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The outbreak of COVID-19 received substantial international attention due to its life-threatening repercussions. Automatic and non-invasive mass screening is pivotal to ensure that the suspected cases can be quarantined, and the spread of the disease can be controlled during the incubation period of the coronavirus. Currently, thermal screening is the only non-invasive mass-screening technique being used. However, with the consumption of paracetamol, the symptoms of fever can be suppressed. Throat inflammation is one of the perceivable symptoms of COVID-19. This paper proposes a novel approach of analyzing the throat images using a Siamese Network based One-Shot learning framework for the identification of Tonsillitis and Pharyngitis for the automatic mass-screening and early-detection of COVID-19 that can be administered without direct contact with the infectious patients, thereby reducing the burden on the medical and paramedical fraternity. Quantitative and qualitative evaluation exhibit promising results and indicate the reliability of the proposed method.

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Sign language is the primary way of communication for speechimpaired people. American Sign Language is one of the most popular sign languages and is used by over 250,000 people around the world. However, this language is only decipherable by the people who have the same disabilities, and it is not understood by masses. Hence, a need arises for an interpreter that can convert this language to text and speech. The existing research focuses on effectively converting sign language to text and speech; however, less emphasis is laid on building a system that is not only accurate but efficient as well which could be run on a compact embedded platform. This is necessary so that a portable interpreter can be made which can be easily carried and used by such people for their convenience making their day to day life easier. In this research, we propose an implementation based on MobileNet architecture, that is not only accurate but efficient as well. Moreover; a demonstration of the entire framework has also been shown on Jetson Nano.

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