Brazil

As commodities milho e soja são produtos consumidos em grande escala no mundo. As flutuações nos preços de mercado têm efeitos de longo alcance sobre os consumidores, agricultores e processadores de grãos. Assim, a previsão dos preços desses grãos tem atraído significativa atenção dos pesquisadores. Os modelos de previsão geralmente usam dados quantitativos de série temporal. No entanto, fatores qualitativos externos podem influenciar os dados em séries temporais, como eventos políticos, crises econômicas e o mercado de câmbio estrangeiro. Essas informações não são explícitas nos dados da série temporal e esses fatores podem influenciar a previsão dos valores das variáveis. Os dados textuais extraídos de notícias, fóruns e redes sociais podem ser uma fonte de conhecimento sobre fatores externos e potencialmente úteis para modelos de previsão de séries temporais. Alguns estudos apresentam técnicas de mineração de texto para combinar dados textuais com séries temporais. Porém, as representações existentes apresentam algumas limitações, como a maldição da dimensionalidade e atributos ineficazes. Este trabalho aplica métodos de pré-processamento em séries temporais e usa representações combinadas com dados textuais para prever o preço futuro do milho e da soja. Os resultados indicam que os métodos utilizados podem ser uma alternativa para melhorar o desempenho de previsão em tarefas de regressão.

BRACIS 2020

Previsão do preço futuro do milho e da soja: Uma análise do uso de informação textual para enriquecer séries temporais.

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

technical paper

Automatic diagnoses of diseases has been a long term challenge for Computer Science and related disciplines. Textual clinical reports can be used as a great source of data for such diagnoses. However, building classification models from them is not a trivial task. The problem tackled in this work is the identification of the medical diagnoses that are indicated in these reports. In the past, several methods have been proposed for addressing this problem, but a method developed for reports in the cardiology area that are written in Portuguese is still needed. In this paper we describe a method that is able to handle the peculiarities of clinical reports, including the medical terminology, and that is implemented to estimate correctly the disease based on raw clinical reports and a list of the possible diagnoses. Experimental results show that our method has a high degree of accuracy, even for infrequent classes and complex databases.

Automated classification of cardiology diagnoses based on textual medical reports

Recording of the LIVE Technical Session 4

Pleanry talk - André Paim

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Recording of the LIVE Technical Session 6

Industry Panel: Applied AI: Bridging the Gap Between Academy and Industry

Recording of the LIVE Technical Session 7

Recording of the LIVE Technical Session 8

Recording of the LIVE Technical Session 9

Plenary talk - Jose Alonso

https://d3smihljt9218e.cloudfront.net/transcript/2397/subtitle/b113e4d321f1717380903b1e4ad68e83.vtt

English (automatic)

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Automated classification of cardiology diagnoses based on textual medical reports

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Next from BRACIS 2020

Automated classification of cardiology diagnoses based on textual medical reports

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Forecasting future corn and soybean prices: an analysis of the use of textual information to enrich time-series.

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