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
    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.
  • Publication
    Correlation Analysis of Emotional EEG in Alpha, Beta and Gamma Frequency Bands
    ( 2021-08-27)
    Choong W.Y.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Murugappan M.
    ;
    Asna Rasyidah Abdul Hamid
    ;
    Bong S.Z.
    ;
    Yuvaraj R.
    ;
    Mohd Iqbal Omar
    ;
    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.
  • 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.
  • 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.
  • 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.