Muhammad Naufal Mansor
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Preferred name
Muhammad Naufal Mansor
Official Name
Muhammad Naufal, Mansor
Alternative Name
Mansor, Muhammad Naufal
Mansor, N.
Naufal Mansor, Muhammad
Mansor, Muhammad Naufal Bin
Mansor, M. N.
Main Affiliation
Scopus Author ID
36469792500
Researcher ID
DHG-8694-2022

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  • Publication
    Development of Surveillance Hovercraft via Arduino
    ( 2024-02-01)
    Muhammad Naufal Mansor
    ;
    Syahrul Affandi Saidi
    ;
    Wan Azani Wan Mustafa
    ;
    Roejhan Md Kawi
    ;
    Mohd Aminudin Jamlos
    ;
    Talib N.A.A.
    ;
    Mohd Zamri Hasan
    The current research focuses on the development of hovercraft via Arduino. The vehicle is designed with bag skirt structure in order to reduce friction for smooth operation. Nowadays, there are a lot of natural disaster occur in everywhere especially flood. However, hovercraft is a vehicle that need a driver to drive which can cause a danger to the rescuer. Based on this problem, a wireless hovercraft is needed to develop. This study explains a hovercraft which is able to control the movement of the hovercraft from the surface. The design of the hovercraft was successfully made by using AutoCAD software. Furthermore, the material of the body was made from the insulation foam while the microprocessor is Arduino UNO R3. There are two brushless DC motors and one servo motor that used for this hovercraft. The first brushless DC motor which is located below the hovercraft is used as a hover operation, while the second motor located behind it is used to ensure the hovercraft move forward. In addition, the performance of the hovercraft was successfully tested on the 3 different surfaces. As a result, the highest performance is on the cement while the lowest is on the grass.
      2  26
  • 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  31
  • 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
    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%.
      2  21