Avishek Garain
- Artificial Intelligence top 5%
- Information Systems top 10%
- Sociology and Political Science
- Computer Vision and Pattern Recognition
- Signal Processing top 10%
- Co-authors
- Ram SarkarBiswarup RayArpan BasuPawan Kumar SinghFabio GiampaoloDipankar DasNorazak SenuAli Ahmadian
- Topics
- Topic Modeling (4 papers)Natural Language Processing Techniques (4 papers)Sentiment Analysis and Opinion Mining (3 papers)
In The Last Decade
Avishek Garain
18 papers receiving 309 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 223
- Information Systems 78
- Sociology and Political Science 62
- Computer Vision and Pattern Recognition 57
- Signal Processing 48
Countries citing papers authored by Avishek Garain
This map shows the geographic impact of Avishek Garain'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 Avishek Garain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Avishek Garain more than expected).
Fields of papers citing papers by Avishek Garain
This network shows the impact of papers produced by Avishek Garain. 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 Avishek Garain. The network helps show where Avishek Garain may publish in the future.
Co-authorship network of co-authors of Avishek Garain
This figure shows the co-authorship network connecting the top 25 collaborators of Avishek Garain. A scholar is included among the top collaborators of Avishek Garain 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 Avishek Garain. Avishek Garain is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 7 | |
| 3 | 11 | |
| 4 | 25 | |
| 5 | 48 | |
| 6 | 9 | |
| 7 | JUNLP@DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Langauges | 3 |
| 8 | Factuality Classification Using BERT Embeddings and Support Vector Machines. | 1 |
| 9 | 148 | |
| 10 | 11 | |
| 11 | 3 | |
| 12 | 6 | |
| 13 | 25 | |
| 14 | Humor Analysis based on Human Annotation(HAHA)-2019: Humor Analysis at Tweet Level using Deep Learning. | 6 |
| 15 | 8 | |
| 16 | 4 | |
| 17 | 8 | |
| 18 | 3 |
About Avishek Garain
Avishek Garain is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Communication, having authored 18 papers that have together received 332 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (4 papers) and Sentiment Analysis and Opinion Mining (3 papers). The work is most often cited by research in Artificial Intelligence (223 citations), Health Informatics (7 citations) and Signal Processing (48 citations). Avishek Garain has collaborated with scholars based in India, Italy and Chile. Frequent co-authors include Ram Sarkar, Biswarup Ray, Arpan Basu, Pawan Kumar Singh, Fabio Giampaolo, Dipankar Das, Norazak Senu, Ali Ahmadian, Sudip Kumar Naskar and Sivaji Bandyopadhyay. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Applied Soft Computing.
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.