Latifah Munirah Kamarudin
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Preferred name
Latifah Munirah Kamarudin
Official Name
Kamarudin, Latifah Munirah
Alternative Name
Kamarudin, Latifah Munirah
Kamarudin, Latifah M.
Kamarudin, L. M.
Kamarudin, Munirah L.
Kamarudin, L.
Main Affiliation
Scopus Author ID
57192974774
Researcher ID
G-8267-2016

Research Output
Now showing 1 - 2 of 2
  • Publication
    COVID-19 mRNA vaccine degradation rate prediction using artificial intelligence techniques: a narrative review
    (IASE, 2024-06)
    Hwai Ing Soon
    ;
    Azian Azamimi Abdullah
    ;
    Hiromitsu Nishizaki
    ;
    Mohd Yusoff Mashor
    ;
    Latifah Munirah Kamarudin
    ;
    Zeti-Azura Mohamed-Hussein
    ;
    Zeehaida Mohamed
    ;
    Wei Chern Ang
    As diseases become more common, the use of mRNA (messenger ribonucleic acid) vaccines is becoming more important. These vaccines can be developed quickly and have a low risk of side effects. However, they are sensitive to environmental conditions, which means they need careful storage and transport, creating challenges in distributing them. Testing the stability of an mRNA vaccine requires a lot of work and time, as it needs many lab tests. Artificial Intelligence (AI) offers a new solution by using the genetic information in RNA sequences to predict how quickly these vaccines might break down. This approach helps address potential shortages of vaccines by avoiding some of the challenges with vaccine distribution. The COVID-19 pandemic has greatly sped up the use of AI in this area. This change is significant because using AI to predict and improve the stability of mRNA vaccines was not well explored before the pandemic. This paper reviews recent studies that use AI to study mRNA vaccines during the COVID-19 pandemic. It points out that the main issue with these vaccines is how long they can be stored before they are no longer effective due to their sensitivity to environmental conditions. By looking at these studies, the paper not only shows how AI and vaccine research are coming together but also points out opportunities for more research. The goal of this review is to outline effective methods to improve the use of mRNA vaccines and encourage more scientific research and development in this field. This is an important step in improving how we deal with pandemics.
  • Publication
    Optimizing hybrid neural networks for precise COVID-19 mRNA vaccine degradation prediction
    (Institute of Advanced Science Extension (IASE), 2024-07)
    Hwai Ing Soon
    ;
    Azian Azamimi Abdullah
    ;
    Hiromitsu Nishizaki
    ;
    Mohd Yusoff Mashor
    ;
    Latifah Munirah Kamarudin
    ;
    Zeti-Azura Mohamed-Hussein
    ;
    Zeehaida Mohamed
    ;
    Wei Chern Ang
    Conventional hybrid models often miss an essential factor that can lead to less effective performance: intrinsic sequence dependence when combining various neural network (NN) architectures. This study addresses this issue by highlighting the importance of sequence hybridization in NN architecture integration, aiming to improve model effectiveness. It combines NN layers—dense, long short-term memory (LSTM), and gated recurrent unit (GRU)—using the Keras Sequential API for defining the architecture. To provide better context, bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU) replace their unidirectional counterparts, enhancing the models through bidirectional structures. Out of 25 NN models tested, 18 four-layer hybrid NN models consist of one-quarter dense layer and the rest BiLSTM and BiGRU layers. These hybrid NN models undergo supervised learning regression analysis, with mean column-wise root mean square error (MCRMSE) as the performance metric. The results show that each hybrid NN model produces unique outcomes based on its specific hybrid sequence. The Hybrid_LGSS model performs better than existing three-layer BiLSTM networks in predictive accuracy and shows lower overfitting (MCRMSEs of 0.0749 and 0.0767 for training and validation, respectively). This indicates that the optimal hybridization sequence is crucial for achieving a balance between performance and simplicity. In summary, this research could help vaccinologists develop better mRNA vaccines and provide data analysts with new insights for improvement.