Ahmad Kadri Junoh
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Ahmad Kadri Junoh
Official Name
Ahmad Kadri, Junoh
Alternative Name
Junoh, Ahmad Kadri
Junoh, A. K.
Ahmad Kadri, Junoh
Junoh, A. K.
Kadri, J. Ahmad
Main Affiliation
Scopus Author ID
38561331300
Researcher ID
FZU-4175-2022

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  • Publication
    An overview of multi-filters for eliminating impulse noise for digital images
    ( 2020-02-01)
    Abdurrazzaq A.
    ;
    Ahmad Kadri Junoh
    ;
    Wan Zuki Azman Wan Muhamad
    ;
    Zainab Yahya
    ;
    Mohd I.
    An image through the digitization process is referred to as a digital image. The quality of the digital image may be degenerating due to interferences on the acquisition, transmission, extraction, etc. This attracted the attention of many researchers to study the causes of damage to the information in the image. In addition to finding cause of image damage, the researchers also looking for ways to overcome this problem. There are many filtering techniques that have been introduced to deal the damage to the information in the image. In addition to eliminating noise from the image, filtering techniques also aims to maintain the originality of the features in the image. Among the many research papers on image filtering there is a lack of review papers which are an important to facilitate researchers in understanding the differences in each filtering technique. Additionally, it helps researchers determine the direction of research conducted based on the results of previous research. Therefore, this paper presents a review of several filtering techniques that have been developed so far.
  • Publication
    Tropical algebra based adaptive filter for noise removal in digital image
    ( 2020-07-01)
    Abdurrazzaq A.
    ;
    Mohd I.
    ;
    Ahmad Kadri Junoh
    ;
    Zainab Yahya
    The concept of the tropical algebra was first introduced to solve problems in mathematical economy such as optimization and approximation problems. In this paper, the concept of tropical algebra is used to build an image filtering algorithm. By using this concept, the lowest and highest pixel values are considered in determining the new pixel value. In addition, adaptive window will also be implemented to help the filtering process become more effective at high density noise. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to evaluate the output image quality produced by the filtering method. In this experiment, the performance of proposed method and existing methods such as switching median filter (SMF), adaptive fuzzy noise switching median filter (NAFSM), modified decision based on unsymmetric trimmed median filter (MDBUTMF), adaptive type-2 fuzzy filter (AT2FF)), based on pixel density filters (BPDF), different applied median filters (DAMF), and tropical SVD filters (TSVD) will be compared. PSNR and SSIM results show that the proposed method outperforms most existing methods: SMF (27.13/0.7954), NAFSM (29.11/0.8459), MDBTUMF (29.18/0.8462), AT2FF (28.10/0.8159), BPDF (25.65/0.7545), DAMF (31.20/0.8833), TSVD (28.39/0.7906), and proposed (31.26/0.8827).
  • Publication
    Distance weighted K-means algorithm for center selection in training radial basis function networks
    ( 2019-03-01)
    Aik L.E.
    ;
    Hong T.W.
    ;
    Ahmad Kadri Junoh
    The accuracies rates of the neural networks mainly depend on the selection of the correct data centers. The K-means algorithm is a widely used clustering algorithm in various disciplines for centers selection. However, the method is known for its sensitivity to initial centers selection. It suffers not only from a high dependency on the algorithm's initial centers selection but, also from data points. The performance of K-means has been enhanced from different perspectives, including centroid initialization problem over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the centers produces by the algorithm. To solve this problem, a new method to find the initial centers and improve the sensitivity to the initial centers of K-means algorithm is proposed. This paper presented a training algorithm for the radial basis function network (RBFN) using improved K-means (KM) algorithm, which is the modified version of KM algorithm based on distance-weighted adjustment for each centers, known as distance-weighted K-means (DWKM) algorithm. The proposed training algorithm, which uses DWKM algorithm select centers for training RBFN obtained better accuracy in predictions and reduced network architecture compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment; hence, the new network was undergoing a hybrid learning process. The network called DWKM-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to proposed method for root mean square error (RMSE) in radial basis function network (RBFN). The proposed method yielded a promising result with an average improvement percentage more than 50 percent in RMSE.
  • Publication
    Determination of flexibility of workers working time through Taguchi method approach
    ( 2020-10-01)
    Saidat S.
    ;
    Ahmad Kadri Junoh
    ;
    Wan Zuki Azman Wan Muhamad
    ;
    Zainab Yahya
    Human factor is one of the important elements in manufacturing world, despite their important role in improvement the production flow, they have been neglected while scheduling for many decades. In this paper the researchers taken the human factor throughout their job performance weightage into consideration while using job shop scheduling (JSS) for a factory of glass industry, in order to improving the workers' flexibility. In other hand, the researchers suggested a new sequence of workers' weightage by using Taguchi method, which present the best flexibility that workers can have, while decreasing the total time that the factory need to complete the whole production flow.
      5  20
  • Publication
    Hurst exponent based brain behavior analysis of stroke patients using eeg signals
    ( 2021-01-01)
    Choong W.Y.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Murugappan M.
    ;
    Omar M.I.
    ;
    Bong S.Z.
