Brazil

Visualizing multidimensional Big Data is defying: high dimensionalities hinder or even preclude visual inspections. A means of tackling this issue is to use DR (Dimensionality Reduction) techniques, producing low-dimensional representations of high-dimensional data. Popular DR algorithms (e.g., Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding), albeit helpful, are computationally expensive. Most have quadratic or cubic ATC (Asymptotic Time Complexity) and/or calculate pairwise distances of the entire data set, exceeding available memory and rendering Big Data DR time-consuming or impracticable. These issues impede the employment of DR for online learning appliances, where recurrent, cumulative model updates are habitual. The stochastic factor of some approaches similarly obstructs any meaningful inspection on how knowledge is spatially disposed. The recently introduced PCS (Polygonal Coordinate System)—an incremental, geometric-based technique with linear ATC—is compelling; however, its restriction to 2-D embeddings amounts to significant information loss. We propose the Big Data ready, incremental PES (Pyramidal Embedding System), which builds on PCS virtues by additionally generating 3-D embeddings through its pyramid-like interspace, mitigating quality degradation. Visual inspections, as well as pairwise distance based statistical analyses, validate the PES ability to retain structural arrangements when embedding high- and low-dimensional data while retaining flexibility in resources consumption.

BRACIS 2020

An Online Pyramidal Embedding Technique for High Dimensional Big Data Visualization

incremental dimensionality reduction

data visualization

feature extraction

embedding

online learning

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

technical paper

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.

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.

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

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.

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

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Next from BRACIS 2020

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

Similar lecture

Towards Zero-Shot Diabetic Retinopathy Grading: Learning Generalized Knowledge via Prompt-Driven Matching and Emulating

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