Exploration of hybrid deep learning algorithms for covid-19 mRNA vaccine degradation prediction system

Coronavirus causes a global pandemic that has adversely affected public health, the economy, including every life aspect. To manage the spread, innumerable measurements are gathered. Administering vaccines is considered to be among the precautionary steps under the blueprint. Among all vaccines, the messenger ribonucleic acid (mRNA) vaccines provide notable effectiveness with minimal side effects. However, it is easily degraded and limits its application. Therefore, considering the cruciality of predicting the degradation rate of the mRNA vaccine, this prediction study is proposed. In addition, this study compared the hybridizing sequence of the hybrid model to identify its influence on prediction performance. Five models are created for exploration and prediction on the COVID-19 mRNA vaccine dataset provided by Stanford University and made accessible on the Kaggle community platform employing the two deep learning algorithms, Long Short-Term Memory (LSTM) as well as Gated Recurrent Unit (GRU). The Mean Columnwise Root Mean Square Error (MCRMSE) performance metric was utilized to assess each model's performance. Results demonstrated that both GRU and LSTM are befitting for predicting the degradation rate of COVID-19 mRNA vaccines. Moreover, performance improvement could be achieved by performing the hybridization approach. Among Hybrid_1, Hybrid_2, and Hybrid_3, when trained with Set_1 augmented data, Hybrid_3 with the lowest training error (0.1257) and validation error (0.1324) surpassed the other two models;the same for model training with Set_2 augmented data, scoring 0.0164 and 0.0175 MCRMSE for training error and validation error, respectively. The variance in results obtained by hybrid models from experimenting claimed hybridizing sequence of algorithms in hybrid modeling should be a concerned. © 2022, Universitas Ahmad Dahlan. All rights reserved.

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

DNA sequence classification is one of the major challenges in biological data processing. The identification and classification of novel viral genome sequences drastically help in reducing the dangers of a viral outbreak like COVID-19. The more accurate the classification of these viruses, the faster a vaccine can be produced to counter them. Thus, more accurate methods should be utilized to classify the viral DNA. This research proposes a hybrid deep learning model for efficient viral DNA sequence classification. A genetic algorithm (GA) was utilized for weight optimization with Convolutional Neural Networks (CNN) architecture. Furthermore, Long Short-Term Memory (LSTM) as well as Bidirectional CNN-LSTM model architectures are employed. Encoding methods are needed to transform the DNA into numeric format for the proposed model. Three different encoding methods to represent DNA sequences as input to the proposed model were experimented: k-mer, label encoding, and one hot vector encoding. Furthermore, an efficient oversampling method was applied to overcome the imbalanced dataset issues. The performance of the proposed GA optimized CNN hybrid model using label encoding achieved the highest classification accuracy of 94.88% compared with other encoding methods © 2022, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Prediction of Priority to Individual for COVID Vaccine Distribution Using Soft Computing Techniques

World is facing impact of COVID pandemic;every industry is facing corollaries to a great extent. With increase in number of COVID cases, it is necessary to control the spread of virus and wipe it out in the best possible manner. COVID-19, which have its rapid spread across the world, is a dangerous episode which entire community is cladding on. Proper treatment and efficient vaccine distribution are the major glitches of medical industry during this untoward outbreak. Priority in distribution of vaccine is extremely vital, to handle availability of vaccines. Our main objective is to develop methodologies with competing process which implements unsupervised k-means clustering and supervised naïve Bayes, decision tree and gradient boost classification algorithms to find the priority of individuals for distributing vaccines. Efficient pathway of distribution is implemented in layered manner based on individual’s priority, assessed in par with impact level of disease on individuals in the community. Priority for vaccine distribution is finalized with classification algorithm with the highest accuracy. It helps to distribute the available vaccines optimally among the population, and it plays major role in reducing the impact of coronavirus on individuals and society. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.