Difficult Aspects of Treatments Patients With Acute Lung Abscesses Who Survived COVID -19 (preprint)

Suppurative diseases of the lungs are topical issues of pulmonology that require detailed study due to the difficulties of diagnosis and high mortality of patients. This is due to several objective and subjective reasons such as the widespread use of antibioticresistant microflora, which is especially important when antibiotics are prescribed unreasonably, even with a mild form of COVID − 19. This, of course, affects COVID patients, who already have reduced immunity. The question of the exact mechanism of development of purulent complications of the lungs after or during infection with COVID − 19 remains open. Clinical and radiological signs of a lung abscess often resemble the symptoms of pneumonia; however, antibacterial, and symptomatic therapy have differences. With gangrene of the lung, the increase in intoxication syndrome can be gradual, which reduces the doctor's alertness regarding the most severe disease and causes inadequate therapy. Comprehensive diagnostic measures, knowledge of the main clinical, instrumental and laboratory parameters are necessary for all physicians.

Face Recognition at varying angles from distant CCTV Footage using Siamese Architecture

Face Recognition is a deeply studied and researched domain. There are quite large solutions and model architectures to tackle majority of the face recognition related concerns. In this work we come up with a more specific version of face recognition which can mainly be used to achieve long distance and natural limitations of CCTV identification in real world scenarios using existing methods to better modifications. The solution which paper proposes using deep learning can be used to recognise a person even if they wear face masks due to the Covid-19 pandemic. One-shot learning is incorporated which can be used to train the specific model with just one image per person of the individual to be recognized. The designed model is modified from Siamese network architecture trained in triplet loss function to achieve these requirements. © 2022 IEEE.