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Medical education in Universiti Sains Malaysia

Zabidi H, Fuad AR

Med J Malaysia, 2002 Dec;57 Suppl E:8-12.
PMID: 12733185

Universiti Sains Malaysia established it's medical school in 1979, the third medical school in Malaysia after Universiti Malaya and Universiti Kebangsaan Malaysia. During the time of its establishment, the university was fortunate to witness a revolution in the world of medical education. PBL-based education was one of the most talked about approach in medical education. The University was fortunate to have experienced medical educators with sufficient foresight to start a medical school that has in its philosophy a community-based integrated curriculum utilizing problem-based learning, one of it's main modes of curricular implementation. Over the last 20 years, the medical curriculum has been revised and fine-tuned twice. The first major curriculum review was undertaken in 1995. One major outcome of this review was a firm commitment to continue with it's original philosophy in medical education at the same time introducing several key strategies to enhance the teaching of medical ethics, attitude formation and reaffirming the need for a lean, integrated curriculum which addresses core knowledge, attitude and skills. A more recent review in 2001 took several approaches including getting the input of students to enhance the original philosophy.
Fulltext Characteristics of mentoring programmes in the early phase of medical training at the Universiti Sains, Malaysia

Mat Nor ZM, Yusoff SBM, Abdul Rahim FA

J Taibah Univ Med Sci, 2017 Aug;12(4):343-348.
PMID: 31435261 DOI: 10.1016/j.jtumed.2017.01.003

OBJECTIVE: Mentoring programmes are important elements of the personal and professional development of medical students. Mentors must focus on the real issues that students face during the mentoring process. This study explores the need for mentoring programmes for first-year medical students at the Universiti Sains, Malaysia (USM).
METHODS: A qualitative case study was conducted with medical students who were in the early phases of their training. Purposive sampling was employed to select the study participants. Data collection was carried out using semi-structured interviews. The interviews were recorded and transcribed verbatim, and they were later analysed using NVivo 10 software and employing open coding, axial coding and selective coding techniques. Nine medical students participated in the study. To ensure trustworthiness of the data, member checks, an audit trail, the Cohen kappa index, and peer checking were utilized.
RESULTS: Based on thematic analysis, four themes and seven categories were identified. Themes include soft skills, an academic overview, social skills and motivation from mentors. Categories include time management, study skills, communication skills, social adjustment, social activities, moral support and personal support.
CONCLUSION: Results indicate that mentoring is essential to medical students in developing their identity and professional maturity. The effectiveness of the mentoring programme is supported by several factors that, as a whole, lead to the development of a professional graduate.
An efficient intrusion detection model based on convolutional spiking neural network

Wang Z, Ghaleb FA, Zainal A, Siraj MM, Lu X

Sci Rep, 2024 Mar 25;14(1):7054.
PMID: 38528084 DOI: 10.1038/s41598-024-57691-x

Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.
Fulltext Noise exposure and noise induced hearing loss among Kuala Lumpur traffic point duty personnel

Thomas N, Mariah AN, Fuad A, Kuljit S, Philip R

Med J Malaysia, 2007 Jun;62(2):152-5.
PMID: 18705450 MyJurnal

Thirty-two points in Kuala Lumpur were selected where traffic personnel were on duty. Sound level readings were taken three times a day. Generally, the traffic noise levels were between 75 dBA to 85 dBA. The maximum sound level recorded was 108.2 dBA. Noise emitted by traffic equipment and vehicles were up to 133 dBA. Results of audiometric tests revealed that out of 30 who were tested, 24 or 80% were positive for noise-induced hearing loss. A questionnaire survey revealed a lack of knowledge on occupational safety and personal protective equipment.
Fulltext Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks

Ghaleb FA, Al-Rimy BAS, Boulila W, Saeed F, Kamat M, Foad Rohani M, et al.

