Noor Anida Abu Talib
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Preferred name
Noor Anida Abu Talib
Official Name
Noor Anida , Abu Talib
Alternative Name
Talib, Noor A.A.
Talib, N. A.Abu
Talib, N. A.
Talib, N. A.A.
Abu Talib, Noor Anida
Abu Talib, N. A.Binti
Main Affiliation
Scopus Author ID
58465259200
57196875994
Researcher ID
CBI-7985-2022

Research Output

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  • Publication
    Arduino IOT Based Inventory Management System Using Load Cell and NodeMCU
    ( 2023-11-01)
    Muhammad Naufal Mansor
    ;
    Noor Anida Abu Talib
    ;
    Syahrul Affandi Saidi
    ;
    Wan Azani Wan Mustafa
    ;
    Zamri N.F.
    Nowadays, everything is made simpler with information and communication technological advancements. It is preferable to track and monitor using devices rather than perform it manually. This resulted in the rapid growth of Internet of Things (IoT) technology and relevant markets. Low cost IoT products has made access to IoT much easier and desirable. These low cost IoT devices and related technologies are widely used in areas such as educational, transportation, tracking, inventory management and many more. The use of Arduino and RFID in the inventory management system lacks in some areas including hardware limitations. In conjunction to the limitation of using an Arduino and RFID technology, this project aims to develop an IoT based inventory management system that incorporates the uses of a NodeMCU and a load cell. In comparison of the NodeMCU to an Arduino, the NodeMCU stands out with the built in Wi-Fi module with much higher processor and additional properties of it being much smaller. While the use of a load cell is much more convenient as to suit all kinds of inventory management needs compared to the use of RFID that suits better to larger scale businesses with larger inventory and massive stocks. Towards the end, this project is expected to ease inventory management by the implementation of IoT with IoT Based Inventory Management System using Load Cell and NodeMCU. The project will generate the inventory count and automatically stores data in the cloud platform. These data can be accessed with internet connection. The project also alerts users when the inventory is low or high in balance. The output of the project is that the project’s working prototype was successfully developed. Overall, the project is a success as all the objectives of the project was successfully achieved.
  • Publication
    Development of Automatic Mini Fan with Human Detector by Using PIR Sensor
    ( 2020-12-18)
    Noor Anida Abu Talib
    ;
    Kamarudin, Farhah
    ;
    Shema S.S.
    ;
    Mohd Azri Abd Aziz
    This project will present the design, construction, development, control and evaluation of an automatic function of electric fan. Fan is the important thing for circulation of air. The development of Automatic Mini Fan with Human Detector System by Using PIR Sensor presented in this project is required to fulfill the requirement of technologies today and it had been fabricate with new design. The automatic mini fan with two types of power supply which is Alternating Current (AC) and Direct Current (DC) can continuously function if one of the power supplies cannot be used. The human detection systems by using PIR sensor are implemented in this project and the detection range is up to 13 feet. The temperature sensors which maximum 70 C that are used in this project can automatically control the speed of fan up to 225 rpm. This automatic mini fan with human detection system contains combination of sensor, controller, motor and two types of power supply that controlled by Arduino UNO as the main controller. This project also presents the expected performance of the automatic mini fan with human detector system which the fan can rotate 0 to 180 and the construction of hardware and software development to gather the performance data. Finally, this project can give many benefits to people because it is portable and can save electricity. The result of this project becomes useful in the future.
  • Publication
    Liquid Composition Identification and Characteristic Measurement Using Ultrasonic Transmission Technique via Neural Network
    ( 2023-08-01)
    Muhammad Naufal Mansor
    ;
    Yahya S.
    ;
    Wan Azani Wan Mustafa
    ;
    Habibah Mokhtaruddin
    ;
    Syahrul Affandi Saidi
    ;
    Ilham Shafini Ahmad Mahyudin
    ;
    Mohd Aminudin Jamlos
    ;
    Noor Anida Abu Talib
    ;
    Mohd Zamri Hasan
    This project is to determine the composition of liquids solvent by using the ultrasonic frequency signal from echoscope scan machine. The transmission technique of ultrasonic signal is focused. On the research experiment, studies on mixing of distilled water with control sodium chloride (Kitchen Salt), kitchen sugar and monosodium glutamate (MSG). The Parameters such as Fast Fourier Transform (FFT) which is the parameters are using to identify the ratio of composition of liquid solvent. The feature extraction of median, average and root mean square (RMS) from FFT is represented with different result analysis such as sensitivity, specificity, accuracy, Area under curve, kappa, F-measure and precision. The results performed more than 90% with Neural Network.
