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20 results on '"Almeida-Cruz, Yudivián"'

7. Balancing Efficiency and Performance in NLP: A Cross-Comparison of Shallow Machine Learning and Large Language Models via AutoML.

Author

Estevanell-Valladares, Ernesto L., Gutiérrez, Yoan, Montoyo-Guijarro, Andrés, Muñoz-Guillena, Rafael, and Almeida-Cruz, Yudivián

Subjects
LANGUAGE models, MACHINE learning, CARBON emissions, SUSTAINABILITY, ECOLOGICAL impact, NATURAL language processing
Abstract

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024
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8. Optimizing Natural Language Processing Pipelines: Opinion Mining Case Study

Author

Estevez-Velarde, Suilan, Gutiérrez, Yoan, Montoyo, Andrés, Almeida-Cruz, Yudivián, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nyström, Ingela, editor, Hernández Heredia, Yanio, editor, and Milián Núñez, Vladimir, editor

Published
2019
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12. Metodología para el análisis y evaluación de daños en escenas aéreas

Author

Díaz Borge, Luis Alberto, Almeida Cruz, Yudivián, Díaz Borge, Luis Alberto, and Almeida Cruz, Yudivián

Abstract

Disasters caused by natural phenomena have always existed throughout the history of mankind, becoming more and more frequent and catastrophic. Due to this growing trend, its analysis and prevention is extremely important to avoid greater evils. Thanks to scientific and technological advances, today there are large volumes of information regarding natural disasters. Once the catastrophe has occurred, accurate and timely data regarding the damage caused is needed in order to provide an effective response. This paper presents a proposal based on neural networks to analyze and mark damage in areas affected by catastrophes, making use of pre- and post-disaster satellite images. Analysis of the results achieved is also carried out, as well as some comparisons with other existing works on the subject., Los desastres causados por fenómenos naturales han existido siempre a través de la historia de la humanidad, siendo cada vez más frecuentes y catastróficos. Debido a esta tendencia creciente, se hace de suma importancia su análisis y prevención para evitar males mayores. Gracias al avance científico-tecnológico, hoy en día se cuenta con grandes volúmenes de información referentes a desastres naturales. Una vez ocurrida la catástrofe se necesitan datos precisos y oportunos respecto a los daños causados con el objetivo de brindar una respuesta eficaz. En este trabajo se presenta una propuesta basada en redes neuronales para analizar y marcar daños en zonas afectadas al paso de catástrofes, haciendo uso de imágenes satelitales pre- y post- desastre. También se realizan análisis de los resultados alcanzados, así como algunas comparaciones con otros trabajos existentes sobre el tema.

Published
2021

13. How Mathematical Approaches Could Help Decision-Making to Epidemic Control? The Successful Experience against COVID-19 in Cuba

Author

Sanchez, Lizet, primary, Lorenzo-Luaces, Patricia, additional, Sebrango, Carlos, additional, Torres, Antonio, additional, Fonte, Claudia, additional, Crespo, Marelis, additional, Valdes, Jose E., additional, Baldoquin, Waldemar, additional, Fonte, Paula, additional, Almeida-Cruz, Yudivián, additional, Estévez-Velarde, Suilan, additional, Piad-Morffis, Alejandro, additional, Morales, Wilfredo, additional, Pérez, Nancy, additional, Araujo, Rafael, additional, del Carmen Pria, Maria, additional, Corral, Ariadna, additional, Noriega, Vivian, additional, Vidal, Maria, additional, Lage, Agustin, additional, Guinovart, Raul, additional, and Mas, Pedro, additional

Published
2020
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14. Demo Application for the AutoGOAL Framework

Author

Estevez-Velarde, Suilan, primary, Piad-Morffis, Alejandro, additional, Gutiérrez, Yoan, additional, Montoyo, Andres, additional, Muñoz-Guillena, Rafael, additional, and Almeida Cruz, Yudivián, additional

Published
2020
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16. Knowledge Discovery in COVID-19 Research Literature

Author

Piad-Morffis, Alejandro, primary, Estevez-Velarde, Suilan, additional, Estevanell-Valladares, Ernesto Luis, additional, Gutiérrez, Yoan, additional, Montoyo, Andrés, additional, Muñoz, Rafael, additional, and Almeida-Cruz, Yudivián, additional

Published
2020
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17. Overlapping Community Detection on a Graph of Chemicals, Diseases and Genes for Drug Repositioning and Adverse Reactions Prediction

Author

García-Ochagavía, María Elena, Almeida-Cruz, Yudivián, Estévez-Velarde, Suilán, Alonso-Reina, Aimée, and Ochagavía-Roque, María Elena

Subjects
biological network, lcsh:Management. Industrial management, lcsh:HD28-70, Biological network, Overlapping community detection, Drug repositioning, Management. Industrial management, adverse reactions, overlapping community detection, HD28-70, Adverse reactions
Abstract

URL del artículo en la web de la Revista: https://www.upo.es/revistas/index.php/gecontec/article/view/4081, Developing a drug from scratch is a very long and expensive process that has a small probability of success. For this reason, pharmaceutical companies are devoting their efforts to find drugs that could be repositioned. When using a drug to treat a disease is necessary to consider what adverse reactions it may cause, this is why the prediction of adverse reactions is highly related to drug repositioning. We propose the detection of overlapping communities over a biological network of chemicals, diseases and genes in order to find drug-disease pairs that could be used as basis for later drug repositioning and adverse reactions prediction analysis. Of the evaluated overlapping community detection algorithms, OSLOM got the best results, producing 724 communities from which was possible to extract 215944 drug-disease pairs not present in the analyzed graph. We illustrate the usefulness of this set through examples of associations between pairs found in the scientific literature., Universidad Pablo de Olavide

Published
2019
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19. Analysis of eHealth knowledge discovery systems in the TASS 2018 Workshop.

Author

Piad-Morffis, Alejandro, Gutiérrez, Yoan, Estévez-Velarde, Suilan, Almeida-Cruz, Yudivián, Montoyo, Andrés, and Muñoz, Rafael

Subjects
DEEP learning, NATURAL language processing, MACHINE learning, TASK analysis, SPANISH language
Abstract

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2019
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20. EVALUACIÓN DE ALGORITMOS DE CLASIFICACION SUPERVISADA PARA EL MINADO DE OPINIÓN EN TWITTER.

Author

Estévez-Velarde, Suilan and Almeida Cruz, Yudivián

Subjects
*CLASSIFICATION algorithms, *MACHINE learning, *ARTIFICIAL intelligence, *MACHINE theory, *SENTIMENT analysis
Abstract

This paper proposes a comprehensive evaluation of supervised classification algorithms without semantic information for opinion mining on Twitter. The evaluation will assess the potential of this approach to tackle the given problem. Also, as result of evaluating, the impact of different variants of the text preprocessing is assessed, as well as different alternatives of dimension reduction techniques in the problem solution. Finally, the results obtained in the experiments offer evidence to direct the use of supervised classification to classify opinions and its valence in a domain of short messages such as Twitter. [ABSTRACT FROM AUTHOR]

Published
2015

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