A Critical Review of Detection and Prediction of Infectious Disease using IOT Sensors

Detection and prediction of infectious disease is a very challenging task due to the lack of substantial evidence of the disease and its behaviours. The effective infection prevention mechanisms through IoT sensors have been explored in the various researches in recent times. Various researchers use the Internet of Things (IoT) to collect real-time sensory information data for the detection and prediction of Infectious Disease. A group of sensors distributed in the workplace to detect or gathered data related to Infectious Disease is one of the major explorations of this paper. Sensors could collect the real-time data gathered in the cloud storage unit and further the user has to be intimated with the real scenario of data suggested. Behind this filtering and analytics process to execute on the gathered data and extract the data in form of user information. This paper explores the Flu, COVID-19, Zika, and H1N1, especially focus on COVID-19 as the recent pandemic. The technique as the Remote Excess of Experts thought IoT data is also the research investigation of this paper. © 2021 IEEE.

COVID-19 Diagnosis Using Transfer-Learning Techniques

COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two-and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis systems achieved excellent results with the VGG-19 architecture. The two-classifier diagnosis system achieved high sensitivity for COVID-19, with 99.5% and 100% on DATASET-1 and DATASET-2, respectively. The three-classifier diagnosis system achieves high sensitivity for COVID-19, with 98.4% and 100% on DATASET-1 and DATASET-2, respectively. The high sensitivity of these diagnostic systems for COVID-19 will significantly improve the speed and precision of COVID-19 diagnosis.