      1
  • Publication
    Aquaponic Ecosystem Monitoring with IOT Application
    ( 2023-08-01)
    Muhammad Naufal Mansor
    ;
    Mohd Zamri Hasan
    ;
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Wan Azani Wan Mustafa
    ;
    Syahrul Affandi Saidi
    ;
    Mohd Aminudin Jamlos
    ;
    Noor Anida Abu Talib
    Aquaculture is an agricultural technology that combines aquaculture (fish farming activities) with hydroponic activities (planting crops without soil media) in one circulation. The most important element in aquaculture is the existence of fish, plants, and bacteria. These three elements form a mutually beneficial relationship or symbiotic mutualism. The main purpose of the aquaculture system is to maintain water quality and reduce ammonia levels from the water so that it can be utilized by other organisms. In addition, aquaculture can also save space and can produce two types of human food simultaneously, plants and livestock. Agricultural technology design with Aquaculture also uses the concept of Internet of Things (IoT) as information from sensors and sensors of value generator is accessible through applications installed on smartphones from anywhere with an Internet connection. Development of monitoring of aquaponic ecosystems with IoT systems was developed using a program using micro-controls to control temperature, humidity, pH levels and water pumps. There are some improvements made to this project.
      2  2
  • Publication
    Application System Development of Accident Prevention and Safety Assistance using IoT Application
    ( 2023-08-01)
    Muhammad Naufal Mansor
    ;
    Mohd Aminudin Jamlos
    ;
    Wan Azani Wan Mustafa
    ;
    Rahmat M.A.
    ;
    Syahrul Affandi Saidi
    ;
    Noor Anida Abu Talib
    ;
    Mohd Zamri Hasan
    The number of road accidents in Malaysia shows a steady increment from 2010 to 2019, reported by the Ministry of Transport Malaysia. This project aims to develop a system to prevent an accident by detecting aggressive driving. If an accident occurred, this system would send an alert for an immediate response, which is crucial to reduce the fatality rate. An accelerometer is utilized to detect aggressive driving and accident events. The method to detect aggressive driving is by determining an abrupt change in acceleration. For accident detection, the vehicle tilt angle and acceleration are monitored. An ESP32 SIM800L microcontroller processes the inputs and alert a web-based cloud service and a set phone number by Short Message Service (SMS). The microcontroller is used due to the embedded Global System for Mobile Communications (GSM) and other wireless communication modules. The small form factor gives an advantage in terms of mounting location flexibility. The alert contains the type of event, time, and location. This report contains the development of the proposed system, which includes the simulation for the system circuit and motion simulation. Accident detection, falls, SMS alerts and online alerts are consistently successful, while aggressive driving detection is inconsistent. Live tracking does not directly work during these detections. In conclusion, this project successfully detects accidents and sends alerts via SMS and internet using a Subscriber Identity Module (SIM) card.
      1
  • Publication
    Leukemia Blood Cells Detection using Neural Network Classifier
    ( 2023-12-01)
    Muhammad Naufal Mansor
    ;
    Mohd Zamri Hasan
    ;
    Wan Azani Wan Mustafa
    ;
    Farah Hanan Mohd Faudzi
    ;
    Syahrul Affandi Saidi
    ;
    Mohd Aminudin Jamlos
    ;
    Noor Anida Abu Talib
    ;
    Ahmad Kadri Junoh
    Image segmentation is an image processing operation performed on the image in order to partition the image into some images based on the information contained in the original image. Image segmentation plays an important role in many medical imaging applications, image segmentation facilitates the anatomy process in a particular body of human body. Classification and clustering are the methods used un data mining for analyzing the data sets and divide them on the basis of some particular classification rules. There are many image segmentation tools that used for medical purpose, so it is necessary to define and/or to improve the image segmentation methods in order to get the best method. In this study, the image of leukemia and red blood cells will be used as samples to determine the best algorithm in image segmentation. The procedure for doing segmentation itself is clustering image, edge detection on image, and image classification. The clustering is to extract important information from an image. The edge detection is to determine the existence of edges of lines in image in order to investigate and localize the desired edge features. Moreover, the classification analyzes the properties of some images and organizes the information into certain categories. In this study, the Neural Network and K-Nearest Neighbor are used for image classification by paired with Local Binary Pattern and Principal Component Analysis. The results revealed that the best method of proven in classifying images is from Local Binary Pattern feature extraction with the average accuracy of 94%.
      1