- Data Mining
- Data Science
- Information Retrieval & Machine Learning
- Computer Intelligence & Vision
- Effective & Efficient Query Processing
- Recommendation Systems
- Multimedia, Spatial and Distributed Databases
Data Fusion and Machine Learning for Information Retrieval
Abstract: The technological advances in data capture, storage, and processing allowed the construction of large databases, especially for multimedia data sharing. These data are used in many contexts, such as education, medicine, biometry, social networks, entertainment, news, among others. Considering the huge volume of data, providing efficient and effective access is critical. This project explores the use of modern machine learning techniques for multimedia information retrieval.
Data Mining using Particle Swarm Optimization
Abstract: This project focus on the study and development of algorithms based on the PSO technique for clustering, optimization, and data classification tasks, especially for qualitative data where the treatment is much more complex and which has several applications.
Increasing the Efficiency of Public Security Actions through Advanced Information Systems
Abstract: In this project, we develop new Information Systems to support Public Security Actions. In special, we collect open data from different sources such as OpenStreetMap, Twitter, IBGE and Web to develop new Information Systems that can support better decision making process related to Public Security Action. For instance, "What is the best place in Feira de Santana to put a new security camera?" In order to answer this question, we employ data about criminality, population and establishment and compute the best best place to put a new security camera taking in account all these information.
Machine Learning for Plant Species Recognition
Abstract: The knowledge of the biodiversity of a region is fundamental for the development of effective productive processes along with the minimization of damages to the environment. At the same time, knowing the characteristics of the species allows the definition of proper preservation policies, and the identification and recognition of flora species is a very important task in the activities of many sectors of society. Consequently, the study and application of modern techniques of representation of characteristics and construction of models for recognition are necessary to allow the development of practical tools. Aligned to the worldwide interested community, this project aims to contribute to the activities of recognition of flora by developing effective and efficient methods with special interest on deep learning approaches.
Preference Queries on Spatial Databases
Abstract: With the population of spatial data , the interest for new technologies that allow to analyze the influence of places of interest to make strategic decisions grows. Computing the influence of a spatial object is complex and requires analyzing a large amount of data to obtain an accurate result. This project aims proposes new algorithms and techniques to support geospatial analysis and compute the influence of places of interest.