Haryati Jaafar
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Haryati Jaafar
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Jaafar, Haryati
Alternative Name
Jaafar, Haryati
Jaafar, H.
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Scopus Author ID
55357649900
Researcher ID
ILC-1943-2023

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  • Publication
    Frog sound identification using extended k-nearest neighbor classifier
    ( 2017-09-21)
    Nordiana Mukahar
    ;
    Bakhtiar Affendi Rosdi
    ;
    Dzati Athiar Ramli
    ;
    Haryati Jaafar
    Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.
  • Publication
    A methodology of nearest neighbor: Design and comparison of biometric image database
    ( 2017-01-06)
    Haryati Jaafar
    ;
    Nordiana Mukahar
    ;
    Dzati Athiar Ramli
    The nearest neighbor (NN) is a non-parametric classifier and has been widely used for pattern classification. Nevertheless, there are some problems encountered that leads to the poor performance of the NN i.e. the samples distribution, weighting issues and computational time for large databases. Hence, various classifiers i.e. k Nearest Neighbor (kNN), k Nearest Centroid Neighborhood (kNCN), Fuzzy k Nearest Neighbor (FkNN), Fuzzy-Based k Nearest Centroid Neighbor (FkNCN) and Improved Fuzzy-Based k Nearest Centroid Neighbor (IFkNCN) were proposed to improve the performance of the NN. This paper presents a review of aforementioned classifiers including the taxonomy, toward the implementation of classifiers in biometric image database. Two databases i.e. finger print and finger vein have been employed and the performance of classifiers were compared in term of processing time and classification accuracy. The results show that the IFkNCN classifier owns the best accuracies to the kNN, kNCN FkNN and FkNCN with 97.66% and 96.74% for fingerprint and finger vein databases, respectively.
  • Publication
    User specific weights based on score distance and EER for weighted sum rule fusion
    ( 2017-01-01)
    Nordiana Mukahar
    ;
    Haryati Jaafar
    ;
    Bakhtiar Affendi Rosdi
    In weighted sum rule fusion, proper weights assignment for different biometric sources is very important. The effective weights assignment procedure based on individual score performance is the vital key in adaptive weighted sum fusion. In this paper, an adaptive user specific weighted sum based on score distance is devised to determine optimal weights. It integrates the performance of the biometric source represented by its Equal Error Rate (EER) and score distance in weights estimation procedure. Although the designated weighted sum fusion requires less setting and less exhaustive search of fusion parameters, it can obtain better performance than previous approaches. The proposed technique outperforms the existing user specific weighted fusion schemes in all experiments by consistently achieves a minimum EER value.
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