ABSTRACT
The worldwide coronavirus (COVID-19) pandemic has accelerated substantially in the 2020, necessitating a global collaborative from various entities to create and speed vaccine development to prevent illnesses and deaths. Because of its fast development, high efficiently, safe administration, and low-cost production, messenger RNA (mRNA) has emerged as a significant technology in this epidemic. However, due of the inadequate in vivo distribution of mRNA, its chemical qualities make it difficult to use the vaccine. As a result, the goal of this study is to create and construct a sequence deep model that will be used to predict the degradation rate of the COVID-19 mRNA vaccine using five reactivity values for each place in the mRNA sequence. The probability degradation rate with/without magnesium at pH10 and 50°C was one of four of these values. The fifth reactivity value shows the likelihood of the RNA sample's secondary structure. The numerical and categorical properties of the deep learning model are the most important. Categorical features are referred from the structures, sequences, and predicted loop of the mRNA sequence, while numerical features are extracted via mathematical computations. 6 models of bidirectional layers models (LSTM, GRU, LSTM+GRU (L_GRU), GRU+LSTM (G_LSTM), LSTM+GRU+LSTM (L_G_LSTM), and GRU+LSTM+GRU (G_L_GRU) give trustworthy projected outcomes because it comprises five reactivity values and validate by mean columnwise root mean square error (MCRMSE). The MCRMSE results are then used to evaluate the performance. The stronger the prediction model, the smaller the values are. The best-fitting model is L_G_LSTM with the MCRMSE difference of 0.007 will be implemented into a Graphical User Interface (GUI) prediction system. © 2022 IEEE.
ABSTRACT
In COVID19 pandemic, new norms have been introduced, including, to leave a record when checking-in to a particular place. This new norm is regulated in order to trace locations that have been visited by someone with positive COVID-19. This paper presents a work on development of check-in location system. The system implemented Near Field Communication (NFC) technology which is mainly utilized two NFC compatible devices where an identification card (IC) is used as a smart object (NFC tag) and the NFC detector as an NFC reader to exchange information. Testing has been conducted in order to observe the system performance, and, the results showed that this system is able to collect information of users who were coming to premise. Also, the information can be checked by authority in order to track someone with positive COVID-19. As conclusion, this system can be an alternative to MySejahtera App. © 2021 Institute of Physics Publishing. All rights reserved.