Aimi Salihah Abdul Nasir
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Aimi Salihah Abdul Nasir
Official Name
Aimi Salihah, Abdul Nasir
Alternative Name
Abdul Nasir, Aimi Salihah
Abdul-Nasir, Aimi Salihah
Nasir, Aimi Salihah Abdul
Main Affiliation
Scopus Author ID
57219027157
Researcher ID
AAG-1747-2021

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  • Publication
    Analysis of the performance of SLIC super-pixel toward pre-segmentation of soil-transmitted helminth
    (AIP Publishing, 2023)
    Loke Siew Wen
    ;
    Lim Chee Chin
    ;
    Aimi Salihah Abdul Nasir
    ;
    Norhanis Ayunie Ahmad Khairudin
    ;
    Chong Yen Fook
    ;
    Mohd Yusoff Mashor
    ;
    Zeehaida Mohamed
    Soil-Transmitted Helminth (STH) infections are one of the most severe health issues in the world including Malaysia and frequently happened in an unsanitary environment within the children group. The helminth infections are diagnosed by inspecting the faeces samples manually through light microscope. However, the manual inspection method to diagnose the helminth egg is a time-consuming and challenging process especially when are huge number of samples. To increase the efficiency and accuracy of the diagnosis, an analysis of super-pixel segmentation with different parameter adjustments on four different species was carried out. This work described a Simple Linear Iterative Clustering (SLIC) super-pixel algorithm that uses different parameter settings to explore more parasites image features for a better segmentation process in the future and to analyse the effect of different SLIC parameter settings towards the pre-segmentation process. There is total 80 images collected from the four helminth egg species which are Ascaris Lumbricoides Ova (ALO), Enterobius Vermicularis Ova (EVO), Hookworm Ova (HWO) and Trichuris Trichiura Ova (TTO). The proposed approach is divided into three steps. First, the images with various lighting conditions are enhanced by the partial contrast stretching (PCS) technique. The simple linear iterative clustering (SLIC) super-pixel algorithm was implemented to the enhanced images as a pre-segmentation algorithm to form super-pixel images. Lastly, image quality assessment will be performed on the SLIC images. The SLIC parameter compactness of super-pixel, m of 5 and number of super-pixels, k of 1000 was selected because they generate the greatest PSNR value, indicating that this combination of parameters could produce high-quality images. In future, a more in-depth analysis of the parameter k and m, which impacts the form of each super-pixel and the pre-segmentation process, might improve the recommended approach.
  • Publication
    Superpixels-based automatic density peaks and fuzzy clustering approach in COVID-19 lung segmentation
    (IEEE, 2023-12)
    Ooi Wei Herng
    ;
    Aimi Salihah Abdul Nasir
    ;
    Fatin Nabilah Shaari
    ;
    Abdul Syafiq Abdull Sukor
    Clustering algorithms that rely on minimizing an objective function suffer from the drawback of requiring manual setting of the number of clusters. This limitation becomes particularly evident when applied to image segmentation, where the large number of pixels can lead to memory overflow issues. To overcome this challenge, a reference of Automatic Fuzzy Clustering Framework (AFCF) for image segmentation method has been used as the comparison to the Density Peaks Clustering (DPC) algorithm. AFCF used superpixel algorithm to reduce the spatial information of data during computation, DPC algorithm to generate decision graph, and prior entropy-based fuzzy clustering (PEFC) algorithm to achieve fully automatic segmentation method in determining the number of cluster and the clustering result. In this study, 50 open-source healthy, COVID-19 and pneumonia infected radiographs dataset are acquired from the Kaggle and Github. The radiographs dataset that segmented by DPC is down sampling to 100*100 pixels due to overloading computation. At the end of the image segmentation, a segmentation performance evaluation is conducted based on sensitivity, specificity, accuracy, precision, recall, F-score and time consumed. The result shows that AFCF algorithm has the better overall performance with higher accuracy of 92.48% and F-score 0.9455. Meanwhile, the most highlighted evaluation index is drop to the time consume comparison, AFCF has around 2.7 times faster processing speed compare to DPC algorithm.
  • Publication
    Design and performance analysis of fuzzy logic controller for solar photovoltaic system
    (Iran University of Science and Technology, 2025-06)
    Kumuthawathe Ananda-Rao
    ;
    Steven Taniselass
    ;
    Afifah Shuhada Rosmi
    ;
    Aimi Salihah Abdul Nasir
    ;
    Nor Hanisah Baharudin
    ;
    Indra Nisja
    This study presents a Fuzzy Logic Controller (FLC)-based Maximum Power Point Tracking (MPPT) system for solar Photovoltaic (PV) setups, integrating PV panels, a boost converter, and battery storage. While FLC is known for its robustness in PV systems, challenges in battery charging and discharging efficiency can affect performance. The research addresses these challenges by optimizing battery charging, preventing overcharging, and enhancing overall system efficiency. The FLC MPPT system is designed to regulate the battery's State of Charge (SOC) while evaluating system performance under varying solar irradiance and temperature conditions. The system is modeled and simulated using MATLAB/Simulink, incorporating the PV system, MPPT algorithm, and models for the PV module and boost converter. System efficiency is assessed under different scenarios, with results showing 97.92% efficiency under Standard Test Conditions (STC) at 1000 W/m² and 25°C. Additionally, mean efficiencies of 97.13% and 96.13% are observed under varying irradiance and temperature, demonstrating the effectiveness of the FLC MPPT in regulating output. The system also extends battery life by optimizing power transfer between the PV module, boost converter, and battery, ensuring regulated SOC.
  • Publication
    Integrating deep transfer learning and image enhancement for enhancing defective photovoltaic cells classification in electroluminescence images
    (Iran University of Science and Technology, 2025-06)
    Aimi Salihah Abdul Nasir
    ;
    Mohammad Faridun Naim Tajuddin
    ;
    Kumuthawathe Ananda-Rao
    ;
    Hanim Suraya Mohd Mokhtar
    ;
    Muhammad Hafeez Abdul Nasir
    The rapid growth of photovoltaic (PV) systems has highlighted the need for efficient and reliable defect detection to maintain system performance. Electroluminescence (EL) imaging has emerged as a promising technique for identifying defects in PV cells; however, challenges remain in accurately classifying defects due to the variability in image quality and the complex nature of the defects. Existing studies often focus on single image enhancement techniques or fail to comprehensively compare the performance of various image enhancement methods across different deep learning (DL) models. This research addresses these gaps by proposing an in-depth analysis of the impact of multiple image enhancement techniques on defect detection performance, using various deep learning models of low, medium, and high complexity. The results demonstrate that mid-complexity models, especially DarkNet-53, achieve the highest performance with an accuracy of 94.55% after MSR2 enhancement. DarkNet-53 consistently outperformed both lower-complexity models and higher-complexity models in terms of accuracy, precision, and F1-score. The findings highlight that medium-depth models, enhanced with MSR2, offer the most reliable results for photovoltaic defect detection, demonstrating a significant improvement over other models in terms of accuracy and efficiency. This research provides valuable insights for optimizing defect detection systems in photovoltaic applications, emphasizing the importance of both model complexity and image enhancement techniques for robust performance.
  • Publication
    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.
      1  27
  • Publication
    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.
      1
  • Publication
    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.
      1  27
  • Publication
    Fast k-means clustering algorithm for malaria detection in thick blood smear
    ( 2020-11-09)
    Aris T.A.
    ;
    Aimi Salihah Abdul Nasir
    ;
    Lim Chee Chin
    ;
    Haryati Jaafar
    ;
    Mohamed Z.
    Lots of people all over the world is threaten by a popular blood infection illness that is called as malaria. According to this fact, immediate diagnosis tests are essential to avoid the malaria parasites from expanding in every part of the body. Malaria detection is based on parasitic count process on thick blood smear samples. Anyhow, this mechanism consist the chances of misinterpretation of parasites on behalf to human flaws. Thus, this research objective is to investigate the segmentation performance for improving malaria detection in thick blood smear images through fast k-means clustering algorithm on various color models. In this research, fast kmeans clustering is used because of its advantage which is no need to retrain cluster center that causes time taken to train the image cluster centers is reduce. Meanwhile, different color models have been utilized in order to identify the most relevant color model that obviously highlight the parasites. Five varied color models namely RGB, XYZ, HSV, YUV and CMY are selected and 15 color components namely R, G, B, X, Y, Z, H, S, V, Y, U, V, C, M and Y component have been derived with the aim to discover which color component is the topnotch for malaria parasites detection. In general, around 100 thick blood smear images have been tested in this study and the outcomes reveal that the best segmentation performance is segmentation through R component of RGB with 99.81% accuracy.
      5  30
  • Publication
    Character segmentation for automatic vehicle license plate recognition based on fast k-means clustering
    ( 2020-11-09)
    Ariff F.N.M.
    ;
    Aimi Salihah Abdul Nasir
    ;
    Haryati Jaafar
    ;
    Zulkifli A.N.
    Automatic vehicle license plate recognition (AVLPR) system is one of application for transportation area under intelligent transport system. This system helps in monitor and identify the vehicle by reading the vehicles license plate numbers and recognize the plate characters automatically. However, various factors such as diversity of plate character viewpoint, shape, format and unstable light conditions at the time of image acquisition were obtained, have challenged the system to segment and recognize the characters. Therefore, this paper, presents an effective procedure approached based on fast k-mean (FKM) clustering. FKM approached have an ability to shortening the time of the image cluster centers process consumed. In addition, the FKM algorithm also able to overcomes the cluster center re-processing problem when constantly added the image in huge quantities. The proposed procedure begins with enhancing the input image by using modified white patch and converted into grayscale image. A total of 100 of images has been tested for the segmentation process with clustering techniques approach used. Template matching is used to standardize the recognition results obtained. The highest achieved was 88.57% of average accuracy for FKM clustering technique compared to k-means clustering where it was only able to achieve an average accuracy of 85.78% and 86.14% for fuzzy c-means. Thus, this show that the most efficient, quicker and more useful algorithm goes to FKM rather than the algorithm for fuzzy c-means (FCM) and k-means (KM). Therefore, it is possible toward consider the proposed FKM clustering as an image segmentation method for segmenting license plate images.
      6  17
  • Publication
    Initial Study of Radio Tomographic Imaging for Human localization by using Simulation Model
    ( 2023-01-01)
    Abdullah M.S.M.
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Nurul Syahirah Khalid
    ;
    Aimi Salihah Abdul Nasir
    This paper explains the details of modelling the simulation works designin setup for the RTI system. Th simulation modelling using software is focused on the interaction of electromagnetic behaviour in a dielectric medium of human inside a monitoring area. The modelling works have involved the criteria of the human, frequency and number of sensor nodes, dielectric properties of the human and last but not least, the configurations of the Radio tomography imaging (RTI) system. The model is then developed in the software to observe and investigate the result.
      1