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.
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Scopus Author ID
36469792500
Researcher ID
DHG-8694-2022

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Environmental lighting towards growth effect monitoring system of plant factory using ANN

2025-01 , Mohamed Mydin Hj M.Abdul Kader , Muhammad Naufal Mansor , Wan Azani Wan Mustafa , Ahmad Anas Nagoor Gunny

Malaysia is currently driven to become another most developed country in the world. Among other priority sector is Food Sustainability. Along the process, our vegetable supply-demand keeps increasing by year. Compared to traditional systems, closed systems or its other name called hydroponic is getting more important for plant production, with artificial light which has many potential advantages, including better quality transplants, shorter production time and less resource use. To gain full profit from it, the quality of vegetables needs to be controlled efficiently. Climate conditions, especially temperature and light intensity, have a significant impact on vegetable growth and yield, as well as nutritional quality. Plant growth and development are influenced by a variety of environmental factors, the most important one is light intensity. Among the problems to be tackled in this research are plant growth manual observation, light intensity variation and abundance of growth-related data to be evaluated manually. Therefore, to solve these problems, the specific type of vegetable used here is lettuce. The proposed methods are, observation of plant growth conducted automatically round the clock in intervals of 15 minutes for the whole month (estimated mature period of lettuce), using images captured. At the same time, the proposed light intensity which is red & white to the ratio of 2:1 (optimum ratio recommended by previous researchers) will be used. The issue of data to be evaluated manually will be solved using Artificial Neural Network (ANN) architecture, in specific Deep Learning. Concisely, the results & analysis shows the research is successfully developed for plant growth monitoring by using artificial neural network which, reached 80% to 90% accuracy in the training and validation session that made the architecture sufficient for determining the growth of the said vegetable. This is indeed foreseen, will highly assist the farmer in better monitoring the growth rate of the plant.

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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.

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Development of Real Time Night Vision Camera Monitoring Robot Integrating DTMF and GPS System

2020-09-21 , Sulong M.M.S. , Mohd Zamri Hasan , Nordiana Shariffudin , Busari M.A. , Muhammad Naufal Mansor

In general, spy robots were employed mostly in military field to patrol over the country border and also can be assigned for a rescue and search mission. Even though this technology has a rapid growth recently, the major problem is what is there in Malaysia security sector, there are many lacks in this technology if compared to other countries. Furthermore, most robots use the RF technology which means the person can only monitor or control the robot within a limited range. Even though the patrol robot can be operated from a long range, there is a circumstance that it can be hard to be located or tracked. Moreover, the ordinary camera can't deliver a better performance under dark circumstances. In view of confinements that have been featured previously, this project plans to develop a mobile patrol robot with wireless night vision camera that can be controlled by using DTMF and GPS system that can be used in military field. There are several parts to be in implement in this project as following; software simulation & hardware development of robot. As example the DTMF technology, GPS system and wireless night vision camera as well are implemented in this project so the working principle of the robot features needs to be simulated and programmed into the robot using Arduino and Proteus software. Only then if the simulation is succeeded the progress is then proceeded to the hardware development of the robot. Later the successful simulation is integrated into the robot to make the robot can be fully operated. To conclude, this robot is supposed to be able to easily be tracked by GPS system and monitored from long range using DTMF while performing such monitoring or guarding duty at the country border or other public areas.

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A modified retinex illumination normalization approach for infant pain recognition system

2014 , Muhammad Naufal Mansor

Pains in newborn babies are monitored in a Neonatal Intensive Care Unit (NICU) for medical treatment. Pain in newborns can be detected by studying their facial appearance. Even though the outcome is acceptable, it is not adequately vigorous to be used in unpredictable, non-ideal situations such as noise and varying illumination environment. First, to improve the noise cancellation robustness an adaptive median filter (AMF) is proposed. Mean and variance of median values are selected to generate a weight for each window part of the images such as 3x3, 5x5 or 7x7. Various linear and nonlinear filters are adopted to eliminate the noise in the images. Quantitative comparisons are performed between these filters with our AMF in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Mean Structural SIMilarity (MSSIM) Index. The average results show improvement in terms of 40.63 db for PSNR, 6.01 for MSE, 258.09 for IEF and 0.97 for MSSIM respectively. In this work a novel method of illumination invariant normalization known as Modified Retinex Normalization (MRT) for preprocessing of infant face recognition is proposed. This is based on a modified retinex model that combines with histogram normalization for filtering the illumination invariant. The proposed method is compared to other methods like Single scale Retinex (SSR), Homomorphic method (HOMO), Single Scale Self Quotient Image (SSQ), Gross and Brajovic Technique (GBT), DCT-Based Normalization (DCT), Gradientfaces-based normalization technique (GRF), Tan and Triggs normalization technique (TT), and Large-and small-scale features normalization technique (LSSF) for evaluation with Infant Classification of Pain Expressions (COPE) database. Several experiments were performed on COPE databases. Single PCA, LBP and DCT feature extraction information yielded a good recognition result. However, by summing these three, it gives more robustness to noise and illumination classification rate because the sum rule was the most resilient to estimate errors and gives higher than 90% accuracies of pain and no pain detection. The new illumination normalization and combination of features gives higher results of more than 90% on five different classifiers with various algorithms such as k-nearest neighbors (k-NN), Fuzzy k-nearest neighbors (FkNN), Linear Discriminat Analysis (LDA), Feed Forward Neural Network (FFNN), Probabilistic Neural Network (PNN), General regression Neural Network (GRNN), SVM Linear kernel (SVMLIN), SVM RBF kernel (SVMRBF), SVM MLP kernel (SVMMLP) and SVM Polynomial kernel (SVMPOL) with different performance measurement such as Sensitivity, Specificity, Accuracy, Area under Curve (AUC), Cohen's kappa (k), Precession , F-Measure and Time Consumption .

