Wan Azani Wan Mustafa
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Preferred name
Wan Azani Wan Mustafa
Official Name
Wan Azani, Wan Mustafa
Alternative Name
Mustafa, W.
Azani Mustafa, Wan
Mustaffa, Wan Azani
Wan Mustafa, Wan Azani
Main Affiliation
Scopus Author ID
57219421621
Researcher ID
J-4603-2014

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  • Publication
    EOG Based Eye Movements and Blinks Classification Using Irisgram and CNN-SVM Classifier
    ( 2023-01-01)
    Zyout A.
    ;
    Alquraan O.
    ;
    Alsalatie M.
    ;
    Alquran H.
    ;
    Alqudah A.M.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohammed F.F.
    ;
    Alkhayyat A.
    The classification of eye movements and blinks is an important task in various fields, including ophthalmology, psychology, and human-computer interaction. In recent years, the use of EOG signals and convolutional neural networks (CNNs) has shown promising results in accurately classifying different types of eye movements and blinks. The Irisgram, which is a two-dimensional representation of the short-time Fourier transform in the shape of a human iris, has been used as a feature for distinguishing between different types of eye movements and blinks. Additionally, CNNs have been utilized to learn the features automatically from Irisgrams and classify the eye movements and blinks based on these learned features. In this paper, we provide a methodology to classify blinks and four eye movements by employing Irisgram as input to the CNN-SVM classifier which achieved test accuracy of 96.2% in the testing dataset.
  • Publication
    Counting Non-Overlapping Abnormal Cervical Cells in Whole Slide Images
    ( 2023-01-01)
    Badarneh A.
    ;
    Alzuet A.
    ;
    Wan Azani Wan Mustafa
    ;
    Alquran H.
    ;
    Alsalatie M.
    ;
    Mohammed F.F.
    ;
    Alkhayyat A.
    Cervical cancer is one of the most common cancer among women globally. The Pap smear test has been widely used to detect cervical cancers according to the morphological characteristics of the cell nuclei on the micrograph. The aim of this paper is to count the non-overlapping abnormal cervical cells in whole slide images automatically by employing various image techniques. The proposed approach consists of four main steps; image enhancement, transform the extended minima, remove small pixels, and count the number of abnormal cells in the image. The proposed system used 250 cervical pap smear images where the overlap between cells is minimal. The performance of the proposed system is evaluated based on comparing the manual counting and automating counting over whole images. Therefore, the accuracy is evaluated mainly on the difference between manual and automated, and it is 92.5%. The proposed method can be used in laboratory to decrease the false positive rates in counting abnormal cells.
  • Publication
    Implementation of High Gain DC/DC Boost Converter
    ( 2023-01-01)
    Zulkifli Z.W.
    ;
    Redzuan N.
    ;
    Muhammad Z.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohammed F.F.
    ;
    Alkhayyat A.
    In order to bypass the ordinary DC/DC boost converter and level up the DC voltage to a higher level, the high gain boost converter is described in this work. With just one semiconductor switch unit, the circuit can level up a little DC voltage to a huge level of DC voltage. In order to prevent the system from becoming saturated, a typical DC/DC boost converter will restrict the boosting production at a specific level. The suggested topology introduces the integration between the high switching state and the transformer, producing a larger output voltage. The PSIM program is used to run the simulation, and a hardware lab scale setup is used to experimentally validate it. The desired output voltage is 200V, which will be generated by a modest 48V DC system. Additionally, the effectiveness of the overshoot study converter and the regular boost converter is compared in this study.
  • Publication
    Heart Arrhythmia Classification Using Deep Learning: A Comparative Study
    ( 2023)
    Radi Omar
    ;
    Alslatie Mohammad
    ;
    Wan Azani Wan Mustafa
    ;
    Alquran Hiam
    ;
    Badarneh Alaa
    ;
    Mohammed F.F.
    ;
    Ahmed Alkhayyat
    Heart arrhythmia is an irregular heartbeat that causes heart problems. It can be classified by their seriousness into serious and non-serious arrhythmia. Mainly to diagnose heart arrhythmias, we use Electrocardiogram (ECG). In this paper, the authors compared three different models of classifiers: Convolutional Neural Network, Dense Neural Network and Long Short-Term Memory to classify cardiac arrhythmia into two types normal and abnormal, using the MIT-BIH database. The results show that CNN and DNN have the best result of the models with 99% accuracy while LSTM shows 60 accuracy percent.
