Mohd Wafi Nasrudin
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Mohd Wafi Nasrudin
Official Name
Mohd Wafi , Nasrudin
Alternative Name
Nasrudin, Mohd Wafi
Wafi, N. M.
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Scopus Author ID
57193691419
Researcher ID
FMA-4535-2022

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  • Publication
    Adopting Ant Colony Optimization Algorithm for Pairwise T-Way Test Suite Generation Strategy
    ( 2021-07-26)
    Nuraminah Ramli
    ;
    Rozmie Razif Othman
    ;
    Hendradi R.
    ;
    Fauzi S.S.M.
    ;
    Ismail I.
    ;
    Mohd Zamri Zahir Ahmad
    ;
    Mohd Wafi Nasrudin
    Combinatorial testing or t-way testing (t represents strength) is useful to detect faults due to interactions. Pairwise testing is one type of t-way testing. The technique is effective in reducing the number of test cases without decreasing the level of coverage. Besides, its purpose is to overcome the problem of exhaustive testing that produces a great number of test cases and is impossible to be implemented due to time and cost constraints. Pairwise T-way Test Suite Generation Strategy based on Ant Colony Optimization (pTTSGA) is introduced to generate a near-optimum test suite size. Experiments have been conducted to evaluate the ability of this strategy for pairwise testing. The results are compared to benchmark results. Overall, pTTSGA produces a comparable test suite size.
  • Publication
    Classification of fish images based on shape characteristic
    ( 2015)
    Mohd Wafi Nasrudin
    This research work has been conducted to analyze and classify the types of fish image based on shape characteristic. The features of characteristic of fish image are extracted by using three Moment Invariants (MI) techniques and Fourier descriptors (FD). The types of Moment invariants are Geometric moment invariant (GMI), United moment invariant (UMI), Zernike moment invariant (ZMI). In the FD’s technique, there are two edge detection have been used to create the boundary of the image, namely Robert cross detection and Sobel cross detection. These feature extraction techniques have been used to analyze the image due to its invariant features of an image based on translation, scaling factor and rotation. There are two ways to examine the performance of feature extraction techniques, namely intra-class analysis and classification analysis. For the intra-class analysis, a set of equations has been implemented to find the best technique among the three different types of moments and Fourier descriptors based on the low value of Total Percentage Min Absolute Error (TPMAE). Meanwhile, for the classification analysis, the Artificial Neural Network (ANN) is explored and adapted to classify the fish images. The feature vectors produce by feature extraction techniques that represent the image are used as the input of classification. The results of the intraclass analysis indicate that the UMI was the best technique among the moment techniques while Fourier descriptor by using the Sobel edge detection shows the lower TPMAE as compared to Robert edge detection. For the classification part, two types of ANN’s which are Multilayer Perceptron (MLP) and Simplified Fuzzy ARTMAP (SFAM) neural networks have been used to classify the image based on fish category. The Leverberg-Marquardt (LM) algorithm is used to train the MLP network in order to check the applicability. Based on the classification that has been computed, the results show that all networks perform good classification performance with overall accuracy is around 90%. However, the MLP trained by Leverberg-Marquardt shows the highest classification performance in classifying the fish images as compared to the SFAM network.
  • Publication
    Development of statistically modelled feature selection method for microwave breast cancer detection
    (Semarak Ilmu Publishing, 2025)
    Vijayasarveswari Veeraperumal
    ;
    Norfadila Mahrom
    ;
    Rafikha Aliana A Raof
    ;
    Phak Len Al Eh Kan
    ;
    Muhammad Amiruddin Ab Razak
    ;
    Bavanraj Punniya Silan
    ;
    Yusnita Rahayu
    ;
    Mohd Wafi Nasrudin
    ;
    Ahmad Ashraf Abdul Halim
    ;
    Nuraminah Ramli
    Microwave technology is very promising tool for breast cancer detection. Microwave transmits and receives UWB signals. UWB signals carries information of the breast cancer. UWB signals need to be pre-processed in order to remove irrelevant and redundant features. Feature extraction and feature selection methods are mostly used to remove the unwanted features. In this paper, a statistically modelled feature selection (SMFS) method is proposed for microwave breast cancer detection. Initially, performance of different feature extraction and feature selection method are analysed using Anova test (p-value) and machine learning (SVM, DT, PNN, NB) accuracy. The best feature extraction and feature selection methods are combined and tested. Based on the performance of feature extraction and feature selection method, Combined Neighbour Component Analysis (feature selection) and Statistical features (feature extraction) are combined and tested. This method is able to achieve up to 85%. The result proves two stage methods are able to improve the accuracy compared to single stage method. Therefore, SMFS is able to detect breast cancer efficiently.
  • Publication
    Edge Enhancement and Detection Approach on Cervical Cytology Images
    ( 2022-09-01)
    Alias N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Mohd Wafi Nasrudin
    ;
    Mansor M.A.s.
