Fathinul Syahir Ahmad Sa'ad
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Fathinul Syahir Ahmad Sa'ad
Official Name
Fathinul Syahir, Ahmad Sa'ad
Alternative Name
Ahmad Saad, Fathinul Syahir
Sa’ad, Fathinul Syahir Ahmad
Main Affiliation
Scopus Author ID
57205307496
Researcher ID
R-5360-2019

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  • Publication
    Nutrient Requirements and Growth Response of Harumanis Mango (Mangiferaindica L.) during Vegetative Shoot Growth Stages: A Mitscherlich Law Analysis
    ( 2023-01-01)
    Erdy Sulino Mohd Muslim Tan
    ;
    Markom M.A.B.
    ;
    Allan Melvin Andrew
    ;
    Abu Hassan Abdullah
    ;
    Norasmadi Abdul Rahim
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Imaduddin Helmi Wan Nordin
    ;
    Abidin M.A.Z.
    ;
    Jamil S.H.F.S.A.
    ;
    Yogesh C.
    This study investigates the nutrient requirements of Harumanis mango (Mangifera indica L) during different vegetative shoot growth stages by analyzing the soil nutrient test value-relative growth relationships. The research utilizes the Mitscherlich Law to model the response of mango yield in relation to varying nutrient levels. The data came from experimental plots, and the results show the asymptotic behavior of mango yield for three essential nutrients: nitrogen (N), phosphorus (P), and potassium (K). For vegetative shoot growth1, the asymptotic yield was estimated at 665.5 with a decline rate of -3.39 concerning N, -2.17 concerning P, and -1.35 concerning K. The coefficient of determination (R2) was 0.934, indicating a high goodness of fit for the model. Similar trends were observed for vegetative shoot growth2 and 3, where the asymptotic yields and nutrient decline rates varied accordingly. This study provides crucial insights into Harumanis mango nutrient needs across growth stages, aiding orchard management for sustainable yields. Applying the Mitscherlich Law enhances our understanding of how nutrients affect mango growth. These findings support targeted fertilization, boosting productivity and orchard efficiency. Future research can explore more growth factors and long-term nutrient impacts.
      4  45
  • Publication
    Analysis of Soil Nutrient (NPK) Test Value - Relative yield Relationship for Harumanis Mango using Modification Arcsine-Log Calibration Curve.
    ( 2023-01-01)
    Erdy Sulino Mohd Muslim Tan
    ;
    Markom M.A.B.
    ;
    Allan Melvin Andrew
    ;
    Abu Hassan Abdullah
    ;
    Norasmadi Abdul Rahim
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Imaduddin Helmi Wan Nordin
    ;
    Abidin M.A.Z.
    ;
    Jamil S.H.F.S.A.
    ;
    Yogesh C.K.
    The cultivation of Harumanis mango (Mangifera indica) is of significant agricultural importance, especially in tropical regions like Malaysia, where it is renowned for its exceptional taste and quality. Maximizing mango yield and maintaining fruit quality are vital aspects of successful cultivation, relying on optimal soil nutrient management, particularly nitrogen (N), phosphorus (P), and potassium (K). In this research, the soil nutrient test value - relative yield relationship for Harumanis mango is investigated using a modification arcsine-log calibration curve. Traditional linear calibration curves may not fully capture the nonlinearities observed in crop responses, potentially leading to inaccurate nutrient requirements for optimal yield. By employing the innovative modification arcsine-log calibration curve, a more precise and robust relationship between soil nutrient test values and relative mango yield is established. Soil samples are collected from mango orchards, and NPK levels are measured using standardized laboratory techniques, alongside corresponding relative mango yields. This study advances precision agriculture by offering precise soil nutrient recommendations for mango farmers. Utilizing calibrated curves improves mango yield, minimizes nutrient waste, and encourages sustainable farming. In conclusion, the modified arcsine-log calibration curve reveals vital insights for optimal Harumanis mango production, benefiting the industry and sustainability.
      1
  • Publication
    Performance analysis of multi-level thresholding for microaneurysm detection
    ( 2022-09-01)
    Choong K.H.
