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
    Investigating the Applicability of Several Fuzzy-Based Classifiers on Multi-Label Classification
    ( 2019-01-01)
    Al-luwaici M.
    ;
    Ahmad Kadri Junoh
    ;
    Ahmad F.K.
    : In the last few decades, fuzzy logic has been extensively used in several domains such as economy, decision making, logic and classification. In specific, fuzzy logic which is a powerful mathematical representation has shown a superior performance with uncertainty real-life applications comparing with other learning approaches. Many researchers utilized the concept of fuzzy logic in solving the traditional single label classification problems of both types: binary classification and multi-class classification. Unfortunately, veiy few researches have utilized fuzzy logic in a more general type of classification that is called Multi-Label Classification (MLC). Hence, this study aims to examine the applicability of fuzzy logic to be used with MLC through evaluating several fuzzy-based classifiers on five different multi-label datasets. The results revealed that the utilizing fuzzy-based classifiers on solving the problem of MLC is promising comparing with a wide range of MLC algorithms that belong to several learning approaches and strategies.
  • Publication
    Contrast virus microscopy images recognition via k-NN classifiers
    ( 2017-07-02)
    Afiq Ahmad Shakri
    ;
    Syahrul Affandi Saidi
    ;
    Muhammad Naufal Mansor
    ;
    Haryati Jaafar
    ;
    Ahmad Kadri Junoh
    ;
    Wan Azani Wan Mustafa
    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 Contrast feature extraction with K-Nearest Neighbor (KNN) classifier under various levels of noise. The real time experiment conducted proved that the proposed method are efficient, robust, and excellent of which it has produced a results accuracy of up to 88% for biological viruses images classification.
  • 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
    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.
  • Publication
    New white blood cell detection technique by using singular value decomposition concept: White blood cell detection technique
    ( 2021-01-01)
    Abdurrazzaq A.
    ;
    Ahmad Kadri Junoh
    ;
    Zainab Yahya
    ;
    Mohd I.
    Segmentation technique is a commonly used method to detect white blood cells. The segmentation technique aims to separate the blood image into several parts based on the similarity of features in the image. Therefore, the detection results do not completely contain white blood cells but also contain other parts with similar features to white blood cells. This study proposes a new detection technique that directly considers the features of white blood cells using singular value decomposition approach. The experimental results show that the proposed method works better in detecting white blood cell nuclei than the existing methods. The existing methods only work well for white blood cells with dense color intensities such as basophil and monocyte. Meanwhile, the proposed method works well overall as it directly compares the level of similarity in white blood cells.
  • Publication
    An emotion assessment of stroke patients by using bispectrum features of EEG Signals
    ( 2020)
    Choong Wen Yean
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Murugappan Murugappan
    ;
    Yuvaraj Rajamanickam
    ;
    Abdul Hamid Adom
    ;
    Mohammad Iqbal Omar
    ;
    Bong Siao Zheng
    ;
    Ahmad Kadri Junoh
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.
  • Publication
    Introduction To Ordinary Differential Equation
    (Penerbit Universiti Malaysia Perlis, 2010)
    Ahmad Kadri Junoh
    ;
    Wan Mohd Khairy Adly Wan Zaimi
    ;
    Wan Nor Munirah Ariffin
    ;
    Shazalina Mat Zin
    This book is intended for students who want to learn about calculus and differential equations in great depth. The core contents of the book discuss about the ‘ First Order and Second Order Differential Equation’. These two (2) chapters provide the students with a lot of examples together with solution methods. Meanwhile, ‘Laplace Transform’ topic introduces the students to an optional method other than the First Order or the Second Order Differential Equation, ‘Fourier Series’ topic discusses about the periodic function of Fourier Series where the techniques from the earlier chapters such as graphs, differentiation and integration will be adapted in this book. Basically, this book provides the basic knowledge of engineering mathematics in order to increase the comprehension of the students in term of differentiation, integration and periodic functions of series. By providing a lot of examples and exercise questions, the author hope this book may increase the understanding level and help the students to solve the questions and at the same time help them to use the theories and concepts which have been learnt to apply in their daily routine problems.