      3  9
  • Publication
    Image Dataset for Cervical Cell Diagnosis - a Review
    ( 2023-01-01)
    Alias N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Alquran H.
    ;
    Ghani M.M.
    ;
    Hanafi H.F.
    ;
    Lah N.H.C.
    ;
    Ismail S.
    ;
    Mohammed F.F.
    ;
    Alkhayyat A.
    Cervical cancer is a prevalent and fatal disease that affects women all over the world. This affects roughly 0.5 million women annually and kills over 0.3 million people. Recently, a significant amount of literature has emerged around the advancement of technologies for identifying cervical cancer cells in women. Previously, diagnosing cervical cancer was done manually, which could lead to false positives or negatives. The best way of interpreting Pap smear images and automatically diagnose cervical cancer are still up for debate among the researchers. Thus, as to encourage talented researchers in this field, an excellent, easily access and expert's validated data for cervical cell has been developed by previous researchers. In this study, datasets have been reviewed from previous studies that can be access for research and study purposes.
      1
  • Publication
    Nucleus Detection Using Deep Learning Approach on Pap Smear Images
    ( 2023)
    Alquran Hiam
    ;
    Wan Azani Wan Mustafa
    ;
    Mohammed F.F.
    ;
    Alkhayyat Ahmed
    Cervical cancer is caused by the abnormal growth of female cervix cells. It is one of the most familiar factors for women's death worldwide. Therefore., early detection of cervical cancer leads to a reduced mortality rate and increased chance of being alive. The Papanicolaou is a common method for screening and identifying the cancerous cells in a woman's cervix. The resultant pap smear images may help the physician diagnose the cervix cells. The crucial part of the cell is the nucleus. Therefore., auto-detection of the nucleus is the core point in this paper. A deep learning algorithm is employed to segment the nucleus in pap smear images. Two network structures., known as ResNet18 and ResNet50., are exploited to detect the nucleus part in the cell. The results are compared with ground truth and between the two structures. Both networks., ResNet18 and ResNet50., perform almost the same., with test accuracy reaching 92%. This work distinguishes it from other work in simplicity., fast., and accuracy. Therefore., it can be recommended to be used in clinical units and rural countries which suffer from the lack of specialist physician.
      3  5
  • Publication
    Analysis of Dual-Way Converter Using Modified H-Bridge Circuit
    ( 2023)
    Nurwani Mohd Redzuan
    ;
    Nadia Anuar
    ;
    Muhammad Z.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohammed F.F.
    ;
    Ahmed Alkhayyat
    This paper presents a modified H-bridge circuit that can operates the common converter in power electronic study namely full wave rectifier. The common topology used to drive the full wave rectifier known as Wheatstone bridge circuit to perform the conversion. However, the default H-bridge circuit has the body drain diode in the power switches which can cause a leakage current and allow the reverse current direction during the conversion. This problem can be solved by modified the default H-bridge with a different topology to block the reverse current phenomenon. This study is simulated using PSIM software and validate thru hardware implementation. The final results show the proposed circuit can handle the conversion perfectly without facing any noise or disturbance.
      7  16
  • Publication
    Roles and Challenge of Social Media in E-Commerce Through Expert Review
    ( 2023)
    Miharaini Md Ghani
    ;
    Wan Azani Wan Mustafa
    ;
    Hafizul Fahri Hanafi
    ;
    Noor Hidayah Che Lah
    ;
    Mohammed F.F.
    ;
    Ahmed Alkhayyat
    Everyone and every business has been profoundly impacted by social media to the point where it can no longer be ignored. Its growth has been meteoric in every market around the globe. E-commerce sites' ability to facilitate social interaction between vendors and buyers is becoming increasingly important in the framework of the current digital revolution. The most popular type of app was social media, followed by games, shopping, and messaging. The primary objective is to provide a comprehensive overview of the research conducted on a particular topic. It also can establish context for a research topic by demonstrating how current research builds on past work. This paper has searched pertinent literature using databases such as Google Scholar, PubMed, Scopus, etc. They employ particular keywords and criteria to identify the most pertinent articles. The identified papers undergo a screening based on their titles and abstracts. The most relevant results are chosen for full-text viewing. The text emphasizes the significance of social media in the evolution of e-commerce and the need for businesses to adapt and utilize these platforms for successful consumer engagement, brand development, and overall growth.