Wan Azani Wan Mustafa
Thumbnail Image
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

Research Output
Now showing 1 - 4 of 4
  • 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
    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.
  • Publication
    Automated Heart Diseases Detection Using Machine Learning Approach
    ( 2023)
    Rand Abedelellah Aburayya
    ;
    Rahaf Ahmed Alomar
    ;
    Dana Khalid Alnajjar
    ;
    Sema Athamnah
    ;
    Hiam Alquran
    ;
    Wan Azani Wan Mustafa
    ;
    Firas Al-Dolaimy
    ;
    Ahmed Alkhayyat
    An extensive number of people might be affected by heart disease (HD), a major health issue that can occur anywhere in the world. Therefore, early diagnosis of cardiac disease is advantageous for treatment. A technology that can easily diagnose heart disease must be developed because the number of people with the condition is rising quickly. In addition, the patient's smoking history affects whether a problem is present or not. The HD system can define the most crucial cardiovascular patient characteristics and identify high-risk patients, but it can also model these characteristics to make it simple and clear to distinguish between them. Age, chest discomfort, blood pressure (BP), gender, cholesterol, and heartbeat are a few examples of factors that are taken into account while applying and comparing machine learning algorithms. The major goal of this article is to create a fundamental machine learning model to enhance accurate cardiac disease diagnosis. In our study, we used a HD dataset to construct a machine-learning-based diagnosis method for heart disease prediction (Logistic Regression, K-Nearest Neighbor (K-NN), Decision Tree, Naive Bayes, Random Forest, and Support Vector Machine (SVM)). To evaluate the performance of classifiers, we employed cross-validation, feature selection techniques, and well-known machine learning metrics like classification accuracy, specificity, and sensitivity. The suggested system makes it simple to distinguish between those who have cardiac disease and those who are healthy. Additionally, each classifier's receiver optimistic curves and area under the curves were calculated. All of the classifiers, feature selection algorithms, preprocessing techniques, validation techniques, and metrics for measuring the performance of the classifiers utilized in this study have been covered. A smaller collection of features and the complete set of features have both been used to validate the performance of the suggested system.