Wan Khairunizam Wan Ahmad
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Preferred name
Wan Khairunizam Wan Ahmad
Official Name
Wan Khairunizam, Wan Ahmad
Alternative Name
Wan, Khairunizam
Ahmad, Wan Khairunizam Wan
Khairunizam, W. A. N.
Main Affiliation
Scopus Author ID
57200576499
Researcher ID
E-6072-2011

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  • Publication
    Virtual Markers based Facial Emotion Recognition using ELM and PNN Classifiers
    ( 2020-02-01)
    Murugappan M.
    ;
    Maruthapillai V.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mutawa A.M.
    ;
    Sruthi S.
    ;
    Yean C.W.
    Detecting different types of emotional expressions from the subject's face is important for developing intelligent systems for a variety of applications. This present work proposed virtual markers based on Facial emotion expression recognition using the Extreme Learning Machine (ELM) and Probabilistic Neural Network (PNN). A facial emotional expression database is developed with 55 undergraduate university students (male: 35, female: 20) of age range between 20-25 years with a mean age of 23.9 years. A HD webcam is used to capture the facial image and Haar Like features and Ada Boost classifier is used to detect the face and eyes through Open CV. A mathematical model based is used to place ten virtual markers called Action Units (AUs) on subjects face at a defined location. Later, Lucas-kanade optical flow algorithm is used to track the marker movement while the subject expressing different emotions and the distance between the center of the face to each marker is used as a feature for classifying emotions. One way Analysis of Variance (ANOVA) is used to test the statistical significance of the features and five fold cross-validation method is used to input the feature for classifiers. In this work, two non-linear classifiers namely, ELM and PNN are used for emotional expression classification. The experimental results give a maximum mean emotion classification rate of 88% and 92% in ELM and PNN classifiers, respectively. Maximum individual class accuracy of happiness-96%, surprise-94%, anger-92%, sadness-88%, disgust-90% and fear 89% is achieved using PNN. The experimental results confirm that the proposed system is able to distinguish six different emotional expressions and could be used as a potential tool for a variety of applications which include, e-learning, pain assessment, psychological counseling, human-machine interaction-based applications, etc.
  • Publication
    Breast Cancer Detection and Classification on Mammogram Images Using Morphological Approach
    ( 2022-01-01)
    Wan Azani Wan Mustafa
    ;
    Azmi A.A.
    ;
    Mohd Aminudin Jamlos
    ;
    Alquran H.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Ismail S.
    ;
    Alkhayyat A.
    ;
    Haron J.
    Breast cancer is one of the most common cancers affecting women worldwide. Mammography is the most well-known and effective method to detect early signs of breast cancer. The purpose of this paper is to detect breast cancer on the mammogram image to classify the disease through morphological techniques. Using conventional methods makes radiology difficult to detect cancer found in the patient's breast. This proposal can be divided into several elements, which are input database, image preprocessing, image segmentation, morphological analysis, and object recognition. First, image preprocessing will be done using the Weiner and Median filters. Second, the thresholding method for image segmentation will be performed, and lastly, morphology will remove imperfections introduced during the image segmentation process. Finally, the image is classified into two classes: normal and cancerous images. A median filter and 0.95 thresholding achieve an accuracy of 93.71%, a sensitivity of 94.36%, and a specificity of 82.53% for the cancerous images.
  • Publication
    Heat transfer improvement in simulated small battery compartment using metal oxide (CuO)/deionized water nanofluid
    ( 2020-02-01)
    Bin-Abdun N.A.
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    ;
    Voon Chun Hong
    ;
    Ibrahim Z.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohd Ridzuan Mohd Jamir
    Improving the heat transfer coefficient of working fluids is essential for achieving the best performance of manufacturing systems. As a replacement of conventional working fluids, nanofluids have a high potential for improving this heat transfer coefficient. However, nanofluids are seldom implemented in actual systems, and several factors should be considered before actual application. Accordingly, this study investigated the thermophysical properties and heat transfer rate of CuO/deionized water nanofluid with and without sodium dodecyl sulfate (SDS) surfactants. Three different volumetric concentrations of the nanofluid were prepared using a two-step preparation method. The experimental steps were divided into two phases: static and dynamic. In these experiments, the thermophysical properties of the prepared nanofluids and the heat transfer coefficient were measured using an apparatus designed based on an actual heat exchanger for a lithium ion polymer battery compartment. The effects of flow rate and surfactants on the heat transfer rate of the nanofluids with varying volumetric concentrations of 0.08%, 0.16%, and 0.40% were analyzed. The results indicate that the heat transfer rate increases considerably as the flow rate increases from 0.5 L/min to 1.2 L/min and with the presence of surfactants. The highest heat transfer rate was obtained at a 0.40% volumetric concentration of CuO/deionized water nanofluid with SDS surfactant.
