Ahmad Kadri Junoh
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Preferred name
Ahmad Kadri Junoh
Official Name
Ahmad Kadri, Junoh
Alternative Name
Junoh, Ahmad Kadri
Junoh, A. K.
Ahmad Kadri, Junoh
Junoh, A. K.
Kadri, J. Ahmad
Main Affiliation
Scopus Author ID
38561331300
Researcher ID
FZU-4175-2022

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  • Publication
    Contrast virus microscopy images recognition via k-NN classifiers
    ( 2017-07-02)
    Afiq Ahmad Shakri
    ;
    Syahrul Affandi Saidi
    ;
    Muhammad Naufal Mansor
    ;
    Haryati Jaafar
    ;
    Ahmad Kadri Junoh
    ;
    Wan Azani Wan Mustafa
    One of the topics that are commonly in focus of object detection and image recognition is virus detection. It is well known that to learn and detecting virus proven to be a challenging and quite complex task for computer systems under different noise level. This research work investigates the performances of preprocessing stages with Contrast feature extraction with K-Nearest Neighbor (KNN) classifier under various levels of noise. The real time experiment conducted proved that the proposed method are efficient, robust, and excellent of which it has produced a results accuracy of up to 88% for biological viruses images classification.
      46  2
  • Publication
    Entropy virus microscopy images recognition via neural network classifiers
    ( 2017-07-02)
    Afiq Ahmad Shakri
    ;
    Syahrul Affandi Saidi
    ;
    Haryati Jaafar
    ;
    Muhammad Naufal Mansor
    ;
    Wan Azani Wan Mustafa
    ;
    Ahmad Kadri Junoh
    One of the topics that are commonly in focus of object detection and image recognition is virus detection. It is well known that to learn and detecting virus proven to be a challenging and quite complex task for computer systems under different noise level. This research work investigates the performances of preprocessing stages with Entropy feature extraction with Feed Forward Neural Network (FFNN) classifier under various levels of noise. The real time experiment conducted proved that the method proposed are efficient, robust, and excellent of which it has produced a results accuracy of up to 88% for biological viruses images classification.
      11  38