Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding this quickly disease spread. The need for automated and scalable methods has increased due to the unavailability of accurate automated toolkits. Recent researches using chest X-ray images suggest they include relevant information about the COVID-19 virus. Hence, applying machine learning techniques combined with radiological imaging promises to identify this disease accurately. It is straightforward to collect these images once it is spreadly shared and analyzed in the world. This paper presents a method for automatic COVID-19 detection using chest Xray images through four convolutional neural networks, namely: AlexNet, VGG-11, SqueezeNet, and DenseNet-121. This method had been providing accurate diagnostics for positive or negative COVID-19 classification. We validate our experiments using a ten-fold cross-validation procedure over the training and test sets. Our findings include the shallow fine-tuning and data augmentation strategies that can assist in dealing with the low number of positive COVID-19 images publicly available. The accuracy for all CNNs is higher than 97.00%, and the SqueezeNet model achieved the best result with 99.20%.
The differentiation between the charcoal produced from (Eucalyptus) plantations and native forests is essential to control, commercialization, and supervision of its production in Brazil. The main contribution of this study is to identify the charcoal origin using macroscopic images and Deep Learning Algorithm.We applied a Convolutional Neural Network (CNN) using VGG-16 architecture, with preprocessing based on contrast enhancement and data augmentation with rotation over the training set images. on the performance of the CNN with fine-tuning using 360 macroscopic charcoal images from the plantation and native forests. The results pointed out that our method provides new perspectives to identify the charcoal origin, achieving results upper 95 % of mean accuracy to classify charcoal from native forests for all compared preprocessing strategies.
People with visual impairments struggle with urban mobility and independent travel, opening up opportunities for technological advances to improve their quality of life. The Internet of Things (IoT) plays an essential role in bringing improvements and accessibility for visually impaired people. Although alternatives aimed to use IoT in urban mobility, those solutions are still in the initial stages and do not supports urban mobility for people with visual impairment. This paper proposed and evaluated a low-cost IoT architecture that uses Single-Border Computers (SBCs) to support urban mobility. A performance evaluation showcased that our low-cost architecture handles bus trace workload and is suitable for supporting impaired people to get information concerning bus location on Smart Cities scenarios.
A obra "CONTEMPORANEIDADE E PROMOÇÃO DA SAÚDE: DESAFIOS, REFLEXÕES E ESTRATÉGIAS" é organizada por Cidianna Emanuelly Melo do Nascimento. O e-book tem como objetivo debater, de maneira ampla e multidisciplinar, a promoção da saúde em diferentes contextos, oportunizando a produção de ações e análises criativas e inovadoras para as subáreas da saúde.
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