Siti Nurul Aqmariah Mohd Kanafiah
Thumbnail Image
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

Filters

27 1
27
Reset filters

Settings
Now showing 1 - 10 of 27
  • Publication
    Recognition of different utility pipes size of ground penetrating radar images at different penetration depth
    ( 2024-02-08)
    Nasri M.I.S.
    ;
    Hasimah Ali
    ;
    Zaidi A.F.A.
    ;
    Shukor S.A.A.
    ;
    Ahmad M.R.
    ;
    Amran T.S.T.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Othman S.M.
    ;
    Elshaikh M.
    Ground Penetrating Radar (GPR) is a geophysical locating method that uses radio waves to capture images below the surface of the ground in a minimally invasive way. It also requires two main essential equipment which is a transmitter and a receiving antenna. To address the problem, this project proposed the hyperbolic recognition of different utility pipes of GPR images at different level of penetration depth. In this framework, the raw data of GPR images were firstly to be pre-processed. The grayscale images were cropped, resized, and enhanced to increase the contrast of the features of the image. Then, the pre-processed GPR images were extracted using the Histogram of Oriented Gradient (HOG) method with three different windows. The extracted HOG features were then used as input to the k-Nearest Neighbor classifier. A series of experiments has been conducted using 10-fold cross-validation technique for training and testing the GPR data. Based on the result obtained, it shows that at depth 20cm the average accuracy is about 99.87%, whereas at depth 40cm the average accuracy achieved 100%. Thus, the result shows that the extracted HOG features exhibit the significant information of hyperbolic signature of different pipe size with different depth of buried object. Therefore the results seem promising in recognizing the hyperbolic of utilities.
  • 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
    Rhinitis phototherapy prototype with timer based on light energy
    ( 2024-05-01)
    Erika Loniza
    ;
    Mita Junita
    ;
    Yessi Jusman
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Kurnia Chairunnisa
    The set of timers in using phototherapy is major problem which has to be resolved to get a good performance of rhinitis phototherapy. This research aims to develop a prototype of phototherapy for allergic rhinitis, incorporating a timer based on light energy. The prototype utilizes a laser diode as a visible light source, specifically with a wavelength of 650 nm. The recommended safe and effective dose of light energy ranges from 1 to 10 Joules, which has been converted into minutes. Measurement tests indicate an average wavelength of 652.40 nm for the right laser, with a measurement uncertainty of ±0.11, and 653.23 nm for the left laser, with a measurement uncertainty of ±0.05. The laser diode source has an average voltage of 1.91 volts and an average current of 1.89 milliamperes, with a measurement uncertainty of ±0.00 and ±0.01, respectively. Additionally, the average discrepancy in the timer is 0.082 minutes for the 10-minute setting and 0.082 minutes for the 20-minute setting. These results confirm the effectiveness and suitability of the developed tool for practical use. The proposed method was useful for rhinitis therapy by using light energy.
  • Publication
    Classification System of Malaria Disease with Hu Moment Invariant and Support Vector Machines
    ( 2022-01-01)
    Jusman Y.
    ;
    Pikriansah
    ;
    Ardiyanto Y.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mohamed Z.
    ;
    Hassan R.
    Malaria is an infectious disease caused by a plasmodium parasite transmitted by the female Anopheles mosquito. According to the World Health Organization (WHO) in 2020 there are an estimated 241 million cases of malaria worldwide with an estimated global death stood at 627. 000. The standard method of malaria diagnosis is by conducting microscopic examination or laboratory test and Rapid Diagnostic Test (RDT). Laboratory tests have a high risk of human error whereas RDT has weaknesses in temperature sensitivity, genetic variation, and antigen resistance in the bloodstream. This research offers a classification system of malaria disease by applying the Hu moment invariant and Support vector Machine (SVM) method with 3 types of malaria parasitic objects, namely falciparum, Malaria and vivax. The classification system uses 3 SVM models, namely linear SVM, polynomial SVM and Gaussian SVM with the Falciparum class as a positive data and malaria and vivax as negative data. The best classification outcome is on the Gaussian SVM model with 96.67% sensitivity and 90% specificity. The mean accuracy of the Gaussian SVM model with a 5-fold cross Validation 90 image sample which is divided into 3 classes is 86.66%.
  • 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
    IoT Based Smart Betta Fish Monitoring system with fish fatality prediction.
    ( 2023-01-01)
    Julida N.L.
    ;
    Othman S.M.
    ;
    Mohd Nasir Ayob
    ;
    Rahim N.A.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Hashim M.S.M.
    ;
    Talib M.T.M.
    ;
    Khalid N.S.
    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.