19 research outputs found

    Learning to Hash-tag Videos with Tag2Vec

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    User-given tags or labels are valuable resources for semantic understanding of visual media such as images and videos. Recently, a new type of labeling mechanism known as hash-tags have become increasingly popular on social media sites. In this paper, we study the problem of generating relevant and useful hash-tags for short video clips. Traditional data-driven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation. We attempt to learn a direct low-cost mapping from video to hash-tags using a two step training process. We first employ a natural language processing (NLP) technique, skip-gram models with neural network training to learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a corpus of 10 million hash-tags. We then train an embedding function to map video features to the low-dimensional Tag2vec space. We learn this embedding for 29 categories of short video clips with hash-tags. A query video without any tag-information can then be directly mapped to the vector space of tags using the learned embedding and relevant tags can be found by performing a simple nearest-neighbor retrieval in the Tag2Vec space. We validate the relevance of the tags suggested by our system qualitatively and quantitatively with a user study

    Introductory Chapter: Pharmacovigilance

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    Clinicodemographic profile of the cases presented with extra pulmonary manifestations during covid 19 pandemic in a tertiary covid care centre at SVPIMSR

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    Aims and objectives: To study severity of COVID 19 virus on various systems in body other than pulmonary system, To foresee adverse outcome of patient who were affected with COVID 19 pneumonia with prominent extra pulmonary manifestations. Methods: A retrospective observational study done in 100 cases of COVID 19 positive patients admitted to SVPIMSR and developed extrapulmonary manifestations over a period of 1 ST April 2020 to 31ST May 2021.Results: Mean age was 60.94 ± 13.91 years and maximum patients were in the age group of 70-79 years, predominantly male were found to have extrapulmonary manifestations, in which cardiovascular system was most commonly involved.COVID-19 patients who developed cardiovascular manifestations had multiple comorbidities.In our study overall mortality was 63% and 53% patients with cardiovascular manifestations were deceased.Conclusion: Patients with COVID-19 may develop extrapulmonary manifestations besides respiratory symptoms. As an emergency physician we should be very careful while managing COVID-19 patients for developing extrapulmonary manifestations. By this we can improve patient‘s survival and reduce mortality. This study guides us for future pandemics of SARS-CoV 2 and we can focus extrapulmonary manifestations beyond pulmonary manifestations
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