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
    Diagnosis of Heart Disease Using Machine Learning Methods
    ( 2021-01-01)
    Azian Azamimi Abdullah
    ;
    Alhadi N.A.
    ;
    Wan Khairunizam Wan Ahmad
    The World Health Organization (WHO) estimated 12 million deaths around the world appear each year from heart disease. Heart disease includes coronary artery disease, heart rhythm problem and heart defects. Each disease has similar symptoms but cause different effects and severity on patient. The common factors of heart disease include high blood pressure, diabetes, cholesterol and age. These factors are independent of each other; thus, the use of artificial intelligence and machine learning will be a suitable choice to model them. Correct diagnosis of heart disease is difficult due to the complicated processes and different system and it is vital because heart disease can lead to a heart attack, chest pain, stroke and sudden death. Hence, an accurate and early detection of heart disease with proper and adequate treatment is needed. The main aim of this research is to identify suitable feature selection method and machine learning algorithms for the diagnosis of heart disease. Chi Square Feature Selection (CSFS), Random Forest Feature Selection (RFFS), Forward Feature Selection (FFS), Backward Feature Selection (BFS) and Exhaustive Feature Selection (EFS) are the feature selection methods applied in this research. These feature selection methods are then implemented in the machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k-Nearest Neighbour (KNN). The performance of these machine learning algorithms is evaluated through accuracy, sensitivity and specificity based on Confusion Matrix, ROC Curve and area under ROC (AUC). Based on the results, combination of RF with RFFS produced the highest accuracy value with 85.25% accuracy.
  • Publication
    Tensile characterizations of oil palm empty fruit bunch (Opefb) fibres reinforced composites in various epoxy/fibre fractions
    ( 2022-10-15)
    Mohd Khairul Faizi Abd Rahman
    ;
    Shahriman Abu Bakar
    ;
    Mohd Shukry Abdul Majid
    ;
    Tamrin S.B.M.
    ;
    Israr H.A.
    ;
    Anas Abdul Rahman
    ;
    Guan N.Y.
    ;
    Zuradzman Mohamad Razlan
    ;
    Kamis N.A.
    ;
    Wan Khairunizam Wan Ahmad
    Oil palm empty fruit bunch (OPEFB) single fibers and reinforced composites were comprehensively characterized through tensile tests to assess their performance as potential reinforcing materials in polymer composites. The performances of OPEFB single fibers and reinforced composites with untreated and treated fibers conditions were compared. The fibers were variously treated with 3% sodium hydroxide, 2% silane, 3% sodium hydroxide mixed with 2% silane, and 3% sodium hydroxide prior to 2% silane for 2 hours soaking time. The highest toughness of the single fibers test was then selected to proceed with composites fabrication. The OPEFB composites were fabricated in 90:10, 80:20, 70:30, and 60:40 epoxy-fibre fractions. The result shows that the selected treated fiber composite exhibits better performance. The selected treated fiber composite increased the highest ultimate tensile strength by 145.3% for the 90:10 fraction. The highest Young’s Modulus was increased by about 166.7% for 70:30 fraction. Next, the highest toughness was increased by 389.5% for the 30:70 fraction. The treated fibers provided a better interlocking mechanism between the matrix and fibers in reinforced composites, thus improving their interfacial bonding.
  • Publication
    Detection of Parkinson's Disease (PD) Based on Speech Recordings using Machine Learning Techniques
    ( 2020-12-20)
    Azian Azamimi Abdullah
    ;
    Norazman N.N.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Saidatul Ardeenawatie Awang
    ;
    Jian F.W.
