Exploration of hybrid deep learning algorithms for covid-19 mrna vaccine degradation prediction system Article Swipe
Soon Hwai Ing
,
Azian Azamimi Abdullah
,
Mohd Yusoff Mashor
,
Zeti‐Azura Mohamed‐Hussein
,
Zeehaida Mohamed
,
Wei Chern Ang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.26555/ijain.v8i3.950
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.26555/ijain.v8i3.950
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 concerned.
Related Topics
Concepts
Computer science
Artificial intelligence
Machine learning
Mean squared error
Blueprint
Deep learning
Artificial neural network
Coronavirus disease 2019 (COVID-19)
Algorithm
Word error rate
Statistics
Medicine
Mathematics
Engineering
Disease
Mechanical engineering
Infectious disease (medical specialty)
Pathology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.26555/ijain.v8i3.950
- http://ijain.org/index.php/IJAIN/article/download/950/ijain_v8i3_p404-416
- OA Status
- gold
- Cited By
- 6
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4320031095
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4320031095Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26555/ijain.v8i3.950Digital Object Identifier
- Title
-
Exploration of hybrid deep learning algorithms for covid-19 mrna vaccine degradation prediction systemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-30Full publication date if available
- Authors
-
Soon Hwai Ing, Azian Azamimi Abdullah, Mohd Yusoff Mashor, Zeti‐Azura Mohamed‐Hussein, Zeehaida Mohamed, Wei Chern AngList of authors in order
- Landing page
-
https://doi.org/10.26555/ijain.v8i3.950Publisher landing page
- PDF URL
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https://ijain.org/index.php/IJAIN/article/download/950/ijain_v8i3_p404-416Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://ijain.org/index.php/IJAIN/article/download/950/ijain_v8i3_p404-416Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Machine learning, Mean squared error, Blueprint, Deep learning, Artificial neural network, Coronavirus disease 2019 (COVID-19), Algorithm, Word error rate, Statistics, Medicine, Mathematics, Engineering, Disease, Mechanical engineering, Infectious disease (medical specialty), PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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