Siti Nurul Aqmariah Mohd Kanafiah
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Preferred name
Siti Nurul Aqmariah Mohd Kanafiah
Official Name
Siti Nurul Aqmariah, Mohd Kanafiah
Alternative Name
Kanafiah, S. N.Aqmariah
Kanafiah, S. N.A.M.
Kanafiah, Siti Nurul Aqmariah Mohd
Aqmariah Kanafiah, S. N.
Kanafiah, S. N. A. M
Main Affiliation
Scopus Author ID
55987982900
Researcher ID
HTR-1815-2023

Research Output

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  • Publication
    An Intelligent Classification System for Trophozoite Stages in Malaria Species
    ( 2022-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mohd Yusoff Mashor
    ;
    Mohamed Z.
    ;
    Way Y.C.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Jusman Y.
    Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnificat i on of t hi n bl ood smear usi ng mi croscope observat i on. However, t he microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of Plasmodium Knowlesi (PK), Plasmodium Falciparum (PF) and Plasmodium Vivax (PV) as early species identification. The process begins with image acquisition, image processing and classification. The image processing involved contrast enhancement using histogram equalisation (HE), segmentation procedure using a combination of hue, saturation and value (HSV) color model, Otsu method and range of each red, green and blue (RGB) color selections, and feature extraction. The features consist of the size of infected red blood cell (RBC), brown pigment in the parasite, and texture using Gray Level Co-occurrence Matrix (GLCM) parts. Finally, the classification method using Multilayer Perceptron (MLP) trained by Bayesian Rules (BR) show the highest accuracy of 98.95%, rather than Levenberg Marquardt (LM) and Conjugate Gradient Backpropagation (CGP) training algorithms.
  • Publication
    Analysis of Thermal Comfort Among Workshop Users: At TVET Technical Institution
    ( 2024-02-01)
    Alias N.M.
    ;
    Umar Kassim
    ;
    Sinar Arzuria Adnan
    ;
    Nur Hidayah Ahmad Zaidi
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Yusoff M.N.
    ;
    Sk Muiz Sk Abdul Razak
    Thermal comfort is a part of indoor environmental quality that should be considered to ensure the occupants' well-being. Unconducive buildings not only bring occupants discomfort but also tend to affect health, disrupt the process of teaching and learning, and reduce work productivity. Thus, this study determines the thermal condition of existing workshop buildings used in Technical and Vocational Education and Training (TVET) implementation. ASHRAE Standard 55 (2017) is referred to in the determination of thermal comfort involving objective measurements and subjective measurements. Observation methods of environmental variables such as air temperature, radiant temperature, air velocity, relative humidity is observed. Evaluations of comfort are based on occupant surveys and environmental measurements. A total of 257 people completed a questionnaire distributed at three technical institutions in Kedah, Malaysia. According to the findings, the average thermal sensation vote is 1.85, which leads to 66.5% of respondents feeling discomfort. Meanwhile, the adaptive model analysis showed that the workshop environmental conditions were out of the comfort zone and did not comply with the ASHRAE 55 standard. Hence, the thermal discomfort factors from the occupants' perspective were identified and widely discussed. As a result, the research findings will benefit parties involved in new building construction or existing building renovations to improve indoor air quality.
  • Publication
    Features Extraction to Differentiate of Spinal Curvature Types using Hue Moment Algorithm
    ( 2020-03-10)
    Salleh M.A.M.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    ;
    Yusof M.I.
    Nowadays, diagnosing the spinal problems is very important to medical field. The objective of this research is to develop feature extraction technique to obtain the features, which automatically differentiate images of normal and abnormal (scoliosis) spinal curvatures. The process to extract features of spinal image start with image acquisition, image processing (i.e. enhancement, filtering, and segmentation). For image processing method, the most important part in this phase is the segmentation using manual threshold method. After the segmentation, hue moment for size and parameter are used to extract features that should be considered based on probabilistic to classify the spine images. The final experimental result shows that the developed features extraction technique can differentiate between normal and scoliosis spine images.
  • Publication
    Automatic Recognition System of Iron Deficiency Anaemia in Human RBC using Machine Learning Techniques
    ( 2023-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Ibrahim W.N.A.B.W.
    ;
    Nordin S.A.
