Abdul Hamid Adom
Thumbnail Image
Preferred name
Abdul Hamid Adom
Official Name
Adom, Abdul Hamid
Main Affiliation
Scopus Author ID
6506600412

Research Output

Filters

43
Reset filters

Settings
Now showing 1 - 10 of 43
  • 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
    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
    Optimal pneumatic actuator positioning and dynamic stability using prescribed performance control with particle swarm optimization: a simulation study
    (Association for Scientific Computing Electronics and Engineering (ASCEE), 2023)
    Addie Irawan
    ;
    Mohd Syakirin Ramli
    ;
    Mohd Herwan Sulaiman
    ;
    Mohd Iskandar Putra Azahar
    ;
    Abdul Hamid Adom
    This paper introduces an optimal control strategy for pneumatic servo systems (PSS) positioning using Finite-time Prescribed Performance Control (FT-PPC) with Particle Swarm Optimization (PSO). Pneumatic servo systems are widely used in industrial automation, as well as medical and cybernetics systems that involve robotics applications. Precision in pneumatic control is crucial not only for the sake of efficiency but also safety. The primary goal of the proposed control strategy is to optimize the convergence rate and finite time of the prescribed performance function in error transformation of the FT-PPC, as well as the Proportional, Integral and Derivative (PID) controller as the inner-loop controller for this system. The study utilizes a dynamic model of a pneumatic proportional valve with a double-acting cylinder (PPVDC) as the targeted plant and performs simulations with a multi-step input trajectory. This offline tuning method is essential for such nonlinear systems to be safely optimized, avoiding major damage to the real-time fine-tuned works on the controller. The results demonstrate that the proposed control strategy surpasses the performance of FT-PPC with a PID controller alone, significantly improving the system's performance, including suppressing overshoot and oscillation in the responses. Further validation through the actual system of PPVDC using the fine-tuned values of FT-PPC and PID with PSO is a future task and more challenging to come, as hardware constraints may vary with different environments such as temperatures.
  • Publication
    EEG signal processing using deep learning for motor imagery tasks: Leveraging signal images
    (Springer, 2025)
    Saidatul Ardeenawatie Awang
    ;
    Abdul Hamid Adom
    ;
    Erdy Sulino Mohd Muslim Tan
    ;
    Husna Najeha Amran
    ;
    Arni Munira Markom
    A novel approach to processing electroencephalography (EEG) signals has emerged, leveraging the utilization of signal images. The application of deep learning techniques in bypassing complex signal and image processing tasks has generated significant interest in this field. However, challenges remain in signal image processing, particularly in handling significant features and image sizes. This study presents a comprehensive investigation of EEG motor imagery signal processing, focusing on the classification of three tasks: eating, drinking, and seeking assistance. Fast Fourier Transform (FFT) is employed to extract signal image features, which are subsequently utilized in a deep learning framework. EEG data were collected from five subjects, and four transfer functions of deep learning models, namely VGG16, VGG19, ResNet50, and ResNet101, were employed for training and classification purposes. The performance of the four models was meticulously evaluated and compared. Notably, VGG16 exhibited superior performance in accurately classifying the EEG motor imagery tasks, achieving an impressive accuracy of 90%, sensitivity of 84%, and specificity of 92%. In conclusion, this study underscores the efficacy of EEG signal image processing through deep learning-based classification techniques. The findings highlight the potential of utilizing signal images in EEG analysis for motor imagery tasks, thereby contributing to the advancement of brain-computer interface technology and enhancing our understanding of neural dynamics.
  • Publication
    Theoretical and numerical study on the effect of ambient temperature towards gas dispersion in indoor environment using CFD approach
    (Semarak Ilmu Publishing, 2023)
    Zaffry Hadi Mohd Juffry
    ;
    Kamarulzaman Kamarudin
    ;
    Abdul Hamid Adom
    ;
    Muhammad Fahmi Miskon
    ;
    Abdulnasser Nabil Abdullah
    The usage of harmful chemical gas and natural gas has been increasing rapidly which increase the risk of gas leakage incident to occur especially in the indoor environment. It is important to learn the gas dispersal behavior in order to mitigate the casualty caused by gas leakage. In addition, one of the factors that contribute to the dispersion of gas is temperature. This paper focuses to study the role of ambient temperature toward gas dispersion in an indoor environment by looking at the theoretical and numerical knowledge of gas diffusion's relationship with temperature. Computational Fluid Dynamics (CFD) has been utilized to simulate gas dispersion at different ambient temperature levels in an indoor environment. This study released ethanol vapor to simulate gas dispersion at 5°C, 25°C, and 40°C of ambient temperature to observe the way gas distribute in the indoor environment. Both results from the theoretical calculation and simulation were compared. The result indicates that the gas diffusivity has an increment of 3.5% for every 5°C increment of the temperature. This causes the gas to diffuse rapidly in the warm air compared to the cool air. This paper also finds out that when the initial ambient temperature which is 5°C, was increased to 25°C and 40°C it causes the spread distance of the gas increased by 13.75% and 32.50% respectively.
