Kasun Amarasinghe

1.5k total citations
31 papers, 1.1k citations indexed

About

Kasun Amarasinghe is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Kasun Amarasinghe has authored 31 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 7 papers in Computer Networks and Communications. Recurrent topics in Kasun Amarasinghe's work include Anomaly Detection Techniques and Applications (9 papers), Network Security and Intrusion Detection (6 papers) and Building Energy and Comfort Optimization (4 papers). Kasun Amarasinghe is often cited by papers focused on Anomaly Detection Techniques and Applications (9 papers), Network Security and Intrusion Detection (6 papers) and Building Energy and Comfort Optimization (4 papers). Kasun Amarasinghe collaborates with scholars based in United States, Spain and Switzerland. Kasun Amarasinghe's co-authors include Milos Manic, Daniel Marino, Juan J. Rodríguez-Andina, Dumidu Wijayasekara, Chathurika S. Wickramasinghe, Craig Rieger, Kevin Kenney, Hemank Lamba, Kit T. Rodolfa and Rayid Ghani and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and IEEE Transactions on Industrial Informatics.

In The Last Decade

Kasun Amarasinghe

30 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kasun Amarasinghe United States 16 437 387 234 209 181 31 1.1k
Jiaming Li Australia 18 415 0.9× 494 1.3× 158 0.7× 183 0.9× 143 0.8× 44 1.3k
Waseem Ullah South Korea 15 431 1.0× 652 1.7× 160 0.7× 286 1.4× 78 0.4× 38 1.4k
Fath U Min Ullah South Korea 20 518 1.2× 614 1.6× 156 0.7× 147 0.7× 79 0.4× 30 1.4k
Decebal Constantin Mocanu Netherlands 17 454 1.0× 306 0.8× 138 0.6× 149 0.7× 243 1.3× 50 1.2k
Daniel Nikovski United States 19 460 1.1× 274 0.7× 194 0.8× 76 0.4× 528 2.9× 114 1.2k
Kenneth Tze Kin Teo Malaysia 23 694 1.6× 262 0.7× 316 1.4× 261 1.2× 504 2.8× 192 1.6k
Enda Barrett Ireland 21 407 0.9× 294 0.8× 195 0.8× 767 3.7× 147 0.8× 53 1.3k
Dimitris Vrakas Greece 16 352 0.8× 294 0.8× 162 0.7× 167 0.8× 112 0.6× 51 885
Dezhi Hong United States 15 178 0.4× 311 0.8× 273 1.2× 159 0.8× 59 0.3× 60 937
Héctor Quintián Spain 19 176 0.4× 300 0.8× 64 0.3× 135 0.6× 113 0.6× 75 809

Countries citing papers authored by Kasun Amarasinghe

Since Specialization
Citations

This map shows the geographic impact of Kasun Amarasinghe'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 Kasun Amarasinghe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kasun Amarasinghe more than expected).

Fields of papers citing papers by Kasun Amarasinghe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kasun Amarasinghe. 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 Kasun Amarasinghe. The network helps show where Kasun Amarasinghe may publish in the future.

Co-authorship network of co-authors of Kasun Amarasinghe

This figure shows the co-authorship network connecting the top 25 collaborators of Kasun Amarasinghe. A scholar is included among the top collaborators of Kasun Amarasinghe 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 Kasun Amarasinghe. Kasun Amarasinghe is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Amarasinghe, Kasun, Kit T. Rodolfa, S. M. Jesus, et al.. (2024). On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods. Proceedings of the AAAI Conference on Artificial Intelligence. 38(19). 20921–20929. 3 indexed citations
2.
Frey, Arun, et al.. (2024). Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance. Proceedings of the AAAI Conference on Artificial Intelligence. 38(20). 22393–22400.
3.
Amarasinghe, Kasun, Kit T. Rodolfa, Hemank Lamba, & Rayid Ghani. (2023). Explainable machine learning for public policy: Use cases, gaps, and research directions. SHILAP Revista de lepidopterología. 5. 24 indexed citations
4.
Wickramasinghe, Chathurika S., Kasun Amarasinghe, Daniel Marino, Craig Rieger, & Milos Manic. (2021). Explainable Unsupervised Machine Learning for Cyber-Physical Systems. IEEE Access. 9. 131824–131843. 47 indexed citations
5.
Amarasinghe, Kasun & Milos Manic. (2019). Explaining What a Neural Network has Learned: Toward Transparent Classification. 1–6. 11 indexed citations
6.
Wickramasinghe, Chathurika S., et al.. (2019). Intelligent Driver System for Improving Fuel Efficiency in Vehicle Fleets. 33. 34–40. 1 indexed citations
7.
Wickramasinghe, Chathurika S., Kasun Amarasinghe, & Milos Manic. (2019). Deep Self-Organizing Maps for Unsupervised Image Classification. IEEE Transactions on Industrial Informatics. 15(11). 5837–5845. 49 indexed citations
8.
Wickramasinghe, Chathurika S., et al.. (2018). Nucleus Basalis of Meynert Stimulation for Dementia: Theoretical and Technical Considerations. Frontiers in Neuroscience. 12. 614–614. 22 indexed citations
9.
Amarasinghe, Kasun, Kevin Kenney, & Milos Manic. (2018). Toward Explainable Deep Neural Network Based Anomaly Detection. 311–317. 90 indexed citations
10.
Wickramasinghe, Chathurika S., Kasun Amarasinghe, Daniel Marino, & Milos Manic. (2018). Deep Self-Organizing Maps for Visual Data Mining. 9. 304–310. 5 indexed citations
11.
Amarasinghe, Kasun, et al.. (2018). Deep Learning and Reconfigurable Platforms in the Internet of Things: Challenges and Opportunities in Algorithms and Hardware. IEEE Industrial Electronics Magazine. 12(2). 36–49. 60 indexed citations
12.
Wickramasinghe, Chathurika S., Daniel Marino, Kasun Amarasinghe, & Milos Manic. (2018). Generalization of Deep Learning for Cyber-Physical System Security: A Survey. 745–751. 62 indexed citations
13.
Amarasinghe, Kasun, Matthew Anderson, Neal Yancey, et al.. (2017). Dynamic user interfaces for control systems. 4. 277–283. 3 indexed citations
14.
Amarasinghe, Kasun, et al.. (2017). Reduction of massive EEG datasets for epilepsy analysis using Artificial Neural Networks. 2. 137–143. 2 indexed citations
15.
Amarasinghe, Kasun, et al.. (2016). EEG feature selection for thought driven robots using evolutionary Algorithms. 752. 355–361. 15 indexed citations
16.
Marino, Daniel, Kasun Amarasinghe, & Milos Manic. (2016). Simultaneous generation-classification using LSTM. 5 indexed citations
17.
Amarasinghe, Kasun, et al.. (2016). VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter. 215–219. 2 indexed citations
18.
Amarasinghe, Kasun, et al.. (2015). Artificial neural networks based thermal energy storage control for buildings. 5421–5426. 20 indexed citations
19.
Manic, Milos, et al.. (2014). Next Generation Emergency Communication Systems via Software Defined Networks. 1–8. 11 indexed citations
20.
Amarasinghe, Kasun, Dumidu Wijayasekara, & Milos Manic. (2014). EEG based brain activity monitoring using Artificial Neural Networks. 61–66. 17 indexed citations

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.

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