    ;
    Tohit E.R.B.M.
    ;
    Ali H.B.
    ;
    Nordiana Shariffudin
    ;
    Siti Marhainis Othman
    Iron Deficiency Anaemia (IDA) is the most common blood disorder. According to WHO, 30% of women aged 15-49 years, 37% of pregnant women, as well as 40% of children aged 6-59 months are anaemic globally. Anaemia can cause premature birth and affect mental, physical, and cognitive development, which in turn will lead to birth weight problems and stunted birth. The process of detecting IDA is usually captured based on a thin blood smear utilizing microscopic observation. Nevertheless, this process can be time-consuming. Moreover, it is challenging to identify the difference between IDA and normal red blood cells (RBCs) because the size is similar based on the observation of the human eye. It will cause difficulty in giving drug treatment to patients. A computeraided diagnosis (CAD) method was created to automatically distinguish between IDA and normal RBCs. The processes started with image acquisition, image processing, and recognition. Additionally, a Graphical User Interface (GUI) is also used to display images. In conclusion, recognition was done using the Multilayer Perceptron (MLP) method. The findings indicate that the proposed automated system is effective at distinguishing between IDA as well as normal RBCs, having an accuracy of 97.58% with regard to training and 98% regarding validation utilizing Levenberg-Marquardt (LM) trained MLP.
  • Publication
    Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data
    ( 2020-03-10)
    Jusman Y.
    ;
    Indra Z.
    ;
    Salambue R.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Nurkholid M.A.F.
    Lung cancer was the most commonly diagnosed cancer as well as the leading cause of cancer death in males in 2008 globally. The way used to detect lung cancer are through examination chest X-ray, Computed Tomography (CT) scan, and Magnetic Resonance Imaging results. The accurate and efisien analysis of the imaging results are important to ensure the minimal time processing. A computed assisted diagnosis system is the crusial research which can conduct the analysis efficiently and efectively. This paper aimed to compare the classification performances of Multi Layered Perceptron (MLP) and Radial Basis Function (RBF) techniques. The public lung cancer datasets was used as training and testing data in the classfication techniques. Ten fold cross validation was used for dividing data before classifying techniques. The accuracy performances are compared to check a better technique for classification step.
  • Publication
    A novel nucleus detection on pap smear image using mathematical morphology approach
    ( 2021-01-01)
    Nahrawi N.
    ;
    Wan Azani Wan Mustafa
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohamad Nur Khairul Hafizi Rohani
    ;
    Hasliza A Rahim @ Samsuddin
    The fourth most common form of cancer among women is cervical cancer with 569, 847 new cases and 311, 365 reported deaths worldwide in 2018. Cervical cancer is classified as the third leading cause of cancer among women in Malaysia, with approximately 1, 682 new cervical cases and about 944 deaths occurred in 2018. Cervical cancer can be detected early by cervical cancer screening. Papanicolaou test, also known as Pap smear test is conducted to detect cancer or pre-cancer in the cervix. The disadvantage of this conventional method is that the sample of microscopic images will risk blurring effects, noise, shadow, lighting and artefact problems. The diagnostic microscopic observation performed by a microbiologist is normally time-consuming and may produce inaccurate results even by experienced hands. Thus, correct diagnosis information is essential to assist physicians to analyze the condition of the patients. In this study, an automated segmentation system is proposed to be used as it is more accurate and faster compared to the conventional technique. Using the proposed method in this paper, the image was enhanced by applying a median filter and Partial Contrast Stretching. A segmentation method based on mathematical morphology was performed to segment the nucleus in the Pap smear images. Image Quality Assessment (IQA) which measures the accuracy, sensitivity and specificity were used to prove the effectiveness of the proposed method. The results of the numerical simulation indicate that the proposed method shows a higher percentage of accuracy and specificity with 93.66% and 95.54% respectively compared to Otsu, Niblack and Wolf methods. As a conclusion, the percentage of sensitivity is slightly lower, with 89.20% compared to Otsu and Wolf methods. The results presented here may facilitate improvements in the detection performance in comparison to the existing methods.
  • Publication
    Analysis of Features Extraction Performance to Differentiate of Dental Caries Types Using Gray Level Co-occurrence Matrix Algorithm
    ( 2020-08-01)
    Jusman Y.
