RSS 1.0Departamento de Computação
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Artificial intelligence for oral cancer diagnosis: What are the possibilities?
; ; Santana, Marcos C.S.
; Pires, Rafael G. ; Papa, João P. ; (Oral Oncology, 2022) [Carta]
Oral cancer could be prevented. The primary strategy is based on prevention. Most patients with oral cancer present to the hospital network with advanced staging and a low chance of cure. This condition may be related to ... -
PL-k NN: A Parameterless Nearest Neighbors Classifier
Jodas, Danilo Samuel; ; ; Papa, Joao Paulo (International Conference on Systems, Signals, and Image Processing, 2022) [Trabalho apresentado em evento]
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The k-Nearest Neighbors is one of the most effective and straightforward models employed in ... -
Future trends in optimum-path forest classification
Papa, João Paulo; (Optimum-Path Forest: Theory, Algorithms, and Applications, 2022) [Capítulo de livro]
In the past years, we have observed an increasing number of applications that require machine learning techniques to sort out problems that are not straightforward to humans. The reasons vary from information that is not ... -
Theoretical background and related works
Afonso, Luis C.S.; ; Papa, João Paulo (Optimum-Path Forest: Theory, Algorithms, and Applications, 2022) [Capítulo de livro]
The Optimum-Path Forest (OPF) is a framework for the design of graph-based classifiers, which covers supervised, semisupervised, and unsupervised applications. The OPF is mainly characterized by its low training and ... -
Optimum-path forest: Theory, algorithms, and applications
; Papa, João Paulo (Optimum-Path Forest: Theory, Algorithms, and Applications, 2022) [Livro]
Optimum-Path Forest: Theory, Algorithms, and Applications was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a ... -
Introduction
; Papa, João Paulo (Optimum-Path Forest: Theory, Algorithms, and Applications, 2022) [Editorial]
Pattern recognition techniques have been consistently applied in various domains, ranging from remote sensing and medicine to engineering, among others. The literature is vast and dominated mainly by Neural Networks in the ... -
Training Transformers for Question Generation Task in Intelligent Tutoring Systems
Santi, Matheus; Manacero, Aleardo ; Peronaglio, Fernanda F. ; Lobato, Renata S. ; Spolon, Roberta ; (Iberian Conference on Information Systems and Technologies, CISTI, 2022) [Trabalho apresentado em evento]
Over the last few years, natural language processing (NLP) technologies have largely evolved, allowing their application in new scenarios with much more significant results. With the introduction of NLP into intelligent ... -
DDIPNET AND DDIPNET+: DISCRIMINANT DEEP IMAGE PRIOR NETWORKS FOR REMOTE SENSING IMAGE CLASSIFICATION
Santos, Daniel F.S.; Pires, Rafael G. ; Passos, Leandro A. ; Papa, João P. (International Geoscience and Remote Sensing Symposium (IGARSS), 2021) [Trabalho apresentado em evento]
Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality ... -
MaxDropoutV2: An Improved Method to Drop Out Neurons in Convolutional Neural Networks
; Roder, Mateus; ; Papa, João Paulo (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022) [Trabalho apresentado em evento]
In the last decade, exponential data growth supplied the machine learning-based algorithms’ capacity and enabled their usage in daily life activities. Additionally, such an improvement is partially explained due to the ... -
DEEP REGRESSOR NETWORKS FOR BLIND IMAGE DEBLURRING
Pires, Rafael G.; Santos, Daniel F.S. ; Passos, Leandro A. ; Papa, João P. (International Geoscience and Remote Sensing Symposium (IGARSS), 2021) [Trabalho apresentado em evento]
Image restoration concerns mainly smoothing noise and deblurring images that were corrupted either during acquisition or transmission. Since traditional deconvolution filters are highly dependent on specific kernels or ... -
An End-to-End Approach for Seam Carving Detection Using Deep Neural Networks
Moreira, Thierry P.; Santana, Marcos Cleison S. ; ; Papa, João Paulo ; da Costa, Kelton Augusto P. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022) [Trabalho apresentado em evento]
Seam carving is a computational method capable of resizing images for both reduction and expansion based on its content, instead of the image geometry. Although the technique is mostly employed to deal with redundant ... -
Learning to weight similarity measures with Siamese networks: A case study on optimum-path forest
De Rosa, Gustavo H.; Papa, João Paulo (Optimum-Path Forest: Theory, Algorithms, and Applications, 2022) [Capítulo de livro]
Recent advances in machine learning algorithms have been aiding humans and improving their decision-making capacities in various applications, such as medical imaging, image classification and reconstruction, object ... -
Detecting atherosclerotic plaque calcifications of the carotid artery through optimum-path forest
Jodas, Danilo Samuel; Roder, Mateus ; Pires, Rafael ; Silva Santana, Marcos Cleison ; ; Passos, Leandro Aparecido (Optimum-Path Forest: Theory, Algorithms, and Applications, 2022) [Capítulo de livro]
Analysis of the atherosclerotic lesions deposited in the carotid artery is so far an essential task for estimating possible cardiovascular disorders in patients. The immediate assessment of such lesion, as well as its ... -
Apriori-roaring: frequent pattern mining method based on compressed bitmaps
Colombo, Alexandre; Spolon, Roberta ; Junior, Aleardo Manacero ; Lobato, Renata Spolon ; (International Journal of Business Intelligence and Data Mining, 2022) [Artigo]
Association rule mining is one of the most common tasks in data analysis. It has a descriptive feature used to discover patterns in sets of data. Most existing approaches to data analysis have a constraint related to ... -
Faults in Cloud Environment
Lobato, Renata Spolon; Silva, Fabio Amorim da ; Spolon, Roberta ; Manacero Jr, Aleardo ; ; ; ; ; ; (Proceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021), 2021) [Trabalho apresentado em evento]
Cloud computing is an area in great expansion in the recently years and have begin to be indispensable for greatest majority of world population, even for those who do not work directly on technical areas. One of the ... -
Simulation Platform on Distributed Shared Memory
; Spolon, Roberta; ; ; ; ; ; ; (Proceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021), 2021) [Trabalho apresentado em evento]
This document presents the work related to a simulation event driven platform, Darfia, engineered through the use of distributed and shared memory (DSM) using the framework Terracotta DSO, in order to facilitate the ... -
Mining negative rules: a literature review focusing on performance
Colombo, Alexandre; Spolon, Roberta ; Lobato, Renata Spolon ; Manacero Junior, Aleardo ; ; ; ; ; (Proceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021), 2021) [Trabalho apresentado em evento]
Mining of frequent patterns and association rules is a Data Mining task that aims to determine consistent relationships among elements in a transaction database. Algorithms that consider the absence of elements perform the ... -
Modeling and simulation of tasks interactions using graphical interface
Peronaglio, Fernando F.; Manacero, Aleardo ; Lobato, Renata S. ; Spolon, Roberta ; ; ; ; ; (Proceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021), 2021) [Trabalho apresentado em evento]
Concurrent processes are very important to the development of modern applications, where several tasks are performed in order to accomplish a certain activity. Unfortunately, many concurrent applications, including hard ... -
INTERVIEW Creative Learning, Computational Thinking, and Robotics in Elementary Education
Yonezawa, Wilson Massashiro (Dialogia, 2022) [Editorial]
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A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest
; ; Papa, Joao Paulo (Proceedings - IEEE International Symposium on Circuits and Systems, 2016) [Trabalho apresentado em evento]
Contextual image classification aims at considering the information about nearby samples in the learning process in order to provide more accurate results. In this paper, we propose a locally-adaptive Optimum-Path Forest ...