Recent Submissions

  • Hierarchical learning using deep optimum-path forest

    Afonso, Luis C.S.; Pereira, Clayton R. Autor UNESP; Weber, Silke A.T. Autor UNESP; Hook, Christian; Falcão, Alexandre X.; Papa, João P. Autor UNESP (Journal of Visual Communication and Image Representation, 2020) [Artigo]
    Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic ...
  • Discovering patterns within the drilling reports using artificial intelligence for operation monitoring

    Colombo, Danilo; Pedronette, Daniel Carlos Guimarães Autor UNESP; Guilherme, Ivan Rizzo Autor UNESP; Papa, João Paulo Autor UNESP; Ribeiro, Luiz Carlos Felix Autor UNESP; Afonso, Luis Claudio Sugi Autor UNESP; Presotto, João Gabriel Camacho Autor UNESP; Sousa, Gustavo José Autor UNESP (Offshore Technology Conference Brasil 2019, OTCB 2019, 2020) [Trabalho apresentado em evento]
    In well drilling activities, the execution of a sequence of operations defined in a well project is a central task. In order to provide proper monitoring, the operations executed during the drilling procedures are reported ...
  • A multi-objective artificial butterfly optimization approach for feature selection

    Rodrigues, Douglas Autor UNESP; de Albuquerque, Victor Hugo C.; Papa, João Paulo Autor UNESP (Applied Soft Computing Journal, 2020) [Artigo]
    Feature selection plays an essential role in machine learning since high dimensional real-world datasets are becoming more popular nowadays. The very basic idea consists in selecting a compact but representative set of ...
  • An efficient parallel implementation for training supervised optimum-path forest classifiers

    Culquicondor, Aldo; Baldassin, Alexandro Autor UNESP; Castelo-Fernández, Cesar; de Carvalho, João P.L.; Papa, João Paulo Autor UNESP (Neurocomputing, 2020) [Artigo]
    In this work, we propose and analyze parallel training algorithms for the Optimum-Path Forest (OPF) classifier. We start with a naïve parallelization approach where, following traditional sequential training that considers ...
  • Manifold learning-based clustering approach applied to anomaly detection in surveillance videos

    Lopes, Leonardo Tadeu Autor UNESP; Valem, Lucas Pascotti Autor UNESP; Guimarães Pedronette, Daniel Carlos Autor UNESP; Guilherme, Ivan Rizzo Autor UNESP; Papa, João Paulo Autor UNESP; Silva Santana, Marcos Cleison Autor UNESP; Colombo, Danilo (VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020) [Trabalho apresentado em evento]
    The huge increase in the amount of multimedia data available and the pressing need for organizing them in different categories, especially in scenarios where there are no labels available, makes data clustering an essential ...
  • Depth-based face recognition by learning from 3D-LBP images

    Neto, João Baptista Cardia; Marana, Aparecido Nilceu Autor UNESP; Ferrari, Claudio; Berretti, Stefano; Del Bimbo, Alberto (Eurographics Workshop on 3D Object Retrieval, EG 3DOR, 2019) [Trabalho apresentado em evento]
    In this paper, we propose a hybrid framework for face recognition from depth images, which is both effective and efficient. It consists of two main stages: First, the 3DLBP operator is applied to the raw depth data of the ...
  • Regression-based finite element machines for reliability modeling of downhole safety valves

    Colombo, Danilo; Lima, Gilson Brito Alves; Pereira, Danillo Roberto; Papa, João P. Autor UNESP (Reliability Engineering and System Safety, 2020) [Artigo]
    Downhole Safety Valve (DHSV) stands for a device widely used in offshore wells to ensure the integrity and avoid uncontrolled leaks of oil and gas to the environment, known as blowouts. The reliability estimation of such ...
  • Evolving Neural Conditional Random Fields for drilling report classification

    Ribeiro, Luiz C.F. Autor UNESP; Afonso, Luis C.S.; Colombo, Danilo; Guilherme, Ivan R. Autor UNESP; Papa, João P. Autor UNESP (Journal of Petroleum Science and Engineering, 2020) [Artigo]
    Oil and gas prospecting is an important economic activity, besides being expensive and quite complex, thus requiring close monitoring to avoid work accidents and mainly environmental damages. An essential source of information ...
  • Does Pooling Really Matter? An Evaluation on Gait Recognition

    dos Santos, Claudio Filipi Goncalves; Moreira, Thierry Pinheiro Autor UNESP; Colombo, Danilo; Papa, João Paulo Autor UNESP (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019) [Trabalho apresentado em evento]
    Most Convolutional Neural Networks make use of subsampling layers to reduce dimensionality and keep only the most essential information, besides turning the model more robust to rotation and translation variations. One of ...
  • Tracking and Re-identification of People Using Soft-Biometrics

    Tavares, Henrique Leal Autor UNESP; Neto, João Baptista Cardia; Papa, João Paulo Autor UNESP; Colombo, Danilo; Marana, Aparecido Nilceu Autor UNESP (Proceedings - 15th Workshop of Computer Vision, WVC 2019, 2019) [Trabalho apresentado em evento]
    The goal of this work is proposing a method of biometric identification using soft-biometrics, that aims the extraction of physical characteristics and estimation of the pose as unique traits of each individual, to name ...
  • Multiple-Instance Learning through Optimum-Path Forest