    ;
    Alquran H.
    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. Method used in this study is the contrast enhancement technique for pre-processing and edge detection-based for segmentation of the nucleus. In this study, the average performance results of the method showed an accuracy of 96.99% in the seven-class problem using Herlev dataset. The present finding also support this study which concluded the results of accuracy achieved for the algorithm used for nucleus detection is improved by 6.15% when comparing to previous work. The accuracy value is in the lines of earlier literature that achieved accuracy of the approach used above 90% for seven class of cells. The major feature of the suggested approach is an improvement in the ability to anticipate which cells are aberrant and which are normal. Adding more classifiers could improve the suggested system even further. Therefore, a cervical cancer screening system might utilize this framework to identify women who have precancerous lesions.
      1  26
  • Publication
    Laguerre Krawtchouk moment invariants feature extraction technique for shape analysis
    ( 2019)
    Mohd Wafi Nasrudin
    The present study is concerned with the development of Feature Extraction (FE) based on the Moment Invariant techniques. The invariant properties errors were identified, when the shape is examined under the Rotation, Translation and Scaling (RTS) factors. Basically, the feature vector extracted from the original image and its counterpart variation should have similarities in their values. The feature vectors produced by the Moment Invariant techniques, that represent the images, are used as the input of classification. The performance of the percentage correct for image classification depends on the feature vectors from the image itself. Therefore, this study is motivated to develop a new algorithm based on the Moment Invariant by using the polynomials coefficients in order to reduce the invariant properties errors. The proposed technique is called as the Laguerre Moment Invariant (LGMI). The LGMI has been hybridized with the existing Moment Invariant techniques, the Zhi-Krawtchouk Moment Invariant (ZhiKMI) and Krawtchouk Moment Invariant (KMI). The new hybrid techniques are then, called as the Zhi-Laguerre Moment Invariant (ZhiLGMI) and the Laguerre-Krawtchouk Moment Invariant (LGKMI) techniques, respectively. There are five (5) existing Moment Invariant techniques that have been utilized in this work, namely the ZhiKMI, KMI, Racah-Krawtchouk Moment Invariant (RKMI), Legendre Moment Invariant (LMI) and Tchebichef Moment Invariant (TMI) techniques, which will be used to compare with the new proposed techniques. There are two main stages to examine the performance of the Moment Invariant techniques, namely the intraclass and interclass analysis. For the intraclass analysis, a set of equations has been implemented to identify the best technique between the Moment Invariants techniques based on the smallest value of Total Percentage Mean Absolute Error (TPMAE). Meanwhile, for the interclass analysis, three (3) types of Artificial Neural Network (ANN), namely Multilayer Perceptron (MLP), Simplified Fuzzy ARTMAP (SFAM) and Quality Threshold ARTMAP (QTAM), have been utilized to classify the shape images based on classes. From the intraclass results, it was found that the spatial quantization error is the main cause of the reduced Moment Invariants capability. However, the proposed LGKMI technique was found to be capable of producing the best feature vectors with the smallest value of TPMAE. The LGKMI technique is also able to classify different images with the highest percentage of correct classification with over 90% of accuracy for all the three (3) Neural Networks employed in the interclass analysis. Based on the results obtained from the intraclass and interclass analysis, it can be concluded that the proposed techniques, particularly the LGKMI technique, is found to be the best Moment Invariants technique in representing the shape feature.
      16  2
  • Publication
    Moment Invariants Technique for Image Analysis and Its Applications: A Review
    ( 2021-07-26)
    Mohd Wafi Nasrudin
    ;
    Yaakob N.S.
    ;
    Nor Azura Abdul Rahim
    ;
    Mohd Zamri Zahir Ahmad
    ;
    Ramli N.
    ;
    Aziz Rashid M.S.
    The Moment invariant is a feature extraction technique used to extract the global features for shape recognition and identification analysis. There are many types of Moment Invariants technique since it was introduced. To date, many applications still use the Moment Invariant technique as feature extraction technique to extract the features of any images. The reason why the Moment Invariants still valid till today because its capabilities to analyze the image due to its invariant features of an image based on rotation, translation and scaling factors. Therefore, this review paper focuses to elaborate the history of Moment invariants and its applications in related fields. The summary about the advantages and disadvantages of Moment Invariants techniques will be described at the end of this review paper.
      28  2
  • Publication
    Cervical cancer situation in Malaysia: A systematic literature review
    ( 2022-01-01)
    Wan Azani Wan Mustafa
    ;
    Halim A.
    ;
    Mohd Wafi Nasrudin
    ;
    Rahman K.S.A.
