Abdul Hamid Adom
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Abdul Hamid Adom
Official Name
Adom, Abdul Hamid
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Scopus Author ID
6506600412

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  • Publication
    Intelligent robot chair with communication aid using TEP responses and higher order spectra band features
    ( 2021)
    Sathees Kumar Nataraj
    ;
    Paulraj Murugesa Pandiyan
    ;
    Sazali Bin Yaacob
    ;
    Abdul Hamid Adom
    In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.
  • Publication
    Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors
    ( 2010)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Masnan, Maz Jamilah
    ;
    Abdul Hallis Abdul Aziz
    ;
    Nazifah Ahmad Fikri
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.
  • Publication
    Assessment of functional and dysfunctional on implant stability measurement for quality of life
    ( 2017-02-01)
    Norshahrizan Nordin
    ;
    Mohd. Mustafa Al Bakri Abdullah
    ;
    Fauziah Che Mat
    ;
    Muhammad Abdullah.
    ;
    Razli Che Razak
    ;
    Abdul Hamid Adom
    This study was conducted to investigate the effect of an implant wearer comprising among orthopedic patients as well as the use of implant dentistry in Northern Malaysia. A total of 100 questionnaires were distributed and 70 questionnaires can be used to record, analyze and test hypotheses. Data for all variables were collected through a questionnaire administered alone and analyzed by using SmartPLS V3. A total of four (4) hypotheses have been formulated and the results show that the hypothesis is supported. The results show that: (1) limit the functionality and quality of life was significantly (0.904) in connection with the implant wearer, (2) physical pain was significantly (0.845) relating to the quality of life, (3) physical discomfort was significantly (0.792) in connection with quality of life, and also (4) social discomfort is significant as well (0.809). This finding suggests that there are positive effects on the implant wearer who through life routine. The results of the study may also serve as a basis for reliable decisions related to quality of life and for the implementation of awareness campaign that increase how the need for humanity in the field of quality involvement.
  • 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
    Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine
    (Wolters Kluwer ‑ Medknow, 2020)
    SatheesKumar Nataraj
    ;
    MP Paulraj
    ;
    Sazali Yaacob
    ;
    Abdul Hamid Adom
    Background: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain–computer interface, i.e., thought‑controlled wheelchair navigation system with communication assistance. Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd‑ball paradigm. The proposed system records EEG signals from 10 individuals at eight‑channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross‑correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross‑correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures (“minimum,” “mean,” “maximum,” and “standard deviation”) were derived from the cross‑correlated signals. Finally, the extracted feature sets were validated through online sequential‑extreme learning machine algorithm. Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross‑correlation signals had the best performance with a recognition rate of 91.93%.
  • Publication
    Design and analysis of the body of an urban concept vehicle for shell eco-marathon capitalized
    ( 2024-03-07)
    Al-Ashwal A.N.T.
    ;
    Rahman M.T.A.
    ;
    Mohd Ridzuan Mohd Jamir
    ;
    Illias S.
    ;
    Abdul Hamid Adom
    ;
    Junoh A.K.
    This paper presents the problem of the body of the vehicle that was designed previously to be competing in the Shell Eco-Marathon competition. The vehicle has difficulties delivering air to the engine bay to help to maintain the temperature of the engine. Side-pods are designed using CATIA software in four different locations on the side of the vehicle, in front of the rear wheel, above the rear wheel, at the front bottom of the rear wheel and the rear top corner of the vehicle. Computational fluid dynamics (CFD) simulations were performed using ANSYS for the four designs to find which design is appropriate in terms of airflow reaching the engine bay. Out of the four designs, it was determined that the side-pod located in front of the rear wheel showed that there is airflow reaching the engine bay, and resulted in the lowest drag coefficient.
      2
  • Publication
    Deep Neural Network for Localizing Gas Source Based on Gas Distribution Map
    ( 2022-01-01)
    Zaffry Hadi Mohd Juffry
    ;
    Kamarulzaman Kamarudin
    ;
    Abdul Hamid Adom
    ;
    Mao X.
