Coronavirus (Covid-19) pandemic has impacted the whole world and has forced health emergencies internationally. The contact of this pandemic has been fallen over almost all the development sectors. A lot of precautionary measures have been taken to control the Covid-19 spread, where wearing a face mask is an essential precaution. Wearing a face mask correctly has been essential in controlling the Covid-19 transmission. Moreover, this research aims to detect the face mask with fine-grained wearing states: face with the correct mask and face without mask. Our work has two challenging tasks due to two main reasons firstly the presence of augmented data set available in the online market and the training of large datasets. This paper represents a mobile application for face mask detection. The fully automated Machine Learning Cloud service known as Google Cloud ML API is used for training the model in TensorFlow file format. This paper highlights the efficiency of the ML model. Additionally, this paper examines the advancement of the cloud technology used for machine learning over the traditional coding methods.
The Internet of Things (IoT) circumscribes the real-world commodity to communicate and interact with each other by the help of Internet. The enormous growth in the field of IoT devices has drawn wide attention towards wireless-based area networks (WBANs) to eliminate implications like lack of central entity, rigid security demands, mass data processing, low-latency service provision and resource limitation. Moreover, this paper represents a broad discussion over IoT healthcare protocols. This paper also brings us an extensive insight over the IoT surrounding healthcare over its privacy, challenges privacy, challenges, and security issues. This paper also proposes a solution to reduce potential causes of human error and to provide with the bestin-class efficiency in the healthcare sector. This paper highlights IoT based approach in the healthcare industry for higher reliability and efficiency.
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