Sara del Río
- Artificial Intelligence top 1%
- Imbalanced Data Classification Techniques 8
- Machine Learning and Data Classification 3
- Evolutionary Algorithms and Applications 2
- Data Stream Mining Techniques 2
- Text and Document Classification Technologies 2
- Fuzzy Logic and Control Systems 1
- Information Systems top 2%
- Data Mining Algorithms and Applications 4
- Management Information Systems top 10%
- Accounting top 10%
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- Electricity Theft Detection Techniques 2
- Co-authors
- Francisco HerreraJosé M. BenítezVictoria LópezAlberto FernándezNitesh V. ChawlaIsaac TrigueroAbdullah BawakidMaría José del Jesús
- Journals
- Information Sciences (1 paper)Fuzzy Sets and Systems (1 paper)BioMed Research International (1 paper)
- Partner nations
- SpainSaudi ArabiaBelgium
In The Last Decade
Sara del Río
13 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 864
- Health Information Management 105
- Information Systems 316
- Management Information Systems 76
- Accounting 73
Countries citing papers authored by Sara del Río
This map shows the geographic impact of Sara del Río'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 Sara del Río with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sara del Río more than expected).
Fields of papers citing papers by Sara del Río
This network shows the impact of papers produced by Sara del Río. 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 Sara del Río. The network helps show where Sara del Río may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Sara del Río, 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 | 2020 | 1 | |
| 2 | 2017 | 166 | |
| 3 | 2016 | 36 | |
| 4 | 2016 | 11 | |
| 5 | 2015 | 95 | |
| 6 | 2015 | 10 | |
| 7 | 2015 | 23 | |
| 8 | 2015 | 79 | |
| 9 | 2015 | 102 | |
| 10 | 2014 | 232 | |
| 11 | 2014 | 155 | |
| 12 | 2014 | 208 | |
| 13 | 2014 | 18 |
About Sara del Río
Sara del Río is a scholar working on Artificial Intelligence, Information Systems and Health Information Management, having authored 13 papers that have together received 1.1k indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (8 papers), Data Mining Algorithms and Applications (4 papers), Machine Learning and Data Classification (3 papers), Evolutionary Algorithms and Applications (2 papers), Electricity Theft Detection Techniques (2 papers), Data Stream Mining Techniques (2 papers), Text and Document Classification Technologies (2 papers) and Fuzzy Logic and Control Systems (1 paper). The work is most often cited by research in Artificial Intelligence (864 citations), Health Information Management (105 citations) and Information Systems (316 citations). Sara del Río has collaborated with scholars based in Spain, Saudi Arabia and Belgium. Frequent co-authors include Francisco Herrera, José M. Benítez, Victoria López, Alberto Fernández, Nitesh V. Chawla, Isaac Triguero, Abdullah Bawakid, María José del Jesús, Sergio Ramírez‐Gallego and Daniel Peralta. Their work appears in journals such as Information Sciences, Fuzzy Sets and Systems and BioMed Research International.
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.