Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos

The discrimination of the interictal and preictal states in epilepsy contributes to the construction of an efficient system of seizure prediction. Here, we performed the classification of the interictal and preictal states for EEG signals of the scalp. The energies of the levels obtained by the signal decomposition of the Wavelet Discrete Transform were used as features for classification. The kNN and SVM classifiers were used in the analysis of the individual EEG channels, which gave indications that the occipital lobe region channels are the most relevant to differentiate between the interictal and preictal states. Using these channels, the classification into two states achieved accuracy of 97.29%, sensitivity of 96.25% and specificity of 98.33%. In addition, the different frequency ranges obtained by Wavelet for the classification were analyzed, and it was observed that the range of 32 Hz to 128 Hz presented greater relevance in the task

Construção de Dicionário de Palavras para Análise de Sentimentos em Tweets

 O crescimento das redes sociais proporcionou uma grande disponibilidadede informações sobre seus usuários, que são de importância para um grande número de interessados, como setores do governo ou empresas. Uma análise útil desses dados é identificar a polaridade do sentimento em um texto sobre determinado assunto. Muitos métodos computacionais já foram propostos neste sentido, porém, devido á sua complexidade , eles costumam ser de difícil interpretação, o que pode não ser ideal para determinados cenários. Neste trabalho buscou-se desenvolver um método de análise de sentimentos de tweets que seja de fácil modificação e fácil interpretação para usuários leigos. A abordagem desenvolvida consiste em criar um dicionário de palavras que pondera cada palavra de acordo com a sua polaridade. Para estimar os pesos de cada palavra foi utilizado um Algoritmo Genético. O dicionário formado permitefácil interpretação e modificação. Quando comparado com outros métodos, os resultados indicaram que a abordagem proposta é promissora

Um modelo algébrico para representação, indexação e classificação automática de documentos digitais.

This paper introduce the idea of representing, indexing and automatically classifying digital documents. The vectorial model of representing documents is simple and allows us to deal with the classification of a great amount of digital documents which were loaded daily in almost 35 Brazilian Digital Library of Thesis and Dissertation. We expect to have another 20 libraries by the end of this year. Using a sample of real documents, we compare this methodology of classification to that done by specialists. The results show that this methodology is promising in reducing the effort of specialists when performing such task

Segmentation of Tuberculosis Bacilli in Conventional Microscopy Images Through Accelerated CNN Using Dilated Convolutions

In this work, we propose a method to achieve microscopy image segmentation,in which a convolutional neural network (CNN) is used. The method is divided in two parts: (i) the CNN is trained for pixelwise classification of image; (ii) the training CNN is accelerated, removing the redundant operations, allowing the classification of the pixels from an entire image patch at the same time. The method was evaluated over a dataset with 120 images obtained using conventional microscopy in sputum smear sheets prepared according to the Ziehl-Neelsen technique. In the experimental evaluations carried out on this dataset, we obtained an accuracy of 97:33% and recall of 96:30%. The accelerated CNN is 44 times faster, maintaining identical prediction results. These results show that the proposed method has the potential to handle the given problem

Comparação de Técnicas para Representação Vetorial de Imagens com Redes Neurais para Aplicações de Recuperação de Produtos do Varejo

ABSTRACTProduct retrieval from images has multiple applications rangingfrom providing information and recommentations for customersin supermarkets to automatic invoice generation in smart stores.However, this task present important challenges such as the largenumber of products, the scarcity of images of items, differencesbetween real and iconic images of the products, and the constantchanges in the portfolio due to the addition or removal of products.Hence, this work investigates ways of generating vector representationsof images using deep neural networks such that theserepresentations can be used for product retrieval even in face ofthese challenges. Experimental analysis evaluated the effect thatnetwork architecture, data augmentation techniques and objectivefunctions used during training have on representation quality. Thebest configuration was achieved by fine-tuning a VGG-16 modelin the task of classifying products using a mix of Randaugmentand Augmix data augmentations and a hierarchical triplet loss as aregularization function. The representations built using this modelled to a top-1 accuracy of 80,38% and top-5 accuracy of 92.62% inthe Grocery Products dataset

A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification

Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation