Abdul Hamid Adom, Prof. Dr.
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/22959
This page provides access to scholarly publications by UniMAP Faculty members and researchers.2024-03-29T08:16:04ZAssessment of Functional and Dysfunctional on Implant Stability Measurement for Quality of Life
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69280
Assessment of Functional and Dysfunctional on Implant Stability Measurement for Quality of Life
Norshahrizan, Nordin; Mohd Mustafa Al Bakri, Abdullah; Fauziah, 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.
Link to publisher's homepage at https://www.matec-conferences.org/
2017-01-01T00:00:00ZDesign and Fabrication of Biodegradable Microneedle Using 3D Rapid Prototyping Printer
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69031
Design and Fabrication of Biodegradable Microneedle Using 3D Rapid Prototyping Printer
Nur Hazwani Azyan, Mansor; Marni Azira, Markom; Erdy Sulino, Mohd Muslim Tan; Abdul Hamid, Adom
Microneedle is known as transdermal drug delivery (TDD) devices that uses to deliver biological fluid into veins and needle that use to tear skin to collect blood sample. It involves with various parameters and designs. This device gain attention as its benefit can eliminate pain and more convenient compared to intravenous injection due to its micron size. Typically,
microneedle is fabricated using MEMS technology. However, this technology requires few
processes such as deposition, etching and moulding, as well as consume much times. This paper
presents a work of design and fabrication of solid microneedle using 3D rapid prototype printing.
A few types of microneedles are designed and they are analysed in terms of stress and force
characteristics. Also, it will study the ability of a few materials to withstand stress while force
exerted on it. The selected materials are polyvinyl alcohol (PVA), polylactic acid (PLA),
polyester resin and acrylonitrile butadiene styrene (ABS). The results show that PVA has the
highest ability to withstand force compared other materials. As conclusion, the design and
fabrication of microneedle using 3D rapid prototyping printer is succeed using PVA material and
real post-analysis can be conducted to test the capability for medical practice.
Link to publisher's homepage at https://iopscience.iop.org/
2019-01-01T00:00:00ZObjective investigation of vision impairments using single trial pattern reversal visually evoked potentials
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/35665
Objective investigation of vision impairments using single trial pattern reversal visually evoked potentials
Vijean, vikneswaran; Hariharan, Muthusamy, Dr.; Sazali, Yaacob, Prof. Dr.; Mohd Nazri, Sulaiman; Abdul Hamid, Adom, Prof. Dr.
Visually evoked potentials (VEPs) originate from the occipital cortex and have long been used as a reliable indicator for vision impairments by ophthalmologists. Any abnormalities in the visual pathways of a person can be diagnosed by analyzing these responses. The amplitudes and latency of VEP responses have been traditionally used for the diagnosis of vision impairments. This paper proposes new ways in which to analyze VEP responses by investigating the time and frequency domain characteristics of the signals. The single trial VEP's are decomposed into six different frequency bands; delta, theta, alpha, beta, gamma1 and gamma2, using digital elliptic filters. Statistical features are extracted from the decomposed VEP's and are analyzed using student two tailed t-test and box plot analysis. Levenberg-Marquardt backpropagation neural network (LMBP) and Extreme Learning Machine (ELM) algorithms are employed for the discrimination of vision impairment. The proposed method gives promising classification accuracy ranging from 90.90% to 96.89%.
Link to publisher's homepage at http://www.elsevier.com/
2013-07-01T00:00:00ZEEG based detection of conductive and sensorineural hearing loss using artificial neural networks
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/34877
EEG based detection of conductive and sensorineural hearing loss using artificial neural networks
Pandiyan, Paulraj Murugesa , Prof. Dr.; Subramaniam, Kamalraj; Sazali, Yaacob, Prof. Dr.; Abdul Hamid, Adom, Prof. Dr.; Hema, C. R.
In this paper, a simple method has been proposed to distinguish the normal and abnormal hearing subjects (conductive or sensorineural hearing loss) using acoustically stimulated EEG signals. Auditory Evoked Potential (AEP) signals are unilaterally recorded with monaural acoustical stimulus from the normal and abnormal hearing subjects with conductive or sensorineural hearing loss. Spectral power and spectral entropy features of gamma rhythms are extracted from the recorded AEP signals. The extracted features are applied to machine-learning algorithms to categorize the AEP signal dynamics into their hearing threshold states (normal hearing, abnormal hearing) of the subjects. Feed forward and feedback neural network models are employed with gamma band features and their performances are analyzed in terms of specificity, sensitivity and classification accuracy for the left and right ears across 9 subjects. The maximum classification accuracy of the developed neural network was observed as 96.75 per cent in discriminating the normal and hearing loss (conductive or sensorineural) subjects. From the neural network models, it has been inferred that network models were able to classify the normal hearing and abnormal hearing subjects with conductive or sensorineural hearing loss. Further, this study proposed a feature band-score index to explore the feasibility of using fewer electrode channels to detect the type of hearing loss for newborns, infants, and multiple handicaps, person who lacks verbal communication and behavioral response to the auditory stimulation.
Link to publisher's homepage at http://www.aicit.org/
2013-05-01T00:00:00Z