    Afonso, Luis C. S.; Colombo, Danilo; Pereira, Clayton R. Autor UNESP; Costa, Kelton A. P. Autor UNESP; Papa, Joao P. Autor UNESP (Proceedings of the International Joint Conference on Neural Networks, 2019) [Trabalho apresentado em evento]
    Multiple-instance (MI) learning aims at modeling problems that are better described by several instances of a given sample instead of individual descriptions often employed by standard machine learning approaches. In ...
  • A Hybrid Approach for Breast Mass Categorization

    Passos, Leandro Aparecido Autor UNESP; Santos, Claudio; Pereira, Clayton Reginaldo Autor UNESP; Afonso, Luis Claudio Sugi; Papa, João P. Autor UNESP (Lecture Notes in Computational Vision and Biomechanics, 2019) [Capítulo de livro]
    Breast cancer is one of the most frequent fatal diseases among women around the world. Early diagnosis is paramount for easing such statistics, increasing the probability of successful treatment and cure. This paper proposes ...
  • JADE-Based Feature Selection for Non-technical Losses Detection

    Pereira, Clayton Reginaldo Autor UNESP; Passos, Leandro Aparecido Autor UNESP; Rodrigues, Douglas; de Souza, André Nunes Autor UNESP; Papa, João P. Autor UNESP (Lecture Notes in Computational Vision and Biomechanics, 2019) [Capítulo de livro]
    Nowadays, non-technical losses, usually caused by thefts and cheats in the energy system distribution, are among the most significant problems an electric power company has to face. Several actions are employed striving ...
  • κ-Entropy Based Restricted Boltzmann Machines

    Passos, Leandro Aparecido; Cleison Santana, Marcos Autor UNESP; Moreira, Thierry Autor UNESP; Papa, Joao Paulo Autor UNESP (Proceedings of the International Joint Conference on Neural Networks, 2019) [Trabalho apresentado em evento]
    Restricted Boltzmann Machines achieved notorious popularity in the scientific community in the last decade due to outstanding results in a wide range of applications and also for providing the required mechanisms to build ...
  • A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising

    Pires, Rafael G.; Santos, Daniel S. Autor UNESP; Souza, Gustavo B.; Levada, Alexandre L. M.; Papa, João Paulo Autor UNESP (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019) [Trabalho apresentado em evento]
    During the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. ...
  • Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks

    Ribeiro, Luiz C.F. Autor UNESP; Afonso, Luis C.S.; Papa, João P. Autor UNESP (Computers in Biology and Medicine, 2019) [Artigo]
    Parkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens the life expectancy of the patients. Many studies have been dedicated to ...
  • A systematic review of fault tolerance solutions for communication errors in open source cloud computing

    Santos, Vinicius Andrade Dos Autor UNESP; Manacero, Aleardo Autor UNESP; Lobato, Renata S. Autor UNESP; Spolon, Roberta Autor UNESP; Cavenaghi, Marcos A. (Iberian Conference on Information Systems and Technologies, CISTI, 2020) [Trabalho apresentado em evento]
    Cloud systems, as any other system, must be reliable. This means that the system should respond correctly in presence of failures, which are quite probable in a distributed, largely independent, system as cloud systems ...
  • Deep Learning from 3DLBP Descriptors for Depth Image Based Face Recognition

    Cardia Neto, Joao Baptista; Marana, Aparecido Nilceu Autor UNESP; Ferrari, Claudio; Berretti, Stefano; Del Bimbo, Alberto; IEEE (2019 International Conference On Biometrics (icb), 2019) [Trabalho apresentado em evento]
    In this paper, we propose a new framework for face recognition from depth images, which is both effective and efficient. It consists of two main stages: First, a hand-crafted low-level feature extractor is applied to the ...
  • kappa-Entropy Based Restricted Boltzmann Machines

    Passos, Leandro Aparecido; Santana, Marcos Cleison Autor UNESP; Moreira, Thierry Autor UNESP; Papa, Joao Paulo Autor UNESP; IEEE (2019 International Joint Conference On Neural Networks (ijcnn), 2019) [Trabalho apresentado em evento]
    Restricted Boltzmann Machines achieved notorious popularity in the scientific community in the last decade due to outstanding results in a wide range of applications and also for providing the required mechanisms to build ...
  • A Novel Siamese-Based Approach for Scene Change Detection With Applications to Obstructed Routes in Hazardous Environments

    Santana, Marcos C. S. Autor UNESP; Passos, Leandro Aparecido Autor UNESP; Moreira, Thierry P. Autor UNESP; Colombo, Danilo; Albuquerque, Victor Hugo C. de; Papa, Joao Paulo Autor UNESP (Ieee Intelligent Systems, 2020) [Artigo]
    The demand for automatic scene change detection has massively increased in the last decades due to its importance regarding safety and security issues. Although deep learning techniques have provided significant enhancements ...

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