Fathinul Syahir Ahmad Sa'ad
Thumbnail Image
Preferred name
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

Research Output

Filters

21
Reset filters

Settings
Now showing 1 - 10 of 21
  • 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.
      39  2
  • 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
    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
    Intelligent irrigation system using rain water harvesting system and fuzzy interface system
    ( 2021-12)
    Azremi Abdullah Al-Hadi
    ;
    Sukhairi Sudin
    ;
    Ahmad Z.A
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    I Ahmad
    ;
    Fadhilnor Abdullah
    ;
    Wan Mohd Faizal Wan Nik
    ;
    A. Deraman
    ;
    N. M Maliki
    ;
    S R S Kamaruzaman
    ;
    S R Romle
    Shortage of water has become a predominant problem all over the world as water plays an important role in agriculture, domestic and industry. In certain parts of the world, farmers face problems watering their crops especially during the dry season. Limited water resources with low efficiency greatly affect crop growth. Therefore, this study proposes an intelligent irrigation system using Rain Water Harvesting (RWH) and Fuzzy Interface System (FIS) for crops watering process. The RWH is a system that collects, centralises and stores rainwater, while the FIS uses temperature and soil moisture sensors to determine the time and amount needed for the watering process. Thus, the intelligent irrigation system will ensure the process of watering the crops to be efficient. The results of this study show that FIS can analyse temperature and soil moisture data, which improves the efficiency of crops watering process and the use of RWH will make it sustainable. The developed project is currently operating at the Institute of Sustainable Agro Technology, i.e. a university-owned agricultural research institute.
      1  17
  • Publication
    Deep CNN-based Planthopper classification using a high-density image dataset
    (MDPI, 2023)
    Mohd Firdaus Ibrahim
    ;
    Siti Khairunniza-Bejo
    ;
    Marsyita Hanafi
    ;
    Mahirah Jahari
    ;
    Mohammad Aufa Mhd Bookeri
    ;
    Fathinul Syahir Ahmad Sa'ad
    Rice serves as the primary food source for nearly half of the global population, with Asia accounting for approximately 90% of rice production worldwide. However, rice farming faces significant losses due to pest attacks. To prevent pest infestations, it is crucial to apply appropriate pesticides specific to the type of pest in the field. Traditionally, pest identification and counting have been performed manually using sticky light traps, but this process is time-consuming. In this study, a machine vision system was developed using a dataset of 7328 high-density images (1229 pixels per centimetre) of planthoppers collected in the field using sticky light traps. The dataset included four planthopper classes: brown planthopper (BPH), green leafhopper (GLH), white-backed planthopper (WBPH), and zigzag leafhopper (ZIGZAG). Five deep CNN models—ResNet-50, ResNet-101, ResNet-152, VGG-16, and VGG-19—were applied and tuned to classify the planthopper species. The experimental results indicated that the ResNet-50 model performed the best overall, achieving average values of 97.28% for accuracy, 92.05% for precision, 94.47% for recall, and 93.07% for the F1-score. In conclusion, this study successfully classified planthopper classes with excellent performance by utilising deep CNN architectures on a high-density image dataset. This capability has the potential to serve as a tool for classifying and counting planthopper samples collected using light traps.
      21  6
  • 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  32
  • 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  37
  • Publication
    An IoT Agricultural System for Harumanis Farm
    ( 2021-12-01)
    Ismail F.A.
    ;
    Muhamad Khairul Ali Hassan
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Khairul Salleh Basaruddin
    ;
    Abdul Halim Ismail
    ;
    Mohd Nasir Ayob
    Internet of Things (IoT) is a revolutionary technology that represents the future of communication and computing. The field of IoT implementation is vast and can be applied in every field. This project is about to develop an IoT system for Harumanis Farm as agriculture is becoming an essential growing sector throughout the world due to the increasing population. The major challenge in the Harumanis sector is to improve the productivity and quality of Harumanis without continuous manual monitoring. IoT improves crop management, cost-effectiveness, crop monitoring and also improves the quality and quantity of the crop. This IoT system completes with several sensors to monitor the Harumanis farm, such as temperature and humidity sensor, pH level sensor, soil moisture sensor, also nitrogen, phosphorous, and potassium (NPK) sensor. The system is a simple IoT architecture where sensors collect information and send it over the Wi-Fi network to the mobile applications.
      1  34
  • Publication
    Development of Ripeness Indicator for Quality Assessment of Harumanis Mango by using Image Processing Technique
    ( 2020-12-18)
    Mohd Nazri Abu Bakar
    ;
    Abu Hassan Abdullah
    ;
    Haniza Yazid
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Ahmad K.
    ;
    Norasmadi Abdul Rahim
    Visual appearance is the main source of information that can be used for quality assessment of mango. In this study, a non-destructive ripeness level estimation for mango of the cultivar Harumanis based on digital image analysis was employed. The changing peel and flesh colour of mango is strongly correlated to ripeness that can be measured as a sensual quality parameter. This measurement of ripeness level has been determined by image analysis technique which needs to attribute external and internal colour feature from image segmentation Multilevel thresholding technique is proposed for colour image segmentation to extract the mango region from the background which every channel of five colour spaces have been applied. Colour analysis technique and Total soluble solids (TSS) is used to compare and evaluate for the prediction. The optimal results were obtained that a∗ channel from L∗a∗b colour space has given more logical and better performance of prediction which is more than 92% accuracy.
      4  29
  • 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  32