Wan Azani Wan Mustafa
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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
    Current Approaches of Artificial Intelligence (AI) in Leading Behavioural Change: The Latest Review
    ( 2024-05-01)
    Ghani M.M.
    ;
    Osman M.N.
    ;
    Omar S.Z.
    ;
    Radzi S.R.K.M.
    ;
    Wan Azani Wan Mustafa
    ;
    Mardatillah A.
    Digital health is plagued by low interest and adherence, but research suggests improvements are possible. Non-health care, for-profit digital enterprises like LinkedIn, Twitter, and Facebook undertake behavioural experiments to enhance user engagement. Today, everyone is impacted by commercial determinants of health, and the selection of unhealthy options compounds economic, social, and racial disparities that already exist. However, healthcare providers tend to prioritize pharmacological approaches at the expense of influencing patients’ actions. Thus, it is crucial to ensure that artificial intelligence (AI) is integrated as well as applied within the systems to improve related fields. Both Scopus and Web of Science (WoS) have indexed the previously examined body of work in their respective databases. This review is going to be divided into three different themes: (1) health, (2) education, and (3) other fields. This paper aims to analyse the existing data or outcomes via analysis, categorization, comparison, and summary. It also may uncover research gaps and explore field possibilities. This review included English-language research and literature from 2022. This study summarises current approaches of Artificial Intelligence (AI) in leading behavioural change from different fields of literature.
  • Publication
    IR 4.0: Smart Farming Monitoring System
    ( 2023-01-01)
    Nasir M.F.
    ;
    Mohd Aminudin Jamlos
    ;
    Wan Azani Wan Mustafa
    ;
    Mohamad Nur Khairul Hafizi Rohani
    ;
    Muhammad Zaid Aihsan
    ;
    Habelalmateen M.I.
    ;
    Ramadan G.M.
    The Internet of Things is the current and future of every field that effects everyone's life by making everything smart. The development of Smart Farming Monitoring with the use of the Internet of Things, changes conventional farming methods by not only making them optimal but also effective for farmers and reducing crop wastage. Therefore, Smart Farm Monitoring of IR 4.0 Implementation is designed to provide a system for monitoring environmental factors in farming in real time. This product will help farmers by creating an easy-to-use user view so users can view data. By implementing various types of sensors and applications such as Raspberry Pi 4B as its main controller, Temperature & Humidity sensor (DHT22), Capacitive Soil Moisture sensor, MQ135 sensor, Light Intensity sensor, ThingSpeak and ThingView, farmers will can monitor parameters and this data will be sent to the database for real-time display and storage purposes. The project is expected to create a smart environment conducive to agriculture and reduce labour costs and water wastage and increase productivity and efficiency. The system is achieved, as the intelligent monitoring of agriculture allows real-time monitoring with less time.
  • Publication
    The Development of an Interactive Animation to Prevent Social Media Fraud
    ( 2020-06-17)
    Lun C.W.
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Wan Nor Ashiqin Wan Ali
    ;
    Ishak N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Wahab M.H.A.
    Technology is the creation, invention, methods or systems of something new that has the purpose to overcome human's limitation, so that the creation can help human to complete the task that they cannot do. However, the world is getting advanced day-by-day, and so do the online frauds, where it is getting more types due to the ease of these technologies. People would use these advanced technologies to scam others, especially through social media. Thus, the number of online scam seems to be rising, and it is an appropriate action that someone does to bring benefits only for themselves. This paper is about the development of an interactive animation to increase the awareness of social media frauds among people and ways to prevent it. Subsequently, we created an animation that can raise the awareness of social media fraud among people, and hence they can also learn effective ways to prevent social media fraud from happening.
  • Publication
    Simulation Study of Metamaterial Effect towards Ultra Wide Band Antenna
    ( 2020-09-21)
    Amirah Othman N.