    ;
    Shafriza Nisha Basah
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Lim Chee Chin
    Diabetic retinopathy (DR) – one of the diabetes complications – is the leading cause of blindness among the age group of 20–74 years old. Fortunately, 90% of these cases (blindness due to DR) could be prevented by early detection and treatment via manual and regular screening by qualified physicians. The screening of DR is tedious, which can be subjective, time-consuming, and sometimes prone to misclassification. In terms of accuracy and time, many automated screening systems based on image processing have been developed to improve diagnostic performance. However, the accuracy and consistency of the developed systems are largely unaddressed, where a manual screening process is still the most preferred option. The main contribution of this paper is to analyse the accuracy and consistency of microaneurysm (MA) detection via image processing by focusing on Otsu’s multi-thresholding as it has been shown to work very well in many applications. The analysis was based on Monte Carlo statistical analysis using synthetic retinal images of retinal images under variation of all stages of DR, retinal, and image parameters – intensity difference between MAs and blood vessels (BVs), MA size, and measurement noise. Then, the conditions – in terms of obtainable retinal and image parameters – that guarantee accurate and consistent MA detection via image processing were extracted. Finally, the validity of the conditions to guarantee accurate and consistent MA detection was verified using real retinal images. The results showed that MA detection via image processing is guaranteed to be accurate and consistent when the intensity difference between MAs and BVs is at least 50% and the sizes of MAs are from 5 to 20 pixels depending on measurement noise values. These conditions are very important as a guideline of MA detection for DR.
      5  40
  • Publication
    Cloud-based System for University Laboratories Air Monitoring
    ( 2020-09-21)
    Abu Hassan Abdullah
    ;
    Sukhairi Sudin
    ;
    Mustafa M.H.
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Khairul Azwan Ismail
    ;
    Muhammad Aizat Abu Bakar
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Abdul Ghapar Ahmad
    ;
    Zahari Awang Ahmad
    ;
    Sara Yasina Yusuf
    Indoor air such as house, shopping complex, hospital, university, office and hotel should be monitor for human safety and wellbeing. These closed areas are prone to harmful air pollutants i.e. allergens, smoke, mold, particles radon and hazardous gas. Laboratories in university are special room in which workers (student, technician, teaching/research assistants, researcher and lecturer) conduct their works and experiment. The activities and the environment will generate specific air pollutant which concentration depending to their parameters. Anyone in the environment that exposure to these pollutants may affect safety and health issue. This paper proposes a study of development of a cloud-based electronic nose system for university laboratories air monitoring. The system consists of DSP33-based electronic nose (e-nose) as nodes which measure main indoor air pollutant along with two thermal comfort variables, temperature and relative humidity. The e-noses are placed at five different laboratories for acquiring data in real time. The data will be sent to a web server and the cloud-based system will process, analyse using Neuro-Fuzzy classifier and display on a website in real time. The system will monitor the laboratories air pollutants and thermal comfort by predict the pollutant concentration and dispersion in the area i.e. Air Pollution Index (API). In case of air hazard safety (e.g., gas spills detection and pollution monitoring), the system will alert the security by activate an alarm and through e-mail. The website will display the API of the area in real-time. Results show that the system performance is good and can be used to monitor the air pollutant in the university laboratories.
      43  2
  • Publication
    Cycling performance prediction based on cadence analysis by using multiple regression
    ( 2021-12-01)
    Sukhairi Sudin
    ;
    Aziz Naim Abdul Aziz
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Nurul Syahirah Khalid
    ;
    Ismail Ishaq Ibrahim
    This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000.Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days' worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable's mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.
      3  16
  • Publication
    A deep neural networks-based image reconstruction algorithm for a reduced sensor model in large-scale tomography system
    (Elsevier Ltd, 2022-12-01)
    Lee C.C.
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Leow P.L.
    ;
    Rahim R.A.
    ;
    Fathinul Syahir Ahmad Sa'ad
    Image reconstruction for soft-field tomography is a highly nonlinear and ill-posed inverse problem. Owing to the highly complicated nature of soft-field, the reconstructed images are always poor in quality. One of the factors that affect image quality is the number of sensors in a tomography system. It is commonly assumed that increasing the number of sensors in a tomography system will improve the ill-posed condition in image reconstruction and hence improve image quality. However, as the number of sensors increases, challenges such as more complicated and expensive hardware, slower data acquisition rates, longer image reconstruction times, and larger sensitivity matrices will arise, resulting in a greater ill-posed condition. Since deep learning (DL) is capable of expressing complex nonlinear functions, the majority of research efforts have been directed toward developing a robust DL-based inverse solver for image reconstruction. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for a large-scale tomography system. This paper proposed an image reconstruction algorithm based on Deep Neural Networks (DNN) to investigate its feasibility in solving the ill-posed inverse problem caused by the reduced sensor model for a large-scale tomography system. The proposed DNN model is based on a supervised, feed-forward, fully connected, backpropagation network. It comprises an input layer, three hidden layers and an output layer. Also, it was trained using large data samples obtained from COMSOL simulation. The relationship between the scattered electromagnetic field measurement and the corresponding true electromagnetic field distribution vector is determined. During the image reconstruction process, the untrained scattered electromagnetic field measurement samples are used as inputs to the trained DNN model, and the model output is an estimate of the electromagnetic field distribution. The results show that the proposed DNN can accurately describe the distribution of electromagnetic field and boundary shape of phantom compared to traditional algorithms (LBP, FBP, Noser and Tikhonov), regardless of the size and number of phantoms within the monitoring area. Hence, the proposed DNN is more robust and has a high degree of generalization.
