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
    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
    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
    Detection of Polycystic Ovary Syndrome (PCOS) Using Machine Learning Algorithms
    ( 2022-01-01)
    Hdaib D.
    ;
    Almajali N.
    ;
    Alquran H.
    ;
    Wan Azani Wan Mustafa
    ;
    Al-Azzawi W.
    ;
    Alkhayyat A.
    One of the most common diseases in women of reproductive age is Polycystic Ovary Syndrome (PCOS). PCOS diagnosis can be tricky, because not everyone with PCOS has polycystic ovaries (PCO), nor does everyone with ovarian cysts have PCOS, hence the pelvic ultrasound as a stand-alone diagnosis is not sufficient. The full diagnostic plan is mainly a combination of a pelvic ultrasound besides blood tests of specific parameters that indicate the presence of PCOS. Since PCOS is a hard-to-diagnose widespread hormonal disorder, blood tests, symptoms, and other parameters with the help of a computer can form a new and easy method to diagnose it. Therefore, we had successfully built a high performing diagnostic model using MATLAB. The data was obtained from the website Kaggle, and the dataset is called Polycystic Ovary Syndrome. In this paper various machine algorithms were employed by utilizing seven classifiers. Results demonstrated that Linear Discriminant classifier exhibits the best performance in terms of accuracy, while in terms of sensitivity, the KNN classifier had the best result. Also, a comparison with four other research papers that exploited the same PCOS dataset was done in terms of implementation platforms, evaluation methods, classifiers, classes, accuracy, and precision of each classifier. Our research excelled among all in terms of accuracy and varied in precedence with precision. MATLAB had shown substantial results and a great model fitting embedded approaches, scoring a high accuracy and precision outcome compared to other studies. Other improvements on the overall PCOS prediction can involve employing preprocessed ultrasound images with the features presented in the dataset.
  • Publication
    SOSFloodFinder: A text-based priority classification system for enhanced decision-making in optimizing emergency flood response
    ( 2024-01-01)
    Kamal S.H.
    ;
    Aziz A.A.
    ;
    Wan Azani Wan Mustafa
    Flooding is a significant concern in nations with frequent precipitation because it can instantly affect multiple regions simultaneously. Due to the unpredictability of their occurrence caused by rapid water level rise, it is challenging to predict such natural disasters accurately. During flooding, prompt rescue efforts are crucial for the affected population. Due to flooded highways and residences, rescue teams may have difficulty locating victims. This hinders the potentially perilous and time-consuming rescue operation. To address this problem, we propose a web-based system that integrates natural language processing (NLP) with global positioning system (GPS) functionality. The SOSFloodFinder system provides automatic classification priorities for text messages sent by flood victims, as well as their most recent or current locations. The classification of text based on priority enables efficient resource allocation during rescue operations. In conclusion, this system has the potential to reduce future flood-related fatalities. Additional research and development are necessary to thoroughly investigate this method’s practical capabilities and effectiveness.
  • Publication
    E-MEDIC: A mobile application for common medicines at pharmacy
    ( 2023-06-12)
    Mahendran B.E.
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Ummi Naiemah Saraih
    ;
    Wahab M.H.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    Humans' and the world's lifestyles have changed dramatically through time as a result of technological advancements. Communication technology has advanced swiftly in the contemporary age, making a significant contribution to human existence. Smartphones are one of the most useful technologies. Mobile apps arose as a result of the evolution of regular phones into smartphones. Mobile applications with various functions give varying levels of aid to human existence. This study aims to give self-medicating patients a guideline and correct content information of over-the-counter (OTC) drugs. Subsequently, it is to develop a mobile application for the medication (E-MEDIC app). Furthermore, it is to ease and save users' time searching for medicines content. The third objective is to evaluate the user's acceptance of the medication mobile application (E-MEDIC app). The qualitative methodologies utilised for the E-MEDIC mobile app included locating two specialists to conduct an Alpha Test to obtain their thoughts and selecting twelve respondents with relevant experience to serve as testers. The experiment includes several areas of the application's functioning, usability, and interface design. All respondents and experts agreed that the E-MEDIC mobile app's functionality, usefulness, and usability are excellent. The E-MEDIC smartphone app, which is based on this initiative, can provide accurate information and content specifics on over-the-counter (OTC) medications to self-medicating patients. If properly implemented in pharmacies, OTC medications, and public health, the E-MEDIC smartphone app offers several benefits. The results from this study are promising.