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Fusion wind and solar generation forecasting via neural network

2021-08-27 , Mahmoud Mustafa Yaseen Mohammed Al Asbahi , Muhammad Naufal Mansor , Mohd Rizal Manan , Mohd Azri Abd Aziz , Roejhan Md Kawi , Farah Hanan Mohd Faudzi

Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore. power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction proposed in this paperwork to support a small microgrids system. The system is a combination of hardware of solar panel, wind turbine, hybrid charge controller, current sensor, voltage sensor circuit, battery, Arduino Mega and personal computer that is install with MATLAB along with artificial neural network model for load forecast. The prediction model is known as Feedforward back propagation (FFBP) artificial neural network (ANN), this method utilizes a learning relationship between wind-solar power output and predicted weather. The FFBP model trained ANN to recognize similar pattern and to predict the output power based on train and tested data and the results achieved 99.5 accuracy, 6.25% MAPE and 1.2 % MAD.

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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.

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Breast Cancer Detection on X-Tray Mammogram Images

2023-01-01 , Azmi M.A.A. , Wan Azani Wan Mustafa , Alquran H. , Aziz A.A. , Muhammad Naufal Mansor , Alzubaidi L.H. , Hussein A.H.A.

Breast cancer (BC) is a common cancer affecting women everywhere in world. Mammography is identified and efficient technique to detect primary BC. The aim of project is for detect BC on mammogram in order to categorize disease by image processing when comparing with a previous technique. Through utilizing conventional methods, it creates it complex for radiology to detect cancer from patient's breast. In addition, there are environmental disturbances and technical problems if using the old method. Image processing techniques was separated to numerous elements. The elements are input, pre-processing, segmentation, morphological, object classification and classification. First pre-processing was done through Weiner and Median filter. Then, thresholding method on segmentation and finally, morphological will eliminate limitations at a segmentation. The image classified into 2 classes like normal and tumor. Both type of images analyzed based on elements. Additionally, it comprises a building of Graphical User Interface (GUI) which is utilized to generate the system as user-friendly. The developed model attain accuracy of 93.71 %, specificity of 82.53 % sensitivity of 94.36% for tumorous images.

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Urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IOT)

2025-01 , Mohamed Mydin Hj M.Abdul Kader , Muhammad Naufal Mansor , Wan Azani Wan Mustafa , Ahmad Anas Nagoor Gunny

As an introduction to this project, the growth-related traits, such as above-ground biomass and leaf area, are critical indicators to characterize the growth of indoor lettuce plants. Currently, non-destructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. It is also one of the problem statements in this project. Based on this project the next problem is manual control of nutrients may cause quality issues to the lettuce plant. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted. This project is about urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IoT). In this project, a method for monitoring the growth of indoor lettuce plants was proposed by using digital images and an ANN using Deep Learning Architecture. DLA is mostly developed by the software of MATLAB or Python to insert and run the coding. DLA is mostly used for image detection, pattern recognition, and natural language processing through the graph for Neural Network. Next, the Internet of Things (IoT) is a medium to store images of indoor lettuce plant growth into the Cloud (Google Drive). Furthermore, it takes indoor lettuce plant images as the input, an ANN was trained to learn the relationship between images and the corresponding growth- related traits with other fixed parameters. The pH level parameters were controlled by other fixed parameters to take the images of indoor lettuce plant growth. The parameters used in this project are temperature and humidity. This helps to compare the results of Artificial Neural Network (ANN), widely adopted methods were also used. Concisely, this project is expected to develop the Deep Learning Architecture using an Artificial Neural Network (ANN) with digital images as a robust tool for the monitoring of the growth of indoor lettuce plants every 30 minutes per day. Generally, focused on an urban farming growth monitoring system using Artificial Neural Network (ANN) and the Internet of Things (IoT).

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Extended median filter for salt and pepper noise

2017-01-01 , Bilal Charmouti , Ahmad Kadri Junoh , Wan Zuki Azman Wan Muhamad , Muhammad Naufal Mansor , Mohd Zamri Hasan , Mohd Yusoff Mashor

Image have a significant importance in many fields in human life such as, in medicine, photography, biology, astronomy, industry and defence. Thus, it attracts the attention of large number of researchers, among them those interested in preserving the image features from any factors that may reduce the image quality. One of these factors is the noise which affects the visual aspect of the image and makes others image processing more difficult. Thus far, solving this noise problem remains a challenge point for the researchers in this field, a huge number of image denoising techniques have been introduced in order to remove the noise with taking care of the image featurs, in other words, get the best similarity to the original image from the noisy one. However, beside the enormous amount of researches and studies which adopt several mathematical concepts (statistics, probabilities, modeling, PDEs, wavelet, fuzzy logic, etc.), the findings proved to be inconclusive yet. From this point, the current study aims to introduce a new denoising method for removing salt & pepper noise from the digital image through developed Median filter, so as to overcome this problem of noise and achieve a good image restoration.

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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.

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