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Damayanthi Herath

ResearcherPublications, citations & collaboration network

Damayanthi Herath is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 44 works, 597 citations, an h-index of 11 and an i10-index of 13.

44
Works
597
Citations
11
h-index
13
i10-index

How has Damayanthi Herath's publication output changed over time?

ScholarIQpublication output · 2011–2025

Output grew0% over the shown period — from 1 works in 2011 to 1 in 2025.

1
2
2
1
1
3
1
2011201720202021202220232025

What are the most-cited papers on Damayanthi Herath?

ScholarIQmost cited works
Semantic segmentation using Vision Transformers: A survey
Hans Thisanke, Chamli Deshan, Kavindu Chamith, Sachith Seneviratne, Rajith Vidanaarachchi, Damayanthi Herath
Engineering Applications of Artificial Intelligence. 2023259 Citations
Chronic Kidney Disease Prediction Using Machine Learning Methods
I.U. Ekanayake, Damayanthi Herath
202094 Citations
DeepSelectNet: deep neural network based selective sequencing for oxford nanopore sequencing
Anjana Senanayake, Hasindu Gamaarachchi, Damayanthi Herath, Roshan Ragel
S19032547. 202341 CitationsOPEN ACCESS
Mobilization of Rock Phosphate Phosphorus through Bacterial Inoculants to Enhance Growth and Yield of Wetland Rice
R.M.C.P. Rajapaksha, Damayanthi Herath, Ayesha P. Senanayake, M. G. T. L. Senevirathne
S159568494. 201133 Citations
CoMet: a workflow using contig coverage and composition for binning a metagenomic sample with high precision
Damayanthi Herath, Sen‐Lin Tang, Kshitij Tandon, David C. Ackland, Saman Halgamuge
S19032547. 201724 CitationsOPEN ACCESS

Related on ScholarIQ

University of Peradeniya
Institution
Semantic segmentation using Vision Transformers: A survey
Paper
Chronic Kidney Disease Prediction Using Machine Learning Methods
Paper
DeepSelectNet: deep neural network based selective sequencing for oxford nanopore sequencing
Paper
Mobilization of Rock Phosphate Phosphorus through Bacterial Inoculants to Enhance Growth and Yield of Wetland Rice
Paper
CoMet: a workflow using contig coverage and composition for binning a metagenomic sample with high precision
Paper
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