Publicações

Arrhythmia, a cardiac condition, is frequently diagnosed by classifying heartbeats using electrocardiograms (ECG). This classification is a crucial step in medical diagnosis and can be significantly improved by employing computational methods to analyze the ECG data. Despite the extensive literature on this subject, the high inter-patient variability and noise in ECG signals pose challenges in the development of computational methods. Deep learning methods represent state-of-the-art solutions to diverse problems in computer vision, signal processing, and pattern recognition, mainly due to advancements enabled by self-supervised learning. In this work, we propose a self-supervised approach for ECG beat classification and a specific pretext task for ECG, termed ECGPuzzle. This approach allows for fine-tuning a deep learning model to an individual, improving the model's generalization. Given the low computational power of medical equipment and the need for on-site training, hardware acceleration is indispensable. Thus, we investigate the feasibility of this proposal on three distinct computational systems and discuss potential manners to train a model on an embedded system.

Accurately identifying plant species and varieties is crucial across various disciplines, such as biology, medicine, and agronomy. While species identification is challenging, variety identification presents an even greater difficulty. Conventional identification methods, although effective, often require specialized and costly equipment, making them less accessible. In this work, we explore the problem of hop variety classification, comparing traditional feature extraction methods with deep learning approaches using the UFOP-HVD dataset. We address two research questions: whether traditional techniques can achieve competitive results given the limited number of images and whether combining traditional techniques and deep learning can improve the current state-of-the-art. Our findings indicate that traditional techniques yield competitive results for hop variety identification, offering advantages such as interpretability, reduced computational costs, and potential integration into mobile devices. Moreover, we introduce an ensemble method that improves the accuracy from 77.16% to 81.90%, establishing a new state-of-the-art for the UFOP-HVD dataset. These results demonstrate the potential of merging traditional methods with deep learning for challenging hop variety classification tasks, providing an initial baseline for future research.

The accurate identification of promoter regions in DNA sequences holds significant importance in the field of bioinformatics. While this problem has garnered substantial attention in the literature, it remains unresolved. Several researchers have achieved notable outcomes by employing diverse machine-learning techniques to predict promoter regions. However, only a few have thoroughly explored the utilization of features derived from the physicochemical properties of DNA across various organism types. This study investigates the advantages of incorporating these features in the training of machine-learning models. The research evaluates and compares the performance of multiple metrics on diverse datasets encompassing both prokaryotic and eukaryotic organisms. The state-of-the-art CNNProm method is employed as the baseline for our experiments. The models and source code associated with this study can be accessed at the following URL of the project's repository: https://anonymous.4open.science/r/bracis-paper-1458/.

In this study, we examine the impact of human mobility on the transmission of COVID-19, a highly contagious disease that has rapidly spread worldwide. To investigate this, we construct a mobility network that captures movement patterns between Brazilian cities and integrate it with time series data of COVID-19 infection records. Our approach considers the interplay between people's movements and the spread of the virus. We employ two neural networks based on Graph Convolutional Network (GCN), which leverage spatial and temporal data inputs, to predict time series at each city while accounting for the influence of neighboring cities. In comparison, we evaluate LSTM and Prophet models that do not capture time series dependencies. By utilizing RMSE (Root Mean Square Error), we quantify the discrepancy between the actual number of COVID-19 cases and the predicted number of cases by the model among the models. Prophet achieves the best average RMSE of 482.95 with a minimum of 1.49, while LSTM performs the least despite having a low minimum RMSE. The GCRN and GCLSTM models exhibit mean RMSE error values of 3059.5 and 3583.88, respectively, with the lowest standard deviation values for RMSE errors at 500.39 and 452.59. Although the Prophet model demonstrates superior performance, its maximum RMSE value of 52,058.21 is ten times higher than the highest value observed in the Graph Convolutional Networks (GCNs) models. Based on our findings, we conclude that GCNs models yield more stable results compared to the evaluated models.

Locating the promoter region in DNA sequences is of paramount importance in bioinformatics. This problem has been widely studied in the literature, but it has not yet been fully resolved. Some researchers have shown remarkable results using convolutional networks that allowed the automatic extraction of features from a DNA chain. However, a single architecture schema that could learn the promoter prediction task competitively for several organisms has not yet been achieved. Thus, researchers must seek new architectures by hand-designing or by Neural Architecture Search for each new evaluated organism dataset. This work proposes a versatile architecture based on a capsule network that can accurately identify promoter sequences in raw DNA data from five different organisms, eukaryotic and prokaryotic. Our architecture, the CapsProm, could help create models with minimum effort to learn the promoter identification task between different datasets. Furthermore, the CapsProm showed competitive results, overcoming the baseline method in five out of seven tested datasets (F1-score). The models and source code are made available at https://github.com/lauromoraes/CapsNet-promoter.

Several recent research papers have shown the usefulness of Deep Learning (DL) techniques for COVID-19 screening in Chest X-Rays (CXRs). To make this technology accessible and easy to use, a natural path is to leverage the widespread use of smartphones. In these cases, the DL models will inevitably be presented with photographs taken with such devices from a computer monitor. Thus, in this work, a dataset of CXR digital photographs taken from computer monitors with smartphones is built and DL models are evaluated on it. The results show that the current models are not able to correctly classify this kind of input. As an alternative, we build a model that discards pictures of monitors such that the COVID-19 screening module does not have to cope with them.