Shazmin Aniza Abdul Shukor
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Preferred name
Shazmin Aniza Abdul Shukor
Official Name
Shazmin Aniza, Abdul Shukor
Alternative Name
Shukor, S. A.
Shukor, S. A.A
S. A.A, Shukor
Shukor, Shazmin Aniza Abdul
Abdul Shukor, Shazmin Aniza
Main Affiliation
Pusat Kecemerlangan Kecerdikan Robotik (COFRI)
Scopus Author ID
57214325384
Researcher ID
GSD-2143-2022

Research Output

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  • Publication
    Validation of Electrical Noise of a DC Motor through Controlled Varistor Cracking: An Experimental Study
    ( 2023-01-01)
    Mohd Rudzuan Mohd Nor
    ;
    Zainudin G.
    ;
    Wan Mohd Nooriman Wan Yahya
    ;
    Sofi Y.
    ;
    Nordiana S.
    ;
    Norlaili S.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Zuradzman Mohamad Razlan
    The varistor is an electronic component that protects the DC motor's circuitry from electrical noise or transients that can cause damage. It works as a voltage-dependent resistor that can change its resistance according to the applied voltage. Once the voltage surpasses a specific threshold, the varistor conducts and directs the excess voltage away from the motor's circuitry. In small DC motor manufacturing, ring varistors are vital for reducing electrical noise, minimizing spark-induced damage to the commutator and brush, and extending the motor's lifespan. Additionally, they prevent damage to electronic parts in the customer's mechanism set. The objective of this study is to investigate the impact of varistor cracks or chips that may occur during the soldering process of varistors to the commutator. To confirm the occurrence of cracks or chips, intentional damage will be inflicted on the varistors. The study aims to determine how the presence of cracked or chipped varistors affects the electrical noise produced by a DC motor during its operation. The resulting spark was observed through an oscilloscope, and it was found that the effect could be substantial, up to 5 to 10 times the rated voltage supplied to the motor. In the next phase of this study, further tests will be conducted on motors without varistors to provide a comparison.
  • Publication
    ORIENTATION-BASED PAIRWISE COARSE REGISTRATION with MARKERLESS TERRESTRIAL LASER SCANS
    ( 2019-10-01)
    Mohd Isa S.N.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Rahim N.A.
    ;
    Maarof I.
    ;
    Yahya Z.R.
    ;
    Zakaria A.
    ;
    Abdullah A.H.
    ;
    Wong R.
    In this paper, pairwise coarse registration is presented using real world point cloud data obtained by terrestrial laser scanner and without information on reference marker on the scene. The challenge in the data is because of multi-scanning which caused large data size in millions of points due to limited range about the scene generated from side view. Furthermore, the data have a low percentage of overlapping between two scans, and the point cloud data were acquired from structures with geometrical symmetry which leads to minimal transformation during registration process. To process the data, 3D Harris keypoint is used and coarse registration is done by Iterative Closest Point (ICP). Different sampling methods were applied in order to evaluate processing time for further analysis on different voxel grid size. Then, Root Means Squared Error (RMSE) is used to determine the accuracy of the approach and to study its relation to relative orientation of scan by pairwise registration. The results show that the grid average downsampling method gives shorter processing time with reasonable RMSE in finding the exact scan pair. It can also be seen that grid step size is having an inverse relationship with downsampling points. This setting is used to test on smaller overlapping data set of other heritage building. Evaluation on relative orientation is studied from transformation parameter for both data set, where Data set I, which higher overlapping data gives better accuracy which may be due to the small distance between the two point clouds compared to Data set II.
  • Publication
    Modelling small artefact for preservation – a case study of Perlis heritage
    (IOP Publishing, 2023)
    L Gopal
    ;
    Shazmin Aniza Abdul Shukor
    Heritage preservation is essential for preserving historical sites and cultural artefact for future generations. However, they are prone to damages and destructions due to weather conditions and other factors. 3D models and reconstructions can aid in the conservation of historical sites and artefact. LiDAR (light detection and ranging) technology can be utilized to obtain accurate 3D representation of object or area of interest. This project aims to preserve one of the historical artefact in Perlis, Malaysia which is known as ‘Batu Nisan Acheh’ or the Acheh Gravestone by using 3D model and reconstruction. iPhone 13 Pro Max LiDAR scanner is used to collect the raw dataset of the artefact with Scaniverse application. MATLAB is employed for data processing which includes data filtering, noise reduction, downsampling and 3D surface reconstruction. In addition, a GUI application is also developed in enabling users to upload their desired point cloud files and produce its 3D model for future usage. Results show that the accuracy, effectiveness, and usability of heritage preservation initiatives are improved by combining iPhone 13 Pro Max LiDAR scanning with MATLAB processing, which is useful for virtual displays, restoration, and future study.
