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
    Assessment of functional and dysfunctional on implant stability measurement for quality of life
    ( 2017)
    Norshahrizan Nordin
    ;
    Mohd. Mustafa Al Bakri Abdullah
    ;
    Fauziah Che Mat
    ;
    Muhammad Safizal 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 campaigns that increase how the need for humanity in the field of quality involvement.
  • 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
    Classification of agarwood oil using an electronic nose
    ( 2010)
    Wahyu Hidayat
    ;
    Ali Yeon Md Shakaff
    ;
    Mohd Noor Ahmad
    ;
    Abdul Hamid Adom
    Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.
  • 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
    Assessment of functional and dysfunctional on implant stability measurement for quality of life
    ( 2017)
    Norshahrizan Nordin
    ;
    Razli Che Razak
    ;
    Mohd. Mustafa Al Bakri Abdullah
    ;
    Muhammad Safizal Abdullah
    ;
    Fauziah Che Mat
    ;
    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
    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
    An emotion assessment of stroke patients by using bispectrum features of EEG signals
    ( 2020-10-01)
    Yean C.W.
    ;
    Ahmad W.K.W.
    ;
    Mustafa W.A.
    ;
    Murugappan M.
    ;
    Rajamanickam Y.
    ;
    Abdul Hamid Adom
    ;
    Omar M.I.
    ;
    Zheng B.S.
    ;
    Junoh A.K.
    ;
    Razlan Z.M.
    ;
    Bakar S.A.
    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.
      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
    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
    ;
    Abdulnasser Nabil Abdullah
    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.
      4
  • Publication
    Improved maturity and ripeness classifications of magnifera indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor
    ( 2012)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Masnan, Maz Jamilah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Mahmad Nor Jaafar
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.
      7  16