Norasmadi Abdul Rahim
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Preferred name
Norasmadi Abdul Rahim
Official Name
Norasmadi, Abdul Rahim
Alternative Name
Rahim, N. A.
Rahim, N. A.S.A.
Rahim, Norasmadi Abd
Rahim, N. Abdul
Abdul Rahim, N.
Rahim, Norasmadi Abdul
Rahim, Norasmadi Bin Abdul
Main Affiliation
Scopus Author ID
36901996000

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  • Publication
    Mitigating Overfitting in Extreme Learning Machine Classifier Through Dropout Regularization
    (Universiti Malaysia Perlis, 2024-02-14)
    Fateh Alrahman Kamal Qasem Alnagashi
    ;
    Norasmadi Abdul Rahim
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Mohamad Hanif Abd Hamid
    Achieving optimal machine learning model performance is often hindered by the limited availability of diverse datasets, a challenge exacerbated by small sample sizes in real-world scenarios. In this study, we address this critical issue in classification tasks by integrating the Dropout technique into the Extreme Learning Machine (ELM) classifier. Our research underscores the effectiveness of Dropout-ELM in mitigating overfitting, especially when data is scarce, leading to enhanced generalization capabilities. Through extensive experiments on synthetic and real-world datasets, our findings consistently demonstrate that Dropout-ELM outperforms traditional ELM, yielding significant accuracy improvements ranging from 0.19% to 16.20%. By strategically implementing dropout during training, we promote the development of robust models that reduce reliance on specific features or neurons, resulting in increased adaptability and resilience across diverse datasets. Ultimately, Dropout-ELM emerges as a potent tool to counter overfitting and bolster the performance of ELM-based classifiers, particularly in scenarios with limited data. Its established efficacy positions it as a valuable asset for enhancing the reliability and generalization of machine learning models, providing a robust solution to the challenges posed by constrained training data.
      15  1
  • Publication
    RSSI-based for device-free localization using deep learning technique
    (MDPI, 2020-06-01)
    Abdul Syafiq Abdull Sukor
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Norasmadi Abdul Rahim
    ;
    Sukhairi Sudin
    ;
    Hiromitsu Nishizaki
    Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.
      3  30