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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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.

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A Study of Coding Learning Amongst Children: Motivation and Learning Performance

2023-01-01 , Hanafi H.F. , Wan Azani Wan Mustafa , Idris M.N. , Ghani M.M. , Alkhayyat A. , Lah N.H.C. , Seng W.Y.

Computer programming and coding now face several obstacles in aiding students to improve their grasp of programming and coding. Furthermore, current programming approaches may more effectively measure children's programming aptitudes and abilities, necessitating a reassessment of programming training difficulties. Such a novel technique may compel educators to teach coding more effectively by crystallising multiple children's cognitive backgrounds. Considering this, the authors performed a comprehensive analysis of the existing literature (2022-2023) to identify critical mental elements and motives that might aid in gaining a broad understanding of coding learning. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used to identify and choose relevant publications from three major internet databases: Scopus, Web of Science, and Eric. Initially, 2250 papers were reviewed and retrieved. However, this number was reduced to just 20 based on selection criteria. Several learning outcomes (assessments) and motivational elements (applications and tools) have substantially influenced children's coding and programming learning. According to the final discussion, children are motivated when exposed to pleasant and pleasurable coding environments.

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Chronic Kidney Disease Detection Using Machine Learning Technique

2022-01-01 , Al-Momani R. , Al-Mustafa G. , Zeidan R. , Alquran H. , Wan Azani Wan Mustafa , Alkhayyat A.

chronic kidney disease is a disorder that disables normal kidney function. The WHO has shown that CKD is a serious disease, ranked as one of the top twenty causes of death. It is recognized that2 million people worldwide suffer from kidney failure and the number of patients diagnosed with CDK continues to expand at a rate of 5-7% annually. late diagnosis of this disease is a life-threatening problem, which, often occurs in remote areas due to the lack of specialized medical personnel, in addition to the high cost of diagnosis. This paper aims at early detection of CDK using machine learning algorithms Artificial Neural Network, Support Vector Machine, and k-Nearest Neighbor. The importance of AI is reflected in the importance of identifying these typically fatal ailments. This study looks at a data set consisting of 400 samples and 13 features. The three classification techniques were evaluated by applying them to the data. The results show that the ANN classifier achieved the best accuracy at 99.2%.

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Effect of direct statistical contrast enhancement technique on document image binarization

2022-01-01 , Wan Azani Wan Mustafa , Haniza Yazid , Alkhayyat A. , Mohd Aminudin Jamlos , Hasliza A Rahim @ Samsuddin

Background: Contrast enhancement plays an important role in the image processing field. Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image. Objective: This paper proposed a novel method based on statistical data from the local mean and local standard deviation. Method: The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories: background, foreground, and problematic (contrast & luminosity) region. Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast variation problem effectively compared to Histogram Equalization (HE), Difference of Gaussian (DoG), and Butterworth Homomorphic Filtering (BHF). Seven (7) types of binarization methods were tested on the corrected image and produced a positive and impressive result. Result: Finally, a comparison in terms of Signal Noise Ratio (SNR), Misclassification Error (ME), F-measure, Peak Signal Noise Ratio (PSNR), Misclassification Penalty Metric (MPM), and Accuracy was calculated. Each binarization method shows an incremented result after applying it onto the corrected image compared to the original image. The SNR result of our proposed image is 9.350 higher than the three (3) other methods. The average increment after five (5) types of evaluation are: (Otsu = 41.64%, Local Adaptive = 7.05%, Niblack = 30.28%, Bernsen = 25%, Bradley = 3.54%, Nick = 1.59%, Gradient-Based = 14.6%). Conclusion: The results presented in this paper effectively solve the contrast problem and finally produce better quality images.

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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.

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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.

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Experimental Analysis of Five-Level Inverter using SEPIC Converter

2022-01-01 , Hariz M.F. , Anuar M.N.K. , Muhammad Z. , Wan Azani Wan Mustafa , Alkhayyat A.

The research in Power Inverter is one of the most rapidly changing technologies in the modern era, with many researchers beginning to replace traditional so-called inverter-based transformers with other sorts of circuits. A major issue with traditional transformers is that they come with a price and occupy up a lot of space. This study will provide a new technique for replacing the traditional transformer idea by combining a dual DC/DC SEPIC converter with a modified H-bridge inverter to create a five-level inverter with multirange voltage selection that is dependent on the duty-cycle of the SEPIC converter. The proposed inverter's input DC voltage is stepped-up, resulting in a five-level output voltage waveform appropriate for driving an AC motor. The recommended inverter is evaluated using a PowerSim simulation with different duty-cycle values.

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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.

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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.

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Simulation Analysis of Single Input Multiple Output Buck Converter

2023-01-01 , Zulkifli Z.W. , Redzuan N. , Muhammad Z. , Wan Azani Wan Mustafa , Alkhayyat A. , Abdulsattar N.F.

This paper presents a modified buck converter with multiple output circuit and its analysis. This Single Input Multiple Output (SIMO) buck converter has attracted researcher heart as this simple circuit can be harness its benefits especially for those that love outdoor activities as this circuit can charge multiple devices with specific current demand. With the proposed topology, the circuit can promise to constant charge using two separated load or USB devices. However, some study needs to be made as to understand the behavior of the circuit completely in terms of the component selection and the impact of faulty selection of duty cycle control. This study is simulated using PSIM software. The results of the analysis are presented in a proper comparison for a better understanding of the operation.

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