74 citations · 291 across the 20 of their papers we have counts for
20 papers
Enhancing Hyper-To-Real Space Projections Through Euclidean Norm Meta-Heuristic Optimization
Luiz C. F. Ribeiro, Mateus Roder, Gustavo H. de Rosa +2
The continuous computational power growth in the last decades has made solving several optimization problems significant to humankind a tractable task; however, tackling some of th…
DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification
Daniel F. S. Santos, Rafael G. Pires, Leandro A. Passos +1
Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technolog…
FEMa-FS: Finite Element Machines for Feature Selection
Lucas Biaggi, João P. Papa, Kelton A. P Costa +2
Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emer…
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks
Mateus Roder, Jurandy Almeida, Gustavo H. de Rosa +3
In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities. Additionally, such an improvement…
PL-kNN: A Parameterless Nearest Neighbors Classifier
Danilo Samuel Jodas, Leandro Aparecido Passos, Ahsan Adeel +1
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The -Nearest Neighbors is one of the most effective…
ComplexWoundDB: A Database for Automatic Complex Wound Tissue Categorization
Talita A. Pereira, Regina C. Popim, Leandro A. Passos +3
Complex wounds usually face partial or total loss of skin thickness, healing by secondary intention. They can be acute or chronic, figuring infections, ischemia and tissue necrosis…
Multimodal Speech Enhancement Using Burst Propagation
Mohsin Raza, Leandro A. Passos, Ahmed Khubaib +1
This paper proposes the MBURST, a novel multimodal solution for audio-visual speech enhancements that consider the most recent neurological discoveries regarding pyramidal cells of…
Canonical Cortical Graph Neural Networks and its Application for Speech Enhancement in Audio-Visual Hearing Aids
Leandro A. Passos, João Paulo Papa, Amir Hussain +1
Despite the recent success of machine learning algorithms, most models face drawbacks when considering more complex tasks requiring interaction between different sources, such as m…
A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic
Renato W. R. de Souza, João V. C. de Oliveira, Leandro A. Passos +3
In the past decades, fuzzy logic has played an essential role in many research areas. Alongside, graph-based pattern recognition has shown to be of great importance due to its flex…
MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks
Claudio Filipi Goncalves do Santos, Mateus Roder, Leandro A. Passos +1
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 improve…
An End-to-End Approach for Seam Carving Detection using Deep Neural Networks
Thierry P. Moreira, Marcos Cleison S. Santana, Leandro A. Passos João Paulo Papa +1
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 mo…
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization
Guilherme C. Oliveira, Gustavo H. Rosa, Daniel C. G. Pedronette +4
Deep learning applications for assessing medical images are limited because the datasets are often small and imbalanced. The use of synthetic data has been proposed in the literatu…
Handling Imbalanced Datasets Through Optimum-Path Forest
Leandro Aparecido Passos, Danilo S. Jodas, Luiz C. F. Ribeiro +3
In the last decade, machine learning-based approaches became capable of performing a wide range of complex tasks sometimes better than humans, demanding a fraction of the time. Suc…
A Review of Deep Learning-based Approaches for Deepfake Content Detection
Leandro A. Passos, Danilo Jodas, Kelton A. P. da Costa +5
Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's inte…
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement
Leandro Aparecido Passos, João Paulo Papa, Javier Del Ser +2
This paper proposes a novel multimodal self-supervised architecture for energy-efficient audio-visual (AV) speech enhancement that integrates Graph Neural Networks with canonical c…
A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks
Mateus Roder, Leandro A. Passos, Luiz Carlos Felix Ribeiro +2
With the advent of deep learning, the number of works proposing new methods or improving existent ones has grown exponentially in the last years. In this scenario, "very deep" mode…
Intestinal Parasites Classification Using Deep Belief Networks
Mateus Roder, Leandro A. Passos, Luiz Carlos Felix Ribeiro +3
Currently, approximately billion people are infected by intestinal parasites worldwide. Diseases caused by such infections constitute a public health problem in most tropical c…
A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines
Leandro Aparecido Passos, João Paulo Papa
Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable rang…
PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection
Leandro Aparecido Passos, Danilo Samuel Jodas, Luiz C. F. Ribeiro +2
Breast cancer is among the most deadly diseases, distressing mostly women worldwide. Although traditional methods for detection have presented themselves as valid for the task, the…
Temperature-Based Deep Boltzmann Machines
Leandro Aparecido Passos Junior, Joao Paulo Papa
Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications, that range from speech recognition to face-base…