Zulkapli Abdullah
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Preferred name
Zulkapli Abdullah
Official Name
Zulkapli, Abdullah
Alternative Name
Abdullah, Zulkapli
Abdullah, Z.
Main Affiliation
Scopus Author ID
57208823927
Researcher ID
EKX-0410-2022

Research Output
Now showing 1 - 2 of 2
  • Publication
    Dual-tree complex wavelet packet transform for voice pathology analysis
    ( 2020-07-01)
    Farah Nazlia Che Kassim
    ;
    Muthusamy H.
    ;
    Vikneswaran Vijean
    ;
    Zulkapli Abdullah
    ;
    Rokiah Abdullah
    Voice pathology analysis has been one of the useful tools in the diagnosis of the pathological voice, as the method is non-invasive, inexpensive, and can reduce the time required for the analysis. This paper investigates feature extraction based on the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) using energy and entropy measures tested with two classifiers, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD) were used. Five datasets of voice samples were used from these databases, including normal and abnormal samples, Cysts, Vocal Nodules, Polyp, and Paralysis vocal fold. To the best of the authors’ knowledge, very few studies were done on multiclass classifications using specific pathology database. File-based and frame-based investigation for two-class and multiclass were considered. In the two-class analysis using the DT-CWPT with entropies, the classification accuracy of 100% and 99.94% was achieved for MEEI and SVD database respectively. Meanwhile, the classification accuracy for multiclass analysis comprised of 99.48% for the MEEI database and 99.65% for SVD database. The experimental results using the proposed features provided promising accuracy to detect the presence of diseases in vocal fold.
      21  4
  • Publication
    DT-CWPT based Tsallis Entropy for Vocal Fold Pathology Detection
    ( 2020-10-26)
    Farah Nazlia Che Kassim
    ;
    Vikneswaran Vijean
    ;
    Muthusamy H.
    ;
    Zulkapli Abdullah
    ;
    Rokiah Abdullah
    ;
    Palaniappan R.
    The study of voice pathology has become one of the valuable methods of vocal fold pathology detection, as the procedure is non-invasive, affordable and can minimise the time needed for the diagnosis. This paper investigates the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) based Tsallis entropy for vocal fold pathology detection. The proposed method is tested with healthy and pathological voice samples from Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD). A pairwise classification using k-Nearest Neighbors (k-NN) classifier gave 91.59% and 85.09% accuracy for MEEI and SVD database respectively. Higher classification accuracy of 93.32% for MEEI and 85.16% for SVD database achieved using Support Vector Machine (SVM) classifier.
      4  40