    There are some neurodegenerative diseases which are unable to cure such as Parkinson's disease (PD) and Hungtinton's disease due to the death of certain parts in the brain that is affecting older adult. PD is an appalling neurodegenerative health disorder that linked to the nervous system which exert influence on motor functions. PD also often known as idiopathic disorder, environmental and genetic factors related, and the causes of PD remain unidentified. To diagnose PD, the clinicians are required to take the history of brain condition for the patient and undergoes various of motor skills examination. Accurate detection of PD plays a crucial role in aiding and providing proper treatment to the patients. Nowadays, there has been recent interest in studying speech-based PD diagnosis. Extracted acoustic attributes are the most important requirement to predict the PD. The experiment was conducted on speech recording dataset consisting of 240 samples. This work studies on the feature selection method, Least Absolute Shrinkage and Selection Operator (LASSO) with multiple machine learnings such as Random Forest (RF), Deep Neural Network (DNN), Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) as the classifier. Throughout this research, train test split method and k-fold cross validation were implemented to evaluate the performance of the classifiers. Through LASSO, Support Vector Machine Grid Search Cross Validation (SVM GSCV) outperformed other 7 models with 100.00 % accuracy, 97.87 % for recall, 65.00 % for specificity and 97.10 % of AUC for 10-fold cross validation. Finally, Graphical User Interface (GUI) was developed and validated through the prediction over UCI speech recording dataset which achieved 96.67 % accuracy for binary classification with 30 samples.
  • Publication
    Blood vessel detection monitoring system and mobile notification for diabetic retinopathy diagnosis
    ( 2020-01-01)
    Mahmud A.S.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohd Al-Hafiz Mohd Nawi
    Disease diagnosis based on retinal image analysis is very popular in order to detect a few critical diseases such as diabetic retinopathy, high blood pressure, cancer and glaucoma. The important part of the retinal is a blood vessel. Besides, the blood vessel study plays an important part in different medical areas such as ophthalmology, oncology, and neurosurgery. The significance of the vessel analysis was helped by the continuous overview in clinical studies of new medical technologies intended for improving the visualization of vessels. In this paper, a new blood vessel detection based on a combination of Kirsch’s templates and Fuzzy C-Means (FCM) was proposed. The main objective of this study is to improve the detection result of FCM and achieved more effective performance compared to the Kirsch’s templates result. The proposed method experimented on 20 images is utilized namely from Digital Retina Images for Vessel Extraction (DRIVE) dataset. The resulting images are compared with the benchmark images based on a few image quality assessment (IQA) such as accuracy, sensitivity and specificity. The total average of accuracy is 92.64%, while sensitivity and specificity obtained was 95.73% and 60.45% respectively. The three parameters of the IQA will then be displayed in a column on the GUI. The second part of the system is for the mobile notification system to send SMS to a mobile phone. In order for the user to obtain the image analysis results, there must be a notification system on the mobile phone. By using the GSM module integrated with Arduino Uno, notification regarding image analysis will be sent to the mobile phone.
  • Publication
    Development of Driving Simulation Experiment Protocol for the Study of Drivers’ Emotions by using EEG Signal
    ( 2024-06-01)
    Abdul Hafiz Abd Halin
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Muhajir Ab. Rahim
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    ;
    Saidatul Ardeenawatie Awang
    The Brain-Computer Interface (BCI) is a field of research that studies the EEG signal in order to elevate our understanding of the human brain. The applications of BCI are not limited to the study of the brain wave but also include its applications. The studies of human emotions specific to the vehicle driver are limited and not vastly explored. The EEG signal is used in this study to classify the emotions of drivers. This research aims to study the emotion classifications (surprise, relax/neutral, focus, fear, and nervousness) while driving the simulated vehicle by analyse the EEG signals. The experiments were conducted in 2 conditions, autonomous and manual drive in the simulated environment. In autonomous driving, vehicle control is disabled. While in manual drive, the subjects are able to control the steering angle, acceleration, and brake pedal. During the experiments, the EEG data of the subjects is recorded and then analyzed.
