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

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  • 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
    Intelligent Classification Procedure for Plasmodium Knowlesi Malaria Species
    ( 2022-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mohd Yusoff Mashor
    ;
    Mohamed Z.
    ;
    Jusman Y.
    ;
    Hasimah Ali
    ;
    Nordiana Shariffudin
    ;
    Siti Marhainis Othman
    Plasmodium knowlesi (PK) is the fifth most prevalent malarial parasite species that causes serious health problems. Generally, PK present in a thin blood smear is observed using a microscope to differentiate between trophozoites (PKT), schizonts (PKS), gametocytes (PKG), and white blood cells (WBCs). This process is time-consuming and strenuous for the human eye. This study developed an intelligent classification procedure for PK using image processing and classification methods. The processes involved starting from image acquisition, and contrast enhancement based on Combination Local and Global Statistical Data (CLGSD), and local contrast stretching (LCS). Subsequently, a segmentation procedure was developed to segment the malaria images into two regions, namely malarial parasites and background regions. The proposed 16 feature sets were extracted, which consisted of the size of the object, size ratio of the object per infected RBC, and seven moments for each object shape based on size and perimeter. Finally, to validate the procedure performance, the proposed procedure was tested using 800 malarial parasites and WBC images. The results showed that the proposed procedure can classify three stages of PK, namely PKT, PKS, and PKG, as well as WBCs with an accuracy of 99.56% for training and 98.84% for validation, using a multi-layer perceptron (MLP) trained using the Levernberg-Marquardt (LM) algorithm.
      1
  • Publication
    IoT Based Smart Betta Fish Monitoring system with fish fatality prediction.
    ( 2023-01-01)
    Julida N.L.
    ;
    Siti Marhainis Othman
    ;
    Mohd Nasir Ayob
    ;
    Norasmadi Abdul Rahim
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Mohd Sani Mohamad Hashim
    ;
    Masturah Tunnur Mohamad Talib
    ;
    Nurul Syahirah Khalid
    This study enlightens the importance of rearing water quality to Betta fish health. A water quality monitoring system was developed based on water quality parameters namely water pH, temperature (°C) and TDS level (ppm). Fuzzy Logic Algorithm was applied to predict the possibility of the fish to get infected by the disease using combination of the water quality parameters value. Graphical User Interface (GUI) was developed to test the efficiency of the fish disease prediction system using fuzzy logic algorithm before the fuzzy rule been embedded to the IOT system. Arduino Uno Wi-Fi R2.0 and Blynk Apps used for enabling the system to update the aquarium water quality to owner in real-time. Hydroponic technology implemented in this project for recirculate rearing water inside the fish tank. Theoretically, the aquaponic system will help regulate the water tank parameters in optimum range and Betta Splendens should be free from all diseases.
      1
  • Publication
    Model Reference Adaptive Controller Design for Electrohydraulic Actuator System with varying disturbance
    (Springer Science and Business Media Deutschland GmbH, 2022-01-01)
    Siti Marhainis Othman
    ;
    Wong Kar Yi
    ;
    Rustam R.A.
    ;
    Rahmat M.F.
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Hashim M.S.M.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    The objective of this study is to design and analyze the performance of the designed controllers on the presence of varying mismatched disturbances. This research provides a clear the selected approaches for the controllers’ design implementation of the electro-hydraulic actuator (EHA) system, an adaptive controller, the Model Reference Adaptive controller (MRAC). Subsequently, this research is considered another controller, the Proportional Integral Derivative (PID) for comparing the best control performance for the electro-hydraulic actuator system with varying mismatched disturbance. PID controller has been tuned by using two different tuning techniques. The Trial-and-error and Ziegler-Nichols tuning method have been proposed for attaining the desired control system response in this research. Simulation results show that the MRAC provides the best response performance among the designed methods for every specific disturbance setting at 0 N, 5000 N and 10,000 N. The MRAC method dominantly achieves the faster response in rise time for every disturbance respectively.