Mourad Sarrouti

498 citations
23 papers · 293 · h-index 11

Impact in

Papers in

    • Topic Modeling 16
    • Natural Language Processing Techniques 9
    • Domain Adaptation and Few-Shot Learning 3
    • Advanced Text Analysis Techniques 2
    • Biomedical Text Mining and Ontologies 11

Mourad Sarrouti

21 papers receiving 281 citations

Peers

Mourad Sarrouti
Comparison fields: 5 of 46
  • Health Informatics 17
  • Artificial Intelligence 216
  • Toxicology 20
  • Computer Vision and Pattern Recognition 41
  • Computational Theory and Mathematics 29
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Citations per year

Countries citing papers authored by Mourad Sarrouti

Since Specialization
Citations

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

Fields of papers citing papers by Mourad Sarrouti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside Mourad Sarrouti, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mourad Sarrouti Line = papers co-authored together Mourad Sarrouti links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201952
2 201739
3 202136
4 201724
5 202122
6 201516
7 201916
8 201713
9 201711
10 202010
11 202110
12 20219
13 20228
14 20178
15
NLM at VQA-Med 2020: Visual Question Answering and Generation in the Medical Domain.
20205
16 20214
17 20214
18
NLM at BioASQ Synergy 2021: Deep learning-based methods for biomedical semantic question answering about COVID-19
20213
19 20171
20
NLM at TREC 2020 Health Misinformation and Deep Learning Tracks.
20201

About Mourad Sarrouti

Mourad Sarrouti is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Sociology and Political Science, having authored 23 papers that have together received 293 indexed citations. Recurring topics across this work include Topic Modeling (16 papers), Biomedical Text Mining and Ontologies (11 papers), Natural Language Processing Techniques (9 papers), Domain Adaptation and Few-Shot Learning (3 papers), Computational Drug Discovery Methods (3 papers), Multimodal Machine Learning Applications (3 papers), Misinformation and Its Impacts (2 papers) and Advanced Text Analysis Techniques (2 papers). The work is most often cited by research in Health Informatics (17 citations), Artificial Intelligence (216 citations), Toxicology (20 citations), Computer Vision and Pattern Recognition (41 citations) and Computational Theory and Mathematics (29 citations). Mourad Sarrouti has collaborated with scholars based in United States and Morocco. Frequent co-authors include Saïd Ouatik El Alaoui, Noureddine En-Nahnahi, Asma Ben Abacha, Dina Demner‐Fushman, Deepak Gupta, Shweta Yadav, Deepak Gupta, Saïd Najah, Travis R. Goodwin and Willie J. Rogers. Their work appears in journals such as Journal of Biomedical Informatics, Computer Methods and Programs in Biomedicine, Artificial Intelligence in Medicine, Pattern Recognition Letters and Information Processing & Management.

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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