IoT-Based Distributed Body Temperature Detection and Monitoring System for the Implementation of Onsite Learning at Schools

During the Covid-19 pandemic, teaching and learning activities were carried out virtually. It has been running for more than one year. When the trend of Covid-19 cases decreased, onsite learning began to be trialed by implementing strict health protocols. One of the important parameters for the first screening is body temperature because 99% of Covid-19 patients have fever. Therefore, a student temperature measurement mechanism is needed before entering the school area. A number of temperature detectors should be located to prevent queues. A distributed real-time monitoring system as well as data records are required for daily evaluations. Therefore, in this study, a distributed system for measuring body temperature was designed and implemented with data recording. This system runs online real-time on an internet network client server application. This system consists of four temperature detectors connected to a mini-computer as data control and an access point to a dedicated network. All sensor nodes can send data simultaneously. A web server application is provided for data storage and access to the client. From testing the proposed system, it is known that the system can send real-time data with a delay of <150 ms on several measurements and other measurements >150 ms because it really depends on the quality of internet service. The application can run an alarm function if it finds a temperature exceeding the threshold. This system has been implemented in one of a private school in the city of Bandung. With this system, it is hoped that it can support onsite learning activities in schools. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Compressive Sensing in Lung Cancer Images for Telemedicine Application

Telemedicine technology as a solution to prevent the spread of Covid-19. Tele-radiology for lung cancer images requires a large bandwidth when the image is transmitted, whereas the available bandwidth is limited. CT-scan lung cancer image has a very large capacity so that it requires a large storage space, while the storage capacity is very limited. On the sender side, the application of compressive sensing as an alternative solution to obtain data compression with a high compression ratio but requires high accuracy on the receiver. In addition to make it easier for medical staff and doctor for diagnosing the type of lung cancer, the recipient requires a lung cancer image classification, which consists of 3 types of cancer, including: adeno carcinoma (ACA), squamous cell carcinoma (SCC), and benign lung cancer (N). This paper proposes a combination method consisting of a Compressive Sensing (CS) algorithm, feature extraction, and KNN classification that can work effectively and efficiently in telemedicine applications. The results showed that CS worked effectively for compression with large compression ratios without having an influence on the accuracy results. The sparse technique FFT provides the highest accuracy compared to IFFT, DWT and without sparsing. The classification using KNN shows that the N image has uniquely extracted characteristics and give accuracy up to 100%, whereas the image of ACA and SCC provide accuracy by 70%. © 2021 ACM.

Covid-19 and Tuberculosis Classification Based on Chest X-Ray Using Convolutional Neural Network

The high rate of patients with tuberculosis (TB) with the graph showing a continual increase requires the research in any sector as the programs to eradicate tuberculosis. One of the applications is the Decision Support System (DSS) that helps the medical experts particularly doctors in diagnosing TB grade 1+. 2+, and 3+ rapidly. Another problem is related to the imbalance between the number of patients and the number of medical practitioners in the condition of pandemic Corona Virus Disease (Covid-19) today. Hence, DSS is highly required and it can be used for the long-term management of Covid. In this study, the rapid classification of normal lung, tuberculosis lung, and Covid-19 lung based on the Chest X-Ray (CXR) image was proposed as the initial step of DSS implementation. The proposed image processing based CXR classification using Deep Learning Convolutional Neural Network (CNN) obtained the highest accuracy rate of 88.37%. This accuracy was obtained in the second scenario with the 208 CXR datasets. The small number of datasets used was related to the limited number of CXR Covid-19 images with good quality brightness. The proposed system developed is expected to help doctors in diagnose lung disease. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.