    ;
    Ahmad Kadri Junoh
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    ;
    Wan Azani Wan Mustafa
    The stroke patients perceive emotions differently with normal people due to emotional disturbances, the emotional impairment of the stroke patients can be effectively analyzed using the EEG signal. The EEG signal has been known as non-linear and the neuronal oscillation under different mental states can be observed by non-linear method. The non-linear analysis of different emotional states in the EEG signal was performed by using hurst exponent (HURST). In this study, the long-range temporal correlation (LRTC) was examined in the emotional EEG signal of stroke patients and normal control subjects. The estimation of the HURST was more statistically significant in normal group than the stroke groups. In this study, the statistical test on the HURST has shown a more significant different among the emotional states of normal subject compared to the stroke patients. Particularly, it was also found that the gamma frequency band in the emotional EEG has shown more statistically significant among the different emotional states.
      34  1
  • Publication
    Hybrid singular value decomposition based alpha trimmed mean-median filter in eliminating high density salt and pepper noise from grayscale image
    ( 2024-07-01)
    Zain M.S.M.
    ;
    Ahmad Kadri Junoh
    ;
    Abdurrazzaq A.
    The use of images has increased over the previous decade and they have the potential to be effective communication tools, similar to social media. In this technological era, uploading information or visual images to the social media seems to be gaining popularity lately. Therefore, a good image is important to provide the right information. However, the information in the image can be lost or corrupted due to the appearance of noise caused by the digitization, transmission or acquisition process. Thus, it is necessary to remove the noise before using the image for subsequent task. In this study, a new method for removing salt and pepper noise in digital image is proposed by using singular value decomposition and alpha trimmed mean median approach. The singular value decomposition will be used in the detection process by considering the distribution of pixel values in the processed image. Next, the detected noisy pixels will be replaced with a new value obtained from the trimmed alpha mean median approach. The experimental process was performed on a grayscale image with a resolution of 512×512 prepared with a salt and pepper noise density varying between 10% to 90% in order to compare the proposed method to other existing methods. The experimental results show that the proposed method has successfully reduced salt and pepper noise in high noise density. In addition, the proposed method provides better filtering results in terms of visual effects and quantitative measurement results compared to the several existing methods.
      1  12
  • Publication
    Comparison of Regression Methods for Estimation of State-of-Health in Lithium-Ion Batteries
    ( 2023-01-01)
    Cahyani D.E.
    ;
    Setyawan F.F.
    ;
    Hariadi A.D.
    ;
    Gumilar L.
    ;
    Ahmad Kadri Junoh
    Lithium-ion batteries are a type of rechargeable battery with a high energy density and an extended cycle life. The development of lithium-ion batteries is very rapid, but lithium-ion batteries have a limited lifespan and their energy storage capacity decreases with time and use. Therefore, the State of Health (SoH) of lithium-ion batteries is crucial when planning battery maintenance. The purpose of this study is to compare regression techniques for estimating the health of Li-ion batteries. XGBoost, Support Vector Regression (SVR), Random Forest Regression, Linear Regression, Gradient Boosting Regression, and Decision Tree Regression are the regression methods utilized in this investigation. All types of batteries from NASA's Prognostics Data Repository were utilized in the investigation. Support Vector Regression (SVR) yields the most accurate results compared to other techniques. The SVR technique yields RMSE, MSE, MAE, and MAPE values of 0.0226, 0.0005, 0.0208, and 0.0264, respectively. This indicates that the SVR method is capable of accurately estimating the SoH of a lithium-ion battery.
      1
  • Publication
    Entropy virus microscopy images recognition via neural network classifiers
    ( 2017-07-02)
    Afiq Ahmad Shakri
    ;
    Syahrul Affandi Saidi
    ;
    Haryati Jaafar
    ;
    Muhammad Naufal Mansor
    ;
    Wan Azani Wan Mustafa
    ;
    Ahmad Kadri Junoh
    One of the topics that are commonly in focus of object detection and image recognition is virus detection. It is well known that to learn and detecting virus proven to be a challenging and quite complex task for computer systems under different noise level. This research work investigates the performances of preprocessing stages with Entropy feature extraction with Feed Forward Neural Network (FFNN) classifier under various levels of noise. The real time experiment conducted proved that the method proposed are efficient, robust, and excellent of which it has produced a results accuracy of up to 88% for biological viruses images classification.
      11  36
  • Publication
    Fibonacci retracement pattern recognition for forecasting foreign exchange market
    ( 2020-01-01)
    Mohd Fauzi Ramli
    ;
    Ahmad Kadri Junoh
    ;
    Mahyun Ab Wahab
    ;
    Wan Zuki Azman Wan Muhamad
    Fibonacci retracement implicates a forecast of future movements in foreign exchange rates (forex) of the previous movement inductive analysis. Fibonacci ratios are used to forecast the retracements level of 0.382, 0.500 and 0.618 and to determine the current trend which provide the mathematical foundation for the Elliott wave theory. K-nearest neighbour (KNN) and linear discriminant analysis (LDA) algorithm are the pattern recognition method for nonlinear feature mining of Elliott wave patterns. Results show that LDA is better than KNN in terms of classification accuracy data which are 99.43%. Among of three levels of Fibonacci retracement results, the 38.2% shows the best forecasting for Great Britain Pound pair to US Dollar currency as major pair by using mean absolute error (MAE), root mean square error (RMSE) and pearson correlation coefficient (r) as the statistical measurements which are 0.001884, 0.000019 and 0.992253 for uptrend and 0.001685, 0.000019 and 0.998806 for downtrend.
      1  61
  • Publication
    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.
      2  30