Romesh Ranawana
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Information Systems
- Signal Processing
- Topics
- Handwritten Text Recognition Techniques (3 papers)Evolutionary Algorithms and Applications (3 papers)Machine Learning in Bioinformatics (2 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionHealth Information Management
- Journals
- Neural Computing and ApplicationsInternational Journal of Knowledge-based and Intelligent Engineering SystemsInternational Journal of Hybrid Intelligent Systems
- Partner nations
- United KingdomSri Lanka
In The Last Decade
Romesh Ranawana
9 papers receiving 251 citations
Peers
Comparison fields: 5 of 77
- Artificial Intelligence 137
- Computer Vision and Pattern Recognition 63
- Molecular Biology 46
- Information Systems 33
- Signal Processing 20
Countries citing papers authored by Romesh Ranawana
This map shows the geographic impact of Romesh Ranawana'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 Romesh Ranawana with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Romesh Ranawana more than expected).
Fields of papers citing papers by Romesh Ranawana
This network shows the impact of papers produced by Romesh Ranawana. 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 Romesh Ranawana. The network helps show where Romesh Ranawana may publish in the future.
Co-authorship network of co-authors of Romesh Ranawana
This figure shows the co-authorship network connecting the top 25 collaborators of Romesh Ranawana. A scholar is included among the top collaborators of Romesh Ranawana 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 Romesh Ranawana. Romesh Ranawana is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 1 | |
| 3 | 4 | |
| 4 | 126 | |
| 5 | 42 | |
| 6 | 8 | |
| 7 | 56 | |
| 8 | 13 | |
| 9 | Use of Fuzzy Feature Description to Recognize Handwritten Alphanumeric Characters. | 2 |
About Romesh Ranawana
Romesh Ranawana is a scholar working on Human-Computer Interaction, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 262 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (3 papers), Evolutionary Algorithms and Applications (3 papers) and Machine Learning in Bioinformatics (2 papers). The work is most often cited by research in Artificial Intelligence (137 citations), Computer Vision and Pattern Recognition (63 citations) and Health Information Management (10 citations). Romesh Ranawana has collaborated with scholars based in United Kingdom and Sri Lanka. Frequent co-authors include Vasile Palade, Asoka S. Karunananda, Daniel Howard and Sachithra Lokuge. Their work appears in journals such as Neural Computing and Applications, International Journal of Knowledge-based and Intelligent Engineering Systems and International Journal of Hybrid Intelligent Systems.
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.