The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images.

Intrusion Detection Systems (IDS) figure as one of the leading solutions adopted in the network security area to prevent intrusions and ensure data and services security. However, this issue requires IDS to be assertive and efficient processing time. Undersampling techniques allow classifiers to be evaluated from smaller subsets in a representative manner, aiming high assertive metrics in less processing time. There are several solutions in literature for IDS projects, but some criteria are not respected, such as the adoption of a replicable methodology. In this work, we selected three undersampling methodologies: random, Cluster centroids, and NearMiss in two novel unbalanced datasets (CIC2017 and CIC2018) for comparison between five classifiers using cross-validation and Wilcoxon statistical test. Our main contribution is a systematic and replicable methodology for using subsampling techniques to balance the data sets adopted in the IDS project. We choose three metrics for classifier's choice in an IDS design: accuracy, f1-measure, and processing time. The results indicate that the under-sampling by Cluster centroids presents the best performance when applied to distance-based classifiers. Moreover, under-sampling techniques influence the process of choosing the best classifier in the design of an IDS.

The amount of non-invasive methods of diagnosis has increased due to the need for simple, quick and painless tests. Due to the growth of technology that provides the means for extraction and signal processing, new analytical methods have been developed to understand the complexity of the voice signals. This paper presents a new idea to characterize signals of healthy and pathological voice based on two mathematical tools widely known in the literature, Wavelet Transform (WT) and Artificial Neural Networks. Four classes of samples were used: one from healthy individuals and three from people with vocal fold nodules, Reinke's edema and neurological dysphonia. All the samples were recorded using the vowel /a/ in Brazilian Portuguese. The work shows that the proposed approach using WT is a suitable technique to discriminate between healthy and pathological voices.