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Abdullah A.N.
    The dynamic characteristic of gas dispersal in uncontrolled environment always leads to inaccurate gas source localization prediction from gas distribution map. Gas distribution map is a representation of the gas distribution over an environment which helps human to observe the concentration of harmful gases at a contaminated area. This paper proposes the utilization of Deep Neural Network (DNN) to predict the gas source location in a gas distribution map. DNN learns from the previous gas distribution map data and patterns to generate a model that is able predict location of gas source. The results indicate that DNN is able to accurately predict the location within the range of 0.8 to 2 m from the actual gas source. This finding shows that DNN has a high potential for utilization in gas source localization application.
      1
  • Publication
    RF signal calibration for improvement of 3D mapping image to locate moisture distribution in rice silo
    ( 2021-12-01)
    Abd Alazeez Almaleeh
    ;
    Ammar Zakaria
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Rahim Y.B.A.
    ;
    Latifah Munirah Kamarudin
    ;
    Abdul Hamid Adom
    Grain storage is an important part of the post-harvest quality assurance process. The moisture level of the grains during storage is one of the primary problems. The current method of measuring rice grain moisture content is based on random sampling, which is relatively localised, and there is no real-time moisture content measurement available. The RF signal was used to build a new technique for detecting moisture and its presence in rice in real-time in this paper. The mapping of an RF signal, in particular, can be transformed into volumetric tomographic images that can be used to forecast moisture distribution.
      4
  • Publication
    A Review: Deep Learning Classification Performance of Normal and COVID-19 Chest X-ray Images
    ( 2021-11-25)
    Marni Azira Markom
    ;
    Taha S.M.
    ;
    Abdul Hamid Adom
    ;
    Abdul Syafiq Abdull Sukor
    ;
    Aimi Salihah Abdul Nasir
    ;
    Haniza Yazid
    ;
    Erdy Sulino Mohd Muslim Tan
    ;
    Arni Munira Markom
    COVID19 chest X-ray has been used as supplementary tools to support COVID19 severity level diagnosis. However, there are challenges that required to face by researchers around the world in order to implement these chest X-ray samples to be very helpful to detect the disease. Here, this paper presents a review of COVID19 chest X-ray classification using deep learning approach. This study is conducted to discuss the source of images and deep learning models as well as its performances. At the end of this paper, the challenges and future work on COVID19 chest X-ray are discussed and proposed.
      1  23
  • Publication
    Thermal and Static Properties Investigation of Different Intake Manifold Materials to Lower Air Intake Temperature for Improved Engine Performance
    ( 2023-04-01)
    Halim S.S.
    ;
    Md. Tasyrif bin Abdul Rahman
    ;
    Abdul Hamid Adom
    ;
    Mohd Al-Hafiz Mohd Nawi
    ;
    Mohd Ridzuan Mohd Jamir
    ;
    Suhaimi Illias
    Formula SAE competition is targeted at students who are interested in designing and developing a Formula-type race car. Rules were imposed to restrict the car’s performance for safety besides encouraging problem-solving skills. One such rule is the requirement of a 20mm restrictor inserted between the carburettor and intake manifold to reduce the air intake. With a constricted airflow creating a bottleneck effect, less air will be provided to the engine for combustion, consequently reducing engine efficiency. The purpose of this project is to overcome this problem despite the restriction imposed by the rules. This is done by choosing an intake manifold material that provides a low air temperature while withstanding the stress and vibrations from the engine. Computational Fluid Dynamics (CFD) software was used to conduct the static, thermal and modal analysis of Aluminium Alloy 6063, Gray Cast Iron, Fibreglass Epoxy and Carbon Fibre Epoxy to choose the material that produces lower intake air temperature while maintaining high strength. Carbon fibre epoxy was found to provide the best durability against static stress while maintaining a lower intake air temperature compared to the other materials tested.
      2