Sathish

28 total papers · 534 total citations
13 papers, 344 citations indexed

About

Sathish is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Sathish has authored 13 papers receiving a total of 344 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Sathish's work include AI in cancer detection (2 papers), Rice Cultivation and Yield Improvement (2 papers) and Medical Image Segmentation Techniques (2 papers). Sathish is often cited by papers focused on AI in cancer detection (2 papers), Rice Cultivation and Yield Improvement (2 papers) and Medical Image Segmentation Techniques (2 papers). Sathish collaborates with scholars based in India. Sathish's co-authors include J. Samuel Manoharan, V. Suba, K. Raja, Arvind Arvind, Kumar, Reddy and Aloka Samantaray and has published in prestigious journals such as JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH, Asian Pacific Journal of Tropical Disease and Legume Research - An International Journal.

In The Last Decade

Sathish

13 papers receiving 266 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Sathish 104 102 90 37 34 13 344
Yiding Lv 101 1.0× 119 1.2× 76 0.8× 23 0.6× 6 0.2× 13 397
Juan Luís Vidal Mazón 71 0.7× 95 0.9× 61 0.7× 6 0.2× 6 0.2× 20 358
Rahul Sharma 104 1.0× 134 1.3× 53 0.6× 17 0.5× 13 0.4× 17 401
Ahmed I. Shahin 200 1.9× 144 1.4× 111 1.2× 39 1.1× 3 0.1× 13 359
Tanvir Ahmad 102 1.0× 92 0.9× 31 0.3× 34 0.9× 4 0.1× 7 332
Fadzil Ahmad 59 0.6× 189 1.9× 34 0.4× 20 0.5× 4 0.1× 27 350
Linh T. Duong 51 0.5× 92 0.9× 145 1.6× 18 0.5× 5 0.1× 16 399
Ziquan Zhu 136 1.3× 116 1.1× 95 1.1× 28 0.8× 3 0.1× 10 361
Esraa Hassan 101 1.0× 148 1.5× 97 1.1× 16 0.4× 2 0.1× 13 384
Umair Muneer Butt 66 0.6× 136 1.3× 24 0.3× 15 0.4× 17 0.5× 18 299

Countries citing papers authored by Sathish

Since Specialization
Citations

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

Fields of papers citing papers by Sathish

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sathish

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

All Works

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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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2026