Comput Intell Neurosci, 2021;2021:2977954.
PMID: 34413885 DOI: 10.1155/2021/2977954

Wireless mesh networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and Internet of Vehicles (IoV). Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth distribution, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms equally distribute the interference reduction on the links to achieve fairness which neither guarantees a fair distribution of the network bandwidth nor eliminates node starvation. In addition, the metaheuristic-based solutions such as genetic algorithm, which is commonly used for WMN, use randomness in creating initial population and selecting the new generation usually leading the search to local minima. To this end, this study proposes a Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) to solve node starvation problem in wireless mesh networks. FA-SCGA-CAA maximizes link fairness while minimizing link interference using a genetic algorithm (GA) with a novel nonlinear fairness-oriented fitness function. The primary chromosome with powerful genes is created based on multicriterion links ranking channel assignment algorithm. Such a chromosome was used with a proposed semichaotic technique to create a strong population that directs the search towards the global minima effectively and efficiently. The proposed semichaotic technique was also used during the mutation and parent selection of the new genes. Extensive experiments were conducted to evaluate the proposed algorithm. A comparison with related work shows that the proposed FA-SCGA-CAA reduced the potential node starvation by 22% and improved network capacity utilization by 23%. It can be concluded that the proposed FA-SCGA-CAA is reliable to maintain high node-level fairness while maximizing the utilization of the network resources, which is the ultimate goal of many wireless networks.
Bibliometric analysis and evaluation of the Journal of Prosthetic Dentistry from 1970 to 2019

Alhajj MN, Al-Sanabani FA, Alkheraif AA, Smran A, Alqerban A, Samran A

J Prosthet Dent, 2021 Jun 23.
PMID: 34175112 DOI: 10.1016/j.prosdent.2021.05.013

STATEMENT OF PROBLEM: A comprehensive bibliometric analysis to determine different aspects of the Journal of Prosthetic Dentistry is lacking.
PURPOSE: The purpose of this bibliometric study was to analyze the characteristics of the Journal of Prosthetic Dentistry between 1970 and 2019.
MATERIAL AND METHODS: The Web of Science Core Collection was used to retrieve 9 categories of the Journal of Prosthetic Dentistry, including keywords and terms used, cited documents published, the countries and organizations of the authors, references, and sources cited during this period. Data were exported to a software program and analyzed for each 10-year period and for the entire 50 years. The highest 10 in each category were reported. Co-occurrence, couthorships, and linkage were also reported.
RESULTS: A total of 11 989 records were reached by the search on the Web of Science Core Collection database; of which, 10 638 (92.9%) were included in the analysis. Articles made up 91.1%, of all records, with 217 review documents (1.8%). The most productive decade was 1980 to 1989 with 2936 documents. The total number of citations of all documents (available period 1980 to 2019) including self-citations was 155 112. During the period 1970 to 2019, 14 837 terms were used. The total number of keywords was 4933 (available period 1990 to 2019). There were 15 382 authors, 82 countries, and 2113 organizations identified in articles published in the Journal of Prosthetic Dentistry during this period, with most from the United States. There were 43 027 authors, 95 324 references, and 14 594 sources cited in the Journal of Prosthetic Dentistry during the period surveyed.
CONCLUSIONS: This bibliometric analysis provided a comprehensive overview of the impactful role of the Journal of Prosthetic Dentistry in contemporary dentistry, particularly in the field of prosthodontics.
Fulltext Effect of Luting Cements On the Bond Strength to Turkom-Cera All-Ceramic Material

Al-Makramani BMA, Razak AAA, Abu-Hassan MI, Al-Sanabani FA, Albakri FM

Open Access Maced J Med Sci, 2018 Mar 15;6(3):548-553.
PMID: 29610618 DOI: 10.3889/oamjms.2018.111

BACKGROUND: The selection of the appropriate luting cement is a key factor for achieving a strong bond between prepared teeth and dental restorations.
AIM: To evaluate the shear bond strength of Zinc phosphate cement Elite, glass ionomer cement Fuji I, resin-modified glass ionomer cement Fuji Plus and resin luting cement Panavia-F to Turkom-Cera all-ceramic material.
MATERIALS AND METHODS: Turkom-Cera was used to form discs 10mm in diameter and 3 mm in thickness (n = 40). The ceramic discs were wet ground, air - particle abraded with 50 - μm aluminium oxide particles and randomly divided into four groups (n = 10). The luting cement was bonded to Turkom-Cera discs as per manufacturer instructions. The shear bond strengths were determined using the universal testing machine at a crosshead speed of 0.5 mm/min. The data were analysed using the tests One Way ANOVA, the nonparametric Kruskal - Wallis test and Mann - Whitney Post hoc test.
RESULTS: The shear bond strength of the Elite, Fuji I, Fuji Plus and Panavia F groups were: 0.92 ± 0.42, 2.04 ± 0.78, 4.37 ± 1.18, and 16.42 ± 3.38 MPa, respectively. There was the statistically significant difference between the four luting cement tested (p < 0.05).
CONCLUSION: the phosphate-containing resin cement Panavia-F exhibited shear bond strength value significantly higher than all materials tested.
Fulltext Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

