Norasmadi Abdul Rahim
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Preferred name
Norasmadi Abdul Rahim
Official Name
Norasmadi, Abdul Rahim
Alternative Name
Rahim, N. A.
Rahim, N. A.S.A.
Rahim, Norasmadi Abd
Rahim, N. Abdul
Abdul Rahim, N.
Rahim, Norasmadi Abdul
Rahim, Norasmadi Bin Abdul
Main Affiliation
Scopus Author ID
36901996000

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  • 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
    Surface Reconstruction from Unstructured Point Cloud Data for Building Digital Twin
    ( 2023-01-01)
    Ismail F.A.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Norasmadi Abdul Rahim
    ;
    Wong R.
    This study highlights on the methods used for surface reconstruction from unstructured point cloud data, characterized by simplicity, robustness and broad applicability from 3D point cloud data. The input data consists of unstructured 3D point cloud data representing a building. The reconstruction methods tested here are Poisson Reconstruction Algorithm, Ball Pivoting Algorithm, Alpha Shape Algorithm and 3D surface refinement, employing mesh refinement through Laplacian smoothing and Simple Smoothing techniques. Analysis on the algorithm parameters and their influence on reconstruction quality, as well as their impact on computational time are discussed. The findings offer valuable insights into parameter behavior and its effects on computational efficiency and level of detail in the reconstruction process, contributing to enhanced 3D modeling and digital twin for buildings.
      1
  • Publication
    Effect of Strength and Conditioning Trainings on Lower Limb Muscles Activity of High-Jumping Athletes
    ( 2021-01-01)
    Hamiza Mohamad Radzi
    ;
    Ahmad Faizal Salleh
    ;
    Norasmadi Abdul Rahim
    ;
    Mohammad Shahril Salim
    ;
    Nurhidayah Omar
    ;
    Hamzah Sakeran
    ;
    Nair S.K.
    In recent years, there has been a proliferation of technology and sport science utilized within an athlete’s physical activity and exercise. This study aims to assess the effectiveness of two strength and conditioning exercises, namely, a customized free-weight exercise and plate-loaded machine exercise, on the lower limb muscle activities of the amateur high jumpers. Six amateur high jumpers were divided into two groups, a customized free-weight group and plate-loaded machine group (control group) and performed exercises as instructed by the coach. The EMG signal of the Rectus Femoris and Bicep Femoris muscles were recorded during the exercises. Metronome was used to control the speed of the exercise and it was standardized for all subjects. The harmstring’s cable pull exercise (customized free-weight) triggered Bicep Femoris more compared to the leg curl exercise (plate-loaded exercise). Similarly, in the case of Rectus Femoris muscle, the front squat exercise (customized free-weight exercise) triggered higher muscular activities compared to the leg extension exercise (plate-loaded exercise). In conclusion, the customized free-weight exercise has indicated higher muscle activities compared to the plate-loaded exercise.
      1
  • Publication
    Mitigating Overfitting in Extreme Learning Machine Classifier Through Dropout Regularization
    (Universiti Malaysia Perlis, 2024-02-14)
    Fateh Alrahman Kamal Qasem Alnagashi
    ;
    Norasmadi Abdul Rahim
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Mohamad Hanif Abd Hamid
    Achieving optimal machine learning model performance is often hindered by the limited availability of diverse datasets, a challenge exacerbated by small sample sizes in real-world scenarios. In this study, we address this critical issue in classification tasks by integrating the Dropout technique into the Extreme Learning Machine (ELM) classifier. Our research underscores the effectiveness of Dropout-ELM in mitigating overfitting, especially when data is scarce, leading to enhanced generalization capabilities. Through extensive experiments on synthetic and real-world datasets, our findings consistently demonstrate that Dropout-ELM outperforms traditional ELM, yielding significant accuracy improvements ranging from 0.19% to 16.20%. By strategically implementing dropout during training, we promote the development of robust models that reduce reliance on specific features or neurons, resulting in increased adaptability and resilience across diverse datasets. Ultimately, Dropout-ELM emerges as a potent tool to counter overfitting and bolster the performance of ELM-based classifiers, particularly in scenarios with limited data. Its established efficacy positions it as a valuable asset for enhancing the reliability and generalization of machine learning models, providing a robust solution to the challenges posed by constrained training data.
      15  1
  • Publication
    Fuzzy logic based prediction of micronutrients demand for harumanis mango growth cycles
    ( 2021-12-01)
    Wan Mohd Nooriman Wan Yahya
    ;
    Abu Hassan Abdullah
    ;
    Norasmadi Abdul Rahim
    ;
    Erdy Sulino Mohd Muslim Tan
    Harumanis is a famous green eating mango cultivar that has been commercially cultivated in Malaysia's state of Perlis. A variety of nutrients are found in soil, all of which are necessary for plant growth. Micronutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K) are essential for Harumanis mango (Mangifera Indica) to growth. The importance of soil fertility in achieving high plant productivity and quality cannot be overstated. It should be used in a moderate amount and in a balanced manner. Predicting appropriate nutrients and the right timing to satisfy the tree's demands is critical. The aim of this study is to create a fuzzy logic-based system to analyse the results of soil tests for nitrogen (N), phosphorus (P), and potassium (K) in the Harumanis mango orchard. The interpreted data are used to estimate N-P-K nutrient levels and indicate the optimal fertilizer solution and application timing for each Harumanis growth stages. The system utilizes Fuzzy Logic Control (FLC) to predict the nutrients demand for Harumanis mango growth. Results shows the system able to calculate and predict values of required N-P-K fertilizer for optimal growth. Thus, assist farmers in predicting the proper amount of N-P-K to apply to Harumanis mango soil.