      2  3
  • Publication
    Effect of Image Thresholding on the Homogenized Properties of Trabecular Bone Model
    ( 2022-01-01)
    Khairul Salleh Basaruddin
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Mohd Hafif Basha Mohamad Jamel Basha
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Muhamad Khairul Ali Hassan
    This paper presents a numerical study to determine the homogenized (apparent) properties of vertebral trabecular bone with different threshold values using homogenization method. Series set of micro-CT images of vertebral trabecular bone was used in the present digital image-based modeling technique to reconstruct the microstructure model. Three image thresholding values were selected based on Otsu’s method. The homogenized properties that include the Young’s moduli, Poisson’s ratio and shear moduli was obtained in this study. The results showed there is significant effect of image threshold on the homogenized properties of vertebral trabecular bone model.
      3  36
  • Publication
    Holonomic Mobile Robot Planners: Performance Analysis
    ( 2022-01-01)
    Aljamali Y.S.
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Khairul Salleh Basaruddin
    ;
    Yazid H.
    ;
    Basha S.N.
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Hassan M.K.A.
    Many algorithms have been proposed to tackle the path planning problem in mobile robots. Among the well-known and established algorithms are the Probabilistic Road Map (PRM) algorithm, A* algorithm, Genetic algorithm (GA), Rapidly-exploring random tree (RRT), and dual Rapidly-exploring random trees (RRT-connect). Hence, this paper will focus on the performance comparison between the aforementioned algorithms concerning computation time, path length, and fail and success rate for producing a path. For the sake of fair and conclusive results, simulation is conducted in two phases with four different environments, namely, free space environment, low cluttered environment, medium cluttered environment, and high cluttered environment. The results show that RRT-connect has a high success rate in producing a feasible path with the least computation time. Hence, RRTs-based sampling algorithms, in general, and RRT-connect, in specific, will be explored in-depth for possible optimization.
      1  33
  • Publication
    Development of aquaculture water quality real-time monitoring using multi-sensory system and internet of things
    ( 2021-12-01)
    Abu Hassan Abdullah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Sukhairi Sudin
    ;
    Zahari Awang Ahmad
    ;
    Ahmad I.
    ;
    Norlida Abu Bakar
    ;
    Shafie Omar
    ;
    Sulaiman S.F.
    ;
    Mohd Hishamuddin Che Mat
    ;
    Nurul Afidah Umoruddin
    ;
    Johari B.H.
    Water quality is an important parameter for the health and growth of aquatic species in aquaculture farming system. The threshold values of the water main parameters should be monitored continuously. Contaminated aquaculture water will affect the health, growth and ability of animals to survive. In addition, it will also affect the harvesting yields based on the number and size of the animals. To overcome this problem, the main water parameters, namely temperature, pH, Dissolved Oxygen and Electrical Conductivity are monitored in real-time using a multi-sensory system and the internet of things. Data is acquired by a developed instrument and transmitted wirelessly via a GPRS/GSM module to a web server database. The data obtained are analyzed and monitored through the website and in real-time. Therefore, corrective action could be taken immediately for contaminated water, indicated by water parameters out of range. The system also provides an early signal to farmers based on a specific range of water quality parameters values. This will help farmers make adjustments to ensure appropriate water quality for the aquaculture system.
      7  43
  • Publication
    Specific Gravity-based of Post-harvest Mangifera indica L. cv. Harumanis for ‘Insidious Fruit Rot’ (IFR) Detection using Image Processing
    ( 2020-01-01)
    Nurul Syahirah Khalid
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Fathinul Syahir Ahmad Sa'ad
    Bruising and internal defects detection is a huge concern for food safety supplied to the consumers. Similar to many other agricultural products, Harumanis cv. has non-uniform quality at harvesting stage. Traditionally, in adapting the specific gravity approach, farmers and agriculturist will estimate the absence of ‘Insidious Fruit Rot’ (IFR) in Harumanis cv. by using floating techniques based on differences in density concept. However, this method is inconvenient and time consuming. In this research, image processing is explored as a method for non-destructive measurement of specific gravity to predict the absence of ‘Insidious Fruit Rot’ (IFR) in Harumanis cv. The predicted specific gravity of 500 Harumanis cv. samples were used and compared with the actual result where it yielded a high correlation,R2 at 0.9055 and accuracy is 82.00%. The results showed that image processing can be applied for non-destructive Harumanis cv. quality evaluation in detecting IFR.
      10  35