Raj P. Gopalan
- Information Systems top 5%
- Computational Theory and Mathematics top 5%
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Molecular Biology
- Co-authors
- Aneesh KrishnaYudho Giri SucahyoAlva ErwinN. R. AchuthanJanne CadamuroAnna CarobeneSergio BernardiniFederico Cabitza
- Topics
- Data Mining Algorithms and Applications (11 papers)Rough Sets and Fuzzy Logic (8 papers)Data Management and Algorithms (8 papers)
- Partner nations
- AustraliaUnited StatesAustria
In The Last Decade
Raj P. Gopalan
25 papers receiving 271 citations
Peers
Comparison fields: 5 of 54
- Information Systems 189
- Computational Theory and Mathematics 139
- Artificial Intelligence 119
- Signal Processing 66
- Molecular Biology 64
Countries citing papers authored by Raj P. Gopalan
This map shows the geographic impact of Raj P. Gopalan'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 Raj P. Gopalan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raj P. Gopalan more than expected).
Fields of papers citing papers by Raj P. Gopalan
This network shows the impact of papers produced by Raj P. Gopalan. 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 Raj P. Gopalan. The network helps show where Raj P. Gopalan may publish in the future.
Co-authorship network of co-authors of Raj P. Gopalan
This figure shows the co-authorship network connecting the top 25 collaborators of Raj P. Gopalan. A scholar is included among the top collaborators of Raj P. Gopalan 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 Raj P. Gopalan. Raj P. Gopalan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | TOWARDS DEEP LEARNING IN GENOME-WIDE ASSOCIATION INTERACTION STUDIES | 7 |
| 5 | 27 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 4 | |
| 9 | 7 | |
| 10 | Detecting SNP Interactions in Balanced and Imbalanced Datasets using Associative Classification | 2 |
| 11 | 2 | |
| 12 | 1 | |
| 13 | 3 | |
| 14 | 1 | |
| 15 | 3 | |
| 16 | CT-ITL: efficient frequent item set mining using a compressed prefix tree with pattern growth | 26 |
| 17 | Efficient mining of long frequent patterns from very large dense datasets | 1 |
| 18 | Fast Frequent Itemset Mining using Compressed Data Representation. | 7 |
| 19 | 1 | |
| 20 | ITL-MINE: Mining Frequent Itemsets More Efficiently. | 5 |
About Raj P. Gopalan
Raj P. Gopalan is a scholar working on Health Informatics, Signal Processing and Information Systems, having authored 28 papers that have together received 313 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (11 papers), Rough Sets and Fuzzy Logic (8 papers) and Data Management and Algorithms (8 papers). The work is most often cited by research in Health Informatics (18 citations), Computational Theory and Mathematics (139 citations) and Information Systems (189 citations). Raj P. Gopalan has collaborated with scholars based in Australia, United States and Austria. Frequent co-authors include Aneesh Krishna, Yudho Giri Sucahyo, Alva Erwin, N. R. Achuthan, Janne Cadamuro, Anna Carobene, Sergio Bernardini, Federico Cabitza, Jochen K. Lennerz and Yanrong Li. Their work appears in journals such as Scientific Reports, Heliyon and Clinical Chemistry and Laboratory Medicine (CCLM).
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.