      2  61
  • 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  69
  • Publication
    Development of Artificial Stingless Bee Hive Monitoring using IoT System on Developing Colony
    ( 2024-01-01)
    Ali M.A.A.C.
    ;
    Bukhari Ilias
    ;
    Norasmadi Abdul Rahim
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Abdul Hamid Adom
    ;
    Saad M.A.H.
    ;
    Hassan M.F.A.
    The trend of stingless bees’ farm in Malaysia has increased recently as it has been proven that its honey gives various benefits to human beings. This trend requires beekeepers to do more frequent inspections of beehives. However, the current practice of opening the cover to inspect the colony and honey will disrupt colony activity. According to a recent study, these stingless bees can only survive between 22 and 38 degrees Celsius, and harsh weather conditions might lead to the collapse of bee colonies. In order to ensure a consistent honey production, the IoT monitoring system will be implemented on an artificial stingless beehive. The system is equipped with an embedded system that utilizes a NodeMCU ESP8266, temperature and humidity sensors, and load cell sensors. Next, honey compartment weight, temperature and humidity inside stingless beehive, and temperature and humidity outside stingless beehive will be uploaded to the Internet of Things (IoT) platforms, namely Thingspeak and Cayenne. The data is sent to Thingspeak via the REST API while to Cayenne by the MQTT API. All data from the artificial stingless bee hive indicating the occurrence of colony rising and has been uploaded to the IoT platform. By analysing the data that were recorded for 13 days, all of the input data such as the weight of the honey compartment, the temperature in the hive, and the humidity in the hive, display its respective characteristics. For the honey compart weight, it has been found that the stingless bee colony is rising as a result of the increasing honey and colony in the compartment weight. Regarding the hive temperature, it has been determined that the temperature inside the hive is stable around 26°C to 38°C in normal weather conditions. Whereas for humidity inside the hive, it is remained between 76.5% and 85.6% due to the moisture from the honey inside the compartment. Lastly, these results indicate that the colony living in the artificial hive of stingless bees is healthy and growing.
      9  23
  • Publication
    A study of extreme learning machine on small sample-sized classification problems
    ( 2021-12-01)
    Ooi B.P.
    ;
    Norasmadi Abdul Rahim
    ;
    Maz Jamilah Masnan
    ;
    Ammar Zakaria
    Extreme learning machine (ELM) is a special type of single hidden layer feedforward neural network that emphasizes training speed and optimal generalization. The ELM model proposes that the weights of hidden neurons need not be tuned, and the weights of output neurons can be calculated by finding the Moore-Penrose generalized inverse method. Thus, the ELM classifier is suitable to use in a homogeneous ensemble model due to the untuned random hidden weights which promote diversity even with the same training data. This paper studies the effectiveness of the ELM ensemble models in solving small sample-sized classification problems. The research involves two variants of the ensemble model: the normal ELM ensemble with majority voting (ELE), and the random subspace method (RS-ELM). To simulate the small sample cases, only 30% of the total data will be used as the training data. Experiment results show that the RS-ELM model can outperform a multi-layer perceptron (MLP) model under the assumptions of a Friedman test. Furthermore, the ELE model has similar performance as an MLP model under the same assumptions.
      1  39
  • Publication
    Connected car: Engines diagnostic via Internet of Things (IoT)
    ( 2017-10-29)
    Azrul Fahmi A.Hamid
    ;
    Md. Tasyrif bin Abdul Rahman
    ;
    Shah Fenner Khan Mohamad Khan
    ;
    Abdul Hamid Adom
    ;
    Muhajir Ab. Rahim
    ;
    Norasmadi Abdul Rahim
    ;
    Ismail M.H.N.
    ;
    Norizan A.
    This paper is about an experiment for performing engines diagnostic using wireless sensing Internet of Thing (IoT). The study is to overcome problem of current standard On Board Diagnosis (OBD-II) data acquisition method that only can be perform in offline or wired method. From this paper it show a method to determined how the data from engines can be collected, make the data can be easily understand by human and sending data over the wireless internet connection via platform of IOT. This study is separate into three stages that is CAN-bus data collection, CAN data conversion and send data to cloud storage. Every stage is experimented with a two different method and consist five data parameter that is Revolution per Minute (RPM), Manifold Air Pressure (MAP), load-fuel, barometric pressure and engine temperature. The experiment use Arduino Uno as microcontroller, CAN-bus converter and ESP8266 wifi board as transfer medium for data to internet.
      8  39
  • Publication
    Rssi-based for device-free localization using deep learning technique
    ( 2020-06-01)
    Abdul Syafiq Abdull Sukor
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Norasmadi Abdul Rahim
    ;
    Sukhairi Sudin
    ;
    Nishizaki H.
    Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.
      7  30