  • Publication
    Specific Gravity-based of Post-harvest Mangifera indica L. cv. Harumanis for ‘Insidious Fruit Rot’ (IFR) Detection using Image Processing
    ( 2020-01-01)
    Nurul Syahirah Khalid
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Fathinul Syahir Ahmad Sa'ad
    Bruising and internal defects detection is a huge concern for food safety supplied to the consumers. Similar to many other agricultural products, Harumanis cv. has non-uniform quality at harvesting stage. Traditionally, in adapting the specific gravity approach, farmers and agriculturist will estimate the absence of ‘Insidious Fruit Rot’ (IFR) in Harumanis cv. by using floating techniques based on differences in density concept. However, this method is inconvenient and time consuming. In this research, image processing is explored as a method for non-destructive measurement of specific gravity to predict the absence of ‘Insidious Fruit Rot’ (IFR) in Harumanis cv. The predicted specific gravity of 500 Harumanis cv. samples were used and compared with the actual result where it yielded a high correlation,R2 at 0.9055 and accuracy is 82.00%. The results showed that image processing can be applied for non-destructive Harumanis cv. quality evaluation in detecting IFR.
      10  32
  • Publication
    Mitigating Overfitting in Extreme Learning Machine Classifier Through Dropout Regularization
    (Universiti Malaysia Perlis, 2024-02-14)
    Fateh Alrahman Kamal Qasem Alnagashi
    ;
    Norasmadi Abdul Rahim
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Mohamad Hanif Abd Hamid
    Achieving optimal machine learning model performance is often hindered by the limited availability of diverse datasets, a challenge exacerbated by small sample sizes in real-world scenarios. In this study, we address this critical issue in classification tasks by integrating the Dropout technique into the Extreme Learning Machine (ELM) classifier. Our research underscores the effectiveness of Dropout-ELM in mitigating overfitting, especially when data is scarce, leading to enhanced generalization capabilities. Through extensive experiments on synthetic and real-world datasets, our findings consistently demonstrate that Dropout-ELM outperforms traditional ELM, yielding significant accuracy improvements ranging from 0.19% to 16.20%. By strategically implementing dropout during training, we promote the development of robust models that reduce reliance on specific features or neurons, resulting in increased adaptability and resilience across diverse datasets. Ultimately, Dropout-ELM emerges as a potent tool to counter overfitting and bolster the performance of ELM-based classifiers, particularly in scenarios with limited data. Its established efficacy positions it as a valuable asset for enhancing the reliability and generalization of machine learning models, providing a robust solution to the challenges posed by constrained training data.
      13  1
  • Publication
    Detection of building fixtures in 3D point cloud data
    ( 2021-12-01)
    Humairah Mansor
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Wong R.
    Building architectural and civil engineering are constantly changing, causes the increases of building spaces as well as renovation works which includes structures such as walls, ceilings and floors, and building fixtures. Building fixtures are objects which is secured to the building, such as lighting fixtures, plug and socket, ceiling fan and so on. It is considered as one of the complex structures in building as the size of the fixtures are small and sometimes are hardly seen immediately. When a certain building changes, the building information need to be updated along with the changes of the building. The process to update the changes has contributed towards complex and huge data to be processed which usually involves tedious and complicated work. Therefore, to recognize the fixtures in building environment before renovation, an object recognition method is applied. This investigation focused on the recognition of lighting fixtures in the environments. By using MATLAB, an algorithm is developed to detect the point cloud data that belongs to the lighting fixtures. The investigation shows that the lighting fixtures can be identified by using Region of Interest (ROI) method within an environment. From the results, the accuracy of the dimensions of the lighting fixtures detected in point cloud data compared to the real one in the environment is 75% and 72% match, which is good but still need an improvement to be closely match with the real dimensions. The finding is hoped to simplify the tasks of determining the fixtures in the building before any changes is done.