    Cervix cancer is one of Malaysia's most significant cancers for women (around 12.9%, with an age-standardised incidence rate of 19.7 per 100, 000). It was higher than other Asian, West, and even worldwide nations. The National Strategic Plan for Cancer Control Program 2016-2020 (Health Ministry) was presented to minimize cancer and mortality. The high incidence of cervical cancer in Malaysia is mainly due to women's insufficient knowledge about its prevention and importance. Compared with traditional literature reviews, the systemic analysis provides many advantages. A clearer review process, a more prominent field of study, and essential priorities that can manage research bias can all help to enhance these reviews. However, better integration, cooperation, and coordination between government and private sector as well as NGOs and professional organisations are essential for optimal cancer control and treatment across the country.
      3  14
  • Publication
    An IoT-based automated gate system using camera for home security and parcel delivery
    ( 2024-02-08)
    Mohd Wafi Nasrudin
    ;
    Jamaluddin A.F.
    ;
    Ismail I.
    ;
    Norfatihah Bahari
    ;
    Vijayasarveswari Veeraperumal
    ;
    Wan Azani Wan Mustafa
    ;
    Abdul Syafiq Abdull Sukor
    ;
    Abdul Rahim A.N.
    The Internet of Things (IoT) has made it possible to set up smart home security and parcel delivery. Therefore, this work proposed an automated gate system using camera for home security and parcel delivery with integrated Internet of Things (IoT). An automated gate system will capture and identify the image of face visitors and delivery riders for admin authentication to open the gate and parcel box. This proposed work is controlled and monitored through mobile apps. The primary purpose and inspiration of this work are to help the delivery rider put the parcel into the parcel box provided if there is no person in the house, and the owner can pick up the parcel without being broken or robbed when she/he comes back home. When the delivery rider presses the button near the gate, the admin will receive the notification "Someone coming,". The admin will click the "okay"button and the system will take a picture using the camera in Blynk App. After the admin verifies that is the delivery rider, the admin will open the box and the delivery rider can access the parcel door box and put the goods inside the box. Another advantage of this work, it also allows familiar people to access our home. The same process with the delivery rider where the visitor needs to press the bell and the admin needs to verify before the visitor can access the single gate. The result indicates that this work is able to monitor and control the gate and parcel door box using an IoT application.
      29  4
  • Publication
    Smart Management Waiting System for Outpatient Clinic
    ( 2023-02-01)
    Mohd Wafi Nasrudin
    ;
    Zainuddin N.S.A.
    ;
    Raja Abdullah Raja Ahmad
    ;
    Rashidah Che Yob
    ;
    Mohd Zamri Zahir Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Vijayasarveswari Veeraperumal
    ;
    Zulkifli N.D.M.
    Queuing has become a common occurrence in malls, train stations, and others. Queuing especially in healthcare intuitions has become a center of attraction because of the long waiting time either at the registration or in receiving treatments. Therefore, in solving this problem, a smart management waiting system for outpatient clinics is developed by using AppGyver and Backendless as the data storage. This system will be operating by QR code scanning for administrators to obtain patients’ personal information before patients obtain the queue number via MyQUEUE mobile application (patients’ interface). By providing queue numbers through the mobile application, patients don’t have to wait in a small uncomfortable waiting lounge instead patients can wait at their desired places such as cafeteria, in their car, and others. Patients also don’t have to worry about missing their turns because there will be a 10 minutes reminder before their turn. Other than that, there is a feature that digitalized the appointment details which means patients don’t have to worry about missing their appointment book or card. The performance of both systems which are the patients’ interface and administrators’ interface is successfully designed and the output obtained. The administrator is able to assign queue numbers, notify patients 10 minutes before consultation time, and assign follow-up appointments to patients.
      2  31
  • Publication
    Cylindrical Dielectric Resonator as Dielectric Matching on Microwave Amplifier for the Unconditionally Stable and Conditionally Stable Transistor at 5 GHz Frequency
    ( 2022-12-01)
    Rashidah Che Yob
    ;
    Mahyuddin N.M.
    ;
    Ain M.F.
    ;
    Mohd Aminudin Jamlos
    ;
    Nur Hidayah Ramli
    ;
    Norfatihah Bahari
    ;
    Mohd Wafi Nasrudin
    ;
    Liyana Zahid
    Stability and matching techniques on microwave amplifier have been an important consideration to maintain their required performances, but typically its frequency dependent. Thus, a frequency variable mechanism is required. The dielectric matching employing the stability and matching techniques on microwave amplifier with cylindrical dielectric resonator has been investigated and realized. The cylindrical dielectric resonator (CDR) with parallel microstrip lines is proposed at 5 GHz frequency for unconditionally stable and conditionally stable transistor as dielectric matching. Hence, the proposed dielectric resonator with +2 mm spacing and 155Ëš of curved configuration indicated the best performances for preliminary study. The result improves the performance of the parallel inhomogeneous CDR by 9.77%. Subsequently, the homogeneous CDR is also successfully working as the variable frequency mechanism for unconditionally stable and conditionally stable transistor at 5 GHz frequency in maintaining their stability performances.
      35  1