Ana Garcı́a-Serrano
About
In The Last Decade
Ana Garcı́a-Serrano
59 papers receiving 523 citations
Peers
Comparison fields: 5 of 84
- Artificial Intelligence 323
- Computer Vision and Pattern Recognition 93
- Molecular Biology 85
- Transportation 80
- Information Systems 76
Countries citing papers authored by Ana Garcı́a-Serrano
This map shows the geographic impact of Ana Garcı́a-Serrano'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 Ana Garcı́a-Serrano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ana Garcı́a-Serrano more than expected).
Fields of papers citing papers by Ana Garcı́a-Serrano
This network shows the impact of papers produced by Ana Garcı́a-Serrano. 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 Ana Garcı́a-Serrano. The network helps show where Ana Garcı́a-Serrano may publish in the future.
Co-authorship network of co-authors of Ana Garcı́a-Serrano
This figure shows the co-authorship network connecting the top 25 collaborators of Ana Garcı́a-Serrano. A scholar is included among the top collaborators of Ana Garcı́a-Serrano 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 Ana Garcı́a-Serrano. Ana Garcı́a-Serrano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | Authorship Verification with neural networks via stylometric feature concatenation. | 1 |
| 3 | LSI2_UNED at eHealth-KD Challenge 2019: A Few-shot Learning Model for Knowledge Discovery from eHealth Documents. | 2 |
| 4 | Key Phrases Annotation in Medical Documents: MEDDOCAN 2019 Anonymization Task. | 1 |
| 5 | UNED @ Retrieving Diverse Social Images Task | 7 |
| 6 | Modelling Techniques for Twitter Contents: A Step beyond Classification based Approaches. | 5 |
| 7 | Using Linked Open Data sources for Entity Disambiguation | 1 |
| 8 | A multimedia IR-based system for the Photo Annotation Task at ImageCLEF2013 | 2 |
| 9 | Using Visual Concept Features in a Multimodal Retrieval System for the Medical Collection at ImageCLEF2012. | 3 |
| 10 | Visual Concept Features and Textual Expansion in a Multimodal System for Concept Annotation and Retrieval with Flickr Photos at ImageCLEF2012 | 1 |
| 11 | MIRACLE (FI) at ImageCLEFphoto 2009 | 2 |
| 12 | MIRACLE Team Report for ImageCLEF IR in CLEF 2006. | 1 |
| 13 | Exploiting Semantic Features for Image Retrieval at CLEF 2005. | 1 |
| 14 | miraQA: Initial Experiments in Question Answering. | 8 |
| 15 | MIRACLE at ImageCLEF 2004 | 1 |
| 16 | Utilizando recursos lingüísticos para mejora de la recuperación deinformación en la Web | 1 |
| 17 | Natural Language Dialogue in a Virtual Assistant Interface | 0 |
| 18 | An Interface Agent with Linguistic Skills | 2 |
| 19 | On the Automatization of Database Conceptual Modelling through Linguistic Engineering | 1 |
| 20 | EMERGENT CO-ORDINATION OF FLOW CONTROL ACTIONS THROUGH FUNCTIONAL CO- OPERATION OF SOCIAL AGENTS | 1 |
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.