      4  20
  • 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.
      1  32
  • Publication
    Auditory evoked potential response and hearing loss: a review
    ( 2015)
    M. P Paulraj
    ;
    Kamalraj Subramaniam
    ;
    Sazali Bin Yaccob
    ;
    Abdul Hamid Adom
    ;
    C. R Hema
    Hypoacusis is the most prevalent sensory disability in the world and consequently, it can lead to impede speech in human beings. One best approach to tackle this issue is to conduct early and effective hearing screening test using Electroencephalogram (EEG). EEG based hearing threshold level determination is most suitable for persons who lack verbal communication and behavioral response to sound stimulation. Auditory evoked potential (AEP) is a type of EEG signal emanated from the brain scalp by an acoustical stimulus. The goal of this review is to assess the current state of knowledge in estimating the hearing threshold levels based on AEP response. AEP response reflects the auditory ability level of an individual. An intelligent hearing perception level system enables to examine and determine the functional integrity of the auditory system. Systematic evaluation of EEG based hearing perception level system predicting the hearing loss in newborns, infants and multiple handicaps will be a priority of interest for future research.
      6  12
  • Publication
    A study of heat insulation methods for enhancing the internal temperature on artificial stingless bee hive
    ( 2024)
    Muhammad Ammar Asyraf Che Ali
    ;
    Bukhari Ilias
    ;
    Norasmadi Abdul Rahim
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Abdul Hamid Adom
    ;
    Mohd Fauzi Abu Hassan
    The stingless bees have gained a large popularity among the beekeepers, particularly in tropical and subtropical regions such as the Americas, Africa, and Southeast Asia. This is because the honey of stingless bees has a distinct flavour and is highly valued for its medicinal qualities. Traditionally, stingless bee colonies constructed from wood logs are fragile and vulnerable to outside attacks. These predator or parasite attacks can cause Colony Collapse Disorder (CCD) if not eliminated. Thus, a PVC, 3D-printed PET-G, and acrylic artificial hive has been created to replace the old one. According to earlier research, stingless bees are especially susceptible to temperatures above 38°C. This paper's main goal is to discuss the results of studies on the best artificial hive insulation method. Over a month and a half, clay, wood powder, polystyrene, bubble aluminium foil, and a water- cooling system were tested as insulators. Results shows that artificial hives with bubble aluminium foil have the biggest average difference between internal and external temperatures (6.4°C) and are closest to traditional hives (8.6°C). The average temperature difference between the artificial hive's exterior and inside was 2.9°C without heat insulation. Clay-insulated artificial hives have the lowest standard deviation value for humidity at 0.46. Since temperature is vital to stingless bee survival, bubble aluminium foil container is the best insulation solution since it increases heat resistance more than other materials.
      4  22
  • Publication
    Review of analysis of EEG signals for stress detection
    ( 2024-03-07)
    Mazlan M.R.
    ;
    Abdul Syafiq Abdull Sukor
    ;
    Abdul Hamid Adom
    ;
    Jamaluddin R.
    Mental stress is one of the major contributors to a variety of health issues. Various measures and diagnoses have been created by neurologists and psychiatrists to determine the intensity of mental stress in its early phases. In the literature, several neuroimaging devices and methods for assessing mental stress have been presented. The key candidate chosen is the electroencephalogram (EEG) signal which contains valuable information regarding mental states and conditions. This paper presents reviews of current works on EEG signal analysis for assessing mental stress. The reviews emphasize the significant disparities in the research outcomes and claims of different results from various data analysis methodologies. Accordingly, methods of EEG signals analysis will be used to study the effect of various extracted features and classification methods that associate with mental stress. Apart from that, the utilization of Artificial Intelligence (AI) approach is also investigated to study its significance towards stress detection.
      1  23
  • Publication
    Classification of agarwood oil using an electronic nose
    (MDPI, 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.
      4  27