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23 results on '"Llavori, Rafael Berlanga"'

1. Highly Heterogeneous XML Collections: How to Retrieve Precise Results?

Author

Sanz, Ismael, Mesiti, Marco, Guerrini, Giovanna, Llavori, Rafael Berlanga, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Larsen, Henrik Legind, editor, Pasi, Gabriella, editor, Ortiz-Arroyo, Daniel, editor, Andreasen, Troels, editor, and Christiansen, Henning, editor

Published
2006
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2. ArHeX: An Approximate Retrieval System for Highly Heterogeneous XML Document Collections

Author

Sanz, Ismael, Mesiti, Marco, Guerrini, Giovanna, Llavori, Rafael Berlanga, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ioannidis, Yannis, editor, Scholl, Marc H., editor, Schmidt, Joachim W., editor, Matthes, Florian, editor, Hatzopoulos, Mike, editor, Boehm, Klemens, editor, Kemper, Alfons, editor, Grust, Torsten, editor, and Boehm, Christian, editor

Published
2006
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3. Approximate Subtree Identification in Heterogeneous XML Documents Collections

Author

Sanz, Ismael, Mesiti, Marco, Guerrini, Giovanna, Llavori, Rafael Berlanga, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bressan, Stéphane, editor, Ceri, Stefano, editor, Hunt, Ela, editor, Ives, Zachary G., editor, Bellahsène, Zohra, editor, Rys, Michael, editor, and Unland, Rainer, editor

Published
2005
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5. A retrieval language for historical documents

Author

Cabo, María José Aramburu, Llavori, Rafael Berlanga, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Quirchmayr, Gerald, editor, Schweighofer, Erich, editor, and Bench-Capon, Trevor J.M., editor

Published
1998
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11. Screening of Radiological Images Suspected of Containing Lung Nodules.

Author

Eixarch, Raúl Pedro Aceñero, Laplaza, Raúl Díaz-Usechi, and Llavori, Rafael Berlanga

Abstract

This paper presents a study about screening large radiological image streams produced in hospitals for earlier detection of lung nodules. Being one of the most difficult classification tasks in the literature, the objective is to measure how well state-of-the-art classifiers can screen out the images stream to keep as many positive cases as possible in an output stream to be inspected by clinicians. The authors performed several experiments with different image resolutions and training datasets from different sources, always taking ResNet-152 as the base neural network. Results over existing datasets show that, contrary to other diseases like pneumonia, detecting nodules is a hard task when using only radiographies. Indeed, final diagnosis by clinicians is usually performed with much more precise images like computed tomographies. [ABSTRACT FROM AUTHOR]

Published
2022
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13. Del Ars Magna a la Inteligencia Artificial Generativa.

Author

Llavori, Rafael Berlanga

Abstract

Con este artículo se pretende dar a conocer los elementos clave en la evolución de la Inteligencia Artificial (IA) en los últimos años, especialmente la Inteligencia Artificial Generativa. Así como la IA predictiva o discriminativa ha tenido un impacto notable en la Medicina en las últimas dos décadas, especialmente en el diagnóstico de patologías a partir de datos clínicos, la IA generativa nos conduce a nuevos retos y oportunidades en numerosos campos como son el tratamiento personalizado, la prevención, la pro acción, nuevas técnicas terapéuticas y, en general, la ampliación de los horizontes de la investigación clínica y biomédica. Para este trabajo hemos realizado un estudio bibliográfico y organizado el mismo en las principales ideas de aplicación de la IA generativa a la Medicina. El principal resultado de este estudio es un conjunto de aplicaciones potenciales de la IA generativa que se están iniciando en diversas áreas de Medicina y que pueden suponer una revolución de la misma a corto o medio plazo. No obstante, hay obstáculos muy importantes que superar, principalmente los referentes a la calidad y precisión de estos sistemas, así como las barreras de privacidad y ética que deben ser respetadas por los sistemas digitales de salud.

Published
2024
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19. ArHeX: An Approximate Retrieval System for Highly Heterogeneous XML Document Collections.

Author

Ioannidis, Yannis, Scholl, Marc H., Schmidt, Joachim W., Matthes, Florian, Hatzopoulos, Mike, Boehm, Klemens, Kemper, Alfons, Grust, Torsten, Boehm, Christian, Sanz, Ismael, Mesiti, Marco, Guerrini, Giovanna, and Llavori, Rafael Berlanga

Abstract

Handling the heterogeneity of structure and/or content of XML documents for the retrieval of information is a fertile field of research nowadays. Many efforts are currently devoted to identifying approximate answers to queries that require relaxation on conditions both on the structure and the content of XML documents [1,2,4,5]. Results are ranked relying on score functions that measure their quality and relevance and only the top-k returned. [ABSTRACT FROM AUTHOR]

Published
2006
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20. Highly Heterogeneous XML Collections: How to Retrieve Precise Results?

Author

Larsen, Henrik Legind, Pasi, Gabriella, Ortiz-Arroyo, Daniel, Andreasen, Troels, Christiansen, Henning, Sanz, Ismael, Mesiti, Marco, Guerrini, Giovanna, and Llavori, Rafael Berlanga

Abstract

Highly heterogeneous XML collections are thematic collections exploiting different structures: the parent-child or ancestor-descendant relationships are not preserved and vocabulary discrepancies in the element names can occur. In this setting current approaches return answers with low precision. By means of similarity measures and semantic inverted indices we present an approach for improving the precision of query answers without compromising performance. [ABSTRACT FROM AUTHOR]

Published
2006
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21. Approximate Subtree Identification in Heterogeneous XML Documents Collections.

Author

Bressan, Stéphane, Ceri, Stefano, Hunt, Ela, Ives, Zachary G., Bellahsène, Zohra, Rys, Michael, Unland, Rainer, Sanz, Ismael, Mesiti, Marco, Guerrini, Giovanna, and Llavori, Rafael Berlanga

Abstract

Due to the heterogeneous nature of XML data for internet applications exact matching of queries is often inadequate. The need arises to quickly identify subtrees of XML documents in a collection that are similar to a given pattern. In this paper we discuss different similarity measures between a pattern and subtrees of documents in the collection. An efficient algorithm for the identification of document subtrees, approximately conforming to the pattern, by indexing structures is then introduced. [ABSTRACT FROM AUTHOR]

Published
2005
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23. A retrieval language for historical documents.

Author

Goos, Gerhard, Hartmanis, Juris, Leeuwen, Jan, Quirchmayr, Gerald, Schweighofer, Erich, Bench-Capon, Trevor J. M., Cabo, María José Aramburu, and Llavori, Rafael Berlanga

Abstract

This paper focuses on a set of structured document applications that we have denoted databases of historical documents. The information into these documents is closely related to the time in which they are created while being still of great usefulness in the future. The main contribution of this paper is the formulation of a group of operators and predicates that express retrieval conditions over the temporal features of documents. Additionally, the resulting retrieval language supports the construction of time series of documents, here named chronicles, which are regarded as a preliminary step towards the implementation of more complex data mining operations over historical document repositories. [ABSTRACT FROM AUTHOR]

Published
1998
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