Adriane Beatriz de Souza Serapião

• Abstract

  Data clustering algorithms divide data into meaningful clusters so that the patterns in the same group are similar in some way, and the patterns in different clusters differ in the same way. The search for clusters involves unsupervised learning, in which it is not known in advance to which group the data belong. The partitioning group attempts to decompose the data into a set of disjoi...

  Automatic bioswarm clustering, AP.R

• Abstract

  In recent years, new technologies provided a huge increase in storage capacity and data processing, creating the demand for analysis of large data volumes captured by scientific instruments or generated by simulations, and resulting in data with a high dimensionality. However, the computational tools for inspection and knowledge extraction from databases not developed in the same level....

  Clustering and swarm intelligence with parallel computing using GPU, AP.R

• Abstract

  The modern control techniques are driven to maximize the performance of operational continuous plants. This technology is rather developed with several available commercial solutions, for instance, PID controller. However, some control processes of the oil industry, such as flow separation systems, that have to carry out the reservoir level control, must be considered under operation wi...

  Development of control strategies for petroleum separation and pumping systems, AP.R

• Abstract

  Automatic image captioning generation is a task that consists of deciphering an image and describing it in natural language phrases. This task combines Natural Language Processing and computer Vision to generate captions. Recently, Deep Learning methods are achieving very promising results for the captions generation problem. This research proposes to investigate new combinations of con...

  Automatic image captioning generation using deep learning, BP.IC

• Abstract

  Data clustering is one of the most important unsupervised techniques of data management, which is used in many scientific and engineering applications, such as machine learning, data mining, pattern recongnitionand image processing. It consists of splitting a dataset into smaller subsets, named clusters. The partition of datasets is obtained by establishing a function that assigns each ...

  Automatic clustering based on nature inspired metaheuristics, BP.IC

• Abstract

  Clustering is a data mining problem that consists on providing a set of data, grouping its objects so that the objects that are more similar to each other can be in the same cluster and the least similar objects can be allocated in other clusters. In general, the number of clusters is previously defined for the separation of objects. However, in many real-world situations, the number of...

  Automatic clustering with swarm intelligence algorithms, BP.IC

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

• Abstract

  Data streams are characterized by continuously generating large amounts of data with a different time interval between each sample. They are an ordered sequence of instances that arrive over time and can be of unbounded size. They have been gaining an increasing amount of attention in recent years due to the numerous real-world applications in dynamic environments that produce non-stati...

  Classification of non-stationary data streams with concept drift and extreme label latency, BE.PQ