Hasimah Ali
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Preferred name
Hasimah Ali
Official Name
Ali, Hasimah
Alternative Name
Ali, H.
Ali, H
Ali, Hashimah
Ali, H. I.
Bt Ali, Hasimah
Main Affiliation
Scopus Author ID
57218540740
Researcher ID
EKZ-6160-2022

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  • Publication
    Autonomous Vehicle: Introduction and Key-elements
    ( 2021-08-27)
    Hafiz Halin
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Hasimah Ali
    The development of autonomous vehicles is undergoing extensive research because the autonomous system must ensure passenger safety. Consumers are concerned about vehicle safety, data privacy, system safety, and the autonomous vehicle's legal liability. Autonomous develop based on several key elements; perception, data processing, path planning, and control system. Comfort and a safe autonomous system can be achieved by creating a controller that can imitate human intelligence and decision-making ability. The proposed controller will be developed from an analysis of the human driving characteristic. The Allied Research Market forecast the autonomous vehicle industries can generate a lot of revenue in the future.
      1
  • Publication
    A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images
    ( 2021-08-27)
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Tengku Amran T.S.
    ;
    Ahmad M.R.
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator's knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD and DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.
      1
  • Publication
    Correlation Analysis of Emotional EEG in Alpha, Beta and Gamma Frequency Bands
    ( 2021-08-27)
    Choong W.Y.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mustafa W.A.
    ;
    Murugappan M.
    ;
    Hamid A.
    ;
    Bong S.Z.
    ;
    Yuvaraj R.
    ;
    Omar M.I.
    ;
    Ahmad Kadri Junoh
    ;
    Hasimah Ali
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    It is aimed at finding the correlation between EEG channels from six induced emotions in normal subjects. The multichannel EEG data was measured by Pearson's correlation coefficient to investigate the linear relationship between channel pairs in alpha, beta and gamma EEG frequency sub-bands. The EEG data were collected from 12 healthy subjects, with six induced emotions by audio-visual stimuli, which were anger, disgust, fear, happiness, sadness and surprise. The 14-channel wireless Emotiv Epoc was used for data collection. The results show that the EEG channels in alpha band was relatively higher correlation than in beta and gamma bands. The highest correlation for all emotions in alpha band were the channel pairs in right frontal region, FC6-F4 and F4-AF4. In beta and gamma bands, the highest correlation pairs involved the right frontal, occipital and parietal regions, which were FC6-F4 and O2-P8.
      2
  • Publication
    Classification of body mass index based face images using facial landmarks approach and PCA plus LDA
    ( 2022-01-01)
    Hasimah Ali
    ;
    Ho Yong Kang
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Mokhtar N.
    Human faces contain rich information. Recent studies found that facial features have relation with human weight or body mass index (BMI). Decoding “facial information” from the face in predicting the BMI could be linked to the various health marker. This paper proposed the classification of body mass index (BMI) using facial landmark approach based on facial images. In this framework, Discriminative Response Map Fitting (DRMF) method has been used as feature extraction technique to detect and locate the facial landmark points on the facial images. About sixty-six (66) facial landmark points were identified. Only nineteen (19) of facial landmark points have been employed to extract the facial features in terms of distance and ratio features. A total of 221 facial landmark features were obtained and used as feature vector to classify the BMI classes. The rationale of using 221 facial landmark features is because these features were able to exhibit the unique characteristic of the BMI classes, which are normal, overweight and obese. Then, the extracted features were further reduced using Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) to map high dimension features into low dimensional feature with maximize between class scatter and minimize within class variations. Later, the reduced features were subjected to k-NN classifiers. A series of experiments has been conducted on MORPH II database using the reduced facial landmark features to classify the three BMI classes. Based on the experimental results, it shows that the reduced features using PCA plus LDA based on k-NN classifier has achieve the highest recognition rate with accuracy of 83.33 %. The obtained results show that the reduced facial landmark features were able to discriminate the three BMI classes of normal, overweight and obese, thus shows the promising results.
      1
  • Publication
    Illumination Effects on Facial Expression Recognition using Empirical Mode Decomposition
    ( 2024-01-01)
    Hasimah Ali
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Muthusamy H.
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    Facial expression recognition (FER) has been acknowledged as a significant modality that could bring facial expression into human-machine interaction and make the interaction more efficient. However, the ability of FER tope rate in a fully automated and robust manner is still challenging. Illumination effects, for example, make the facial expression images always contaminated with different levels of ambient noise (such as brightness variation) in acluttered background. Thus, this paper aims to investigate the illumination effects (brightness variations) on facial expression recognition using empirical mode decomposition reconstruction techniques. In this framework, firstly, the noisy facial expression images were simulated with the illumination effects using different brightness levels of 30%,40%, 50%, 60%, and 70%. Then, the EMD will decompose the noisy facial expression images into a small set of intrinsic mode functions (IMF), namely IMF1, IMF2, IMF3, and residue. Based on property held by EMD, the signals are decomposed into several IMF components, each with a different time scale. Because the last several IMFs represent the majority of illumination effects, various reconstruction techniques for IMFs have been investigated atvarious brightness levels. Feature reduction techniques Principal component analysis (PCA) and linear discriminant analysis (LDA) have been employed to reduce the high-dimensional space of IMF features into low-dimensional IMF features. The reduced IMF reconstructions were then used as input to the k-nearest neighbour classifier to recognise the seven facial expressions. A series of experiments have been conducted on the JAFEE database using various reconstruction IMFs together with PCA plus LDA. Based on the results obtained, the reconstruction of IMF1 + IMF2+ IMF3 shows the highest accuracy in high illumination conditions, which is 99.06%.
      1
  • Publication
    Classification of Body Mass Index Based Facial Images using Empirical Mode Decomposition
    ( 2021-06-11)
    Hasimah Ali
    ;
    Yee, O.S.
    ;
    Wan Khairunizam Wan Ahmad
    Human faces contain rich information. Recent studies found that facial features have relation with human weight or body mass index (BMI). Decoding "facial information"from the face in predicting the BMI could be linked to the various health marker. This paper proposed the classification of body mass index (BMI) based on appearance based features of facial images using empirical mode decomposition (EMD) as feature extraction technique. The facial images that describe the body mass index was extracted using EMD to obtain a set of significant features. In this framework, the facial image was decomposed using EMD to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF features which exhibit the unique pattern were used to classify the BMI. The obtained features were then fed into machine learning classifier such as k-nearest neighbour and support vector machines (SVM) to classify the three BMI classes namely normal, overweight and obese. The obtained results show that the IMF2 feature using SVM classifier achieved recognition rate of 99.12% which show promising result.
      3  3