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Beginning in December 2019, the spread of the novel Coronavirus (COVID-19) has exposed weaknesses in healthcare systems across the world. To sufficiently contain the virus, countries have had to carry out a set of extraordinary measures, including exhaustive testing and screening for positive cases of the disease. It is crucial to detect and isolate those who are infected as soon as possible to keep the virus contained. However, in countries and areas where there are limited COVID-19 testing kits, there is an urgent need for alternative diagnostic measures. The standard screening method currently used for detecting COVID-19 cases is RT-PCR testing, which is a very time-consuming, laborious, and complicated manual process. Given that nearly all hospitals have X-ray imaging machines, it is possible to use X-rays to screen for COVID-19 without the dedicated test kits and separate those who are infected and those who are not. In this study, we applied deep convolutional neural networks on chest X-rays to determine this phenomena. The proposed deep learning model produced an average classification accuracy of 90.64% and F1-Score of 89.8% after performing 5-fold cross-validation on a multi-class dataset consisting of COVID-19, Viral Pneumonia, and normal X-ray images.

Link to this article: http://journals.cambridge.org/abstract_S1351324911000118 How to cite this article: FAZEL KESHTKAR and DIANA INKPEN (2012). A hierarchical approach to mood classication in AbstractIn this article, we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a data set to train and evaluate our method. We present extensive error analysis and discuss the difficulty of the task.

One of the most complex tasks in digital image processing is image segmentation. This paper proposes a novel image segmentation algorithm that uses a biologically inspired technique based on swarm intelligence and a cellular automata model. The proposed swarm intelligence-based algorithm operates on the image pixel data and a region/neighborhood map to form a context in which they can merge. The swarm intelligent algorithm also tries to find similar pixels using a sensor function, which is then utilized by swarm agents to determine the next appreciate pixel in the region/segment area. In addition, the paper introduces a cellular automata-based dynamic flow algorithm to guide swarm agents to choose the best possible advancing direction to avoid traffic jam and inconsistency. The suggested image segmentation strategy is tested on a set of dental radiographs.

Because paraphrasing is one of the crucial tasks in natural language understanding and generation, this paper introduces a novel technique to extract paraphrases for emotion terms, from nonparallel corpora. We present a bootstrapping technique for identifying paraphrases, starting with a small number of seeds. WordNet Affect emotion words are used as seeds. The bootstrapping approach learns extraction patterns for six classes of emotions. We use annotated blogs and other data sets as texts from which to extract paraphrases, based on the highest scoring extraction patterns. The results include lexical and morphosyntactic paraphrases, that we evaluate with human judges.