Index

About Me

Publications

Journal: Analysis of social networks as a strategy to support health surveillance during Covid-19.

Month/Year: July/2020

Institution/Location:: Revista Estudos Avançados

ABSTRACT: The large volume of data generated on social networks is used by companies to monitor public opinion about their products and services. These data may contain useful information for health surveillance, such as in assessing the impact of public policies or identifying fake news. This work presents results of studies that demonstrate how analysis of data from social networks may be applied to surveillance activities, using the covid-19 pandemic as a case study. An approach based on data science was used, with information extracted through machine learning algorithms. Results indicate that this approach can reveal useful information for surveillance activities, providing a real-time view of aspects related to the pandemic.


Journal: Extended Paper: Use of Spatial Visualization for Pattern Discovery in Evapotranspiration Estimation

Month/Year: December/2018

Institution/Location:: Revista Brasileira de Cartografia

ABSTRACT: In Water Resources area, data are obtained from various sources, such as measuring instruments and satellites. Often such data may contain patterns that are not easily identified, either because of the large volume of data sets or because the analysis requires the use of several data dimensions. In this way, this study proposes the application of machine learning resources and spatial visualization to identify patterns in the estimation of an important component of the hydrological cycle: the evapotranspiration. This work is expected to contribute to an approach to estimate evapotranspiration, using spatial resources for pattern identification and model generation.


Conference Paper: Use of Spatial Visualization for Pattern Discovery in Evapotranspiration Estimation

Month/Year: December/2017

Institution/Location:: XVIII Brazilian Symposium on GeoInformatics - Salvador - Brazil

ABSTRACT: In Water Resources area, data are obtained from various sources, such as measuring instruments and satellites. Often such data may contain patterns that are not easily identified, either because of the large volume of data sets or because the analysis requires the use of several data dimensions. In this way, this study proposes the application of machine learning resources and spatial visualization to identify patterns in the estimation of an important component of the hydrological cycle: the evapotranspiration. This work is expected to contribute to an approach to estimate evapotranspiration, using spatial resources for pattern identification and model generation.


Conference Paper: A Software Tool for Analysis and Forecast of Hydrometeorological Variables

Month/Year: November/2016

Institution/Location:: IV Conference of Computational Interdisciplinary Science - São José dos Campos - Brazil

ABSTRACT: The Data Deluge Era represents a challenge for researchers, providing them a growing and complex data mass, many times difficult to extract information from this data mass. In order to extracting value from these data, computational resources have been increasingly used in scientific research, integrating Computer Science to other knowledge areas. Due to its interdisciplinary aspect, Data Science emerges as an approach to enable this integration, using tools and methods from many knowledge areas to transform these data in useful information, through of a Data Science Lifecycle. Aiming to automate phases from this lifecycle, it was developed a software using as case study the process of analysis and forecast of hydrometeorological variables. In this paper, it was described this software as well the study case used to validate the software developed.


Dissertation: Application of Data Science Techniques in Evapotranspiration Estimation

Month/Year: July/2016

Course: Master's in Informatics

Institution/Location: Federal University of the State of Rio de Janeiro (UNIRIO) - Rio de Janeiro - Brazil

ABSTRACT: The studies related to water resources have great relevance in many areas such as irrigation, water supply and power generation. The efficient use of these resources depends on many factors, like the correct estimation of certain variables related to the hydrological cycle, such as evapotranspiration. However, the most precise models currently applied for estimating evapotranspiration require variables that are not always available or are too complex to obtain in some regions, due to the lack of measuring instruments. In these cases, the precision of the evapotranspiration estimative is decreased, compromising its validity depending on the context. This research consisted in the application of Data Science techniques over meteorological data provided by the Brazilian National Institute of Meteorology (INMET), in order to generate a model for estimating evapotranspiration, using a "data-driven" approach. As a Data Science project, this research had high level of interaction with a domain expert from Hydrology area. This interactive process was necessary for definition of the research question, experimental scenarios and for results evaluation, generated by the successive runs of the Data Science lifecycle used in this research. Through interaction with the domain expert, the main objective of this research was defined to simplify the current methods for evapotranspiration estimation, without loss of precision in relation to the historical results. In order to automate the experimental runs, we developed a software program that supports all the steps of the Data Science lifecycle to enable the reproducibility of the experimental results. After successive runs of the experiment with scenarios defined together with the domain expert, we found a model that fits the goals defined in the first step of the lifecycle. Finally, for results analysis by the expert domain, graphs were generated to compare the results of different scenarios, as well as maps with layers of the Brazilian biomes and climate types, aiming to identify possible patterns among results and vegetation and climate type.