  • Publication
    Performance Comparison of Machine Learning Algorithms for Classification of Chronic Kidney Disease (CKD)
    ( 2020-06-17)
    Azian Azamimi Abdullah
    ;
    Hafidz S.A.
    ;
    Wan Khairunizam Wan Ahmad
    Kidney is one of the vital organs in a human body while ironically, chronic kidney disease (CKD) is one of the main causes of death in the world. Due to the low rate of loss of kidney function, the disease is often overlooked until it is in a really bad condition. Dysfunctional kidney may lead to accumulation of wastes in blood which would affect several other systems and functions of the body such as blood pressure, red blood cell production, vitamin D and bone health. Machine learning algorithms can help in classifying the patients who have CKD or not. Even though several studies have been made to classify CKD on patients using machine-learning tool, not many researchers perform pre-processing and feature selection technique to obtain quality and dependable result. Machine learning used with feature selection techniques are shown to have better and more dependable result. In this study, feature selection methods such as Random Forest feature selection, forward selection, forward exhaustive selection, backward selection and backward exhaustive selection were identified and evaluated. Then, machine learning classifiers such as Random Forest, Linear and Radial SVM, Naïve Bayes and Logistic Regression were implemented. Lastly, the performance of each machine-learning model was evaluated in terms of accuracy, sensitivity, specificity and AUC score. The results showed that Random Forest classifier with Random Forest feature selection is the most suitable machine learning model for classification of CKD as it has the highest accuracy, sensitivity, specificity and AUC with 98.825%, 98.04%, 100% and 98.9% respectively which outperformed other classifiers.
  • Publication
    Development of Computer Aided System for Classification of Gastrointestinal Lesions
    ( 2022-01-01)
    Azian Azamimi Abdullah
    ;
    Kamardin N.A.A.
    ;
    Wan Khairunizam Wan Ahmad
    Colorectal cancer is a major global health problem and is one of the major contributors to deaths worldwide. Because malignant transformations are rare, endoscopic resection of hyperplastic polyps exacerbates medical costs, including those for resection and unnecessary pathological assessment. The proposed work is based on assessment of exploratory data analysis and visualization, initial pre-processing step followed by selection of attributes and performance assessment of proposed supervised machine learning algorithms. For features selection, two method were implemented (Boruta and SelectFromModel (SFM) Random Forest) to compare the performances of the models. The comparative analysis of machine learning included Random Forest (RF), Support Vector Machines (SVM), Deep Neural Network (DNN). For Boruta algorithm, it is shown that RF has the highest accuracy of 90.32%, sensitivity of 93.05% and specificity of 95.95%. Therefore, the adenoma detection rate (ADR) is desirable for improvement. A computer-aided system is developed which offers the opportunity to evaluate the presence of colorectal polyps objectively during colonoscopy.
  • Publication
    Thermal Management System Analysis Concentrate on Air Forced Cooling for Small Space Compartment and Heat Load
    ( 2021-12-01)
    Yahaya M.N.
    ;
    Ghani A.Z.A.
    ;
    Zuradzman Mohamad Razlan
    ;
    Rahman A.A.
    ;
    Bakar S.A.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Harun A.
    ;
    Hashim M.S.M.
    ;
    Mohd Khairul Faizi Abd Rahman
    ;
    Ishak Ibrahim
    ;
    Kamarrudin N.S.
