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
    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.
  • 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.
  • 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
    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
    Leukemia Blood Cells Detection using Neural Network Classifier
    ( 2023-12-01)
    Muhammad Naufal Mansor
    ;
    Mohd Zamri Hasan
    ;
    Wan Azani Wan Mustafa
    ;
    Farah Hanan Mohd Faudzi
    ;
    Syahrul Affandi Saidi
    ;
    Mohd Aminudin Jamlos
    ;
    Noor Anida Abu Talib
    ;
    Ahmad Kadri Junoh
    Image segmentation is an image processing operation performed on the image in order to partition the image into some images based on the information contained in the original image. Image segmentation plays an important role in many medical imaging applications, image segmentation facilitates the anatomy process in a particular body of human body. Classification and clustering are the methods used un data mining for analyzing the data sets and divide them on the basis of some particular classification rules. There are many image segmentation tools that used for medical purpose, so it is necessary to define and/or to improve the image segmentation methods in order to get the best method. In this study, the image of leukemia and red blood cells will be used as samples to determine the best algorithm in image segmentation. The procedure for doing segmentation itself is clustering image, edge detection on image, and image classification. The clustering is to extract important information from an image. The edge detection is to determine the existence of edges of lines in image in order to investigate and localize the desired edge features. Moreover, the classification analyzes the properties of some images and organizes the information into certain categories. In this study, the Neural Network and K-Nearest Neighbor are used for image classification by paired with Local Binary Pattern and Principal Component Analysis. The results revealed that the best method of proven in classifying images is from Local Binary Pattern feature extraction with the average accuracy of 94%.
      1
  • Publication
    An Experimental Framework for Assessing Emotions of Stroke Patients using Electroencephalogram (EEG)
    ( 2020-06-17)
    Wan Khairunizam Wan Ahmad
    ;
    Yean C.W.
    ;
    Murugappan M.
    ;
    Ahmad Kadri Junoh
    ;
    Zuradzman Mohamad Razlan
    ;
    Wan Azani Wan Mustafa
    ;
    Shahriman Abu Bakar
    ;
    Ibrahim Z.
    ;
    Nurhafizah S.
    This research aims to assess the emotional experiences of stroke patients using Electroencephalogram (EEG) signals. Since emotion and health are interrelated, thus it is important to analyse the emotional states of stroke patients for neurofeedback treatment. Moreover, the conventional methods for emotional assessment in stroke patients are based on observational approaches where the results can be fraud easily. The observational-based approaches are conducted by filling up the international standard questionnaires or face to face interview for symptom recognition from psychological reactions of patients and do not involve experimental study. This paper introduces an experimental framework for assessing emotions of the stroke patient. The experimental protocol is designed to induce six emotional states of the stroke patient in the form of video-audio clips. In the experiments, EEG data are collected from 3 groups of subjects, namely the stroke patients with left brain damage (LBD), the stroke patients with right brain damage (RBD), and the normal control (NC). The EEG signals exhibit nonlinear properties, hence the non-linear methods such as the Higher Order Spectra (HOS) could give more information on EEG in the signal's analysis. Furthermore, the EEG classification works with a large amount of complex data, a simple mathematical concept is almost impossible to classify the EEG signal. From the investigation, the proposed experimental framework able to induce the emotions of stroke patient and could be acquired through EEG.
      3
  • Publication
    Investigation of the Brain Activation Pattern of Stroke Patients and Healthy Individuals During Happiness and Sadness
    ( 2022-01-01)
    Choong W.Y.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Murugappan M.
    ;
    Wan Azani Wan Mustafa
    ;
    Abdul Hamid Adom
    ;
    Bong S.Z.
    ;
    Ahmad Kadri Junoh
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    This study aimed to assess the emotional experiences of stroke patients and normal people using electroencephalogram (EEG) signals in happiness and sadness. The brain behaviors under both emotional states in the EEG signal were analyzed through signal processing methods. In this study, the EEG signals of normal control (NC) and stroke patients with left brain damage (LBD) and right brain damage (RBD) were analyzed through Hjorth parameters. The extracted Hjorth parameters showed significant differences between happiness and sadness in alpha, beta, and gamma frequency bands, which implied the two emotions exhibiting different brain behavior in different EEG bands. The topographic mapping of the Hjorth parameters presented different activation patterns in the subject groups, and the higher frontal activation can be observed from the NC group for both emotions. Also, the Hjorth Mobility and Complexity parameters were lower in LBD and RBD in the frontal regions of the alpha band. The significant difference channels between the emotions were analyzed by statistical analysis using ANOVA. Moreover, the features of each subject group were used for emotion classification by the application of machine learning-based algorithm. The KNN classification results achieved an average accuracy of 92.35% for NC, 90.84% for LBD, and 95.59% for RBD in classifying happiness and sadness. The emotion classification showed that the emotional dominance frequency bands were the beta and gamma bands. However, the alpha band activity showed left frontal lateralization in the NC group, while right frontal lateralization in the LBD and RBD groups suggested different brain activation of the stroke groups and the controls during happiness and sadness, which reflected the emotional impairment in stroke groups.
      2