Joao Paulo Papa | Universidade Estadual Paulista "Júlio de Mesquita Filho" (original) (raw)

Papers by Joao Paulo Papa

Research paper thumbnail of A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising

Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 2019

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Research paper thumbnail of A Deep Boltzmann Machine-Based Approach for Robust Image Denoising

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2018

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Research paper thumbnail of LibOPT: An Open-Source Platform for Fast Prototyping Soft Optimization Techniques

ArXiv, 2017

Optimization techniques play an important role in several scientific and real-world applications,... more Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which ends up fostering the research and development of new techniques and applications. In this work, we present a new library for the implementation and fast prototyping of nature-inspired techniques called LibOPT. Currently, the library implements 15 techniques and 112 benchmarking functions, as well as it also supports 11 hypercomplex-based optimization approaches, which makes it one of the first of its kind. We showed how one can easily use and also implement new techniques in LibOPT under the C paradigm. Examples are provided with samples of source-code using benchmarking functions.

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Research paper thumbnail of A nature-inspired feature selection approach based on hypercomplex information

Applied Soft Computing, 2020

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Research paper thumbnail of Quaternion-based Deep Belief Networks fine-tuning

Applied Soft Computing, 2017

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Research paper thumbnail of Handling dropout probability estimation in convolution neural networks using meta-heuristics

Soft Computing, 2017

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Research paper thumbnail of Improving land cover classification through contextual-based optimum-path forest

Information Sciences, 2015

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Research paper thumbnail of Social-Spider Optimization-based Support Vector Machines applied for energy theft detection

Computers & Electrical Engineering, 2016

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Research paper thumbnail of Parkinson's disease identification through optimum-path forest

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010

Artificial intelligence techniques have been extensively used for the identification of several d... more Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification.

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Research paper thumbnail of Improving Parkinson's disease identification through evolutionary-based feature selection

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011

Parkinson's disease (PD) automatic identification has been actively pursued over several work... more Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification.

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Research paper thumbnail of Electrical consumers data clustering through Optimum-Path Forest

2011 16th International Conference on Intelligent System Applications to Power Systems, 2011

Page 1. 1 Electrical Consumers Data Clustering Through Optimum-Path Forest Caio CO Ramos, André N... more Page 1. 1 Electrical Consumers Data Clustering Through Optimum-Path Forest Caio CO Ramos, André N. Souza, Member, IEEE, Rodrigo YM Nakamura, Jo˜ao P. Papa Abstract—Non-technical losses identification has been paramount in the last decade. ...

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Research paper thumbnail of How Far Do We Get Using Machine Learning Black-Boxes?

International Journal of Pattern Recognition and Artificial Intelligence, 2012

With several good research groups actively working in machine learning (ML) approaches, we have n... more With several good research groups actively working in machine learning (ML) approaches, we have now the concept of self-containing machine learning solutions that oftentimes work out-of-the-box leading to the concept of ML black-boxes. Although it is important to have such black-boxes helping researchers to deal with several problems nowadays, it comes with an inherent problem increasingly more evident: we have observed that researchers and students are progressively relying on ML black-boxes and, usually, achieving results without knowing the machinery of the classifiers. In this regard, this paper discusses the use of machine learning black-boxes and poses the question of how far we can get using these out-of-the-box solutions instead of going deeper into the machinery of the classifiers. The paper focuses on three aspects of classifiers: (1) the way they compare examples in the feature space; (2) the impact of using features with variable dimensionality; and (3) the impact of usi...

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Research paper thumbnail of A binary cuckoo search and its application for feature selection

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Research paper thumbnail of Optimizing Optimum-Path Forest Classification for Huge Datasets

2010 20th International Conference on Pattern Recognition, 2010

Abstract Traditional pattern recognition techniques can not handle the classification of large da... more Abstract Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for ...

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Research paper thumbnail of Binary Flower Pollination Algorithm and Its Application to Feature Selection

Studies in Computational Intelligence, 2014

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Research paper thumbnail of Fast and accurate holistic face recognition using Optimum-Path Forest

2009 16th International Conference on Digital Signal Processing, 2009

Abstract This paper presents a novel, fast and accurate holistic method for face-recognition usin... more Abstract This paper presents a novel, fast and accurate holistic method for face-recognition using the Optimum-Path Forest (OPF) classifier. Our objective is to improve the face recognition accuracy against traditional methods and to reduce the computational effort in face recognition tasks. During the feature extraction stage we apply principal component analysis to reduce feature vectors in several dimensionalities. Experiments using face images from three public datasets (ORL, CBCL and YALE) present good results. ...

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Research paper thumbnail of Feature selection through gravitational search algorithm

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

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Research paper thumbnail of Robust and fast Vowel Recognition Using Optimum-Path Forest

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010

Abstract The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamenta... more Abstract The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is ...

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Research paper thumbnail of Fast Non-Technical Losses Identification Through Optimum-Path Forest

2009 15th International Conference on Intelligent System Applications to Power Systems, 2009

Abstract Fraud detection in energy systems by illegal consumers is the most actively pursued stud... more Abstract Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as artificial neural networks and support vector machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the optimum-path forest classifier for a fast non-technical losses recognition, which has been ...

