Shan Suthaharan

67 papers receiving 1.4k citations

Hit Papers

Machine Learning Models and Algorithms for Big Data Class...20152026201820222015100200300400

Peers

Shan Suthaharan
Comparison fields: 5 of 160
  • Artificial Intelligence 438
  • Computer Vision and Pattern Recognition 370
  • Computer Networks and Communications 302
  • Signal Processing 136
  • Electrical and Electronic Engineering 128
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José C. Cabaleiro Spain
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Jyotsna Kumar Mandal India
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Citations per field
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Citations per year

Countries citing papers authored by Shan Suthaharan

Since Specialization
Citations

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

Fields of papers citing papers by Shan Suthaharan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shan Suthaharan

This figure shows the co-authorship network connecting the top 25 collaborators of Shan Suthaharan. A scholar is included among the top collaborators of Shan Suthaharan 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 Shan Suthaharan. Shan Suthaharan 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
#WorkIndexed citations
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Automated choroid layer segmentation based on wide-field SS-OCT images using deep residual encoder-decoder architecture
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An automated choroid segmentation approach using transfer learning and encoder-decoder networks
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5 10
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Multimodal ophthalmic image registration using Hessian feature spaces
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7 10
8 24
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11 106
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Fuzzy Logic Strategy of Prognosticating TCP's Timeout ad Retransmission.
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A New Edge Enhancement Technique for Image/ Video Segmentation and Compression
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A New Linear Post-Filtering Technique to Reduce Transform Coding Block-Edge Artifact at Low Bit Rates
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SNR Optimization Using Genetic Algorithm.
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Adaptive. Neighbourhood Image Filtering for Mpeg-1 Coded Images
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20 11

About Shan Suthaharan

Shan Suthaharan is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Computer Networks and Communications, having authored 79 papers that have together received 1.5k indexed citations. Recurring topics across this work include Image and Video Quality Assessment (12 papers), Chaos-based Image/Signal Encryption (11 papers) and Advanced Image Processing Techniques (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (370 citations), Artificial Intelligence (438 citations) and Computer Networks and Communications (302 citations). Shan Suthaharan has collaborated with scholars based in United States, Australia and South Korea. Frequent co-authors include Selima Sultana, Firoozeh Karimi, Christopher Leckie, Sutharshan Rajasegarar, Marimuthu Palaniswami, Arumugam Nallanathan, B. Kannan, V. Jeyakumar, K.R. Rao and Guoyin Li. Their work appears in journals such as Scientific Reports, Investigative Ophthalmology & Visual Science and Pattern Recognition Letters.

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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