ORCID Profile
0000-0003-1397-8174
Current Organisation
International Islamic University Malaysia
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Publisher: American Scientific Publishers
Date: 10-2016
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 09-2014
Publisher: Science Publishing Corporation
Date: 08-06-2018
DOI: 10.14419/IJET.V7I2.34.13913
Abstract: The ubiquitous computing has made consumers life easy, it has given the new way to interact with family and friends and perform many activities which were impossible in previous time. One of the profound achievement of ubiquitous computing is Mobile Payment and an advanced mode of the mobile payment is the near field communication mobile payment. In this study the authors have proposed theoretical near field communication mobile payment model that is based on extended unified technology acceptance and use of technology (UTAUT2) .In this paper, the author have performed the pilot study to validate the variables and to verify their reliability among the proposed items. The results has proven that there is a reliability among the items in variables, as the Cronbach’s alpha value for the variables is above or equal to 0.7.
Publisher: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Date: 23-08-2019
DOI: 10.35940/IJITEE.I3291.0789S319
Abstract: Three-dimensional (3D) printed model becomes more popular as the flexibility to print 3D model has become cheaper to produce. This research sought to assess the effect of using 3D printed model as a tool in teaching on optometry students. Another primary focus of this research was to assess the confidence level and enjoyment level among the second-year optometry students on traditional lecture method and compared it with the use of 3D printed model as a teaching aid. Confidence is important as an optometrist, especially when making the right clinical judgement. Enjoyment is also important as it may help learning process become effective and fast. A total of 36 second-year optometry students were selected to participate in this research. The 3D printed model was based on ophthalmoscope that had been printed using PRUSA 3000 3D printer. The students were ided into two groups – one group was exposed to the lecture only and another group was exposed to 3D model in addition to the traditional lecture. Two sets of questionnaires were given to assess their confidence and enjoyment level before and after each learning session. The confidence level assessment and the enjoyment level comprise of three statements on each topic answered by the students using 5-point Likert scale. The results showed that there are significance differences between lecture only group and lecture with 3D-printed model group especially as a visualization tool (P = 0.001) and it is considered to be enjoyable and stimulating (P = 0.008). This study demonstrates that the usage of 3D printed model as teaching aid does affect the confidence level and enjoyment level of students
Publisher: Hindawi Limited
Date: 15-11-2021
DOI: 10.1155/2021/6211006
Abstract: Introduction. Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients. Aim. To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 s les of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. Results. The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%). Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.
Publisher: Hindawi Limited
Date: 18-08-2020
DOI: 10.1155/2020/8828855
Abstract: The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.
Publisher: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Date: 23-08-2019
DOI: 10.35940/IJITEE.I3290.0789S319
Abstract: Breast cancer is the leading cancer that occurs in women globally. The use of machine learning has been introduced to supplement the work in breast cancer studies. There are undisputed pieces of evidence of the existence of publications pertaining to the use of decision tree in breast cancer-related research. However, little is known regarding the types and frequencies of the searched articles. The main objective of this paper is to unearth the broad variety of articles related to breast cancer research that utilized decision trees. The Scopus database was chosen to examine the trend, frequencies and themes of the related publications from the year 2013 until 2018. The study was also intended to disclose the categories of articles based on the areas of breast cancer that have employed the decision trees method. A total of 259 articles from Scopus database were found to meet the inclusion criteria. The analysis of the frequency of published articles generally shows an upward trend. The majority of articles targeted diagnosis of breast cancer (37.8%) in comparisons with other categories. Even though the number of articles found is adequate, several categories of breast cancer are lacking in publications specifically the survivability, incidence, and recurrence of breast cancer among patients. There is a need to redirect the focus of breast cancer research on these categories for future efforts
Publisher: Informa UK Limited
Date: 24-01-2017
DOI: 10.1080/02713683.2016.1250277
Abstract: The goal of this study was to predict visual acuity (VA) and contrast sensitivity function (CSF) with tissue redness grading after pterygium surgery. A total of 67 primary pterygium participants were selected from patients who visited an ophthalmology clinic. We developed a semi-automated computer program to measure the pterygium fibrovascular redness from digital pterygium images. The final outcome of this software is a continuous scale grading of 1 (minimum redness) to 3 (maximum redness). The region of interest (ROI) was selected manually using the software. Reliability was determined by repeat grading of all 67 images, and its association with CSF and VA was examined. The mean and standard deviation of redness of the pterygium fibrovascular images was 1.88 ± 0.55. Intra-grader and inter-grader reliability estimates were high with intraclass correlation ranging from 0.97 to 0.98. The new grading was positively associated with CSF (p < 0.01) and VA (p < 0.01). The redness grading was able to predict 25% and 23% of the variance in the CSF and the VA, respectively. The new grading of pterygium fibrovascular redness can be reliably measured from digital images and showed a good correlation with CSF and VA. The redness grading can be used in addition to the existing pterygium grading.
No related grants have been discovered for Mohd Izzuddin Mohd Tamrin.