  • 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
    Perpendicular High Isolation MIMO Antenna
    ( 2022-01-01)
    Mohd Aminudin Jamlos
    ;
    Sabri N.H.M.
    ;
    Wan Azani Wan Mustafa
    ;
    Khairul Najmy Abdul Rani
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Husna H.
    This research presented a perpendicular high isolation MIMO antenna for LTE advance application. A high gain perpendicular MIMO antenna is concentrated on designing used in LTE advance application. The issues of low isolation of conventional antenna can be solved by structuring a MIMO antenna in order to increase the isolation in LTE advance application. Generally, the array antenna design causes a bigger antenna size and has a mutual coupling which lead to spectral efficiency damage and reduce the MIMO antenna framework performance. The substrate material like FR-4 is choosing as a dielectric substrate due to its good performances for many applications beside it has a low cost and more usable. The advantage of copper such as has a great relative material, cheaper and easy to construct is choose in this project as a conductive material. ADS software has been utilized for the structure stage to design the antenna. Then, the results are evaluated in terms of return loss (S11 and S22), mutual coupling (S12 and S21), match impedance, directivity, radiation pattern, gain and radiated power. Vector Network Analyzer (VNA) is used to measure the fabricated antenna. The factor of cable loses and the soldering technique will make the measurement result was slightly change from the simulating result. However, the antenna design satisfied the proficiency necessity of the antenna which the frequency is drop at 2.5 GHz with the return loss is below than −10 dB.
  • Publication
    Deep CNN-LSTM Network Integration for COVID-19 Classification
    ( 2023-01-01)
    Shaari F.N.
    ;
    Abdul Nasir A.S.
    ;
    Herng O.W.
    ;
    Wan Azani Wan Mustafa
    The COVID-19 virus outbreak has exceeded our expectations and shattered all previous records for virus outbreaks. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes progressive respiratory failure and severe alveolar damage might be deadly. During the pandemic, to curb the virus' spread and ease the strain on the healthcare systems, these has arisen an imperative for swift and precise detection of COVID-19 through computer-aided diagnosis. This paper aims to study the effects of the original and five image enhancement techniques which are Modified Global Contrast stretching (MGCS), Adaptive Gamma Correction with Weighting Distribution (AGCWD), Lowlight (LL), Multi-scale Retinex 2 (MSR2), and Contrast Enhancement using Heat Conduction Matrix (CEHCM) on chest X-ray (CXR) images on the classification process. As a matter of fact, to attain accurate and quick COVID-19 detection, a standard convolutional neural network (CNN) and long short-term memory (LSTM) were developed. A total of 15000 CXR images consisting of COVID-19, normal, and pneumonia were collected from various data repositories to implement this study. The experimental result shows the best classification performance of the CNN-LSTM model is achieved when the system is fed with CXR images enhanced by the lowlight (LL) image enhancement technique, which achieves accuracy, sensitivity, specificity, precision, and F1-score of 99.65%, 99.80%, 99.95%, 99.90%, and 99.85%, respectively.
  • Publication
    Automated Classification of Skin Lesions Using Different Classifiers
    ( 2023-01-01)
    Al-Tawalbeh J.
    ;
    Alshargawi B.
    ;
    Al-Daraghmeh M.
    ;
    Alquran H.
    ;
    Wan Azani Wan Mustafa
    ;
    Al-Dolaimy F.
    ;
    Alkhayyat A.
    Human skin cancer is the most common death. Skin cancer is defined as the abnormal growth of skin cells that most commonly occurs in areas of the body that are exposed to sunlight, but it can occur anywhere on the body. In their early stages, the majority of skin cancers are curable. As a result, detecting skin cancer early and quickly can save a patient's life. The incidence of malignant melanoma, the most dangerous type of skin cancer, rises year after year. Detecting skin cancer from a skin lesion is difficult due to artifacts, low contrast, mole, scar, etc. Due to the new technological advancements, early detection of skin cancer is now possible. This paper uses K-nearest neigbour (KNN), Artificial neural network (ANN) and support vector machine (SVM) classifiers for segmented and non-segmented groups and shows 95.8% overall accuracy for all classes, with the sensitivity of 97%, 91.4% and 99.7% for Benign, melanoma, seborrheic keratosis, respectively as well a precision of 92.4%, 96.6% and 99.7%, respectively. With all automatically extracted features, the accuracy is better in a non-segmented case. This paper could be extended and further processed to meet an everyday demand of how the lesions are classified or if there are any cancers.