      1
  • Publication
    An analysis of kinect-based human fall detection system
    (IEEE, 2020)
    Nor Asilah Saidin
    ;
    Shazmin Aniza Abdul Shukor
    Human fall detection system has become one of the most important things especially for indoor environment application. This system has been used in respective areas of elderly care and at child care houses. It helps to detect any human fall and will alert the caretaker about the accident. Kinect sensor can be used to perform the detection due to its capability in scanning and tracking human as well as its affordability. One of the widely used algorithm in human fall detection using Kinect is the skeleton-based method where it works by calculating the distances of every joint with the floor-plane. The joints are detected using the skeleton space coordinate system. When the floor-plane is not visible and the Y-coordinate is less than the given value, a fall is detected. Due to its widely usage, there is a need to study its performance to know the best condition that this algorithm could offer. Performance of selected parameters were observed through a few experiments conducted using Visual Studio as the interface. In this work, a mobile-based Kinect is used due to its mobility and better future implementation for indoor navigation. The best parameter can be identified quantitatively in order to choose the ideal scene that can be used to detect human fall detection using this skeleton-based method. Among the parameters are the distance of the human to the Kinect, the light intensity, the time to track human and the speed of fall. It can be concluded that the most ideal conditions would be at a distance of 3 meters to 3.5 meters with lightings of 1007 lux and of 2 persons at the scene. These conditions can be helpful for others when considering to use the algorithm for human fall detection using Kinect.
      7  4
  • Publication
    Performance evaluation of different devices and algorithms for modelling small artefact
    (IOP Publishing, 2023)
    H Tazudin
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Jalal Johari
    3D reconstruction and modelling play important roles in various applications, specifically in heritage preservation. With the aid of suitable hardware like the 3D sensors as well as respective data processing methods, the work has become more feasible in realizing the aim to conserve and preserve more artefacts. However, too many choices and alternatives might lead to different results which may affecting the preservation purpose. The objective of this work is to analyze and evaluate the performance of different devices and algorithms for small artefact modelling. Two 3D sensors, iPhone 13 Pro Max LiDAR and Structure sensor were selected to collect data of small artefact to be reconstructed and modelled. Two main, important surface reconstruction algorithms which are Poisson and Ball-Pivoting methods were also selected to be tested. Specifications of the sensors’ capabilities as well as modelling results of the artefact are examined. Different parameters of the algorithms were selected to study their effect. These findings will help to learn more about 3D sensors and the suitable modelling methods in making them better for usage in a variety of areas, including archaeology, architecture, and the protection of cultural heritage.
      7  2
  • Publication
    A study of heat insulation methods for enhancing the internal temperature on artificial stingless bee hive
    ( 2024)
    Muhammad Ammar Asyraf Che Ali
    ;
    Bukhari Ilias
    ;
    Norasmadi Abdul Rahim
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Abdul Hamid Adom
    ;
    Mohd Fauzi Abu Hassan
    The stingless bees have gained a large popularity among the beekeepers, particularly in tropical and subtropical regions such as the Americas, Africa, and Southeast Asia. This is because the honey of stingless bees has a distinct flavour and is highly valued for its medicinal qualities. Traditionally, stingless bee colonies constructed from wood logs are fragile and vulnerable to outside attacks. These predator or parasite attacks can cause Colony Collapse Disorder (CCD) if not eliminated. Thus, a PVC, 3D-printed PET-G, and acrylic artificial hive has been created to replace the old one. According to earlier research, stingless bees are especially susceptible to temperatures above 38°C. This paper's main goal is to discuss the results of studies on the best artificial hive insulation method. Over a month and a half, clay, wood powder, polystyrene, bubble aluminium foil, and a water- cooling system were tested as insulators. Results shows that artificial hives with bubble aluminium foil have the biggest average difference between internal and external temperatures (6.4°C) and are closest to traditional hives (8.6°C). The average temperature difference between the artificial hive's exterior and inside was 2.9°C without heat insulation. Clay-insulated artificial hives have the lowest standard deviation value for humidity at 0.46. Since temperature is vital to stingless bee survival, bubble aluminium foil container is the best insulation solution since it increases heat resistance more than other materials.
      4  19
  • Publication
    An Intelligent Classification System for Trophozoite Stages in Malaria Species
    ( 2022-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mohd Yusoff Mashor
    ;
    Mohamed Z.
    ;
    Way Y.C.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Jusman Y.
    Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnificat i on of t hi n bl ood smear usi ng mi croscope observat i on. However, t he microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of Plasmodium Knowlesi (PK), Plasmodium Falciparum (PF) and Plasmodium Vivax (PV) as early species identification. The process begins with image acquisition, image processing and classification. The image processing involved contrast enhancement using histogram equalisation (HE), segmentation procedure using a combination of hue, saturation and value (HSV) color model, Otsu method and range of each red, green and blue (RGB) color selections, and feature extraction. The features consist of the size of infected red blood cell (RBC), brown pigment in the parasite, and texture using Gray Level Co-occurrence Matrix (GLCM) parts. Finally, the classification method using Multilayer Perceptron (MLP) trained by Bayesian Rules (BR) show the highest accuracy of 98.95%, rather than Levenberg Marquardt (LM) and Conjugate Gradient Backpropagation (CGP) training algorithms.
      24  1