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
    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
    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
    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.
  • Publication
    Ground penetrating radar for buried utilities detection and mapping: a review
    ( 2021-12-01)
    Hasimah Ali
    ;
    Ideris N.S.M.
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Amran T.S.T.
    ;
    Ahmad M.R.
    ;
    Rahim N.A.
    ;
    Shazmin Aniza Abdul Shukor
    This paper presents a review on Ground Penetrating Radar (GPR) detection and mapping of buried utilities which have been widely used as non-destructive investigation and efficiently in terms of usage. The reviews cover on experimental design in GPR data collection and survey, pre-processing, extracting hyperbolic feature using image processing and machine learning techniques. Some of the issues and challenges facing by the GPR interpretation particularly in extracting the hyperbolas pattern of underground utilities have also been highlighted.
  • Publication
    Wireless mass air flow device for thermal comfort data acquisition
    ( 2024)
    Ismail I. Ibrahim
    ;
    M. N. I. Mohamad Zubir
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Muhajir Ab. Rahim
    ;
    K. Kohlhof
    This paper aims to build and implement an IoT-based mass air flow sensor device using the FS7 sensor from IST Innovative Sensor Technology and the ESP32. The scope of the project includes design and implementation of the device, the evaluation of its performance, and the presentation of the results. To achieve its objective, the project will employ literature evaluation, hardware design, programming, testing, and data analysis methodologies. The IoT-based mass air flow sensing device has the potential to improve the performance of air flow-dependent systems by providing real-time data, remote monitoring and control, and enhanced precision and dependability. In addition, it will be calibrated, maintained, and upgraded remotely, decreasing the need for on-site maintenance and extending the device's lifespan.
  • Publication
    Automatic Recognition System of Iron Deficiency Anaemia in Human RBC using Machine Learning Techniques
    ( 2023-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Ibrahim W.N.A.B.W.
    ;
    Nordin S.A.
    ;
    Tohit E.R.B.M.
    ;
    Ali H.B.
    ;
    Nordiana Shariffudin
    ;
    Siti Marhainis Othman
    Iron Deficiency Anaemia (IDA) is the most common blood disorder. According to WHO, 30% of women aged 15-49 years, 37% of pregnant women, as well as 40% of children aged 6-59 months are anaemic globally. Anaemia can cause premature birth and affect mental, physical, and cognitive development, which in turn will lead to birth weight problems and stunted birth. The process of detecting IDA is usually captured based on a thin blood smear utilizing microscopic observation. Nevertheless, this process can be time-consuming. Moreover, it is challenging to identify the difference between IDA and normal red blood cells (RBCs) because the size is similar based on the observation of the human eye. It will cause difficulty in giving drug treatment to patients. A computeraided diagnosis (CAD) method was created to automatically distinguish between IDA and normal RBCs. The processes started with image acquisition, image processing, and recognition. Additionally, a Graphical User Interface (GUI) is also used to display images. In conclusion, recognition was done using the Multilayer Perceptron (MLP) method. The findings indicate that the proposed automated system is effective at distinguishing between IDA as well as normal RBCs, having an accuracy of 97.58% with regard to training and 98% regarding validation utilizing Levenberg-Marquardt (LM) trained MLP.
  • Publication
    Computational fluid dynamic (CFD) of air conditioning system for human thermal comfort analysis: A simulation study
    ( 2019-04-08)
    Ali A.M.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Rahim N.A.
    ;
    Zuradzman Mohamad Razlan
    ;
    Zul Azhar Zahid Jamal
    ;
    Kohlhof K.
    Human thermal comfort is very important especially in an indoor environment because it may effect human's health and welfare. Air conditioning (AC) system has become a necessary tool for indoors to maintain human comfort. This is especially applied to places with high strength of solar radiation, high relative humidity, high air temperature, and low air speed areas which are considered the most critical climate effect for indoors such as in Thailand, Malaysia, and Singapore. However, the current mechanism of the AC system allows the user to set it into maximum cooling, i.e. at the lowest temperature with highest fan speed. This setting does not necessarily create a thermally comfortable environment inside the room, but could contribute towards negative impact to human. Thus, there is a need to study the possibility of integrating the element of thermal comfort with the AC system. This project will concentrate on a case study of the effect of AC towards human thermal comfort in an indoor environment by using computational fluid dynamic (CFD) flow simulation. It adapts an enhanced Predicted Mean Vote (PMV)-based algorithm in creating the thermal comfort environment. The simulation uses SOLIDWORKS software and concentrates on a small sized room with one person doing sedentary work. The flow simulation is done on four conditions-maximum cooling setting and other three that is based on the enhanced PMV algorithm, which were then analyzed and compared. Based on the results, it is shown that the enhanced PMV-based algorithm could provide a thermally comfortable environment compared to the maximum cooling setting.
  • Publication
    IoT-based Carbon Monoxide (CO) Real-Time Warning System Application in Vehicles
    ( 2021-12-01)
    Kamarudin A.A.A.