Monografia de Final de Curso / Monograph: Um Estudo sobre as Metodologias de Produção de Material Didático para Educação à Distância

Course: Specialization in EAD Planning, Implementation and Management

Month/Year: November/2015

Institution/Location: Fluminense Federal University (UFF) - Niterói - Brazil

ABSTRACT:


Conference Paper: KDD application on Meteorological Data for Identification of Regional Patterns in Estimation of Evapotranspiration

Month/Year: October/2015

Institution/Location:: XXX Brazilian Symposium on Databases (SBBD) - National Laboratory of Scientific Computing (LNCC) - Petrópolis - Brazil

ABSTRACT: The evaporation is defined as the sum of the evaporated water with the water transpired from the vegetation. It has great importance in irrigation, that represents 75% of global water consumption and, therefore, its estimate is crucial to better management of this resource. Currently, this estimate is made by mathematical methods, using data obtained from measuring stations, whose absence prevents the use of these methods. This initial study aims, from the data available at measuring stations, apply the KDD process to identify whether there are patterns in the estimation in places with similar characteristics, which could be used for similar locations without measuring stations.


Conference Paper: Application of Knowledge Discovery in Databases in Evapotranspiration Estimation: an Experiment in the State of Rio de Janeiro

Month/Year: May/2015

Institution/Location: XI Brazilian Symposium on Information Systems (SBSI) - Goiânia - Brazil

ABSTRACT: With the growing volume of data in various areas such as Hydrology, there is a need for using information systems to aid in handling such data. This article is a report of an experiment that used knowledge discovery techniques to estimate an important component of the hydrological cycle: evapotranspiration. The experiment reported in this article was done with weather data and showed that some algorithms, such as M5P, present good results when compared to historical data of the estimated evapotranspiration.


Conference Paper: A view about RFID technology in Brazil

Month/Year: July/2010

Institution/Location: PICMET 2010 Conference - Phuket - Thailand

ABSTRACT: In times of big business competition, technology can be a very important tool for companies to get an advantage over their competitors. One example is a Radio-Frequency Identification (RFID) technology is getting more investments from companies around world. In Brazil, this technology is getting more investments from companies, universities and the government. This paper will present the real situation about RFID technology in Brazil and some case studies of RFID deployment in Brazilian companies from some sectors, such as aircraft industry, military and auto based industry. The main objective of this paper is to map RFID technology in Brazil, showing our advances and our difficulties. As a result, research on RFID deployments can be used in other research projects. Another result is that companies can analyze these case studies to get a better idea about their own RFID deployment projects.


Monografia de Final de Curso / Monograph: Uso de Agentes na Tecnologia RFID / Use of Agents in RFID Technology

Month/Year: June/2010

Institution/Location: University of Campinas (UNICAMP) - São Paulo - Brazil

ABSTRACT: The use of software agents is increasingly common. Characteristics of some agents, such as mobility, are Fundamental for application in some areas. In technology RFID, the use of software agents can This paper aims to present a review of the work done On agents applied in RFID technology.


Lectures

ADempiere

ADempiere - Fiscal & Contábil

O que é RFID?

2o UDF Tech Day (02/10/2018) - Machine Learning

Machine Learning na Climatologia