ABSTRACT
This paper projects machine learning as a valuable tool for the restriction of the Covid-19 pandemic escalation in the global scenario. The proposed system involves detection of masked or unmasked people and a temperature sensing system for ensuring Covid-19 appropriate protocol is followed to allow only healthy person(s) in public/crowded places. The integration of Arduino Uno and MLX90614 non-contact temperature sensor, along with a MobileNetV2 machine learning model, is performed for complete execution. The system will classify a person as a masked or unmasked individual using ML techniques and detect their body temperature. If the individual meets the appropriate requirements, the system will enable them to access via the gate, which will be controlled by a servo motor in conjunction with a temperature sensor module. © 2022 IEEE.
ABSTRACT
Mucormycosis, often known as 'black fungus' in India, is a fungal illness that has been identified in COVID-19 patients on an irregular basis. This case report describes a patient who was diagnosed with rhino-orbital-cerebral mucormycosis infection following COVID-19 infection. The COVID-19 infection weakens the patient's immune system, making the patient more susceptible to other fungal infections. He had surgery for this as well as ocular exenteration surgery. A physiotherapy treatment was intended to battle the post-surgery problems, including COVID-19 infection. The goal of the physiotherapy was to improve the patient's facial muscles after surgery. Following recovery from the COVID-19 infection, it also aimed to strengthen the patient's respiratory capacity and increase his exercise tolerance. As a result, the patient's quality of life improves. It included the facial proprioceptive neuromuscular facilitation (PNF) exercise as well as a well -planned cardiovascular exercise programme. The 6-minute walk test, peak expiratory flow rate, and quality of life questionnaire were used to assess the efficacy of the physiotherapy treatment.