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
    Automatic detection of embolic signal for stroke prevention
    ( 2017-01-01)
    Noor Salwani Ibrahim
    ;
    Ng Yan Duan
    ;
    Dzati Athiar Ramli
    ;
    Haryati Jaafar
    Transcranial Doppler (TCD) ultrasound is an essential tool in clinical diagnosis to determine the occurrence of embolism in stroke patients. However, it requires manual attention and the accuracy will deteriorate due to fatigue factor. Instead of depending on human observer as a gold standard to detect the emboli, this study proposes an automated emboli detection system based on three detection methods i.e. time-domain intensity, frequency-domain intensity and time-frequency intensity hybrid. Experimental studies of 240 samples of six data sets were employed. The performance evaluations of each method are measured in term of accuracy percentage and processing speed while human observation is also done as the golden standard for accuracy comparison. The best result is achieved by the time-frequency intensity hybrid method where 90.74 % of the embolic signals and 100 % of the non-embolic signals were successfully identified. The performance of this method is promising as the accuracy achieved by human observation was 87.45 and 100 % for embolic signals and non-embolic signals, respectively.
  • 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
    Deep neural network approach to frog species recognition
    ( 2017-10-10)
    Norsalina Hassan
    ;
    Dzati Athiar Ramli
    ;
    Haryati Jaafar
    Automatic frog species recognition based on acoustic signal has received attention among biologists for environmental studies as it can detect, localize and document the declining population of frog species efficiently compared to the manual survey. In this study, we investigate the possibility of the use of Deep Neural Network (DNN) as a classifier for a frog species recognition system. The Mel-Frequency Cepstral Coefficients (MFCCs) is utilized as features and prior to the feature extraction, we also investigate the capability of automatic segmentation of syllables based on the Sinusoidal Modulation (SM), Energy with Zero Crossing Rate (E+ZCR) and Short-Time Energy with Time Average Zero Crossing Rate (STE+STAZCR). We also evaluate several DNN parameter's setting so as to discover the optimum parameter values for our developed system. 55 different species of frog with 2674 syllables from our in-house database have been tested. Experimental results based on DNN classifier showed that the STE+STAZCR method gives the accuracy of 99.03%, which reveals the viability of DNN as a classifier. In future, further research on DNN parameter optimization will be conducted for system improvement.
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