Ghaleb FA, Kamat MB, Salleh M, Rohani MF, Abd Razak S

PLoS One, 2018;13(11):e0207176.
PMID: 30457996 DOI: 10.1371/journal.pone.0207176

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects.
Fulltext Smart Home Privacy Protection Methods against a Passive Wireless Snooping Side-Channel Attack

Nassiri Abrishamchi MA, Zainal A, Ghaleb FA, Qasem SN, Albarrak AM

Sensors (Basel), 2022 Nov 07;22(21).
PMID: 36366261 DOI: 10.3390/s22218564

Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to data leakage; this privacy issue has motivated researchers to seek a secure solution to overcome this challenge. In this regard, wireless signal eavesdropping is one of the most severe threats that enables attackers to obtain residents' sensitive information. Even if the system encrypts all communications, some cyber attacks can still steal information by interpreting the contextual data related to the transmitted signals. For example, a "fingerprint and timing-based snooping (FATS)" attack is a side-channel attack (SCA) developed to infer in-home activities passively from a remote location near the targeted house. An SCA is a sort of cyber attack that extracts valuable information from smart systems without accessing the content of data packets. This paper reviews the SCAs associated with cyber-physical systems, focusing on the proposed solutions to protect the privacy of smart homes against FATS attacks in detail. Moreover, this work clarifies shortcomings and future opportunities by analyzing the existing gaps in the reviewed methods.
Fulltext A Kullback-Liebler divergence-based representation algorithm for malware detection

Aboaoja FA, Zainal A, Ghaleb FA, Alghamdi NS, Saeed F, Alhuwayji H

PeerJ Comput Sci, 2023;9:e1492.
PMID: 37810364 DOI: 10.7717/peerj-cs.1492

BACKGROUND: Malware, malicious software, is the major security concern of the digital realm. Conventional cyber-security solutions are challenged by sophisticated malicious behaviors. Currently, an overlap between malicious and legitimate behaviors causes more difficulties in characterizing those behaviors as malicious or legitimate activities. For instance, evasive malware often mimics legitimate behaviors, and evasion techniques are utilized by legitimate and malicious software.
PROBLEM: Most of the existing solutions use the traditional term of frequency-inverse document frequency (TF-IDF) technique or its concept to represent malware behaviors. However, the traditional TF-IDF and the developed techniques represent the features, especially the shared ones, inaccurately because those techniques calculate a weight for each feature without considering its distribution in each class; instead, the generated weight is generated based on the distribution of the feature among all the documents. Such presumption can reduce the meaning of those features, and when those features are used to classify malware, they lead to a high false alarms.
METHOD: This study proposes a Kullback-Liebler Divergence-based Term Frequency-Probability Class Distribution (KLD-based TF-PCD) algorithm to represent the extracted features based on the differences between the probability distributions of the terms in malware and benign classes. Unlike the existing solution, the proposed algorithm increases the weights of the important features by using the Kullback-Liebler Divergence tool to measure the differences between their probability distributions in malware and benign classes.
RESULTS: The experimental results show that the proposed KLD-based TF-PCD algorithm achieved an accuracy of 0.972, the false positive rate of 0.037, and the F-measure of 0.978. Such results were significant compared to the related work studies. Thus, the proposed KLD-based TF-PCD algorithm contributes to improving the security of cyberspace.
CONCLUSION: New meaningful characteristics have been added by the proposed algorithm to promote the learned knowledge of the classifiers, and thus increase their ability to classify malicious behaviors accurately.
Fulltext Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique

Ali AM, Ghaleb FA, Al-Rimy BAS, Alsolami FJ, Khan AI

Sensors (Basel), 2022 Sep 15;22(18).
PMID: 36146319 DOI: 10.3390/s22186970

Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community's behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency-inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.
Spatiotemporal proximity of rubella cases to the occurrence of congenital rubella syndrome in Yogyakarta, Indonesia

Sriwahyuni E, Sriwahyuni E, Fuad A, Ahmad RA, Ahmad RA, Rustamaji R, et al.