    Battery thermal management system (BTMS) plays an important thing as to control of the battery thermal behaviours. Recently, most of the manufacturer either in automobile, motorcycle, and electric vehicle (EV) industry are using this application of BTMS for their product. It is because BTMS promising the extend the period and lifespan of the battery and the battery system controlling the temperature distribution and circulation on the system. Lithium-ion battery is one of the common usages in BTMS. Lithium-ion battery promising the goals such as higher performance, better cycle stability, and improved protection are being followed with the selection and engineering of acceptable electrode materials. It also shows a goal for future such as high of the energy storage due to higher energy density by weight among other rechargeable batteries. However, there still have factor that are limiting the performance/application when using lithium-ion as battery thermal management system (BTMS). For example, the performance, cost, life, and protection of the battery. The main reason is therefore important in order to achieve optimum efficiency whenworking under different conditions. Hence, the best range of temperature and the cooling capacity of lithium-ion battery need to evaluate in order to increasing the lifespan of lithium-ion battery at the same time can increasing the performance of the cell. This study found that the higher the velocity of air, the higher the cooling capacity that gain from the surrounding. It also was strongly related to the dry bulb temperature of surrounding air.
  • Publication
    Blood Vessels Segmentation in Eye Fundus Images Using Image Processing Algorithms
    ( 2023-01-01)
    Al-Quraan O.
    ;
    Alquran H.
    ;
    Alsalatie M.
    ;
    Wan Azani Wan Mustafa
    ;
    Wan Khairunizam Wan Ahmad
    The retinal blood vessels have a huge impact on the diagnosis of eye diseases in addition to other systematic diseases in the human body. In this paper, we presented an automated segmentation method to extract retinal blood vessels, starting with preprocessing, then passing the image into segmentation stage using Bradley technique, and lastly, morphological operations. The proposed method was assessed and tested on STARE dataset, followed by comparing the auto-segmented images to the manually segmented ones. The comparison results Accuracy, Sensitivity, and Specificity were 94.63%, 95.02%, and 80.73% respectively.
  • Publication
    A review of the application and effectiveness of heat storage system using phase change materials in the built environment
    ( 2021-05-03)
    Ibrahim Z.
    ;
    Newby S.
    ;
    Hassani V.
    ;
    Ya'akub S.R.
    ;
    Shahriman Abu Bakar
    ;
    Zuradzman Mohamad Razlan
    ;
    Wan Khairunizam Wan Ahmad
    Global warming is the most significant threat that civilization faced within the 21st century. Buildings, which account for 40% of global consumption of energy and greenhouse gas emissions, play a key role in global warming. It is estimated that their destructive impact will grow by 1.8 percent per year by 2050, indicating that future energy consumption and emissions will be more critical than they are today. Therefore, the use of a latent heat storage system using phase change materials (PCM) is one of the effective ways of storing thermal energy and has the advantages of high-energy storage density and the isothermal nature of the storage process. PCM has been widely used in latent heat thermal storage systems for heat pumps, solar engineering, and spacecraft thermal control applications. Thermal energy conservation by latent heat is an ideal way to increase the thermal inertia of building envelopes, which would minimize temperature fluctuations, contributing to increased occupants' thermal comfort. For this reason, high-density PCM can be used effectively. This paper reviews recent studies of the application and effectiveness of using PCM in the built environment.
  • 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
    An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images
    ( 2012)
    Ismail. Ibrahim
    ;
    Zuwairie Ibrahim
    ;
    Kamal Khalil
    ;
    Musa Mohd Mokji
    ;
    Syed Abdul Rahman Syed Abu Bakar
    ;
    Norrima Mokhtar
    ;
    Wan Khairunizam Wan Ahmad
    Because decisions made by human inspectors often involve subjective judg- ment, in addition to being intensive and therefore costly, an automated approach for printed circuit board (PCB) inspection is preferred to eliminate subjective discrimination and thus provide fast, quantitative, and dimensional assessments. In this study, defect classi cation is essential to the identi cation of defect sources. Therefore, an algorithm for PCB defect classi cation is presented that consists of well-known conventional op- erations, including image difference, image subtraction, image addition, counted image comparator, ood- ll, and labeling for the classi cation of six different defects, namely, missing hole, pinhole, underetch, short-circuit, open-circuit, and mousebite. The de- fect classi cation algorithm is improved by incorporating proper image registration and thresholding techniques to solve the alignment and uneven illumination problem. The improved PCB defect classi cation algorithm has been applied to real PCB images to successfully classify all of the defects.