhav Rupapara more than expected).
Fields of papers citing papers by Vaibhav Rupapara
This network shows the impact of papers produced by Vaibhav Rupapara. 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 Vaibhav Rupapara. The network helps show where Vaibhav Rupapara may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Vaibhav Rupapara, 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 | 2023 | 9 | |
| 2 | 2023 | 17 | |
| 3 | 2022 | 86 | |
| 4 | 2022 | 16 | |
| 5 | 2022 | 8 | |
| 6 | 2022 | 38 | |
| 7 | 2022 | 14 | |
| 8 | 2021 | 9 | |
| 9 | 2021 | 127 | |
| 10 | A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysisbreakdown → | 2021 | 200 |
| 11 | 2021 | 35 | |
| 12 | 2021 | 65 | |
| 13 | 2021 | 27 | |
| 14 | 2021 | 21 | |
| 15 | Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniquesbreakdown → | 2021 | 245 |
| 16 | 2020 | 72 | |
| 17 | 2020 | 12 | |
| 18 | 2020 | 8 | |
| 19 | 2020 | 11 |
About Vaibhav Rupapara
Vaibhav Rupapara is a scholar working on Medical Laboratory Technology, Health Information Management and Artificial Intelligence, having authored 19 papers that have together received 1.0k indexed citations. Recurring topics across this work include Spam and Phishing Detection (6 papers), Sentiment Analysis and Opinion Mining (5 papers), Imbalanced Data Classification Techniques (4 papers), Web Data Mining and Analysis (3 papers), Artificial Intelligence in Healthcare (3 papers), Advanced Malware Detection Techniques (3 papers), Misinformation and Its Impacts (2 papers) and Machine Learning in Healthcare (2 papers). The work is most often cited by research in Health Information Management (175 citations), Artificial Intelligence (597 citations) and Information Systems (232 citations). Vaibhav Rupapara has collaborated with scholars based in United States, Pakistan and South Korea. Frequent co-authors include Furqan Rustam, Saleem Ullah, Gyu Sang Choi, Arif Mehmood, Muhammad Umer, Michele Nappi, Seyedali Mirjalili, Saima Sadiq, Imran Ashraf and Abid Ishaq. Their work appears in journals such as PLoS ONE, Expert Systems with Applications and IEEE Access.
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.