Med J Malaysia, 2020 05;75(Suppl 1):41-47.
PMID: 32483106

INTRODUCTION: Rubella infection during early pregnancy may cause fatal consequences such as congenital rubella syndrome (CRS). The incidence rate (IR) of CRS confirmed cases in Yogyakarta, Indonesia between July 2008 and June 2013 was high at 0.05 per 1,000 live births. This study aimed to discover the spatiotemporal pattern of rubella and CRS and also identify whether the proximity of rubella cases was associated with the occurrence of CRS cases.
METHODS: This observational research used a spatiotemporal approach. We obtained CRS and rubella surveillance data from Dr. Sardjito Hospital, Provincial, and District Health Offices in Yogyakarta, Indonesia during January-April 2019. The home addresses of rubella and CRS cases were geocoded using the Global Positioning System. Average of the nearest neighbour and space-time permutation analyses were conducted to discover the spatiotemporal patterns and clusters of rubella and CRS cases.
RESULTS: The peak of rubella cases occurred in 2017 (IR: 22.3 per 100,000 population). Twelve confirmed cases of CRS were found in the 2016-2018 period (IR: 0.05 per 1,000 live births). The occurrence of CRS in Yogyakarta was detected 6-8 months after the increase and peak of rubella cases. The spatiotemporal analysis showed that rubella cases were mostly clustered, while CRS cases were distributed in a dispersed pattern. Rubella cases were found within a buffer zone of 2.5 km from any CRS case.
CONCLUSIONS: Rubella cases were spatiotemporally associated with the occurrence of CRS in Yogyakarta. We recommend strengthening the surveillance system of CRS and rubella cases in order to contain any further spreading of the disease.
Effect of Surface Treatments on the Bond Strength to Turkom-Cera All-ceramic Material

Razak AA, Abu-Hassan MI, Al-Makramani BM, Al-Sanabani FA, Al-Shami IZ, Almansour HM

J Contemp Dent Pract, 2016 Nov 01;17(11):920-925.
PMID: 27965501

AIM: The aim of this study was to evaluate the effect of surface treatments on shear bond strength (SBS) of Turkom-Cera (Turkom-Ceramic (M) Sdn. Bhd., Puchong, Malaysia) all-ceramic material cemented with resin cement Panavia-F (Kuraray Medical Inc., Okayama, Japan).
MATERIALS AND METHODS: Forty Turkom-Cera ceramic disks (10 mm × 3 mm) were prepared and randomly divided into four groups. The disks were wet ground to 1000-grit and subjected to four surface treatments: (1) No treatment (Control), (2) sandblasting, (3) silane application, and (4) sandblasting + silane. The four groups of 10 specimens each were bonded with Panavia-F resin cement according to manufacturer's recommendations. The SBS was determined using the universal testing machine (Instron) at 0.5 mm/min crosshead speed. Failure modes were recorded and a qualitative micromorphologic examination of different surface treatments was performed. The data were analyzed using the one-way analysis of variance (ANOVA) and Tukey honestly significant difference (HSD) tests.
RESULTS: The SBS of the control, sandblasting, silane, and sandblasting + silane groups were: 10.8 ± 1.5, 16.4 ± 3.4, 16.2 ± 2.5, and 19.1 ± 2.4 MPa respectively. According to the Tukey HSD test, only the mean SBS of the control group was significantly different from the other three groups. There was no significant difference between sandblasting, silane, and sandblasting + silane groups.
CONCLUSION: In this study, the three surface treatments used improved the bond strength of resin cement to Turkom-Cera disks.
CLINICAL SIGNIFICANCE: The surface treatments used in this study appeared to be suitable methods for the cementation of glass infiltrated all-ceramic restorations.

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