Shazmin Aniza Abdul Shukor
Thumbnail Image
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
Now showing 1 - 8 of 8
  • Publication
    A study of embedded fuzzy logic to determine artificial stingless bee hive condition and honey volume
    ( 2024)
    Bukhari Ilias
    ;
    Norasmadi Abdul Rahim
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Abdul Hamid Adom
    ;
    Muhammad Ammar Asyraf Che Ali
    ;
    Mohd Al-Haffiz Saad
    ;
    Mohd Fauzi Abu Hassan
    Stingless Bee is particularly nutrient-dense in his honey. Therefore, numerous beekeepers for the Stingless Bee have begun this agricultural enterprise, particularly in Malaysia. However, beekeepers encounter challenges when caring for an excessively large stingless bee colony. Due to the risk of causing colony disruption, the beekeeper cannot always access the hives to monitor honey volume and hive condition. Consequently, the purpose of this paper is to aid beekeepers and prevent disruption to bee colonies by determining the condition of the hive and the quantity of honey using an embedded fuzzy logic system. Artificial hives have been created in order to easily measure the weight of a hive of stingless bees and to divide the honey compartment from the brood compartment in order to calculate the honey volume. Since the stingless bee designs its colony with honey on top and larvae on the bottom, honey volume can be determined by weighing the honey compartment using load cell and internal humidity using dht22. DHT22 is used for measuring the internal temperature and humidity, as previous papers have stated that the hive condition can be determined using the internal temperature and humidity. Morever, FLDa (Fuzzy Logic Designer app) by MATLAB was subsequently utilised to construct membership function, rules, fuzzification, and defuzzification. Then, the same input, membership function, and rules that used in FLDa will be implemented on the Nodemcu ESP8266 using eFLL (Embedded Fuzzy Logic Library). A comparison between the crisp output from FLDa and the crisp output from eFLL was conducted to determine whether eFLL is suitable for use in the NodeMCU ESP8266. As a consequence, the standard deviationand averaged percentage error of differences for hive condition, which is 0.22 and 0.17%, isless than the honey volume, which is 0.49 and 0.66%, because hive condition has a strict correlation with temperature. The hive condition will be rated bad (0% when the temperature is cold or hot state), but it will be rated good (100% when the temperature is normal state). As for honey volume, the majority of results correspond to the percentage of honey compartment weight, unless the humidity is dry state, which will cause the value to be cut in half. Finally, the fuzzy logic system is effectively implemented into an embedded system, making it easier for the beekeeper to monitor the hive condition and honey volume without interfering with the activity of stingless bees.
  • 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
    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.
  • 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.
  • Publication
    Feature extraction for underground object reconstruction from Ground Penetrating Radar (GPR) data
    ( 2022)
    Shazmin Aniza Abdul Shukor
    ;
    Havenderpal Singh
    ;
    Nurush Syamimie Mahmud
    ;
    H. Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    T.S. Tengku Amran
    ;
    M.R. Ahmad
    Ground Penetrating Radar (GPR) is very beneficial for underground object scanning and detection. It utilises radar pulses as the signal, hence it able to penetrate surfaces in obtaining the underneath information without disturbing and destructing the ground. However, its radargram output in hyperbolic signal are very challenging to be analysed. Thus, suitable algorithm has to be designed and developed to interpret the data. This work highlights on the usage of drop-flow algorithm in detecting important features of the hyperbolic signal. Previous study has shown that these features is promising in understanding and further, reconstructing the GPR data. Results show that the features extracted from the hyperbolic signal able to be identified for further processing, which is necessary for visualization purpose.
  • 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.
  • 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.
  • Publication
    An overview of object detection from building point cloud data
    ( 2021-06-11)
    Humairah Mansor
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Wong R.
    3D laser scanner, also known as LiDAR (Light Detection and Ranging), is a device that able to collect dense representation of its surroundings. Its data in point cloud form is commonly used to monitor complex environments like the highways, infrastructures and buildings. The rapid development of 3D laser scanner nowadays has assisted the process of managing complicated and huge areas, especially in building and facility management. As the advancement in architectural and civil engineering increases, building spaces change frequently, as well as renovations work which consists of several items like structures (walls, ceilings, floors) and building fixtures (doors, windows). This has contributed towards complex and huge data to be processed which usually involves tedious and complicated work. Therefore, this data needs to be handled efficiently. Object recognition and classification is one of the most important process in point cloud data since it provides a full detail of building information. Object recognition is used to recognize multiple objects in point cloud data and classification process is used to classify the objects into a class based on the criteria of the objects. These processes reduce the noise and size of point cloud data to be processed. This paper provides an overview on data processing approaches, which focused on the process of object detection and classification, especially for buildings, as part of Building Information Management (BIM) and the possibility of future research in BIM modelling.