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Research paper thumbnail of A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection

Computers & Electrical Engineering, 2011

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Research paper thumbnail of A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising

Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 2019

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Research paper thumbnail of A Deep Boltzmann Machine-Based Approach for Robust Image Denoising

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2018

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Research paper thumbnail of LibOPT: An Open-Source Platform for Fast Prototyping Soft Optimization Techniques

ArXiv, 2017

Optimization techniques play an important role in several scientific and real-world applications,... more Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which ends up fostering the research and development of new techniques and applications. In this work, we present a new library for the implementation and fast prototyping of nature-inspired techniques called LibOPT. Currently, the library implements 15 techniques and 112 benchmarking functions, as well as it also supports 11 hypercomplex-based optimization approaches, which makes it one of the first of its kind. We showed how one can easily use and also implement new techniques in LibOPT under the C paradigm. Examples are provided with samples of source-code using benchmarking functions.

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Research paper thumbnail of A nature-inspired feature selection approach based on hypercomplex information

Applied Soft Computing, 2020

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Research paper thumbnail of Quaternion-based Deep Belief Networks fine-tuning

Applied Soft Computing, 2017

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Research paper thumbnail of Handling dropout probability estimation in convolution neural networks using meta-heuristics

Soft Computing, 2017

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Research paper thumbnail of Improving land cover classification through contextual-based optimum-path forest

Information Sciences, 2015

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Research paper thumbnail of Social-Spider Optimization-based Support Vector Machines applied for energy theft detection

Computers & Electrical Engineering, 2016

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Research paper thumbnail of Parkinson's disease identification through optimum-path forest

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010

Artificial intelligence techniques have been extensively used for the identification of several d... more Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Improving Parkinson's disease identification through evolutionary-based feature selection

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011

Parkinson's disease (PD) automatic identification has been actively pursued over several work... more Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Electrical consumers data clustering through Optimum-Path Forest

2011 16th International Conference on Intelligent System Applications to Power Systems, 2011

Page 1. 1 Electrical Consumers Data Clustering Through Optimum-Path Forest Caio CO Ramos, André N... more Page 1. 1 Electrical Consumers Data Clustering Through Optimum-Path Forest Caio CO Ramos, André N. Souza, Member, IEEE, Rodrigo YM Nakamura, Jo˜ao P. Papa Abstract—Non-technical losses identification has been paramount in the last decade. ...

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Research paper thumbnail of How Far Do We Get Using Machine Learning Black-Boxes?

International Journal of Pattern Recognition and Artificial Intelligence, 2012

With several good research groups actively working in machine learning (ML) approaches, we have n... more With several good research groups actively working in machine learning (ML) approaches, we have now the concept of self-containing machine learning solutions that oftentimes work out-of-the-box leading to the concept of ML black-boxes. Although it is important to have such black-boxes helping researchers to deal with several problems nowadays, it comes with an inherent problem increasingly more evident: we have observed that researchers and students are progressively relying on ML black-boxes and, usually, achieving results without knowing the machinery of the classifiers. In this regard, this paper discusses the use of machine learning black-boxes and poses the question of how far we can get using these out-of-the-box solutions instead of going deeper into the machinery of the classifiers. The paper focuses on three aspects of classifiers: (1) the way they compare examples in the feature space; (2) the impact of using features with variable dimensionality; and (3) the impact of usi...

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Research paper thumbnail of A binary cuckoo search and its application for feature selection

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Optimizing Optimum-Path Forest Classification for Huge Datasets

2010 20th International Conference on Pattern Recognition, 2010

Abstract Traditional pattern recognition techniques can not handle the classification of large da... more Abstract Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for ...

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Research paper thumbnail of Binary Flower Pollination Algorithm and Its Application to Feature Selection

Studies in Computational Intelligence, 2014

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Research paper thumbnail of Fast and accurate holistic face recognition using Optimum-Path Forest

2009 16th International Conference on Digital Signal Processing, 2009

Abstract This paper presents a novel, fast and accurate holistic method for face-recognition usin... more Abstract This paper presents a novel, fast and accurate holistic method for face-recognition using the Optimum-Path Forest (OPF) classifier. Our objective is to improve the face recognition accuracy against traditional methods and to reduce the computational effort in face recognition tasks. During the feature extraction stage we apply principal component analysis to reduce feature vectors in several dimensionalities. Experiments using face images from three public datasets (ORL, CBCL and YALE) present good results. ...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Feature selection through gravitational search algorithm

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

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Research paper thumbnail of Robust and fast Vowel Recognition Using Optimum-Path Forest

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010

Abstract The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamenta... more Abstract The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is ...

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Research paper thumbnail of Fast Non-Technical Losses Identification Through Optimum-Path Forest

2009 15th International Conference on Intelligent System Applications to Power Systems, 2009

Abstract Fraud detection in energy systems by illegal consumers is the most actively pursued stud... more Abstract Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as artificial neural networks and support vector machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the optimum-path forest classifier for a fast non-technical losses recognition, which has been ...

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Research paper thumbnail of A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection

Computers & Electrical Engineering, 2011

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Research paper thumbnail of Bio-Inspired Computation and Applications in Image Processing

A sample chapter of the Book on "Bio-inspired Computation and Applications in Image Processing" ... more A sample chapter of the Book on
"Bio-inspired Computation and Applications in Image Processing"
(Elsevier, 2016).

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