Lasitha Vidyaratne

1.0k total citations
31 papers, 566 citations indexed

About

Lasitha Vidyaratne is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Neurology. According to data from OpenAlex, Lasitha Vidyaratne has authored 31 papers receiving a total of 566 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 7 papers in Neurology. Recurrent topics in Lasitha Vidyaratne's work include Brain Tumor Detection and Classification (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Advanced Neural Network Applications (4 papers). Lasitha Vidyaratne is often cited by papers focused on Brain Tumor Detection and Classification (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Advanced Neural Network Applications (4 papers). Lasitha Vidyaratne collaborates with scholars based in United States and United Kingdom. Lasitha Vidyaratne's co-authors include Khan M. Iftekharuddin, Mahbubul Alam, Linmin Pei, Chris H. Pappas, Malachi Schram, Sarah Cousineau, Majdi I. Radaideh, Xian Yeow Lee, Dan Lu and T. Britton and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Lasitha Vidyaratne

29 papers receiving 545 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lasitha Vidyaratne United States 9 223 137 134 126 121 31 566
Ming Yu China 13 63 0.3× 57 0.4× 214 1.6× 83 0.7× 148 1.2× 73 741
Mohammad-Parsa Hosseini United States 11 457 2.0× 146 1.1× 87 0.6× 48 0.4× 110 0.9× 14 766
Seda Arslan Tuncer Türkiye 13 118 0.5× 36 0.3× 165 1.2× 31 0.2× 148 1.2× 55 542
Yavuz Erdem Türkiye 14 142 0.6× 64 0.5× 229 1.7× 63 0.5× 109 0.9× 73 727
Mahbubul Alam United States 9 70 0.3× 58 0.4× 78 0.6× 63 0.5× 89 0.7× 28 311
Devon Hjelm United States 6 456 2.0× 75 0.5× 154 1.1× 59 0.5× 342 2.8× 11 945
Mohammad Zavid Parvez Bangladesh 14 375 1.7× 184 1.3× 145 1.1× 163 1.3× 369 3.0× 46 982
Weibei Dou China 12 135 0.6× 92 0.7× 219 1.6× 117 0.9× 132 1.1× 65 596
Vinayak K. Bairagi India 14 202 0.9× 109 0.8× 201 1.5× 92 0.7× 88 0.7× 68 568
Tongguang Ni China 13 110 0.5× 93 0.7× 199 1.5× 106 0.8× 224 1.9× 54 583

Countries citing papers authored by Lasitha Vidyaratne

Since Specialization
Citations

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

Fields of papers citing papers by Lasitha Vidyaratne

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lasitha Vidyaratne

This figure shows the co-authorship network connecting the top 25 collaborators of Lasitha Vidyaratne. A scholar is included among the top collaborators of Lasitha Vidyaratne 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 Lasitha Vidyaratne. Lasitha Vidyaratne 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.
Lee, Xian Yeow, et al.. (2025). Exploring LLM-based Agentic Frameworks for Fault Diagnosis. Annual Conference of the PHM Society. 17(1).
2.
3.
Vidyaratne, Lasitha, et al.. (2024). Generating Troubleshooting Trees for Industrial Equipment using Large Language Models (LLM). 116–125. 6 indexed citations
4.
Schram, Malachi, Steven Goldenberg, Lasitha Vidyaratne, et al.. (2023). Multi-module-based CVAE to predict HVCM faults in the SNS accelerator. SHILAP Revista de lepidopterología. 13. 100484–100484. 5 indexed citations
5.
Lee, Xian Yeow, Lasitha Vidyaratne, Mahbubul Alam, et al.. (2023). XDNet: A Few-Shot Meta-Learning Approach for Cross-Domain Visual Inspection. 4375–4384. 3 indexed citations
6.
Vidyaratne, Lasitha, et al.. (2023). Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–7. 1 indexed citations
7.
Vidyaratne, Lasitha, et al.. (2023). 3D far-field Lidar sensing and computational modeling for human identification. Applied Optics. 63(8). C15–C15. 2 indexed citations
8.
Vidyaratne, Lasitha, et al.. (2022). Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory. Frontiers in Artificial Intelligence. 4. 718950–718950. 4 indexed citations
9.
Vidyaratne, Lasitha, et al.. (2021). Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information. ePubs (Science and Technology Facilities Council, Research Councils UK). 11 indexed citations
10.
Vidyaratne, Lasitha, et al.. (2021). INITIAL STUDIES OF CAVITY FAULT PREDICTION AT JEFFERSON LABORATORY. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
11.
Pei, Linmin, et al.. (2020). Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images. Scientific Reports. 10(1). 19726–19726. 99 indexed citations
12.
Carpenter, Adam, et al.. (2019). SRF Cavity Fault Classification Using Machine Learning At CEBAF. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 5 indexed citations
13.
Alam, Mahbubul, et al.. (2019). Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction. Frontiers in Neuroscience. 13. 966–966. 42 indexed citations
14.
Vidyaratne, Lasitha, et al.. (2018). Glioblastoma and Survival Prediction. Lecture notes in computer science. 10670. 358–368. 24 indexed citations
15.
Alam, Mahbubul, Lasitha Vidyaratne, & Khan M. Iftekharuddin. (2018). Novel deep generative simultaneous recurrent model for efficient representation learning. Neural Networks. 107. 12–22. 6 indexed citations
16.
Alam, Mahbubul, et al.. (2018). Deep learning and texture-based semantic label fusion for brain tumor segmentation. PubMed. 2018. 12–12. 7 indexed citations
17.
Alam, Mahbubul, Lasitha Vidyaratne, & Khan M. Iftekharuddin. (2018). Sparse Simultaneous Recurrent Deep Learning for Robust Facial Expression Recognition. IEEE Transactions on Neural Networks and Learning Systems. 29(10). 4905–4916. 39 indexed citations
18.
Bussey, D. B. J., et al.. (2017). Convolutional neural network transfer learning for robust face recognition in NAO humanoid robot. 1. 1–7. 3 indexed citations
19.
Alam, Mahbubul, Lasitha Vidyaratne, & Khan M. Iftekharuddin. (2016). Efficient feature extraction with simultaneous recurrent network for metric learning. 2. 1195–1201. 2 indexed citations
20.
Alam, Mahbubul, Lasitha Vidyaratne, & Khan M. Iftekharuddin. (2015). Novel hierarchical Cellular Simultaneous Recurrent neural Network for object detection. 1–7. 5 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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