Latifah Munirah Kamarudin
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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

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Now showing 1 - 5 of 5
  • Publication
    Non-Contact Breathing Signal Classification Using Hybrid Scalogram Image Representation Feature
    ( 2022-01-01)
    Muhammad Husaini
    ;
    Latifah Munirah Kamarudin
    ;
    Nishizaki Hiromitsu
    ;
    Kamarudin I.K.
    ;
    Ibrahim M.A.
    ;
    Ammar Zakaria
    ;
    Toyoura M.
    ;
    Mao X.
    When monitoring human vital signs, breathing is one of the most critical physiological metrics. In areas with limited resources and a shortage of trained medical professionals, automated analysis of abnormal breathing patterns may prove advantageous to healthcare systems. In this paper, we implemented the architecture of five transfer learning models to classify individuals' breathing patterns using our proposed method which uses hybrid scalogram image-based features. We implemented the Sleep Breathing Detection Algorithm (SBDA) for extracting the actual breathing signals from ultra-wideband (UWB) radar for the pre-processing method. Later, the signals were converted to hybrid scalogram image-based representations before being classified using the VGG16, DenseNet, Xception, ResNet, and MobileNet models. The performance of the proposed method was validated using two other image representations: a standard image and a spectrogram image. The overall result showed that the proposed method obtained the highest classification accuracy on the test set for all pre-trained models.
      3  13
  • Publication
    3D grape bunch model reconstruction from 2D images
    ( 2023)
    Woo Yan San
    ;
    Zhuguang Li
    ;
    Tamura Shun
    ;
    Buayai Prawit
    ;
    Nishizaki Hiromitsu
    ;
    Makino Koji
    ;
    Latifah Munirah Kamarudin
    ;
    Xiaoyang Mao
    A crucial step in the production of table grapes is berry thinning. This is because the market value of table grape production is significantly influenced by bunch compactness, bunch form and berry size, all of which are primarily regulated by this task. Grape farmers must count the number of berries in the working bunch and decide which berry should be eliminated during thinning, a process requiring extensive viticultural knowledge. However, the use of 2D pictures for automatic berry counting and identifying the berries to be removed has limitations, as the number of visible berries might vary greatly depending on the direction of view. In addition, it is extremely important to understand the 3D structure of a bunch when considering future automation via robotics. For the reasons stated, obtaining a field-applicable 3D grape bunch model is needed. Thus, the contribution of this study is a novel technology for reconstructing a 3D model of a grape bunch with uniquely identified berries from 2D images captured in the real grape field environment.
      1  21
  • 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
    Integration of dual band radio waves and ensemble-based approach for rice moisture content determination and localisation
    (Elsevier, 2024-09)
    Noraini Azmi
    ;
    Latifah Munirah Kamarudin
    ;
    Ahmad Shakaff Ali Yeon
    ;
    Ammar Zakaria
    ;
    Hiromitsu Nishizaki
    ;
    Xiaoyang Mao
    ;
    Latifah Mohamed
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Mohd Hafiz Fazalul Rahiman
    Maintaining optimal moisture content in grain storage is critical to ensuring adequate supply throughout the year, but it presents a significant challenge. Current moisture measurement methods often necessitate sophisticated and costly equipment. This paper introduces an approach employing real-time rice moisture content determination and detection of spoilage (specifically wet spots) within a storage facility achieved through the utilisation of radio waves operating at 2.4 GHz and 868 MHz, along with an ensemble-based machine learning algorithm. Experimental samples spanning from 12% to 30% moisture levels were collected, then subjected to pre-processing, and subsequently employed to train the Ensemble-based Rice Moisture Content and Localisation (eRMCL) algorithm. The eRMCL produced an effective prediction of both rice moisture content and the localisation of wet spots within the grain storage unit. The results show that compared to support vector machine, random forest, and machine learning methods, the eRMCL algorithm had the best performance metrics, with an accuracy of 94.8% in predicting the moisture content and location of spoilage in storage. The measurement of moisture content and the identification of wet spots in rice storage using the dual frequency wave approach were found to be more accurate than with a single frequency band. Thus, the dual frequency band is a novel method for the determination of the moisture content of stored rice and the localisation of the spoilage area.
  • 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.