  • Publication
    Study of eddy current density distribution in a contactless breast cancer detection mechanism using magnetic induction spectroscopy
    ( 2017-01-01)
    Gowry Balasena
    ;
    Shahriman Abu Bakar
    ;
    Zulkarnay Zakaria
    ;
    Ryojun Ikeura
    ;
    Zuradzman Mohamad Razlan
    ;
    Hazry Desa
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Cheng Ee Meng
    ;
    Mohd Afendi Rojan
    Breast cancer is a throbbing disease that no longer needs an introduction. This is especially true among women due to their unique breast structure that naturally has more breast tissues compared to that of man’s. It is been forecasted that in 2015, a minimum of 60290 new cases of breast cancer will be reported. The goal of this study is to analytically evaluate the changes in the induced Eddy current densities as a function of di-electrical properties of the breast tissue with respect to tumor positioning as well as its size. This is achieved by running numerical simulations on the proposed mechanism of magnetic induction to detect tumors among healthy breast tissue via a 2D breast model configuration. The analytical results presented in this article, proved that the multi frequency magnetic induction principle is viable in detecting the breast lesions as small as 0.2 cm non-invasively through the distributions of the induced Eddy current density. While important pattern of the induced current were reflected when the tumors are located at the far ends of the breast diameter. The minimum results computational time with the proposed system is 10 s.
  • 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
    Approach to enhance the heat transfer of valve seats through thermal analysis
    ( 2022-02-05)
    Hassan M.A.S.M.
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    ;
    Mohd Afendi Rojan
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Ibrahim Z.
    ;
    Ishak A.A.
    ;
    Rahman A.A.
    ;
    Mohd Ridzuan Mohd Jamir
    The valve seat insert is a component of the engine cylinder head, whose primary function is to seal the combustion chamber and absorb the valve's heat, releasing it to the engine cylinder head. The valves experience high temperatures owing to high thermal loading and low heat absorption in the valve seat, which can potentially damage the engine. Therefore, the thermal characteristics of the valve seat must be optimised to increase the heat transmission between the valve and its seat. Here, three copper alloy valve seats, brass, beryllium copper, and bronze copper, were tested against the existing sintered iron valve seat, and their temperature maps were determined using actual engine operation conditions. The instantaneous heat transfer coefficients of the valves, seats, and engine cylinder head during the four-stroke cycle were evaluated using a one-dimensional thermal simulation analysis. The values obtained were used to assess the finite-element model using a three-dimensional thermal simulation in the Ansys software. The results show that the brass, beryllium-, and bronze-copper valve seats increased the overall heat flux by 4.46%, 4.16%, and 2.06%, respectively, compared to those for sintered iron. Thus, the results are essential to improve the thermal characteristics of the copper alloy valve seat imposed on the cylinder head. For validation, an experimental engine thermal survey and uncertainty magnification factors were used to validate the model. The results indicate that the maximum difference between the simulation and experimental values is 8.42%. Therefore, this approach offers a direct and comprehensible application for evaluating the temperature distribution, heat gradient, and heat flux of the cylinder head of air-cooled spark-ignition moped motorcycle engines using copper alloy valve seat materials at intermediate engine speeds. Furthermore, this method is applicable as a platform for the automotive industry to improve the heat transfer of the structural parts of internal combustion engines.
  • Publication
    Niblack algorithm modification using maximum-minimum (Max-min) intensity approaches on low contrast document images
    ( 2020-01-01)
    Wan Azani Wan Mustafa
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mat Yusoff A.S.
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Mohamad Nur Khairul Hafizi Rohani
    In recent decades, detection or segmentation has been one of the major interesting research subjects due to the analysis of the information. However, most of the historical document has degraded and low contrast problem. Recently, many binarization methods were proposed in order to segment the text region from the background region in the low-quality image. In this paper, an improved binarization method was inspired by Niblack method was presented. The modification focuses to find the optimum threshold value by using the Maximum-Minimum intensity technique. The main target is to reduce the unwanted detection image and increase the resultant performance compared to the original Niblack method. The proposed method was applied to the document images from H-DIBCO 2012 and H-DIBCO 2014 dataset. The results of the numerical simulation indicate that the target was achieved by the F-Measure by F-measure (58.706), PSNR (10.778) and Accuracy (86.876). This finding will give a new benchmark to other researchers to propose an advance binarization method.