Haryati Jaafar
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Haryati Jaafar
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Jaafar, Haryati
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Jaafar, Haryati
Jaafar, H.
Main Affiliation
Scopus Author ID
55357649900
Researcher ID
ILC-1943-2023

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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.

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Improvising non-uniform illumination and low contrast images of soil transmitted helminths image using contrast enhancement techniques

2021-01-01 , Norhanis Ayunie Ahmad Khairudin , Aimi Salihah Abdul Nasir , Lim Chee Chin , Haryati Jaafar , Mohamed Z.

Image enhancement plays an important role in image processing and computer vision. It is used to enhance the visual appearance in an image and also to convert the image suited to the requirement needed for image processing. In this paper, image enhancement is used to produce a better image by enhancing the image quality and highlighting the morphological features of the helminth eggs. Result obtained from enhancement is prepared for segmentation and classification process. The helminth eggs used in this paper are Ascaris Lumbricoides Ova (ALO) and Trichuris Trichiura Ova (TTO). In this study, several enhancement techniques have been performed on 100 images of ALO and TTO which have been captured under three different illuminations: normal, under-exposed and over-exposed images. The techniques used are global contrast stretching, limit contrast, linear contrast stretching, modified global contrast stretching, modified linear contrast stretching, partial contrast and reduce haze. Based on results obtained from these techniques, modified linear contrast stretching and modified global contrast stretching are able to equalize the lighting in the non-uniform illumination images of helminth eggs. Both techniques are suitable to be used on non-uniform illumination images and also able to improve the contrast in the image without affecting or removing the key features in ALO and TTO images as compared to the other techniques. Hence, the resultant images would become useful for parasitologist in analyzing helminth eggs.

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Effect of TiOâ‚‚/eggshell composite using sol gel method photoanode for dye-sensitized solar cell applications (DSSC) and comparison using k-nearest neighbors method

2025-04 , Hidayani Jaafar , Haryati Jaafar , Zainal Arifin Ahmad , Muhammad Asyraf Mat Asri

This study investigated the impact of TiOâ‚‚/eggshell (TE) composite with different ratios via sol gel method and used for the development of photoanodes in DSSC. The impact of eggshell incorporation into TiOâ‚‚ on its structural, optical properties, electrochemical properties and photovoltaic performance were investigated. The absorption spectrum revealed a reduction in the energy band gap as eggshell concentration increased, leading to an enhancement in the DSSC properties. Addition of eggshell enhances the electrochemical properties of the photoanodes. The EIS results confirm that eggshell incorporation can lower the charge transfer resistance and enhanced the efficiency to 2.95 % using natural dye sensitizer for TE 3:10. In this research also, integration with machine learning was conducted using k-Nearest Neighbors (kNN) to predict the highest efficiency based on various samples at EIS analysis. The k-Nearest Neighbors (kNN) algorithm was employed to identify the sample with the highest efficiency, showing that DSSCs with TE 3:10 exhibited the highest efficiency, with a prediction accuracy of 90 %. To validate the kNN results, manual measurements were performed, and the findings presented in Nyquist plots confirmed that kNN predictions are equally reliable as manual measurements for efficiency estimation.

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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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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.

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An Application of Principal Component Analysis in Aspergillus Species Identification

2022-01-01 , Nur Rodiatul Raudah Mohamed Radzuan , Haryati Jaafar , Farah Nabilah Zabani

Aspergillus sp. is one of the filamentous fungi that has a number of benefits in the food industry. Despite its important roles in industry level, they have several shortcomings especially to immunocompromised individuals that appear to be highly susceptible to disease or infection. Normally, the identification of species was manually screened by the trained microscopists but, the machine learning application becomes as an alternative to identify the species of Aspergillus. However, the development of machine learning is not straightforward and time consuming if the data is not well presented. In order to fasten the identification process of Aspergillus while retaining its characteristics, principal component analysis (PCA) and principal component analysis and Histogram of Oriented Gradient (PCAHOG) were employed to reduce the dimensionality of the dataset. Different values of eigen in PCA were executed and the classification by support vector machine (SVM) with two different kernels such as polynomial and radial basis function (RBF) was done afterwards. Based on the performance evaluation, PCAHOG-SVM (Polynomial) with eigenvalue of 48 outperformed the others with accuracy of 99.43% for training number of 18. Moreover, three Aspergillus sp. have been recorded 100% of accuracy with the same number of trainings.

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An Identification of Aspergillus Species: A Comparison on Supervised Classification Methods

2022-01-01 , Nur Rodiatul Raudah Mohamed Radzuan , Haryati Jaafar , Aimi Salihah Abdul Nasir

Aspergillus is one of the well-known existed saprophytic fungi that can withstand with various environments. Other can be beneficial in food industry, it also can be infectious to human and animals and normally, it attacks those with low immunity level. In order to keep the treatment in track with more accurate analysis, identification of Aspergillus plays an important role. Identification of Aspergillus is solely based on its characteristic and currently, there are two methods used which are microscopic and macroscopic examinations to observe its features. It handled by experienced microscopist and a few confirmations had to be done before presenting out the final result. Therefore, to prevent misidentification, an automated based identification is proposed. In this paper, different supervised classifiers are tested and compared to observe their ability to detect different 162 of Aspergillus images. The features have been extracted by using Principal component analysis (PCA) and several classifiers such as k- nearest neighbour (kNN), Sparse Representation Classifier (SRC), Support Vector Machine (SVM), Improved Fuzzy-Based k Nearest Centroid Neighbor (IFkNCN) and Kernal Sparse Representation Classifier (KSRC) are employed. Based on its accuracy, Aspergillus flavus recorded 80% of accuracy for all the classifiers.

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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.

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Comparability of edge detection techniques for automatic vehicle license plate detection and recognition

2021-01-01 , Fatin Norazima Mohamad Ariff , Aimi Salihah Abdul Nasir , Haryati Jaafar , Zulkifli A.N.

License plate recognition system is one of the famous topics in image processing to identify the vehicle registration number. This system has been given a lot of beneficial toward transportation system, especially for security system. However, to get the perfect segmentation on alphabet shape for recognition purpose is quite challenging due to the non-uniform condition of image acquisition. Hence this paper proposes a methodology for segmentation of license plate number by using edge-based segmentation. In this study, image segmentation based on edge detection has been chosen due to the sharpness and detail in detecting the shape of an object. Since there are various types of edge detection techniques have been proposed by the previous researchers, several edge detection techniques from the most commonly used techniques have been chosen to be compared and analyze the results of various edge detection for license plate recognition. In this paper, several types of edge detection techniques such as Approxcanny, Canny, Chan-Vese, Kirsch, Prewitt, Robert, Sobel, Quadtree and Zero Crossing edge detector have been compared through greyscale images. Grayscale image has been enhancing before by modified white patch. Then, the holes area of the segmented license plate image are filled to obtain the characters, followed by step for removing the unwanted objects from the segmented license plate images. Later, the characters of the license plate are recognized based on template matching approach. This recognition analysis consists of two stages. First stage is all edge detector techniques have been used same standard values in removing the noise. Five edge detectors with best performance have been selected for next stage. In the second stage, the unwanted objects have been removed with appropriate values which are suitable for each of the edge detection techniques. The final result shows that Chan-Vese conquers the analysis with highest accuracy of edge detection obtained in license plate recognition.

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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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