    ;
    Mohd Aminudin Jamlos
    ;
    Wan Azani Wan Mustafa
    ;
    Syed Zulkarnain Syed Idrus
    In this paper, the design of a metamaterial ultra-wideband (UWB) antenna with a goal towards application in microwave imaging systems for detecting unwanted cells in human tissue, such as in cases of breast cancer, heart failure and brain stroke detection is proposed. The metamaterial unit cell is constructed using circular split ring resonator (CSRR) technique and wire, to attain a design layout that simultaneously exhibits both a negative magnetic permeability and a negative electrical permittivity and attached as superstrate in front of the UWB antenna. This design results in an astonishing negative refractive index that enables amplification of the radiated power of this reported antenna, and therefore, high antenna performance. A Rogers (RT5880) substrate material is used to design and print this reported antenna, and has the following characteristics: thickness of 0.51 mm, relative permeability of one, relative permittivity of 2.70 and loss tangent of 0.02. The metamaterial antenna is design to be operated at frequency between 300MHz to 30GHz which is suitable for biomedical application such as Microwave Imaging. The overall metamaterial antenna size is 90 mm 50 mm 0.51 mm. The design and simulation has been carried out using Computer Simulation Technology Microwave Studio (CST MWS).
  • 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
    H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner
    ( 2023-02-01)
    Yacob Y.M.
    ;
    Alquran H.
    ;
    Wan Azani Wan Mustafa
    ;
    Alsalatie M.
    ;
    Sakim H.A.M.
    ;
    Lola M.S.
    Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.
  • Publication
    A Review of Learner’s Model for Programming in Teaching and Learning
    ( 2024-02-01)
    Hanafi H.F.
    ;
    Selamat A.Z.
    ;
    Ghani M.M.
    ;
    Wan Azani Wan Mustafa
    ;
    Harun M.F.
    ;
    Naning F.H.
    ;
    Huda M.
    ;
    Alkhayyat A.
    Over recent years, computer science (CS) teachers and instructors have faced several challenges in helping students strengthen their understanding of programming. The existing assessment methods could be more effective in assessing students' programming skills and knowledge, thus requiring a review of issues surrounding the instruction of programming courses. Against this backdrop, the authors systematically reviewed the current literature to identify several socio-cognitive factors that can help develop a learner model for learning programming. Specifically, the Systematic Reviews and Meta-Analyses (PRISMA) technique was utilized to identify and select relevant articles from three primary online databases: Scopus, Web of Science, and Eric. Initially, 401 relevant papers were identified and retrieved, further reduced to only 24 articles based on specific selection criteria. As revealed, several demographic factors (such as gender, age, ethnicity, and socioeconomic status) and socio-cognitive factors (motivation, attitude, and interest) have been shown to impact student learning of programming significantly. The authors' findings from the systematic literature review helped synthesize the essential elements of the learner model that must be carefully considered and utilized. Arguably, the use of such a new learner model can compel instructors to teach programming more effectively by clarifying several students' socio-cognitive backgrounds, which collectively have a significant impact on student learning of programming courses or subjects at the primary, secondary, and tertiary levels of education, especially in the Malaysian educational context.
  • Publication
    Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning
    ( 2022-12-01)
    Qasmieh I.A.
    ;
    Alquran H.
    ;
    Zyout A.
    ;
    Al-Issa Y.
    ;
    Wan Azani Wan Mustafa
    ;
    Alsalatie M.
    A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency.
  • Publication
    Cervical Cancer Detection Techniques: A Chronological Review
    ( 2023-05-01)
    Wan Azani Wan Mustafa
    ;
    Ismail S.
    ;
    Mokhtar F.S.
    ;
    Alquran H.
    ;
    Al-Issa Y.
    Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide.
  • Publication
    The Usage of Virtual Reality Technology Through Histopology Application to Increase Orang Asli Imagination Skill in History
    ( 2020-06-17)
    Fadzil N.A.S.N.M.
    ;
    Lah H.A.
    ;
    Wan Azani Wan Mustafa
    Studies show that mobility problems in rural schools among students make the Teaching and Facilitation process less effective. Teachers need to incorporate the elements of Historical Thinking Skills (KPS) in tourism education embodied in the history syllabus of Year Six. However, this element cannot be implemented due to the constraints it has. Preliminary research has shown that Orang Asli students have severe problems with the process of imagination and empathy that they do not reach the minimum KPS level. Thus, one intervention was undertaken - the development of Histopology applications. It is an online application built on ASSURE and MICUP models integrating Google Earth and Google Streer View based on Virtual Reality technology that creates 360-degree views for students to visualize tourist attractions in Malaysia. As a result, the minimum level of KPS was achieved, plus it also fostered the love for the country through the depiction of uniqueness of Malaysia's tourist destinations.