S. Varghese

1.2k total citations
2 papers, 20 citations indexed

About

S. Varghese is a scholar working on Health, Toxicology and Mutagenesis, Artificial Intelligence and Environmental Chemistry. According to data from OpenAlex, S. Varghese has authored 2 papers receiving a total of 20 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Health, Toxicology and Mutagenesis, 1 paper in Artificial Intelligence and 1 paper in Environmental Chemistry. Recurrent topics in S. Varghese's work include Imbalanced Data Classification Techniques (1 paper), Per- and polyfluoroalkyl substances research (1 paper) and Chemical Analysis and Environmental Impact (1 paper). S. Varghese is often cited by papers focused on Imbalanced Data Classification Techniques (1 paper), Per- and polyfluoroalkyl substances research (1 paper) and Chemical Analysis and Environmental Impact (1 paper). S. Varghese collaborates with scholars based in Qatar, India and Australia. S. Varghese's co-authors include Pritha Chatterjee, Venkataramana Gadhamshetty, Maryam Al‐Ejji, Probir Das, Ali Altaee, Alaa H. Hawari and Mohamed Arselene Ayari and has published in prestigious journals such as SHILAP Revista de lepidopterología and Journal of environmental chemical engineering.

In The Last Decade

S. Varghese

1 paper receiving 20 citations

Peers

S. Varghese
Courtney Lemeris United States
Burçin Önem Türkiye
Dayana Delgado United States
Rida Akram Pakistan
Grace M. Lee United States
S. Varghese
Citations per year, relative to S. Varghese S. Varghese (= 1×) peers Georgios Varotsis

Countries citing papers authored by S. Varghese

Since Specialization
Citations

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

Fields of papers citing papers by S. Varghese

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Varghese

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

All Works

2 of 2 papers shown
1.
Varghese, S., Mohamed Arselene Ayari, Probir Das, et al.. (2025). Machine learning-aided prediction of COD removal in the electrocoagulation process using a super learner model. Journal of environmental chemical engineering. 13(5). 117469–117469.
2.
Varghese, S., et al.. (2024). Advances in bioremediation strategies for PFAS-contaminated water and soil. SHILAP Revista de lepidopterología. 3(1). 100126–100126. 20 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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