Haryati Jaafar
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Preferred name
Haryati Jaafar
Official Name
Jaafar, Haryati
Alternative Name
Jaafar, Haryati
Jaafar, H.
Main Affiliation
Scopus Author ID
55357649900
Researcher ID
ILC-1943-2023

Research Output
Now showing 1 - 4 of 4
  • Publication
    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.
      1
  • Publication
    Curvature-based active region segmentation for improved image processing of Aspergillus species
    (Semarak Ilmu Publishing, 2025-04)
    Haryati Jaafar
    ;
    Nur Rodiatul Raudah Mohamed Radzuan
    ;
    Farah Nabilah Zabani
    ;
    Fatin Norazima Mohamad Ariff
    ;
    Fatin Nadia Azman Fauzi
    Aspergillus is one of the most ubiquitous of the airborne saprophytic fungi that can withstand various climatic conditions and could cause multiple type of illness. It can be beneficial to humankind and also can be infectious to humans and animals. Direct microscopic is used by trained microscopist as one of the alternatives in identification process to any specimen that suspected of having fungal infection. Confirmation towards identification is often necessary as the structure of Aspergillus is complex and dissimilar in each cycle. In addition, the structure of some species of Aspergillus are the almost same, which can be incorrectly recognized. In prevention of misidentification, computer-based Aspergillus species identification is proposed. The detection process is the earliest and important process hence, this paper proposed an active region-based segmentation method in order to detect the presence of fungi. This method is literally not depending on the gradient or sharp edges of the object and implementing level set function for curve evolution which able to reduce the computational cost. Originally, this function was developed for tracking fluid interfaces but in this study, this function has been applied to fungi database. Two different methods were tested and compared to observe their ability to segment different 80 of Aspergillus images which included four species. Experiments conducted have been compared with the baseline technique and the proposed method is outperformed in terms of accuracy, specificity with average of 90% and PSNR value of greater than 40dB. Meanwhile the active contour (snake) was slightly underperformed but well performed particularly in terms of sensitivity with greater than 80% for all the species. Moreover, upon scrutinizing the dice coefficients provided in both tables, it becomes apparent that there is a lack of significant variance in the values, except in the instance of Aspergillus fumigatus (active region-based) that which produces a result below 36%.
      2  3
  • Publication
    Implementation of Fuzzy Gamma Adaptive Histogram Equalization for Penicillium and Aspergillus Species
    ( 2022-01-01)
    Farah Nabilah Zabani
    ;
    Haryati Jaafar
    ;
    Harun A.
    This paper proposes a new approach in image enhancement method to enhance the structure of fungi namely, Penicillium and Aspergillus. The new approach combines the methods of adaptive histogram equalization (AHE), gamma correction and fuzzy logic. Previous methods of enhancing the structure of fungi is barely available as the current conventional method of identifying a fungi species relies on detecting the fungi directly from a microscope instead of examining it in the form of an image. It is widely known that microscopic images of fungi are usually low in contrast. Plus, the structure of fungi is complex and changes according to its level of maturity. Thus, a new approach in image enhancement is investigated to improve the appearance of the structure of fungi. The performance of the proposed method is evaluated on the collected database consisting of 194 microscopic coloured image of fungi. The result obtained shows that the proposed method, Fuzzy Gamma Adaptive Histogram Equalization (FGAHE) has a better performance in terms of enhancing the structure of fungi by obtaining a PSNR value of 14.28 dB, 20.37 dB and 21.60 dB from the species of Penicillium, Aspergillus fumigatus and Aspergillus terreus respectively.
      1  18
  • Publication
    An evaluation of fuzzy in image enhancement: design and comparison for Penicillium and Aspergillus species
    (Semarak Ilmu Publishing, 2025-02)
    Haryati Jaafar
    ;
    Farah Nabilah Zabani
    ;
    Nur Rodiatul Raudah Mohamed Radzuan
    ;
    Fatin Norazima Mohamad Ariff
    ;
    Azirah Baharum
    The main focus in this study is to enhance and classify the image of a type of filamentous fungi named Penicillium and Aspergillus. For image enhancement, fuzzy-partition gamma adaptive histogram equalization (FpGAHE) is proposed to improve the quality of an image, in particular the low quality of a microscopic image. Two stages have been considered in this technique. In the first stage, a fuzzy partition is developed to handle the inconsistency of the grey level values of the images by introducing a fuzzy set. In the second stage, surrounding neighbourhood is employed to avoid the imbalance data and reduce the drastically changes of brightness values of the image. The performances are evaluated into two parts i.e., image processing and image classification by using the collected microscopic images of fungi species. To evaluate the effectiveness of the proposed technique, the existing techniques, HE, AHE, CLAHE, GC and AGC is compared to the FpGAHE. In image processing, the result attained shows that the proposed technique has a better performance by obtaining the highest value for the PSNR, SSIM and FSIM evaluation for the species of A. terreus in clean condition. Meanwhile, in image classification, five different nearest neighbour classifiers have been tested. The results show the proposed FpGAHE with Improved Fuzzy-Based k Nearest Centroid Neighbour (IFkNCN) classifier perform the best result compare to other nearest neighbour classifier by obtaining the value of 92.59 and 93.95 for the salt and pepper and Gaussian noise corrupted images respectively.