Silvia Acid
Impact in
- Artificial Intelligence top 10%
- Bayesian Modeling and Causal Inference
- Machine Learning and Data Classification
- AI-based Problem Solving and Planning
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- Data Quality and Management
Papers in
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- Bayesian Modeling and Causal Inference 8
- Machine Learning and Algorithms 2
- Machine Learning and Data Classification 2
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- Data Quality and Management 4
- Co-authors
- Luis M. de Campos (9 shared papers)Juan M. Fernández‐Luna (2 shared papers)Javier G. Castellano (1 shared paper)Juan F. Huete (2 shared papers)
- Journals
- International Journal of Approximate Reasoning (2 papers)Artificial Intelligence in Medicine (1 paper)Data Mining and Knowledge Discovery (1 paper)Journal of Artificial Intelligence Research (1 paper)International Journal of Intelligent Systems (1 paper)
- Partner nations
- Spain
In The Last Decade
Silvia Acid
9 papers receiving 247 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 194
- Management Science and Operations Research 55
- Signal Processing 30
- Health Information Management 10
- Computational Theory and Mathematics 36
Countries citing papers authored by Silvia Acid
This map shows the geographic impact of Silvia Acid'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 Silvia Acid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Silvia Acid more than expected).
Fields of papers citing papers by Silvia Acid
This network shows the impact of papers produced by Silvia Acid. 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 Silvia Acid. The network helps show where Silvia Acid may publish in the future.
Co-authors
The 4 scholars most cited alongside Silvia Acid, 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 | 2003 | 69 | |
| 2 | 2004 | 60 | |
| 3 | 2005 | 49 | |
| 4 | 2001 | 38 | |
| 5 | 2003 | 31 | |
| 6 | 2011 | 13 | |
| 7 | 2011 | 7 | |
| 8 | 2001 | 1 | |
| 9 | 2012 | 1 |
About Silvia Acid
Silvia Acid is a scholar working on Artificial Intelligence, Management Science and Operations Research, Information Systems, Signal Processing and Computational Theory and Mathematics, having authored 9 papers that have together received 269 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (8 papers), Data Quality and Management (4 papers), Machine Learning and Algorithms (2 papers), Data Management and Algorithms (2 papers), Rough Sets and Fuzzy Logic (2 papers), Machine Learning and Data Classification (2 papers), Risk and Safety Analysis (1 paper) and Fuzzy Systems and Optimization (1 paper). The work is most often cited by research in Artificial Intelligence (194 citations), Management Science and Operations Research (55 citations), Signal Processing (30 citations), Health Information Management (10 citations) and Computational Theory and Mathematics (36 citations). Silvia Acid has collaborated with scholars based in Spain. Frequent co-authors include Luis M. de Campos, Juan M. Fernández‐Luna, Javier G. Castellano and Juan F. Huete. Their work appears in journals such as International Journal of Approximate Reasoning, Artificial Intelligence in Medicine, Data Mining and Knowledge Discovery, Journal of Artificial Intelligence Research and International Journal of Intelligent Systems.
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.