    ;
    Tamarena R.I.
    ;
    Puspita S.
    ;
    Saleh E.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    This study analyzes the features extraction performance of dental caries image using Gray Level Cooccurrence Matrix (GLCM) algorithm for contrasted two types of caries is based on the theory of GV Black, namely: Dental caries Class 3 and Class 4. The study aims to determine the pixel value and quantization value of the GLCM used for an automated classification system of dental caries types. The analysis is conducted by using variations of pixel distances and quantization value to perform features on the image in values such as contrast, correlation, energy, and homogeneity. Then these values are used as input to the classification stage Knearest neighbor (KNN). Result performed on four data sets containing 60 images of each set is an accuracy value. The highest performance obtained is 80% of accuracy in 100 and 200 of pixel distances and 16 and 32 of quantization value. The pixel distances and quantization values are recommended to be used for an automated classification system of dental caries types based on X-ray images.
  • Publication
    An Improved Performance of Chest Physiotherapy using Vibration
    ( 2023-01-01)
    Loniza E.
    ;
    Setiawan R.A.
    ;
    Jusman Y.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Chairunnisa K.
    Data storage capabilities is a major problem which must be resolved in a chest physiotherapy device using vibration method to give effectiveness to therapist. This study aims to design a vibration-based Chest Physiotherapy tool with data storage capabilities, specifically tailored to address the clearance of secretions or phlegm in bronchitis patients. The research methodology employs frequency testing, input voltage measurement, and timer testing to compare the performance of the designed tool against a reference tool set consisting of a tachometer, Avometer, and stopwatch. The selection of the vibration method as the primary focus is justified by its comprehensive nature, encompassing other relevant methodologies. A notable innovation in this study is the inclusion of a memory card data storage mode, enabling real-time control and assessment of therapist effectiveness during therapy sessions. By leveraging the vibration-based chest physiotherapy method with enhanced data storage features, optimal airflow can be restored, overcoming blockages caused by the presence of secretions in the external respiratory tract.
  • Publication
    Contrast enhancement approaches on medical microscopic images: a review
    ( 2021-01-01)
    Nahrawi N.
    ;
    Wan Azani Wan Mustafa
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mohd Aminudin Jamlos
    ;
    Wan Khairunizam Wan Ahmad
    Nowadays, there are many method for medical identification that exist for example based on microscopic and nonmicroscopic. Microscopic is a method that use microscope to capture an image and identify the disease based on the image captured. The image quality of medical image is very important for patient diagnosis. Image with poor contrast and the quality of the image is not good may lead to the mistaken decision, even in experienced hands. Therefore, contrast enhancement methods was proposed in order to enhance the image quality. Contrast enhancement is a process that improves the contrast of an image to make various features more easily perceived. Contrast enhancement is widely used and plays important roles in image processing application. This paper review the contrast enhancement techniques was used in microscopic images. There are microscopic images for cervical cancer, leukemia, malaria, tuberculosis and anemia.
  • Publication
    Color contrast enhancement on pap smear images using statistical analysis
    ( 2021-01-01)
    Nahrawi N.
    ;
    Wan Azani Wan Mustafa
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mashor M.Y.
    In the conventional cervix cancer diagnosis, the Pap smear sample images are taken by using a microscope,causing the cells to be hazy and afflicted by unwanted noise. The captured microscopic images of Pap smear may suffer from some defects such as blurring or low contrasts. These problems can hide and obscure the important cervical cell morphologies, leading to the risk of false diagnosis. The quality and contrast of the Pap smear images are the primary keys that could affect the diagnosis’ accuracy. The paper's main objective is to propose the best contrast enhancement to eliminate contrast problems in images and cor-rect them in color images to ensure smooth segmentation. In this paper, the med-ian and standard deviation are used for the image's global and local data where the problem region is normalized by using a special proposed formula. The expected resulting image shows only the object (nuclei and cytoplasm), and a background without any noise. The results were compared with CLAHE, HE, and Gray World, and the performance was evaluated based on PSNR, RMSE, and MAE. Proposed method shows higher PSNR and RMSE value while lower value for MAE compared to other methods. This paper's main impact will help doctors in identifying the patient's disease, such as cervical cancer, based on a Pap smear analysis, and increase the accuracy percentages as compared to the conventional method.