    ;
    Nurul Syahirah Khalid
    ;
    Ismail Ishaq Ibrahim
    ;
    Muhammad Aizat Abu Bakar
    ;
    Mahadi M.Z.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Sukhairi Sudin
    ;
    Mohd Zamri Hasan
    ;
    Humairah Mansor
    The project is about develop a system and application for detect the presence of Carbon Monoxide(CO) in car, since recently there are many cases of drowning while sleeping in car due to inhaling CO. The build system are able to detect the presence of CO and provide warning about level of CO to the users. It uses Blynk application to monitors level of CO inside the vehicle, MQ-9 gas sensor as the input sensor, ESP 8266 as medium to send data to the application via IoT-based and the level concentration of CO is displayed on the LCD in real-time displayed. For the output, it has 3 different condition based on the level concentration of CO. This project has been testing in six different situation. Based on the result, ambience air and in car with open window situation have lowest of CO level. Meanwhile, the highest of CO level is detect in smoke that are produced from fuel combustion of the car exhaust at distance 5 cm. Additionally, Principal Component Analysis (PCA) is used to analysed the ability of this system in clustering for each situation. As a result, PCA have clearly clustering data for every situation with the value of PC1 is 71.82% and PC2 is 28.18%, hence it is verified that the build system is able to applied in detecting the presence of CO. This project is believed able in helping to reduce the numbers of cases people drowning while sleeping due to inhaling CO in the car.
      1
  • Publication
    Hand-held shelf life decay detector for non-destructive fruits quality assessment
    ( 2024)
    Nurul Syahirah Khalid
    ;
    Nordiana Shariffudiin
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Humairah Mansor
    ;
    Masturah Tunnur Mohamad Talib
    ;
    Sukhairi Sudin
    ;
    Ismail I. Ibrahim
    ;
    Mohd Zamri Hasan
    ;
    Siti Marhainis Othman
    ;
    N.D.N Dalila
    ;
    M.Thaqif B.N Ashimi
    Perishable food such as fruits have a limited shelf life and can quickly degrade if not properly stored. One method for detecting decay in these foods is the use of ethylene gas. Ethylene is a naturally occurring hormone that is released by fruits as they ripen. By measuring the levels of ethylene in the storage area, it is possible to detect when fruits and vegetables are starting to degrade. This information can then be used to act, such as removing spoiled produce and adjusting storage conditions, to extend the shelf life of the remaining products. By utilizing ethylene gas for early detection of decay, it is possible to improve food safety and reduce food waste. The project aims to utilized ethylene gas from perishable food such as fruits before decay. This project proposed portable or hand-held detection ethylene gas by including temperature and humidity. The sensor will be measuring the level of ethylene gas, temperature and humidity. Next, machine learning method; K-Nearest Neighbour(KNN) were used to evaluate the accuracy of the proposed system. This project, a hand-held decay detector for perishable food products is believed can help to prevent food waste by detecting early signs of spoilage in fruits.
      1  2
  • Publication
    Development of Artificial Stingless Bee Hive Monitoring using IoT System on Developing Colony
    ( 2024-01-01)
    Ali M.A.A.C.
    ;
    Bukhari Ilias
    ;
    Norasmadi Abdul Rahim
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Abdul Hamid Adom
    ;
    Saad M.A.H.
    ;
    Hassan M.F.A.
    The trend of stingless bees’ farm in Malaysia has increased recently as it has been proven that its honey gives various benefits to human beings. This trend requires beekeepers to do more frequent inspections of beehives. However, the current practice of opening the cover to inspect the colony and honey will disrupt colony activity. According to a recent study, these stingless bees can only survive between 22 and 38 degrees Celsius, and harsh weather conditions might lead to the collapse of bee colonies. In order to ensure a consistent honey production, the IoT monitoring system will be implemented on an artificial stingless beehive. The system is equipped with an embedded system that utilizes a NodeMCU ESP8266, temperature and humidity sensors, and load cell sensors. Next, honey compartment weight, temperature and humidity inside stingless beehive, and temperature and humidity outside stingless beehive will be uploaded to the Internet of Things (IoT) platforms, namely Thingspeak and Cayenne. The data is sent to Thingspeak via the REST API while to Cayenne by the MQTT API. All data from the artificial stingless bee hive indicating the occurrence of colony rising and has been uploaded to the IoT platform. By analysing the data that were recorded for 13 days, all of the input data such as the weight of the honey compartment, the temperature in the hive, and the humidity in the hive, display its respective characteristics. For the honey compart weight, it has been found that the stingless bee colony is rising as a result of the increasing honey and colony in the compartment weight. Regarding the hive temperature, it has been determined that the temperature inside the hive is stable around 26°C to 38°C in normal weather conditions. Whereas for humidity inside the hive, it is remained between 76.5% and 85.6% due to the moisture from the honey inside the compartment. Lastly, these results indicate that the colony living in the artificial hive of stingless bees is healthy and growing.
      1