Gustavo Enrique de Almeida Prado Alves Batista

• Abstract

  Insects are undoubtedly important to agriculture, the environment and human health. Many insect species are beneficial to the environment and humans. For example, insects are responsible for pollinating at least two-thirds of all food consumed in the world. Due to its importance to humans, the recent decline in populations of pollinator insects, especially bees, is considered a serious en…

  Intelligent traps and sensors: an innovative approach to control insect pests and disease vectors, AP.ESCIENCE.R

• Abstract

  Dengue is the most important viral disease transmitted by mosquitoes and one of the major public health concerns in Brazil and most of the tropical and subtropical countries in of the world. Dengue epidemics are also responsible for a significant economic and social impact on the populations in which they occur. Currently, due to lack of an effective vaccine against dengue, epidemics can …

  Intelligent tools to vector control and population orientation against dengue fever, AP.PIPE

• Abstract

  We propose a focused, interdisciplinary research project on data mining and data visualization with a specific focus on marine data. This data is particularly challenging for data mining as it presents only a very sparse set of data points with respect to the volume of the marine space that is being modelled and investigated. It also presents a set of challenges in visualization of data a…

  Research on geo-spatial marine biology data mining using time series, text mining and visualization, AP.R

(Only some records are available in English at this moment)

• Abstract

  Insects are undoubtedly important to agriculture, the environment and human health. Many insect species are beneficial to the environment and humans. For example, insects are responsible for pollinating at least two-thirds of all food consumed in the world. Due to its importance to humans, the recent decline in populations of pollinator insects, especially bees, is considered a serious en…

  Intelligent traps and sensors: an innovative approach to control insect pests and disease vectors, BP.PD

• Abstract

  Despite the relatively maturity of batch-mode supervised learning research, in which the data typifies stationary problems, many real world applications deal with data streams whose statistical properties change over time, causing what is known as concept drift. For such applications a large body of research has been done in the last years, with the objective of creating new models that a…

  Data stream classification with concept drift and extreme verification latency, BP.MS

• Abstract

  Time series are ubiquitous in day-by-day of human beings. Consequently, the area of time series analysis has attracted the attention and effort of many researchers around the world. Due to the great interest in time series, many analysis methods have been proposed to in recent decades. Several of these methods have one thing in common: in their cores, there is a similarity function used a…

  Time series analysis by similarity in large scale, BP.DR

(Only some records are available in English at this moment)

• Abstract

  Dynamic Time Warping (DTW) is certainly the most relevant distance for time series analysis. The main issue with DTW is its computational time complexity, which is quadratic to the number of observations. Given the importance of DTW to time series mining, a large body of research has been proposed to accelerate DTW calculations. However, all the recent advances in speeding up DTW are conf…

  Speeding up All-Pairwise Dynamic Time Warping distance matrix for time series mining, BE.EP.DR

• Abstract

  Machine Learning has proven its importance by its rapidly growth in number of published works and consequently maturity. However, although there is a crescent concern about correctly assessing new propositions, the currently used evaluation methodology can be not enough to compare data stream classifiers for real world scenarios. Evaluation, nowadays, is limited to the case where, once th…

  Data stream analysis and evaluation under the presence of verification latency, BE.EP.MS

• Abstract

  Learning from data stream is an important research area that has growing up in the last years. A many learning algorithm applied in data stream has been development. For a learning system succeeds in acquiring knowledge is necessary that it has some desirable properties: (i) update the decision model along the time; (ii) perform online learning, processing the sample as soon as it is avai…

  Method for extracting knowledge from data stream - algorithm k- nearest neighbor incremental anytime, BE.EP.MS

(Only some records are available in English at this moment)