David Ruano-Ordás
- Information Systems top 5%
- Spam and Phishing Detection 16
- Artificial Intelligence top 5%
- Text and Document Classification Technologies 8
- Data Stream Mining Techniques 4
- Sentiment Analysis and Opinion Mining 2
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- Network Security and Intrusion Detection 6
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- Metabolomics and Mass Spectrometry Studies 2
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- Analytical Chemistry and Chromatography 2
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- Computational Drug Discovery Methods 2
- Co-authors
- José R. MéndezFlorentino Fdez‐RiverolaVítor Basto-FernandesIryna YevseyevaRosalía LazaReyes PavónJ. F. GálvezMichael Emmerich
- Partner nations
- SpainPortugalUnited Kingdom
In The Last Decade
David Ruano-Ordás
27 papers receiving 348 citations
Peers
Comparison fields: 5 of 82
- Information Systems 220
- Artificial Intelligence 248
- Computer Networks and Communications 134
- Signal Processing 28
- Health Informatics 3
Countries citing papers authored by David Ruano-Ordás
This map shows the geographic impact of David Ruano-Ordás'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 David Ruano-Ordás with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Ruano-Ordás more than expected).
Fields of papers citing papers by David Ruano-Ordás
This network shows the impact of papers produced by David Ruano-Ordás. 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 David Ruano-Ordás. The network helps show where David Ruano-Ordás may publish in the future.
Co-authorship network
The 13 scholars most cited alongside David Ruano-Ordás, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 9 | |
| 2 | 2024 | 1 | |
| 3 | 2021 | 2 | |
| 4 | 2021 | 2 | |
| 5 | 2021 | 1 | |
| 6 | 2021 | 10 | |
| 7 | 2021 | 7 | |
| 8 | 2020 | 9 | |
| 9 | 2019 | 7 | |
| 10 | 2019 | 5 | |
| 11 | 2018 | 17 | |
| 12 | 2018 | 60 | |
| 13 | 2017 | 28 | |
| 14 | 2017 | 5 | |
| 15 | 2017 | 13 | |
| 16 | 2016 | 12 | |
| 17 | 2015 | 2 | |
| 18 | 2015 | 12 | |
| 19 | 2012 | 37 | |
| 20 | 2012 | 31 |
About David Ruano-Ordás
David Ruano-Ordás is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications, having authored 27 papers that have together received 357 indexed citations. Recurring topics across this work include Spam and Phishing Detection (16 papers), Text and Document Classification Technologies (8 papers), Network Security and Intrusion Detection (6 papers), Data Stream Mining Techniques (4 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Analytical Chemistry and Chromatography (2 papers), Sentiment Analysis and Opinion Mining (2 papers) and Computational Drug Discovery Methods (2 papers). The work is most often cited by research in Information Systems (220 citations), Artificial Intelligence (248 citations) and Computer Networks and Communications (134 citations). David Ruano-Ordás has collaborated with scholars based in Spain, Portugal and United Kingdom. Frequent co-authors include José R. Méndez, Florentino Fdez‐Riverola, Vítor Basto-Fernandes, Iryna Yevseyeva, Rosalía Laza, Reyes Pavón, J. F. Gálvez, Michael Emmerich, Helge Janicke and Rongfang Liu. Their work appears in journals such as Expert Systems with Applications, Sensors and Information Sciences.
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.